ALBERT

Description: Since mineral supplements for horses commonly contain macro minerals, although the requirement for such is usually covered by roughage-based diets, the aim of this study was to investigate the effects of different dietary calcium levels on mineral serum concentrations and bone metabolism. The trial was conducted in 30 young warmblood stallions (2–3 years) that were divided into two groups for a five-month feeding trial. The groups were fed a hay- and oat-based diet and were either supplied with high (Ca-High) or moderate (Ca-Moderate) calcium excess. While in Ca-High calcium supply was about 2–2.5-fold of the requirement, in Ca-Moderate calcium requirements were slightly surpassed (1.5–1.6-fold). In order to monitor the effects of the different calcium supply, blood samples were taken during the trial and analysed for levels of macro and trace elements as well as concentrations of two bone markers. In Ca-Moderate a trend towards higher phosphorus serum levels compared to Ca-High was observed which was significant at the end of the trial (p = 0.0002). Furthermore, results showed no influence of the diet on bone markers. Results support the idea that forage-based rations for horses do not necessarily have to be supplemented with macro minerals but with trace elements.

Description: Transport infrastructure (TI) plays a crucial role in socioeconomic development. The increase of TI inequality, an all-pervading phenomenon in both developed and developing countries, has been an obstacle to sustainable economic growth. The relationship between TI inequality and economic growth has attracted considerable interest over the past three decades. However, the relationship remains obscure, and people find it impossible to utilize to develop economies. This study collected a panel of empirical data from 1982 to 2015 from China to calculate the Gini coefficient and conduct the Granger causality test. The data analysis results show that TI inequality is not always conducive to economic growth. A softening TI inequality helps address the issues of uneven economic growth across regions in the long term. The short-term effects of improving TI inequality at the national level are reflected in the network effect. In addition, the “social filters” facilitate the region to absorb the economic benefits brought by the improvement of TI inequality. These findings offer a way to address the increase of TI inequality and shed light on the ways to improve transport investment from the perspective of economic growth.

Description: This case series study aimed to evaluate the safety, feasibility, and positive outcome of the neurorehabilitation multimodal protocol (NRMP) in 16 chronic post-surgical IVDD Hansen type I dogs, with OFS 0/DPP− (n = 9) and OFS 1/DPP+ (n = 7). All were enrolled in the NRMP for a maximum of 90 days and were clinically discharged after achieving ambulation. The NRMP was based on locomotor training, functional electrical stimulation, transcutaneous electrical spinal cord stimulation, and 4-aminopyridine (4-AP) pharmacological management. In the Deep Pain Perception (DPP)+ dogs, 100% recovered ambulation within a mean period of 47 days, reaching OFS ≥11, which suggests that a longer period of time is needed for recovery. At follow-up, all dogs presented a positive evolution with voluntary micturition. Of the DPP− dogs admitted, all achieved a flexion/extension locomotor pattern within 30 days, and after starting the 4-AP, two dogs were discharged at outcome day 45, with 78% obtaining Spinal Reflex Locomotion (SRL) and automatic micturition within a mean period of 62 days. At follow-up, all dogs maintained their neurological status. After the NRMP, ambulatory status was achieved in 88% (14/16) of dogs, without concurrent events. Thus, an NRMP may be an important therapeutic option to reduce the need for euthanasia in the clinical setting.

Description: The threshing of frozen corn is accompanied by breakage and adherence, which influence the cleaning performance when the corn-cleaning mixture is separated and cleaned. In order to reduce the impurity ratio and loss ratio during frozen corn cleaning and provide theoretical support for frozen corn combine harvesting, this study employed a self-made air-screen cleaning system with adjustable parameters. The optimal process parameters of frozen corn cleaning were determined by using the response surface method (RSM). The influences of the fan speed (FS), vibrational frequency (VF), and screen opening (SO) on the cleaning performance were explored. The results showed that all three process parameters had significant effects on the impurity ratio (IR) and loss ratio (LR). The fan speed had the most significant impact. The cleaning performance was optimal when the fan speed was 102.7 rad/s, the vibration frequency was 6.42 Hz, and the screen opening was 21.9 mm, corresponding to a 0.80% impurity ratio and a 0.61% loss ratio. The predicted values of the regression models were consistent with the experimental results with a relative error of less than 5%. The reliability and accuracy of regression models were established and confirmed.

Description: The muskrat is a neozoon species that has occupied many countries of continental North Europe after its introduction from north America as fur animals. Due to its burrowing activity it damages river and canal banks and structures of flood control. For this reason, the eradication of this alien species is recommended. Muskrats are also of parasitological interest since they can act as suitable intermediate hosts for Echinococcus multilocularis. On the other hand, little is known on the other helminths that infect muskrats. A total of 130 muskrats of different age groups trapped in different habitats in the Barnim district of the Brandenburg state by a professional hunter were examined for parasites and seven trematodes (Echinostoma sp., Notocotylus noyeri, Plagiorchis elegans, Plagiorchis arvicolae, Psilosostoma simillimum, P. spiculigerum, Opisthorchis felineus and four larval cestode species (Hydatigera taeniaeformis, Taenia martis, Taenia polyacantha, Taenia crassiceps) were detected. Larval stages of E. multilocularis were not found. O. felineus was found for the first time in muskrats in Germany. All the named parasites were present in Europe prior to the introduction of muskrats. With a prevalence of 48.9%, Strobilocercus fasciolaris, the larval stage of the cat tapeworm, H. taeniaeformis, was the most frequent parasite found in adult muskrats.

Description: Water uptake is a seminal process in seed germination. Salt and polyethylene glycol (PEG) are known to retard seed germination rates and percentages, which is often attributed to osmotic effects. Here, we quantified water uptake in wheat seeds killed with a hot needle, finding evidence of three distinct water uptake pools. The fast pool was unaffected by salt, and likely represents cell walls and other apoplastic material. Water uptake into the medium and slow pools was slowed by salt addition, with the medium pool thought to be cellular, while the slow pool is presumably related to endosperm hydration. Salt caused a minor decrease in the water uptake rates and maximum seed water content, while PEG strongly suppressed both parameters. Seeds transferred between water and salt solutions followed the water uptake trajectories of the solution into which they were transferred. Seeds transferred from PEG to water achieved final seed water contents similar to water control seeds, while seeds transferred from water to PEG achieved significantly higher final water contents than PEG controls. This work confirms that salt and PEG have distinct effects on water uptake by wheat seeds.

Description: The south of Portugal is one of the regions that will be most affected by the impacts of climate change (CC), with an expected increase in water scarcity. Irrigated super-intensive olive orchards occupy a large area of the used agricultural surface in the Alentejo region, south of Portugal, making it necessary to adapt this crop to the effects of CC. This study assessed the impacts of CC and defined adaptation measures concerning irrigation management of the super-intensive olive orchard. To compute the crop irrigation requirement (CIR), the soil water balance model ISAREG was combined with climate data relative to the reference period 1971–2000 and to the representative concentration pathways RCP4.5 and RCP8.5 for the periods 2011–2040, 2041–2070, and 2071–2100. The growing degree-days (GDD) approach was used to estimate olive phenology for these CC scenarios. Unchanged irrigation management with an average CIR increase up to 16% in RCP4.5 and 31% in RCP8.5 is expected. By adopting higher levels of water deficit, water savings of up to 22% can be realized. The results showed that the anticipated CIR increase for the CC scenarios can be mitigated through regulated deficit irrigation strategies.

Description: 5-aminolevulinic acid (ALA), a key precursor in the biosynthesis of porphyrins, can improve plant tolerance to various environmental stresses. However, it is unclear whether ALA can improve tolerance in poplar. Here, we investigated the effects of ALA on poplars under drought and salt stresses. ALA pretreatment exhibited less morphological damage, reduced leaf malonaldehyde content (MDA) and electrolyte leakage (EL), and increased leaf relative water content (RWC), proline (PRO), superoxide dismutase (SOD), and peroxidase (POD) content under stresses. Furthermore, exogenous ALA mitigated the decrease in photosynthetic capacity, and restored the chlorophyll content (Chl), net CO2 assimilation rate, stomatal conductance (Gs), transpiration rate (Tr), maximal photochemical quantum yield of PSII (Fv/Fm), actual quantum yield of photosynthesis (YII), and electron transfer rate (ETR) of poplar under various stresses. qRT-PCR showed that ALA up-regulated the expression of antiporters and aquaporins genes, which are associated with Na+ exclusion in the leaf cells and the transport activity of aquaporins. In summary, ALA pretreatment significantly improved the stress tolerance of poplar, decreasing the degree of membrane lipid peroxidation and promoting the photosynthesis and antioxidant capacity of leaves. In addition, our results showed that ALA might mediate Na+ transporter and aquaporins activity, thereby increasing the salt tolerance of poplar.

Description: This paper presents the design of NaviPBx, an ultrasound-navigated prostate cancer biopsy system. NaviPBx is designed to support an affordable and sustainable national healthcare program in Senegal. It uses spatiotemporal navigation and multiparametric transrectal ultrasound to guide biopsies. NaviPBx integrates concepts and methods that have been independently validated previously in clinical feasibility studies and deploys them together in a practical prostate cancer biopsy system. NaviPBx is based entirely on free open-source software and will be shared as a free open-source program with no restriction on its use. NaviPBx is set to be deployed and sustained nationwide through the Senegalese Military Health Service. This paper reports on the results of the design process of NaviPBx. Our approach concentrates on “frugal technology”, intended to be affordable for low–middle income (LMIC) countries. Our project promises the wide-scale application of prostate biopsy and will foster time-efficient development and programmatic implementation of ultrasound-guided diagnostic and therapeutic interventions in Senegal and beyond.

Description: Traditional research methods in software defect prediction use part of the data in the same project to train the defect prediction model and predict the defect label of the remaining part of the data. However, in the practical realm of software development, the software project that needs to be predicted is generally a brand new software project, and there is not enough labeled data to build a defect prediction model; therefore, traditional methods are no longer applicable. Cross-project defect prediction uses the labeled data of the same type of project similar to the target project to build the defect prediction model, so as to solve the problem of data loss in traditional methods. However, the difference in data distribution between the same type of project and the target project reduces the performance of defect prediction. To solve this problem, this paper proposes a cross-project defect prediction method based on manifold feature transformation. This method transforms the original feature space of the project into a manifold space, then reduces the difference in data distribution of the transformed source project and the transformed target project in the manifold space, and finally uses the transformed source project to train a naive Bayes prediction model with better performance. A comparative experiment was carried out using the Relink dataset and the AEEEM dataset. The experimental results show that compared with the benchmark method and several cross-project defect prediction methods, the proposed method effectively reduces the difference in data distribution between the source project and the target project, and obtains a higher F1 value, which is an indicator commonly used to measure the performance of the two-class model.

Description: A field experiment was conducted to investigate the effectiveness of different herbicides for controlling wild onion (Asphodelus tenuifolius) in cumin (Cuminum cyminum L.) during the rabi seasons (2018–2019 and 2019–2020) at Agricultural Research Station, Agriculture University, Jodhpur, Rajasthan. The experiment comprised eight herbicidal weed management treatments for wild onion applied to cumin in a three-replication randomized block design. Among the herbicidal weed management treatments, early post-emergence (8 DAS) application of oxyfluorfen 200 g/ha resulted in the lowest weed density and dry matter of Asphodilus tenuifolius, with maximum weed (Asphodilus tenuifolius) control efficiency at 40 days after sowing (DAS) during both experimental years. Likewise, the highest total efficiency of weed control was recorded with the application of oxyfluorfen 200 g/ha at 8 DAS. Oxyflourfen 200 g/ha used early post emergence (8 DAS) reduced the weed index more effectively than the other herbicides. It also recorded the highest number of branches/plant, plant height, umbels/plant, umbellates/umbel, seeds/umbellates, and seed yield. However, application of oxyflourfen @ 200 g/ha 8 DAS–early POE and pendimethalin 38.7 CS 500 g/ha + oxyfluorfen @ 150 g/ha 8 DAS–early POE were statistically similar in terms of plant growth, yield, and yield attributes. The net returns (366.49 USD/ha in 2018–2019 and 175.72 USD/ha in 2019–2020) and B:C ratio (1.70 and 1.33 in 2018–2019 and 2019–2020, respectively) were also superior, with oxyfluorfen 200 g/ha applied early post emergence.

Description: This paper combines the interval analysis tools with the nonlinear model predictive control (NMPC). The NMPC strategy is formulated based on an uncertain dynamic model expressed as nonlinear ordinary differential equations (ODEs). All the dynamic parameters are identified in a guaranteed way considering the various uncertainties on the embedded sensors and the system’s design. The NMPC problem is solved at each time step using validated simulation and interval analysis methods to compute the optimal and safe control inputs over a finite prediction horizon. This approach considers several constraints which are crucial for the system’s safety and stability, namely the state and the control limits. The proposed controller consists of two steps: filtering and branching procedures enabling to find the input intervals that fulfill the state constraints and ensure the convergence to the reference set. Then, the optimization procedure allows for computing the optimal and punctual control input that must be sent to the system’s actuators for the pendulum stabilization. The validated NMPC capabilities are illustrated through several simulations under the DynIbex library and experiments using an inverted pendulum.

Description: This study evaluated the effects of the duration of ZH supplementation and days on feed (DOF) on performance, carcass characteristics, and saleable meat yield of Nellore young bulls. The fixed effects included the duration (0, 20, 30, or 40 d before slaughter plus a 3 d ZH withdrawal period—8.33 mg of ZH/kg of DM) and DOF (90 and 117 d). Feed efficiency (G:F) linearly increased when the duration of ZH supplementation increased (p 〈 0.01). Nellore bulls fed ZH had greater HCW (p 〈 0.01), dressing percentage (p 〈 0.01) and Longissimus muscle area (LMA) (p 〈 0.01), but less 12th-rib fat (p = 0.04) than the control group. The hot carcass weight (HCW) (p 〈 0.01), and dressing percentage increased linearly (p 〈 0.01) with the increase of duration of ZH supplementation. The HCW, ossification, and 12th-rib fat increased with DOF (p 〈 0.01). The ZH supplemented group had most of the individual cuts of hindquarters and total saleable meat increased compared with the control. Zilpaterol hydrochloride was effective in improving hot carcass weight, hindquarter, and saleable meat yields of Nellore bulls when fed for at least 20 d before slaughter, independently of days on feed.

Description: Healthy, weaned, coccidial-free male rabbits from two breeds (New Zealand white (NZ) and V-line (VL)) were divided into 10 equal groups (5 groups each for NZ and VL) (3 replicates/group, 6 rabbits/replicate, 18 rabbits/group). All rabbits were inoculated with 5 × 104 Eimeria spp. oocysts (E. intestinalis (67%), E. magna (22%), and E. media (11%)) except for the rabbits in the first group (G1), which were inoculated with a sterile solution and served as a negative control. The remaining four groups were treated as follows: G2, no treatment/positive control, G3, treated with neem leaf extract, G4, treated with pomegranate peel extract (PPE), and G5, treated with a combination of neem leaf extract and PPE. For both breeds, our results showed that the use of neem leaf and/or pomegranate peel extract resulted in improved growth performance, with a significant improvement in relative feed conversion ratio (FCR) compared to the positive control groups, which recorded the worst values, as well as a significant (p ≤ 0.05) reduction in mean oocyst count compared to the positive control groups. We also observed downregulation of mRNA levels of IL-1βα, IL6, and TNF-α in the herbal treatment groups compared with the mRNA levels of these genes in the positive control groups. Herbal treatment with neem leaf and/or pomegranate peel extracts had positive effects on the NZ and VL rabbits experimentally infected with mixed Eimeria species, as evidenced by their healthy appearance, good appetite, no mortalities, an anticoccidial index 〉 120, and a significantly higher total return and net profit when compared to the positive control groups of both breeds. In NZ rabbits, the treatment with neem leaf extract alone (G3) or in combination with PPE (G5) recorded the most efficient economic anticoccidial activity.

Description: The short form of the leptin receptor (LRa) plays a key role in the transport of leptin to the central nervous system (CNS). Here, the resistin (RSTN)-mediated expression of LRa in the preoptic area (POA), ventromedial and dorsomedial nuclei (VMH/DMH),arcuate nucleus (ARC) and the anterior pituitary gland (AP)was analyzed considering the photoperiodic (experiment 1) and nutritional status (experiment 2) of ewes. In experiment 1, 30 sheep were fed normally and received one injection of saline or two doses of RSTN one hour prior to euthanasia. RSTN increased LRa expression mainly in the ARC and AP during long days (LD) and only in the AP during short days (SD). In experiment 2, an altered diet for 5 months created lean or fat sheep. Twenty sheep were divided into four groups: the lean and fat groups were given saline, while the lean-R and fat-R groups received RSTN one hour prior to euthanasia. Changes in adiposity influenced the effect of RSTN on LRa mRNA transcript levels in the POA, ARC and AP and without detection of LRa in the VMH/DMH. Overall, both photoperiodic and nutritional signals influence the effects of RSTN on leptin transport to the CNS and are involved in the adaptive/pathological phenomenon of leptin resistance in sheep.

Description: This paper reveals the relationship between the Miller plateau voltage and the displacement currents through the gate–drain capacitance (CGD) and the drain–source capacitance (CDS) in the switching process of a power transistor. The corrected turn-on Miller plateau voltage and turn-off Miller plateau voltage are different even with a constant current load. Using the proposed new Miller plateau, the turn-on and turn-off sequences can be more accurately analyzed, and the switching power loss can be more accurately predicted accordingly. Switching loss models based on the new Miller plateau have also been proposed. The experimental test result of the power MOSFET (NCE2030K) verified the relationship between the Miller plateau voltage and the displacement currents through CGD and CDS. A carefully designed verification test bench featuring a power MOSFET written in Verilog-A proved the prediction accuracy of the switching waveform and switching loss with the new proposed Miller plateau. The average relative error of the loss model using the new plateau is reduced to 1/2∼1/4 of the average relative error of the loss model using the old plateau; the proposed loss model using the new plateau, which also takes the gate current’s variation into account, further reduces the error to around 5%.

Description: One important type of animal welfare concern is “natural living” (i.e., that animals are able to express natural behaviours that are important to them, and to engage with aspects of the natural world that they find important). The aims of this narrative review were to describe the behavioural development of calves (Bos taurus) in natural settings and use this to identify characteristics of natural systems that may be important to consider relative to this natural living conception of animal welfare. At birth, calves are licked by their mothers and soon stand to suckle for colostrum, and during the milk-feeding period, calves spend much of their time lying down. In natural systems, calves perform a variety of social behaviours with herd-mates, and slowly transition from their mother’s milk to eating solid food, by gradually increasing time spent grazing and ruminating. In contrast, on most commercial dairy systems, dairy calves are removed from their mothers at birth, housed individually, fed restricted amounts of milk and weaned abruptly at a young age. The results of this review suggest that accommodating key natural behaviours, for example through the use of teat feeding of milk, social housing, and gradual weaning, can help address welfare concerns.

Description: The Society of Automotive Engineers (SAE) defines six levels of driving automation, ranging from Level 0 to Level 5. Automated driving systems perform entire dynamic driving tasks for Levels 3–5 automated vehicles. Delegating dynamic driving tasks from driver to automated driving systems can eliminate crashes attributed to driver errors. Sharing status, sharing intent, seeking agreement, or sharing prescriptive information between road users and vehicles dedicated to automated driving systems can further enhance dynamic driving task performance, safety, and traffic operations. Extensive simulation is required to reduce operating costs and achieve an acceptable risk level before testing cooperative automated driving systems in laboratory environments, test tracks, or public roads. Cooperative automated driving systems can be simulated using a vehicle dynamics simulation tool (e.g., CarMaker and CarSim) or a traffic microsimulation tool (e.g., Vissim and Aimsun). Vehicle dynamics simulation tools are mainly used for verification and validation purposes on a small scale, while traffic microsimulation tools are mainly used for verification purposes on a large scale. Vehicle dynamics simulation tools can simulate longitudinal, lateral, and vertical dynamics for only a few vehicles in each scenario (e.g., up to ten vehicles in CarMaker and up to twenty vehicles in CarSim). Conventional traffic microsimulation tools can simulate vehicle-following, lane-changing, and gap-acceptance behaviors for many vehicles in each scenario without simulating vehicle powertrain. Vehicle dynamics simulation tools are more compute-intensive but more accurate than traffic microsimulation tools. Due to software architecture or computing power limitations, simplifying assumptions underlying convectional traffic microsimulation tools may have been a necessary compromise long ago. There is, therefore, a need for a simulation tool to optimize computational complexity and accuracy to simulate many vehicles in each scenario with reasonable accuracy. This research proposes a traffic microsimulation tool that employs a simplified vehicle powertrain model and a model-based fault detection method to simulate many vehicles with reasonable accuracy at each simulation time step under noise and unknown inputs. Our traffic microsimulation tool considers driver characteristics, vehicle model, grade, pavement conditions, operating mode, vehicle-to-vehicle communication vulnerabilities, and traffic conditions to estimate longitudinal control variables with reasonable accuracy at each simulation time step for many conventional vehicles, vehicles dedicated to automated driving systems, and vehicles equipped with cooperative automated driving systems. Proposed vehicle-following model and longitudinal control functions are verified for fourteen vehicle models, operating in manual, automated, and cooperative automated modes over two driving schedules under three malicious fault magnitudes on transmitted accelerations.

Description: The aim of the present research was to analyze the farmers’ intention towards participation in the management and conservation of wetlands through the lens of the extended theory of planned behavior (TPB). To do this, a cross-sectional survey of Iranian farmers was carried out. To select the samples, a multi-stage random sampling process with a proportional assignment was employed. The research instrument was a researcher-made questionnaire whose validity and reliability were verified using various quantitative and qualitative indicators. The results of the extended TPB using structural equation modeling showed that four variables, namely moral norms of participation in management and conservation (MNPMC), attitude towards participation in management and conservation (APMC), subjective norms towards participation in management and conservation (SNPMC), and self-concept about participation in management and conservation (SCPMC) had positive and significant impacts on intention towards participation in management and conservation (IPMC). The results also revealed that that entering MNPMC and SCPMC into TPB could increase its explanatory power. Also, the fit indicators supported the extended TPB. From a practical point of view, the present study provides justifications and insights for the use of MNPMC, APMC, SNPMC, and SCPMC in policies and programs intended to encourage farmers and local communities to participate in wetlands management and conservation.

Description: In machine learning and data science, feature selection is considered as a crucial step of data preprocessing. When we directly apply the raw data for classification or clustering purposes, sometimes we observe that the learning algorithms do not perform well. One possible reason for this is the presence of redundant, noisy, and non-informative features or attributes in the datasets. Hence, feature selection methods are used to identify the subset of relevant features that can maximize the model performance. Moreover, due to reduction in feature dimension, both training time and storage required by the model can be reduced as well. In this paper, we present a tri-stage wrapper-filter-based feature selection framework for the purpose of medical report-based disease detection. In the first stage, an ensemble was formed by four filter methods—Mutual Information, ReliefF, Chi Square, and Xvariance—and then each feature from the union set was assessed by three classification algorithms—support vector machine, naïve Bayes, and k-nearest neighbors—and an average accuracy was calculated. The features with higher accuracy were selected to obtain a preliminary subset of optimal features. In the second stage, Pearson correlation was used to discard highly correlated features. In these two stages, XGBoost classification algorithm was applied to obtain the most contributing features that, in turn, provide the best optimal subset. Then, in the final stage, we fed the obtained feature subset to a meta-heuristic algorithm, called whale optimization algorithm, in order to further reduce the feature set and to achieve higher accuracy. We evaluated the proposed feature selection framework on four publicly available disease datasets taken from the UCI machine learning repository, namely, arrhythmia, leukemia, DLBCL, and prostate cancer. Our obtained results confirm that the proposed method can perform better than many state-of-the-art methods and can detect important features as well. Less features ensure less medical tests for correct diagnosis, thus saving both time and cost.

Description: With the rapid development of deep learning, computer vision has assisted in solving a variety of problems in engineering construction. However, very few computer vision-based approaches have been proposed on work productivity’s evaluation. Therefore, taking a super high-rise project as a research case, using the detected object information obtained by a deep learning algorithm, a computer vision-based method for evaluating the productivity of assembling reinforcement is proposed. Firstly, a detector that can accurately distinguish various entities related to assembling reinforcement based on CenterNet is established. DLA34 is selected as the backbone. The mAP reaches 0.9682, and the speed of detecting a single image can be as low as 0.076 s. Secondly, the trained detector is used to detect the video frames, and images with detected boxes and documents with coordinates can be obtained. The position relationship between the detected work objects and detected workers is used to determine how many workers (N) have participated in the task. The time (T) to perform the process can be obtained from the change of coordinates of the work object. Finally, the productivity is evaluated according to N and T. The authors use four actual construction videos for validation, and the results show that the productivity evaluation is generally consistent with the actual conditions. The contribution of this research to construction management is twofold: On the one hand, without affecting the normal behavior of workers, a connection between construction individuals and work object is established, and the work productivity evaluation is realized. On the other hand, the proposed method has a positive effect on improving the efficiency of construction management.

Description: Creating a widely excepted model on the measure of intelligence became inevitable due to the existence of an abundance of different intelligent systems. Measuring intelligence would provide feedback for the developers and ultimately lead us to create better artificial systems. In the present paper, we show a solution where learning as a process is examined, aiming to detect pre-written solutions and separate them from the knowledge acquired by the system. In our approach, we examine image recognition software by executing different transformations on objects and detect if the software was resilient to it. A system with the required intelligence is supposed to become resilient to the transformation after experiencing it several times. The method is successfully tested on a simple neural network, which is not able to learn most of the transformations examined. The method can be applied to any image recognition software to test its abstraction capabilities.

Description: Physical inactivity increases the risk of many adverse health conditions, including the world’s major non-communicable diseases, such as coronary heart disease, type 2 diabetes, and breast and colon cancers, shortening life expectancy. There are minimal medical care and personal trainers’ methods to monitor a patient’s actual physical activity types. To improve activity monitoring, we propose an artificial-intelligence-based approach to classify physical movement activity patterns. In more detail, we employ two deep learning (DL) methods, namely a deep feed-forward neural network (DNN) and a deep recurrent neural network (RNN) for this purpose. We evaluate the two models on two physical movement datasets collected from several volunteers who carried tri-axial accelerometer sensors. The first dataset is from the UCI machine learning repository, which contains 14 different activities-of-daily-life (ADL) and is collected from 16 volunteers who carried a single wrist-worn tri-axial accelerometer. The second dataset includes ten other ADLs and is gathered from eight volunteers who placed the sensors on their hips. Our experiment results show that the RNN model provides accurate performance compared to the state-of-the-art methods in classifying the fundamental movement patterns with an overall accuracy of 84.89% and an overall F1-score of 82.56%. The results indicate that our method provides the medical doctors and trainers a promising way to track and understand a patient’s physical activities precisely for better treatment.

Description: A previous study revealed low survival rates for Japanese larch (Larix kaempferi) summer-planted seedlings grown in Hiko-V-120 containers. This study examines nursery practices that could potentially prevent deterioration of the seedling water balance after planting to improve the survival rate of this species, which has a low drought tolerance. During summer planting, we tested (1) drought hardening or high-potassium fertilization for two months before planting, (2) antitranspirant or topping treatment at planting, and (3) the use of the JFA-150 container with a larger capacity and lower growing density than the Hiko-V-120 container. Drought hardening increased seedling drought tolerance because of the low leaf:root ratio, due to lower leaf mass production, resulting in increased survival from 74% to 93% in Hiko-V-120 containers. When JFA-150 containers were used, the leaf:root ratio was lower because of higher root mass, resulting in an increase in survival to 87%, with the highest survival of 97% when combined with drought hardening. The application of antitranspirant increased survival to over 90%, whereas topping did not, probably because of severer competition from weeds. High-potassium fertilization did not affect seedling traits or survival. For better survival of summer-planted container-grown Japanese larch seedlings, it is recommended that they be grown in containers providing sufficient cell volume and density for root growth while the seedlings are in the nursery and that irrigation be withheld for two months before planting. In addition, to obtain higher survival, an antitranspirant can be applied at planting at a cost.

Description: Contagious agalactia is associated with mastitis, keratoconjunctivitis, arthritis, pneumonia, and septicemia in small ruminants in countries with large dairy industries worldwide. The causative agents belong to four (sub)species of the Mycoplasma genus that have remained essentially susceptible to antimicrobials, including to the widely-used tetracycline family. However, some clinical isolates have been detected that show increased minimum inhibitory concentrations of tetracyclines, although they do not harbor the mutation in the 16SrRNA gene usually associated with resistance. The present work aimed to assess whether efflux pumps, infrequently described in mycoplasmas, could participate in the observed moderate loss of susceptibility. General efflux mechanisms were measured (i) using the fluorescence property of ethidium bromide when accumulated intracellularly and intercalated in the mycoplasma genomes, its active extrusion resulting in a temperature-dependent decrease in fluorescence and (ii) monitoring the growth inhibition of mycoplasmas by subinhibitory concentrations of tetracycline with or without reserpine, a known inhibitor of efflux in other bacteria. Both methods revealed non-specific efflux phenomena in most of the isolates tested, although their efficacy was difficult to quantify. This property could contribute to the acquisition of mutations conferring resistance by maintaining intracellular concentrations of tetracyclines at subinhibitory levels.

Description: Reliable estimations of parameter values and associated uncertainties are crucial for crop model applications in agro-environmental research. However, estimating many parameters simultaneously for different types of response variables is difficult. This becomes more complicated for grapevines with different phenotypes between varieties and training systems. Our study aims to evaluate how a standard least square approach can be used to calibrate a complex grapevine model for simulating both the phenology (flowering and harvest date) and yield of four different variety–training systems in the Douro Demarcated Region, northern Portugal. An objective function is defined to search for the best-fit parameters that result in the minimum value of the unweighted sum of the normalized Root Mean Squared Error (nRMSE) of the studied variables. Parameter uncertainties are estimated as how a given parameter value can determine the total prediction variability caused by variations in the other parameter combinations. The results indicate that the best-estimated parameters show a satisfactory predictive performance, with a mean bias of −2 to 4 days for phenology and −232 to 159 kg/ha for yield. The corresponding variance in the observed data was generally well reproduced, except for one occasion. These parameters are a good trade-off to achieve results close to the best possible fit of each response variable. No parameter combinations can achieve minimum errors simultaneously for phenology and yield, where the best fit to one variable can lead to a poor fit to another. The proposed parameter uncertainty analysis is particularly useful to select the best-fit parameter values when several choices with equal performance occur. A global sensitivity analysis is applied where the fruit-setting parameters are identified as key determinants for yield simulations. Overall, the approach (including uncertainty analysis) is relatively simple and straightforward without specific pre-conditions (e.g., model continuity), which can be easily applied for other models and crops. However, a challenge has been identified, which is associated with the appropriate assumption of the model errors, where a combination of various calibration approaches might be essential to have a more robust parameter estimation.

Description: Single image super-resolution task aims to reconstruct a high-resolution image from a low-resolution image. Recently, it has been shown that by using deep image prior (DIP), a single neural network is sufficient to capture low-level image statistics using only a single image without data-driven training such that it can be used for various image restoration problems. However, super-resolution tasks are difficult to perform with DIP when the target image is noisy. The super-resolved image becomes noisy because the reconstruction loss of DIP does not consider the noise in the target image. Furthermore, when the target image contains noise, the optimization process of DIP becomes unstable and sensitive to noise. In this paper, we propose a noise-robust and stable framework based on DIP. To this end, we propose a noise-estimation method using the generative adversarial network (GAN) and self-supervision loss (SSL). We show that a generator of DIP can learn the distribution of noise in the target image with the proposed framework. Moreover, we argue that the optimization process of DIP is stabilized when the proposed self-supervision loss is incorporated. The experiments show that the proposed method quantitatively and qualitatively outperforms existing single image super-resolution methods for noisy images.

Description: The characteristics of bridge pile-group foundation have a significant influence on the dynamic performance of the superstructure. Most of the existing analysis methods for the pile-group foundation impedance take the trait of strong specialty, which cannot be generalized in practical projects. Therefore, a project-oriented numerical solution algorithm is proposed to compute the dynamic impedance of bridge pile-group foundation. Based on the theory of viscous-spring artificial boundary, the derivation and solution of the impedance function are transferred to numerical modeling and harmonic analysis, which can be carried out through the finite element method. By taking a typical pile-group foundation as a case study, the results based on the algorithm are compared with those from existing literature. Moreover, an impact experiment of a real pile-group foundation was implemented, the results of which are also compared with those resulting from the proposed numerical algorithm. Both comparisons show that the proposed numerical algorithm satisfies engineering precision, thus showing good effectiveness in application.

Description: Urban greenery is widely acknowledged as a key element for creating livable urban environments and improving residents’ quality of life. However, only a few current studies on the subject of urban greenery focus on a human visual perspective and take street greenery into consideration. Street greenery is an indispensable component of urban vegetation to which residents have a higher frequency of access. Additionally, few studies focused on the disparity of the green view at a micro-level, such as at a county or community level. This study explored the spatial distribution of street greenery and its influential factors using the green view index (GVI) as the main evaluation indicator. Compared to other traditional indicators of greenery, such as the normalized difference vegetation index (NDVI) and accessibility, GVI is recognized as a human-oriented indicator to evaluate the quantity of greenery viewed by human eyes in daily life. The downtown area of Shanghai was chosen as the case study, as it reflects the common phenomenon of street greenery in many megacities globally. In addition, county/jiedao (the same administrative area as county in China) level was selected as the minimum geographical unit to evaluate the disparity of GVI and its influential factors to fill the knowledge gap. We analyzed 233,000 pieces of street-view images from Baidu Map and other correlated data. The results showed (1) the street greenery of 70% of the downtown area of Shanghai is less than the recommended comforFogre visual environment; (2) street greenery is spatially clustered in Huangpu district, Xuhui district, college town, and the Century Park of Shanghai; (3) street-greenery distribution is positively correlated with housing price and street network density, and negatively correlated with the ratio of society vulnerability; however, it is uncorrelated to population density. According to these findings, local municipalities could improve urban planning and design by introducing a more human-oriented green-space policy that improves social equity.

Description: Medical image analysis research requires medical image datasets. Nevertheless, due to various impediments, researchers have been described as “data starved”. We hypothesize that implicit evolving community standards require researchers to use ever-growing datasets. In Phase I of this research, we scanned the MICCAI (Medical Image Computing and Computer-Assisted Intervention) conference proceedings from 2011 to 2018. We identified 907 papers involving human MRI, CT or fMRI datasets and extracted their sizes. The median dataset size had grown by 3–10 times from 2011 to 2018, depending on imaging modality. Statistical analysis revealed exponential growth of the geometric mean dataset size with an annual growth of 21% for MRI, 24% for CT and 31% for fMRI. Thereupon, we had issued a forecast for dataset sizes in MICCAI 2019 well before the conference. In Phase II of this research, we examined the MICCAI 2019 proceedings and analyzed 308 relevant papers. The MICCAI 2019 statistics compare well with the forecast. The revised annual growth rates of the geometric mean dataset size are 27% for MRI, 30% for CT and 32% for fMRI. We predict the respective dataset sizes in the MICCAI 2020 conference (that we have not yet analyzed) and the future MICCAI 2021 conference.

Description: Climate change has inherent multidisciplinary characteristics, and predicting the future of a single field of work has a limit. Therefore, this study proposes a water-centric nexus approach for the agriculture and forest sectors for improving the response to climate change in the Korean Peninsula. Two spatial models, i.e., Environmental Policy Integrated Climate and Integrated Valuation of Ecosystem Services and Tradeoffs, were used to assess the extent of changes in agricultural water demand, forest water supply, and their balance at the watershed level in the current and future climatic conditions. Climate changed has increased the agricultural water demand and forest water supply significantly in all future scenarios and periods. Comparing the results with RCP8.5 2070s and the baseline, the agricultural water demand and forest water supply increased by 35% and 28%, respectively. Water balance assessment at the main watershed level in the Korean Peninsula revealed that although most scenarios of the future water supply increases offset the demand growth, a risk to water balance exists in case of a low forest ratio or smaller watershed. For instance, the western plains, which are the granary regions of South and North Korea, indicate a higher risk than other areas. These results show that the land-use balance can be an essential factor in a water-centric adaptation to climate change. Ultimately, the water-centric nexus approach can make synergies by overcoming increasing water demands attributable to climate change.

Description: Sex differences in a variety of cognitive traits have long been reported in various species, including dogs. However, only a few canine studies have taken the possible effect of reproductive hormones into account. The aim of this study was to investigate the effects of sex and reproductive status of pet dogs (N = 1032) on two cognitive traits: inhibitory control and social cognition. Inhibitory control was assessed using the cylinder test, and the dogs’ tendency to initiate social contact with a human during a problem-solving situation was assessed using the unsolvable task. Female dogs had a significantly higher success rate in the cylinder test compared to males, and they spent significantly more time in human-directed behavior during the unsolvable task. In contrast, males spent significantly more time in independent behavior during the unsolvable task. Reproductive status had no significant effect on the results of the cylinder test or the unsolvable task. Our results showed that female dogs asked for more help/used a more cooperative strategy during a problem-solving situation and had greater inhibitory control compared to males. According to our results, it seems likely that these sex differences were not influenced to a large extent by reproductive hormones.

Description: Heritabilities of workability (WT) traits—milking speed (MS) and temperament (MT)—as well as genetic and phenotypic correlations between these traits in the population of Polish Holstein-Friesian (PHF) cows were estimated. The estimation of genetic parameters was performed twice: first with the use of pedigree data; and second with the use of pedigree and genomic data. Phenotypic data from routinely conducted MS and MT evaluations for 1,045,511 cows born from 2004 to 2013 were available; the cows were evaluated from 2011 to 2015. The main dataset was reduced based on imposed restrictions (e.g., on age of calving, stage of lactation and day of first trial milking). The dataset prepared in this manner comprised 391,615 cows. It was then reduced to daughters of 10% randomly selected sires for computational reasons. Finally, for genetic parameter estimation, 13,280 records of cows were used. The linear observation model included additive random effects of animal, fixed effects of herd-year-season of calving subclass (HYS) and lactation phase, fixed regressions on cow age at calving and the percent of HF breed genes in the cow genotype. Heritabilities estimated based on pedigree data were 0.12 (±0.0067) for MS and 0.08 (±0.0063) for MT, the genetic correlation between MS and MT was estimated at 0.05 (±0.0002) and the phenotypic correlation coefficient was estimated at 0.14 (±0.0004). The inclusion of genomic information of sire bulls had no clear effect on the size of the estimated WT genetic parameters. The heritabilities of MS and MT were 0.11 (±0.0065) and 0.09 (±0.0012), respectively. The genetic and phenotypic correlation coefficients were 0.07 (±0.0003) and 0.12 (±0.0005), respectively. The sizes of the obtained heritabilities of WT and of the genetic and phenotypic correlation between these traits indicate the possibility of effective population improvement for both WT traits.

Description: Low pressure fluid transport (1) applications often require low and precise volumetric flow rates (2) including low leakage to reduce additional costly and complex sensors. A peristaltic pump design (3) was realized, with the fluid’s flexible transport channel formed by a solid cavity and a wobbling plate comprising a rigid and a soft layer (4). In operation, the wobbling plate is driven externally by an electric motor, hence, the soft layer is contracted and unloaded (5) during pump-cycles transporting fluid from low to high pressure sides. A thorough characterization of the pump system is required to design and dimension the components of the peristaltic pump. To capture all these parameters and their dependencies on various operation-states, often complex and long-lasting dynamic 3D FE-simulations are required. We present, here, a holistic design methodology (6) including analytical as well as numerical calculations, and experimental validations for a peristaltic pump with certain specifications of flow-rate range, maximum pressures, and temperatures. An experimental material selection process is established and material data of candidate materials (7) (liquid silicone rubber, acrylonitrile rubber, thermoplastic-elastomer) are directly applied to predict the required drive torque. For the prediction, a semi-physical, analytical model was derived and validated by characterizing the pump prototype.

Description: Many smart city and society applications such as smart health (elderly care, medical applications), smart surveillance, sports, and robotics require the recognition of user activities, an important class of problems known as human activity recognition (HAR). Several issues have hindered progress in HAR research, particularly due to the emergence of fog and edge computing, which brings many new opportunities (a low latency, dynamic and real-time decision making, etc.) but comes with its challenges. This paper focuses on addressing two important research gaps in HAR research: (i) improving the HAR prediction accuracy and (ii) managing the frequent changes in the environment and data related to user activities. To address this, we propose an HAR method based on Soft-Voting and Self-Learning (SVSL). SVSL uses two strategies. First, to enhance accuracy, it combines the capabilities of Deep Learning (DL), Generalized Linear Model (GLM), Random Forest (RF), and AdaBoost classifiers using soft-voting. Second, to classify the most challenging data instances, the SVSL method is equipped with a self-training mechanism that generates training data and retrains itself. We investigate the performance of our proposed SVSL method using two publicly available datasets on six human activities related to lying, sitting, and walking positions. The first dataset consists of 562 features and the second dataset consists of five features. The data are collected using the accelerometer and gyroscope smartphone sensors. The results show that the proposed method provides 6.26%, 1.75%, 1.51%, and 4.40% better prediction accuracy (average over the two datasets) compared to GLM, DL, RF, and AdaBoost, respectively. We also analyze and compare the class-wise performance of the SVSL methods with that of DL, GLM, RF, and AdaBoost.

Description: This work aimed to (i) determine the reliability of a portable Bluetooth colourimeter for fruit colour measurements; (ii) characterise the changes in quantitative skin colour attributes in a nectarine cultivar in response to time from harvest; and (iii) determine the influence of row orientation and training system on nectarine skin colour. The skin colour attributes measured with the colourimeter, namely L*, a* and b*, were calibrated and validated against a reference spectrophotometer. C* and h° were obtained from a* and b*. Skin colour was measured in situ from 42 days before to 6 days after harvest on ‘Majestic Pearl’ nectarines subjected to different row orientations and training systems. Validation models showed high reliability of colour estimations. The trends of colour attributes over time were characterised by cubic regression models, with h° proving to be the best parameter to describe changes of colour over time, with a clear link to the maturation process. No significant effects of row orientation and training system on skin colour were observed at harvest. Overall, the device proved reliable for fruit colour detection. Results of this study highlight the potential of h° as a quantitative index to monitor ripening prior to harvest in ‘Majestic Pearl’ nectarines.

Description: The SARS-CoV-2 virus has spread worldwide, and the World Health Organization has declared COVID-19 pandemic, proclaiming that the entire world must overcome it together. The chest X-ray and computed tomography datasets of individuals with COVID-19 remain limited, which can cause lower performance of deep learning model. In this study, we developed a model for the diagnosis of COVID-19 by solving the classification problem using a self-supervised learning technique with a convolution attention module. Self-supervised learning using a U-shaped convolutional neural network model combined with a convolution block attention module (CBAM) using over 100,000 chest X-Ray images with structure similarity (SSIM) index captures image representations extremely well. The system we proposed consists of fine-tuning the weights of the encoder after a self-supervised learning pretext task, interpreting the chest X-ray representation in the encoder using convolutional layers, and diagnosing the chest X-ray image as the classification model. Additionally, considering the CBAM further improves the averaged accuracy of 98.6%, thereby outperforming the baseline model (97.8%) by 0.8%. The proposed model classifies the three classes of normal, pneumonia, and COVID-19 extremely accurately, along with other metrics such as specificity and sensitivity that are similar to accuracy. The average area under the curve (AUC) is 0.994 in the COVID-19 class, indicating that our proposed model exhibits outstanding classification performance.

Description: Defective hard candies are usually produced due to inadequate feeding or insufficient cooling during the candy production process. The human-based inspection strategy needs to be brought up to date with the rapid developments in the confectionery industry. In this paper, a detection and classification method for defective hard candies based on convolutional neural networks (CNNs) is proposed. First, the threshold_li method is used to distinguish between hard candy and background. Second, a segmentation algorithm based on concave point detection and ellipse fitting is used to split the adhesive hard candies. Finally, a classification model based on CNNs is constructed for defective hard candies. According to the types of defective hard candies, 2552 hard candies samples were collected; 70% were used for model training, 15% were used for validation, and 15% were used for testing. Defective hard candy classification models based on CNNs (Alexnet, Googlenet, VGG16, Resnet-18, Resnet34, Resnet50, MobileNetV2, and MnasNet0_5) were constructed and tested. The results show that the classification performances of these deep learning models are similar except MnasNet0_5 with the classification accuracy of 84.28%, and the Resnet50-based classification model is the best (98.71%). This research has certain theoretical reference significance for the intelligent classification of granular products.

Description: Visual Question Generation (VQG) from images is a rising research topic in both fields of natural language processing and computer vision. Although there are some recent efforts towards generating questions from images in the open domain, the VQG task in the medical domain has not been well-studied so far due to the lack of labeled data. In this paper, we introduce a goal-driven VQG approach for radiology images called VQGRaD that generates questions targeting specific image aspects such as modality and abnormality. In particular, we study generating natural language questions based on the visual content of the image and on additional information such as the image caption and the question category. VQGRaD encodes the dense vectors of different inputs into two latent spaces, which allows generating, for a specific question category, relevant questions about the images, with or without their captions. We also explore the impact of domain knowledge incorporation (e.g., medical entities and semantic types) and data augmentation techniques on visual question generation in the medical domain. Experiments performed on the VQA-RAD dataset of clinical visual questions showed that VQGRaD achieves 61.86% BLEU score and outperforms strong baselines. We also performed a blinded human evaluation of the grammaticality, fluency, and relevance of the generated questions. The human evaluation demonstrated the better quality of VQGRaD outputs and showed that incorporating medical entities improves the quality of the generated questions. Using the test data and evaluation process of the ImageCLEF 2020 VQA-Med challenge, we found that relying on the proposed data augmentation technique to generate new training samples by applying different kinds of transformations, can mitigate the lack of data, avoid overfitting, and bring a substantial improvement in medical VQG.

Description: Cucumber, a widely cultivated vegetable, is mostly grown under greenhouse conditions. In recent years, the overuse of inorganic fertilizers for higher yield attainment adversely has affected human health and the environment. Therefore, a greenhouse experiment was designed to evaluate the effects of different nutrient sources (poultry manure (PM) and mineral fertilizer (MF)) on productivity-enhancing parameters of cucumber via univariate and multivariate analyses. Amounts of PM and MF (NPK15:15:15) were added to coco-peat per cubic meter by weight/volume (w/v) ratios as follows: T1 (control), 60 kg PM; T2, 30 kg PM + 3 kg MF; T3, 30 kg PM + 5 kg MF, and T4, 30 kg PM + 7 kg MF. The univariate analysis performed on the collected data illustrated the significant enhancement in growth and productivity for the integrated use of PM and MF. Multivariate analyses (correlation, clustering, and Principal Component Analysis) validated the results of univariate analysis by differentiating treatments into two groups. The three treatments obtained a distinguished group from T1 (Control) and did not show significant differences among each other, with a maximum yield increase by T2 (74.6%). According to these results, T2 could improve cucumber productivity under greenhouse conditions. It can be taken as recommendations for better quality and yield enhancement in future improvement programs and cucumber-related farming communities.

Description: Italian dry-cured ham production requires pigs to be slaughtered at 160 ± 16 kg at 9 months of age (control, C). The study explored three alternatives, based on different feeding conditions: (1) allowing pigs to express their growth potential by letting them reach 160 ± 16 kg slaughter weight (SW) at younger slaughter age (SA) (younger Age, YA); (2) allowing pigs to express their growth potential by maximizing their SW at 9 months SA (greater weight, GW); (3) increasing the SA required to reach 160 ± 16 kg SW (older age, OA). Pigs (336 C21 Goland, 95 kg initial body weight) were slaughtered on average at 257, 230, 257, and 273 d SA and 172.7, 172.3, 192.9, and 169.3 SW kg for the four treatments, respectively. C pigs had an average daily gain (ADG) of 715 g/d and feed efficiency (FE) of 0.265 (gain to feed). Compared to C, YA pigs had higher ADG (+32%), FE (+7.5%), and better ham adiposity; GW pigs had higher carcass weight (+12%), ADG (+25%), trimmed ham weight (+10.9%), and better ham adiposity. OA treatment affected ADG (−16.4%), FE (−16.6%), and trimmed ham weight (−3.6%). YA and GW could be promising alternatives to C as they improved FE and ham quality traits.

Description: Many factors govern the flow of deposited nitrogen (N) through forest ecosystems and into stream water. At the Fernow Experimental Forest in WV, stream water nitrate (NO3−) export from a long-term reference watershed (WS 4) increased in approximately 1980 and has remained elevated despite more recent reductions in chronic N deposition. Long-term changes in species composition may have altered forest N demand and the retention of deposited N. In particular, the abundance and importance value of Acer saccharum have increased since the 1950s, and this species is thought to have a low affinity for NO3−. We measured the relative uptake of NO3− and ammonium (NH4+) by six important temperate broadleaf tree species and estimated stand uptake of total N, NO3−, and NH4+. We then used records of stream water NO3− and stand composition to evaluate the potential impact of changes in species composition on NO3− export. Surprisingly, the tree species we examined all used both mineral N forms approximately equally. Overall, the total N taken up by the stand into aboveground tissues increased from 1959 through 2001 (30.9 to 35.2 kg N ha−1 yr−1). However, changes in species composition may have altered the net supply of NO3− in the soil since A. saccharum is associated with high nitrification rates. Increases in A. saccharum importance value could result in an increase of 3.9 kg NO3−-N ha−1 yr−1 produced via nitrification. Thus, shifting forest species composition resulted in partially offsetting changes in NO3− supply and demand, with a small net increase of 1.2 kg N ha−1 yr−1 in NO3− available for leaching. Given the persistence of high stream water NO3− export and relatively abrupt (~9 year) change in stream water NO3− concentration circa 1980, patterns of NO3− export appear to be driven by long-term deposition with a lag in the recovery of stream water NO3− after more recent declines in atmospheric N input.

Description: Biochar, a pyrogenic carbon, has been receiving incremental attention for potential contribution to soil health, agricultural productivity enhancement while mitigating climate change by sequestering carbon and reducing greenhouse gas (GHG) emissions. However, it is not well-known to us how far rice husk biochar (RHB) application rates could increase phosphorus (P) bioavailability and plant performance when co-applied with P and lime. Here, we present data of a pot experiment consisting of eleven treatments to evaluate RHB, lime, and phosphorus effect on soil phosphorus availability, CO2 emission, nutrient uptake, and yield performance of maize. Co-application of RHB (10 and 15 t ha−1) and lime (100% and 75%) was made with different rates of P (100%, 75%, and 50%). Our result revealed that, at harvest, the combined application of RHB, lime, and phosphorus fertilizer significantly increased soil pH, P availability and decreased Al and Fe toxicity relative to the control while increasing maize yield. The maximum soil pH increased by 36.75%, the highest available P increased by 158.75%, whilst, the exchangeable Al content reduced by 96.84% compared to the control treatment. However, the difference in biomass production and yield among different lime, RHB, and P were minimal, with the largest grain yield (15.50 t ha−1) was recorded in the T6 treatments (75% lime + 10 t ha−1 RHB + 100% Triple superphosphate). The increment in biomass and grain yield could have occurred due to lime and RHB mediated changes in soil properties, including enhancement of soil pH, availability of P, and other nutrients. This increased availability then increased nutrient uptake and biomass production. Our results suggest that the combined application of lime and RHB could bring favorable changes in soil properties while sacrificing some carbon from soils.

Description: The introduction of new species in forest management must be undertaken with a degree of care, to help prevent the spread of invasive species. However, new species with higher profitability are needed to increase forest products value and the resilience of rural populations. Paulownia tomentosa has an extremely fast growth. The objective and novelty of this work was to study the potential use of young Paulownia trees grown in Portugal by using heat treatment to improve its properties, thereby allowing higher value applications of the wood. The average chemical composition of untreated and heat-treated wood was determined. The extractive content was determined by successive Soxhlet extraction with dichloromethane (DCM), ethanol and water as solvents. The composition of lipophilic extracts was performed by injection in GC-MS with mass detection. Insoluble and soluble lignin, holocellulose and α-cellulose were also determined. Physical (density and water absorption and dimensional stability) and mechanical properties (bending strength and bending stiffness) and termite resistance was also determined. Results showed that extractive content increased in all solvents, lignin and α-cellulose also increased and hemicelluloses decreased. Compounds derived from the thermal degradation of lignin were found in heat-treated wood extractions. Dimensional stability improved but there was a decrease in mechanical properties. Resistance against termites was better for untreated wood than for heat-treated wood, possibly due to the thermal degradation of some toxic extractives.

Description: This paper describes the physical and tensile properties of corn bracts during a whole harvest period by using two corn cultivars, aiming to realize efficient peeling with minimum energy performance and decrease the incidence of ear damage. The value range and change rule of tensile properties were obtained by combining mechanical experiments and numerical statistics. Meanwhile, mathematical models were established for tensile properties depending on bract moisture content and bract thickness. The experimental results show that the tensile properties of leaf blade were affected by the orientation between pulling force and longitudinal vein, and that parallel orientation was greater than perpendicular. Further, the tensile properties of leaf sheaths depended on the angle between pulling force and natural growth direction of the bract in the following order: 0° 〉 90° 〉 180°. A larger pulling force angle can improve the probability of bract fracturing at the root of leaf sheaths, which helps reduce bract residue on the peduncle. In addition, the fracture mechanisms of leaf blades and sheaths were expressed from physiological and morphological perspectives. The experimental results are believed to be able to provide theoretical guidance by which to design and optimize corn-peeling devices.

Description: Handover usually deals with the mobility of the end users in a mobile network to assure about the ongoing session of a user. It is observed that frequent handover results in call dropping due to latency. In order to overcome this issue, a fuzzy based handover decision scheme for mobile devices using a predictive model is proposed. First, an MFNN (Multi-layer Feed Forward Network) is used to determine the next cell of the user along with best hand off time. To obtain the best access network, multiple-attribute Access Network Selection Function (ANSF) is used. The fuzzy rule is applied by considering the parameter data rate, reliability, signal strength, battery power and mobility as input and the output obtained is the optimal network. The proposed scheme selects the best access network and enhances the quality of services.

Description: The accurate and timely assessment of pasture quantity and quality (i.e., nutritive characteristics) is vital for effective pasture management. Remotely sensed data can be used to predict pasture quantity and quality. This study investigated the ability of Sentinel-2 multispectral bands, convolved from proximal hyperspectral data, in predicting various pasture quality and quantity parameters. Field data (quantitative and spectral) were gathered for experimental plots representing four pasture types—perennial ryegrass monoculture and three mixtures of swards representing increasing species diversity. Spectral reflectance data at the canopy level were used to generate Sentinel-2 bands and calculate normalised difference indices with each possible band pair. The suitability of these indices for prediction of pasture parameters was assessed. Pasture quantity parameters (biomass and Leaf Area Index) had a stronger influence on overall reflectance than the quality parameters. Indices involving the 1610 nm band were optimal for acid detergent fibre, crude protein, organic matter and water-soluble carbohydrate concentration, while being less affected by biomass or LAI. The study emphasises the importance of accounting for the quantity parameters in the spectral data-based models for pasture quality predictions. These explorative findings inform the development of future pasture quantity and quality models, particularly focusing on diverse swards.

Description: The physical-geographical features of the mountainous area of Montenegro cause difficulties in farmers’ life and work. The organization of the agricultural production faces a number of problems that limit the overall development of rural areas. Some agricultural crops, such as raspberry, have found optimal growth conditions and produce appropriate yields associated with good fruit quality in such mountainous area. The Willamette variety dominates the production and has broadly expanded, as some new varieties, most notably, Tulameen, Fertȍdi Zamatos, and Glen Ample. The aim of this paper was to examine the biological and production characteristics of two raspberry varieties—one floricane (Tulameen) and one primocane (Polka)—grown in two localities. We confirmed the general rule that at lower altitudes, these varieties (570 m a.s.l, Bijelo Polje) are characterized by earlier vegetation, flowering, and fruit ripening in comparison to plants at higher altitude (1040 m a.s.l, Mojkovac). The Tulameen variety started flowering on 15 May in Bijelo Polje (the flowering phenophase lasted for 29 days, until 12 June) and on 25 May in Mojkovac (the flowering phenophase lasted for 27 days, until 20 June). That is a delay of 11 days in relation to different locations. The Polka variety started flowering on 25 June in Bijelo Polje (57 days, until 20 August) and on 1 July in Mojkovac (flowering for 67 days, until 5 September). That is a delay of 7 days. The Tulameen variety started maturation on 14 June in Bijelo Polje (maturation lasted for 27 days, until 10 July) and on 22 June in Mojkovac (26 days, until 17 July), with a delay of 9 days. The Polka variety started maturation on 23 July in Bijelo Polje (the maturation phenophase lasted for 55 days, until 15 September) and on 5 August in Mojkovac (52 days, until 25 September), with a delay of 14 days. The results showed that the Polka variety had significantly higher total phenol content than the Tulameen variety (4.43 and 4.03 mg, respectively). In terms of locality, the Mojkovac raspberries had higher total phenol content than the Bijelo Polje raspberries. Polka raspberries also had a higher content of total flavonoids than Tulameen, whereas differences between localities in regard to the content of total flavonoids in the fruit were not significant. The content of condensed tannins and gallotannins in the raspberry fruit was similar in relation to both varieties and localities. Finally, the total antioxidant capacity was significantly higher in the Polka compared to the Tulameen variety, whereas the differences between localities were not statistically significant.

Description: In this paper, we construct novel numerical algorithms to solve the heat or diffusion equation. We start with 105 different leapfrog-hopscotch algorithm combinations and narrow this selection down to five during subsequent tests. We demonstrate the performance of these top five methods in the case of large systems with random parameters and discontinuous initial conditions, by comparing them with other methods. We verify the methods by reproducing an analytical solution using a non-equidistant mesh. Then, we construct a new nontrivial analytical solution containing the Kummer functions for the heat equation with time-dependent coefficients, and also reproduce this solution. The new methods are then applied to the nonlinear Fisher equation. Finally, we analytically prove that the order of accuracy of the methods is two, and present evidence that they are unconditionally stable.

Description: IP fragmentation is still prevalent on the Internet. Defragmented traffic is a prerequisite for many network processing algorithms. This work focuses on the size and organization of a flow table, which is an essential ingredient of the hardware IP defragmentation block. Previous research suggests that fragmented IP traffic is highly local, and a relatively small flow table (on the order of a thousand entries) can process most of the traffic. Samples of IP traffic were obtained from public data sources and used for a statistical analysis, revealing the key factors in achieving design goals. The findings were backed by an extensive design space exploration of the software defragmentation model, which resulted in the efficiency estimates. To provide a robust score of the simulation model, a new validation technique is employed that helps to overcome the limitations of the samples.

Description: The use of gradient descent training to optimize the performance of a rule-fact network expert system via updating the network’s rule weightings was previously demonstrated. Along with this, four training techniques were proposed: two used a single path for optimization and two use multiple paths. The performance of the single path techniques was previously evaluated under a variety of experimental conditions. The multiple path techniques, when compared, outperformed the single path ones; however, these techniques were not evaluated with different network types, training velocities or training levels. This paper considers the multi-path techniques under a similar variety of experimental conditions to the prior assessment of the single-path techniques and demonstrates their effectiveness under multiple operating conditions.

Description: The black soil of Northeast China has a strong adhesion ability because of its unique physical properties, and the soil often adheres to the surface of the grouser shoes of tracked vehicles during the operation. The adhesion performance depends largely on structure parameters of the grouser shoes. The grouser height, the grouser thickness, and the grouser splayed angle were selected as structure parameters. The adhesion force and adhesive soil mass were selected as indicators of adhesion performance. Therefore, the mathematical model between structure parameters and response indicators was established by the response surface method (RSM). The optimal parameters combination was that the grouser height was 20 mm, grouser thickness was 6.34 mm, and grouser splayed angle was 40.45°. The average data of verification experiments occurred when the adhesion force reached 1.11 kPa and adhesive soil mass reached 22.68 g. Compared with the average value of un-optimized experiment results, the adhesion force and adhesive soil mass reduced by 12.84% and 4.63%, respectively. The relative error of the predicted values and measured values was less than 5%, proving the reliability of the regression models. This study could provide a reference method for parameters optimization, and a new structure of the grouser shoes of tracked vehicles will be designed to reduce adhesion.

Description: Gram negative organisms are frequently isolated from Caretta caretta turtles, which can act as reservoir species for resistant microorganisms in the aquatic environment. C. caretta, which have no history of treatment with antimicrobials, are useful sentinel species for resistant microbes. In this culture-based study, commensal bacteria isolated from oral and cloacal samples of 98 healthy C. caretta were compared to clinical isolates from the wounds of 102 injured animals, in order to investigate the presence of AMR bacteria in free-living loggerheads from the Adriatic Sea. A total of 410 isolates were cultured. Escherichia coli and genera such as Serratia, Moraxella, Kluyvera, Salmonella were isolated only in healthy animals, while Acinetobacter, Enterobacter, Klebsiella and Morganella were isolated only from the wounds of the injured animals. When tested for susceptibility to ampicillin, amoxicillin + clavulanic acid, ceftazidime, cefuroxime, gentamicin, doxycycline, ciprofloxacin and enrofloxacin, the clinical isolates showed highly significant differences in AMR rates vs. commensal isolates for all the drugs tested, except for doxycycline. The detection of high AMR rates in loggerheads is of clinical and microbiological significance since it impacts both the choice of a proper antibiotic therapy and the implementation of conservation programs.

Description: The chemical composition of plant components of three maize hybrids harvested at the beginning of six reproductive stages of maturity was compared. The hybrids evaluated included Maximus VIP3, Defender VIP and Feroz VIP, which were evaluated at each of following stages: R1 (grain formation), R2 (milky grain), R3 (pasty grain), R4 (floury grain), R5 (hard grain) and R6 (ripe grain). The advancement in maturation was linearly related to the crude protein (CP) content of the stem, whole plant, and leaves, and there was a difference among the hybrids. Between R4 and R5 stages, Maximus and Defender presented the highest CP contents for husk (6.58 and 5.42% for Maximus; 5.54 and 5.17% for Defender). The neutral detergent fiber (NDF) of the leaves showed a quadratic relationship with the advancement of maturation but did not differ among the hybrids. For all the hybrids, the NDF content in the husk and cobs increased linearly during the reproductive stages (〉77 and 78%, respectively, for the three hybrids in R6). Defender had the lowest NDF content of the cob in R3. The acid detergent lignin contents did not differ among stages in the stems, and showed a linear decrease throughout the whole plant, though the contents did not differ among the hybrids. Due to the differences observed, recommendations for harvest based on the maturity stage for each hybrid should be taken into consideration. There seems to be no important distinction among hybrids for harvesting and use of straw. Despite the reduction in grain yield, an early harvest for earlage or snaplage can provide lower lignin content in husk and cob, as well as higher protein content in the husk, favoring the nutritional value of the vegetative fraction (husk and/or cob).

Description: Rice production in Tanzania, with 67% of its territory considered semi-dry and having average annual rainfall of 300 mm, must be increased to feed an ever-growing population. Water for irrigation and low soil fertility are among the main challenges. One way to decrease water consumption in paddy fields is to change the irrigation regime for rice production, replacing continuous flooding with alternate wetting and drying. In order to assess the impact of different irrigation regimes and nitrogen fertilizer applications on growth, yield, and water productivity of rice, a greenhouse pot experiment with soil from lowland rice ecology was conducted at Sokoine University of Agriculture, Tanzania during the 2019 cropping season. The experiment was split-plot based on randomized complete block design with 12 treatments and 3 replications. Water regimes were the main factors comparing continuous flooding (CF) and alternate wetting and drying (AWD) with nitrogen fertilizer levels as the subfactor, comparing absolute control (no fertilizer) with 0 (P and K fertilizers), 60, 90, 120, and 150 kg Nha−1. Alternate wetting and drying (AWD) significantly improved water productivity by 8.3% over CF (p 〈 0.05). Water productivity (WP) ranged from 0.6 to 1.5 kg of rice per m3 of water. Average water use ranged from 36 to 82 L per season, and water saving was up to 34.3%. Alternate wetting and drying significantly improved yields (p 〈 0.05) by 13.3%, and the yield ranged from 21.8 to 118.2 g pot−1. The combination of AWD water management and 60 kg N ha−1 nitrogen fertilization application was found to be the optimal management, however there was no significant difference between 60 and 90 kg N ha−1, in which case 60 kg N ha−1 is recommended because it lowers costs and raises net income. Nitrogen levels significantly affected water productivity, water use, and number of irrigations. Nitrogen levels had significant effect (p 〈 0.05) on plant height, number of tillers, flag leaf area, chlorophyll content, total tillers, number of productive tillers, panicle weight, panicle length, 1000-grain weight, straw yield, grain yield, and grain harvest index. The results showed that less water can be used to produce more crops under alternative wetting and drying irrigation practices. The results are important for water-scarce areas, providing useful information to policy makers, farmers, agricultural departments, and water management boards in devising future climate-smart adaptation and mitigation strategies.

Description: The knowledge of genetic variability and breeding techniques is crucial in crop improvement programs. This information is especially important in underutilized crops such as Bambara groundnut, which have limited breeding systems and genetic diversity information. Hence, this study evaluated the genetic variability and established the relationship between the yield and its components in Bambara groundnut based on seed weight using multivariate analysis. A field trial was conducted in a randomized complete block design with three replications on 28 lines. Data were collected on 12 agro-morphological traits, and a statistical analysis was conducted using SAS version 9.4 software, while the variance component, genotypic and phenotypic coefficient variation, heritability, and genetic advance values were estimated. A cluster analysis was performed using NT-SYS software to estimate the genetic relations among the accessions. The results showed significant variability among the accessions based on the yield and yield component characteristics. The evaluated lines were grouped into seven primary clusters based on the assessed traits using the UPGMA dendrogram. Based on the overall results, G5LR1P3, G1LR1P3, G4LR1P1, G2SR1P1 and G3SR1P4 performed the best for the yield and yield components. These improved lines are recommended for large-scale evaluation and utilization in future breeding programs to develop high-yield Bambara groundnut varieties.

Description: The computing resources of today’s smartphones are underutilized most of the time. Using these resources could be highly beneficial in edge computing and fog computing contexts, for example, to support urban services for citizens. However, new challenges, especially regarding job scheduling, arise. Smartphones may form ad hoc networks, but individual devices highly differ in computational capabilities and (tolerable) energy usage. We take into account these particularities to validate a task execution scheme that relies on the computing power that clusters of mobile devices could provide. In this paper, we expand the study of several practical heuristics for job scheduling including execution scenarios with state-of-the-art smartphones. With the results of new simulated scenarios, we confirm previous findings and better comprehend the baseline approaches already proposed for the problem. This study also sheds some light on the capabilities of small-sized clusters comprising mid-range and low-end smartphones when the objective is to achieve real-time stream processing using Tensorflow object recognition models as edge jobs. Ultimately, we strive for industry applications to improve task scheduling for dew computing contexts. Heuristics such as ours plus supporting dew middleware could improve citizen participation by allowing a much wider use of dew computing resources, especially in urban contexts in order to help build smart cities.

Description: The intricate nature of congenital heart disease requires understanding of the complex, patient-specific three-dimensional dynamic anatomy of the heart, from imaging data such as three-dimensional echocardiography for successful outcomes from surgical and interventional procedures. Conventional clinical systems use flat screens, and therefore, display remains two-dimensional, which undermines the full understanding of the three-dimensional dynamic data. Additionally, the control of three-dimensional visualisation with two-dimensional tools is often difficult, so used only by imaging specialists. In this paper, we describe a virtual reality system for immersive surgery planning using dynamic three-dimensional echocardiography, which enables fast prototyping for visualisation such as volume rendering, multiplanar reformatting, flow visualisation and advanced interaction such as three-dimensional cropping, windowing, measurement, haptic feedback, automatic image orientation and multiuser interactions. The available features were evaluated by imaging and nonimaging clinicians, showing that the virtual reality system can help improve the understanding and communication of three-dimensional echocardiography imaging and potentially benefit congenital heart disease treatment.

Description: Friction nonlinear disturbance is one of the main factors affecting the control performance of servo tracking system. In this paper, an improved Active Disturbance Rejection Control (ADRC) scheme of dual-axis servo turntable is researched to achieve accurate tracking. Firstly, the mathematical dynamics model of dual-axis servo tracking turntable system is established. The Elastoplastic model is used to describe nonlinear friction, in which the immeasurable part is extended to be a new state. Secondly, considering the smooth and monotonic increasing property of hyperbolic tangent function, an improved tracking differentiator is introduced, which can provide better noise attenuation performance. Thirdly, based on adjustable parameter systematic pole placement method, the fuzzy control algorithm is applied to realize the intelligent tuning of the improved Extended State Observer (ESO) gains, in which the input of the fuzzy controller is the estimation error, while the output is the observer bandwidth. Finally, the improved ADRC system is transformed into a Lurie system, then the extended circle criteria are adopted to analyze the absolute stability of the proposed system. Simulation and experimental verification of the improved ADRC method for the dual-axis turntable tracking servo system are conducted. Results illustrate the effectiveness and robustness of the proposed controller.

Description: Root-knot nematode (RKN) is a serious threat to crops worldwide due to the difficulty in controlling it and the limited eco-friendly alternatives to deal with the biotic stress it causes. In the present work, water-extractable fractions obtained from vermicompost (WSFv), vermicompost enriched with Trichoderma asperellum (WSFta) and T. virens (WSFtv) were tested as biotechnological tools to reduce the impacts of RKN on gas exchange, water use efficiency (WUE) and nutrient concentration in tomato and bell pepper plants. The plants were infected with 5000 eggs and eventual J2 of RKN and then treated with the water-extractable fractions for seven weeks. It was observed that the addition of WSFta, WSFtv and WSFv increased the CO2 assimilation, stomatal conductance and WUE in the tomato plants. In the bell pepper plants, WSFta, WSFtv, WSFv increased the stomatal conductance, while WUE was higher in the treatment with WSFtv. In fact, the parameters associated with the gas exchange were usually higher in the bell pepper than in the tomato plants. Overall, higher contents of N, Mg, B and Mn were detected when the extracts were applied in both bell pepper and tomato plants. The application of the water-extractable fractions, inoculated or not with Trichoderma, attenuates the RKN damage on the gas exchange parameters and successfully enhanced the nutrient concentration in the infected tomato and bell pepper plants, showing that it could be an important and promising tool for reducing the damage caused by this pathogen. We suggest that both the tomato and pepper plants can cope with the dilemma between growth and stress response via stomata regulation that are modulated by the WSF and Trichoderma.

Description: Conventionally, public key certificates bind one subject with one static public key so that the subject can facilitate the services of the public key infrastructure (PKI). In PKI, certificates need to be renewed (or revoked) for several practical reasons, including certificate expiration, private key breaches, condition changes, and possible risk reduction. The certificate renewal process is very costly, especially for those environments where online authorities are not available or the connection is not reliable. A dynamic public key certificate (DPKC) facilitates the dynamic changeover of the current public–private key pairs without renewing the certificate authority (CA). This paper extends the previous study in several aspects: (1) we formally define the DPKC; (2) we formally define the security properties; (3) we propose another implementation of the Krawczyk–Rabin chameleon-hash-based DPKC; (4) we propose two variants of DPKC, using the Ateniese–Medeiros key-exposure-free chameleon hash; (5) we detail two application scenarios.

Description: In modulation identification issues, like in any other classification problem, the performance of the classification task is significantly impacted by the feature characteristics. Feature weighting boosts the performance of machine learning algorithms, particularly the class of instance-based learning algorithms such as the Minimum Distance (MD) classifier, in which the distance measure is highly sensitive to the magnitude of features. In this paper, we propose an improved version of the Salp Swarm optimization Algorithm (SSA), called ISSA, that will be applied to optimize feature weights for an MD classifier. The aim is to improve the performance of a blind digital modulation detection approach in the context of multiple-antenna systems. The improvements introduced to SSA mainly rely on the opposition-based learning technique. Computer simulations show that the ISSA outperforms the SSA as well as the algorithms that derive from it. The ISSA also exhibits the best performance once it is applied for feature weighting in the above context.

Description: The purpose of this study was to investigate the feasibility of a time-of-flight (TOF) brain positron emission tomography (PET) providing high-quality images. It consisted of 30 detector blocks arranged in a ring with a diameter of 257 mm and an axial field of view of 52.2 mm. Each detector block was composed of two detector modules and two application-specific integrated circuit (ASIC) chips. The detector module was composed of an 8 × 8 array of 3 × 3 mm2 multi-pixel photon counters and an 8 × 8 array of 3.11 × 3.11 × 15 mm3 lutetium yttrium oxyorthosilicate scintillators. The 64-channel individual readout ASIC was used to acquire the position, energy, and time information of a detected gamma ray. A coincidence timing resolution of 187 ps full width at half maximum (FWHM) was achieved using a pair of channels of two detector modules. The energy resolution and spatial resolution were 6.6 ± 0.6% FWHM (without energy nonlinearity correction) and 2.5 mm FWHM, respectively. The results of this study demonstrate that the developed TOF brain PET could provide excellent performance, allowing for a reduction in radiation dose or scanning time for brain imaging due to improved sensitivity and signal-to-noise ratio.

Description: Detecting trace amounts of explosives to ensure personal safety is important, and this is possible by using laser-based spectroscopy techniques. We performed surface-enhanced Raman scattering (SERS) using plasmonic nanogap substrates for the solution phase detection of some nitro-based compounds, taking advantage of the hot spot at the nanogap. An excitation wavelength of 785 nm with an incident power of as low as ≈0.1 mW was used to excite the nanogap substrates. Since both RDX and PETN cannot be dissolved in water, acetone was used as a solvent. TNT was dissolved in water as well as in hexane. The main SERS peaks of TNT, RDX, and PETN were clearly observed down to the order of picomolar concentration. The variations in SERS spectra observed from different explosives can be useful in distinguishing and identifying different nitro-based compounds. This result indicates that our nanogap substrates offer an effective approach for explosives identification.

Description: During transmission of digital images, secret messages can be embedded using data hiding techniques. Such techniques can transfer private secrets without drawing the attention of eavesdroppers. To reduce the amount of transmitted data, image compression methods are widely applied. Hiding secret data in compressed images is a hot issue recently. In this paper, we apply the de-clustering concept and the indicator-free search-order coding (IFSOC) technique to hide information into vector quantization (VQ) compressed images. Experimental results show that the proposed two-layer reversible data hiding scheme for IFSOC-encoded VQ index table can hide a large amount of secret data among state-of-the-art methods with a relatively lower bit rate and high security.

Description: In this work, we propose a mechanism for knowledge transfer between Convolutional Neural Networks via the geometric regularization of local features produced by the activations of convolutional layers. We formulate appropriate loss functions, driving a “student” model to adapt such that its local features exhibit similar geometrical characteristics to those of an “instructor” model, at corresponding layers. The investigated functions, inspired by manifold-to-manifold distance measures, are designed to compare the neighboring information inside the feature space of the involved activations without any restrictions in the features’ dimensionality, thus enabling knowledge transfer between different architectures. Experimental evidence demonstrates that the proposed technique is effective in different settings, including knowledge-transfer to smaller models, transfer between different deep architectures and harnessing knowledge from external data, producing models with increased accuracy compared to a typical training. Furthermore, results indicate that the presented method can work synergistically with methods such as knowledge distillation, further increasing the accuracy of the trained models. Finally, experiments on training with limited data show that a combined regularization scheme can achieve the same generalization as a non-regularized training with 50% of the data in the CIFAR-10 classification task.

Description: Animals vary on intrinsic characteristics such as temperament and stress responsiveness, and this information can be useful to experimentalists for identifying more homogeneous subsets of animals that show consistency in risk for a particular research outcome. Such information can also be useful for balancing experimental groups, ensuring animals within an experiment have similar characteristics. In this review, we describe the BioBehavioral Assessment Program at the California National Primate Research Center, which, since its inception in 2001, has been providing quantitative information on intrinsic characteristics to scientists for subject selection and balancing, and to colony management staff for management purposes. We describe the program and review studies relating to asthma, autism, behavioral inhibition, etc., where the BBA Program was used to select animals. We also review our work, showing that factors such as rearing, ketamine exposure, and prenatal experience can affect biobehavioral organization in ways that some investigators might want to control for in their studies. Attention to intrinsic characteristics of subject populations is consistent with the growing interest in precision medicine and can lead to a reduction in animal numbers, savings in time and money for investigators, and reduced distress for the animals.

Description: Android apps are daily installed by billions of users worldwide, who grant access to an extensive set of sensitive personal data. Different techniques have been developed over the years to understand how apps protect or harm their users’ privacy. However, these results have been produced in different research domains and addressing privacy from different perspectives, resulting in a growing but scattered body of knowledge. To bridge this gap, we have carried out a systematic mapping study to provide practitioners and researchers with an overview of the state-of-the-art technique, published between 2016 and 2020, to assess privacy in Android apps. In this paper, we highlight the most relevant findings, identify and analyse the most pressing gaps, and discuss the promising research directions.

Description: Research was conducted in connection with the pressure exerted by man on the environment through the use of pesticides. The aim of the study was to assess the impact of pesticides on soil and to evaluate the effect of these changes on greenhouse gas emissions into the atmosphere. The research was carried out on soil sown with oilseed rape. The activity of protease and urease, ammonification, nitrification in soil, as well as CO2 (carbon dioxide) and N2O (nitrous oxide) gas emissions from soil were assessed. The analyses were carried out directly after harvest and 2 months after. Pesticides most frequently negatively affected the tested parameters, in particular enzymatic activities. Of the two herbicides used, Roundup had a stronger negative impact on microbial activity. The application of pesticides, especially the fungicide, resulted in an increase in gas emissions to the atmosphere over time. Pesticides disturbed soil environmental balance, probably interfering with qualitative and quantitative relationships of soil microorganism populations and their metabolic processes. This led to the accumulation of microbial activity products in the form of, among others, gases which contribute to the greenhouse effect by escaping from the soil into the atmosphere.

Description: Land suitability assessment is fundamental in space control planning and land development because of its effects on land use and urban layout. Rainstorms and waterlogging have become one of the most common natural disasters in the coastal areas of China. As a result, the concept of an ecological sponge city was incorporated into the construction of cities in the future. Taking Shenzhen–Shantou special cooperation zone (SSCZ), we constructed a storm flooding model based on the SCS flow generation model and GIS to explore the spatial distribution characteristics of the flooding risk in a rainstorm of 100-year lasting 1 h. Combined with population and economic indicators, a radial basis function (RBF) network was utilized to evaluate the environmental risk, the vulnerability of disaster-bearing bodies, and the rain–flood resilience of sponge cities. The self-organizing feature mapping (SOFM) model was used for cluster analysis. Spatial differences were found in the construction suitability of the study area. A suitable construction area (73.59% of the entire area) was located downtown. The construction of the artificial spongy body in the highest vulnerable area (3.25%) needs to be strengthened. The control construction area (3.3%) is located along the banks of the river, with relatively high risk and low resilience of flood control engineering. Ecological construction (19.85%) serves as the sponge body of ecological buffer. The factors of waterlogging, ecology, population, and economy could be integrated comprehensively by applying neural network methods for urban planning and construction.

Description: The silvopastoral system has the potential to alleviate the negative impacts of heat stress on livestock. Through a preliminary study, we assessed the thermal environment experienced by hair coat lambs, as well as the impacts on their bio-thermal and behavioural responses, when either kept in either the silvopastoral system, or exposed to full sun. Twelve hair coat lambs (Dorper × Santa Ines) were randomly assigned to a silvopastoral system or full sun exposure during the summer (from January to February 2017). Parameters, including air temperature, black globe temperature, relative humidity, wind speed, and ground surface temperature, were measured daily for both thermal environments. From 14:00 to 16:00, lambs kept in silvopastoral areas experienced lower levels of air temperature, radiant heat load, and ground surface temperature. Consequently, they had a lower hair coat surface and lower body rectal temperatures. Lambs exposed to a shaded environment spent more time grazing and walking, and less time standing at rest. In conclusion, lambs kept in a silvopastoral system experienced lower levels of radiant heat load and ground surface temperature. In addition, the animals showed a reduced requirement for evaporative cooling and expressed behaviours that indicated a comfortable thermal environment.

Description: A conventional interdigital bandpass filter (BPF) is characterized by coupled and tapped lines and affords low insertion loss (IL) and easy fractional bandwidth (FBW) adjustment. However, the maximum FBW of the filter is limited to 30%, beyond that, its gap size increases, thereby rendering filter fabrication impractical on a standard printed circuit board. In addition, the filter size cannot be changed because it dictates the operational frequency of the filter. Hence, in this study, we propose a compact interdigital BPF based on a spiral and folded stepped impedance resonator (SIR), which affords low IL and excellent group delay. The spiral, folded structure facilitates drastic FBW adjustment: the center frequency and adjustable range of the FBW of the designed BPF are 800 MHz and 80 to 180%, respectively. Additionally, the proposed BPF can adjust the FBW by k-factor which can adjust from 80 to 180%. The insertion and return losses of the proposed filter are 0.043 dB and 17.1 dB, respectively, and the group delay is 0.098 ns. The total filter size is only 13.8 mm × 5.98 mm, which corresponds to a size reduction by factors of 〉2/8 relative to a conventional filter and 2.1 relative to the latest BPF design. The group delay difference between the BPF and other filters is 0.15 ns. In addition, the range of adjustable FBW for the filter is 1.36 times different than for other filters.

Description: A test suite is a set of test cases that evaluate the quality of software. The aim of whole test suite generation is to create test cases with the highest coverage scores possible. This study investigated the efficiency of a multiple-searching genetic algorithm (MSGA) for whole test suite generation. In previous works, the MSGA has been effectively used in multicast routing of a network system and in the generation of test cases on individual coverage criteria for small- to medium-sized programs. The performance of the algorithms varies depending on the problem instances. In this experiment were generated whole test suites for complex programs. The MSGA was expanded in the EvoSuite test generation tool and compared with the available algorithms on EvoSuite in terms of the number of test cases, the number of statements, mutation score, and coverage score. All algorithms were evaluated on 14 problem instances with different corpus to satisfy multiple coverage criteria. The problem instances were Java open-source projects. Findings demonstrate that the MSGA generated test cases reached greater coverage scores and detected a larger number of faults in the test class when compared with the others.

Description: Fusarium head blight (FHB) is one of the major threats for wheat production worldwide. It reduces yield, quality, and feeding value of wheat grains. In addition, mycotoxins produced by Fusarium pathogens can have a negative effect on livestock and human health. The aim of this study was to assess changes in technological quality traits and end-use quality of winter wheat varieties after artificial inoculation with Fusarium spp. over three years. Differences in dough development duration and extensibility were measured as the means of relative reductions due to different environments and varieties’ characteristics. Differences in dough softening during kneading were determined as the means of relative increases due to FHB inoculation. In addition, dough had reduced strength, was stickier, and therefore was more difficult to handle, due to a decrease of the average energy value and resistance to extension in FHB-inoculated wheat, compared to naturally infected plants. Dough development time, stability, and resistance usually varied in a similar way, with FHB-resistant varieties showing a good response to FHB inoculation and maintaining good quality. Increasing the level of Fusarium spp. contamination in more FHB-susceptible wheat varieties worsened their technological quality, primarily, the sedimentation value and the gluten index, and hence had a negative effect on the rheological properties.

Description: Molecular genetic techniques (FISH, RT-PCR, and metagenomic analysis) were used to investigate the comparative functional biodiversity in the prokaryotic complex in grassland and forests’ sod-podzol under polycyclic aromatic hydrocarbon influence. The polluted samples showed a decrease in the biomass of the prokaryotic community representatives and a change in the metabolically active dominants–representatives of the Bacteria and Archaea domains compared to the control samples. The suppression of the metabolic activity of prokaryote cells under the influence of PAHs in sod-podzolic soil under meadow vegetation was more pronounced compared to soils under forest vegetation. The representatives of prokaryotes that are sensitive and resistant to the studied PAHs were identified. The representatives of the phylogenetic groups from the bacterial complex resistant to PAH pollution were Proteobacteria (Alphaproteobacteria), Bacteroidetes, Firmicutes, Chloroflexi, and Thaumarhaeota in the archaeal complex. Representatives of the phylum Acidobacteria and Actinobacteria (Streptosporangiales) are noted among those sensitive to PAH contamination. The presence and expression of the functional alkane monooxygenase (alkB) gene were established in all the experimental variants studied. In the plant variants, the number of copies of alkB genes increased by an order of magnitude and the biomass of metabolically active prokaryotic representatives with the functional alkB gene doubled compared to the unpolluted territories. The copy number index of the alkB gene can be used as one of the parameters when characterizing an ecosystem for the presence of PAH pollutants.

Description: This paper presents a dual-mode low-density parity-check (LDPC) decoding architecture that has excellent error-correcting capability and a high parallelism design for fifth-generation (5G) new-radio (NR) applications. We adopted a high parallelism design using a layered decoding schedule to meet the high throughput requirement of 5G NR systems. Although the increase in parallelism can efficiently enhance the throughput, the hardware implementation required to support high parallelism is a significant hardware burden. To efficiently reduce the hardware burden, we used a grouping search rather than a sorter, which was used in the minimum finder with decoding performance loss. Additionally, we proposed a compensation scheme to improve the decoding performance loss by revising the probabilistic second minimum of a grouping search. The post-layout implementation of the proposed dual-mode LDPC decoder is based on the Taiwan Semiconductor Manufacturing Company (TSMC) 40 nm complementary metal-oxide-semiconductor (CMOS) technology, using a compensation scheme of grouping comparison for 5G communication systems with a working frequency of 294.1 MHz. The decoding throughput achieved was at least 10.86 Gb/s without evaluating early termination, and the decoding power consumption was 313.3 mW.

Description: The widespread use of artificial intelligence (AI) in civil engineering has provided civil engineers with various benefits and opportunities, including a rich data collection, sustainable assessment, and productivity. The trend of construction is diverted toward sustainability with the aid of digital technologies. In this regard, this paper presents a systematic literature review (SLR) in order to explore the influence of AI in civil engineering toward sustainable development. In addition, SLR was carried out by using academic publications from Scopus (i.e., 3478 publications). Furthermore, screening is carried out, and eventually, 105 research publications in the field of AI were selected. Keywords were searched through Boolean operation “Artificial Intelligence” OR “Machine intelligence” OR “Machine Learning” OR “Computational intelligence” OR “Computer vision” OR “Expert systems” OR “Neural networks” AND “Civil Engineering” OR “Construction Engineering” OR “Sustainable Development” OR “Sustainability”. According to the findings, it was revealed that the trend of publications received its high intention of researchers in 2020, the most important contribution of publications on AI toward sustainability by the Automation in Construction, the United States has the major influence among all the other countries, the main features of civil engineering toward sustainability are interconnectivity, functionality, unpredictability, and individuality. This research adds to the body of knowledge in civil engineering by visualizing and comprehending trends and patterns, as well as defining major research goals, journals, and countries. In addition, a theoretical framework has been proposed in light of the results for prospective researchers and scholars.

Description: Elevated or unnatural levels of arsenic (As) and phosphorus (P) concentrations in soils and waterbodies from anthropogenic sources can present significant hazards for both natural ecosystems and human food production. Effective, environmentally friendly, and inexpensive materials, such as biochar, are needed to reduce mobility and bioavailability of As and P. While biochar features several physicochemical properties that make it an ideal contaminant sorbent, certain modifications such as mineral-impregnation can improve sorption efficiencies for targeted compounds. Here, we conducted sorption experiments to investigate and quantify the potential utility of magnesium (Mg) for improving biochar sorption efficiency of P and As. We synthesized a Mg-modified walnut shells-derived biochar and characterized its ability to remove As and P from aqueous solutions, thereby mitigating losses of valuable P when needed while, at the same time, immobilizing hazardous As in ecosystems. SEM-EDX, FTIR and elemental analysis showed morphological and functional changes of biochar and the formation of new Mg-based composites (MgO, MgOHCl) responsible for improved sorption potential capacity by 10 times for As and 20 times for P. Sorption efficiency was attributed to improved AEC, higher SSA, chemical forms of sorbates and new sorption site formations. Synthetized Mg-composite/walnut shell-derived biochar also removed 〉90% of P from real samples of wastewater, indicating its potential suitability for contaminated waterbody remediation.

Description: Radars with mmWave frequency modulated continuous wave (FMCW) technology accurately estimate the range and velocity of targets in their field of view (FoV). The targeted angle of arrival (AoA) estimation can be improved by increasing receiving antennas or by using multiple-input multiple-output (MIMO). However, obtaining target features such as target type remains challenging. In this paper, we present a novel target classification method based on machine learning and features extracted from a range fast Fourier transform (FFT) profile by using mmWave FMCW radars operating in the frequency range of 77–81 GHz. The measurements are carried out in a variety of realistic situations, including pedestrian, automotive, and unmanned aerial vehicle (UAV) (also known as drone). Peak, width, area, variance, and range are collected from range FFT profile peaks and fed into a machine learning model. In order to evaluate the performance, various light weight classification machine learning models such as logistic regression, Naive Bayes, support vector machine (SVM), and lightweight gradient boosting machine (GBM) are used. We demonstrate our findings by using outdoor measurements and achieve a classification accuracy of 95.6% by using LightGBM. The proposed method will be extremely useful in a wide range of applications, including cost-effective and dependable ground station traffic management and control systems for autonomous operations, and advanced driver-assistance systems (ADAS). The presented classification technique extends the potential of mmWave FMCW radar beyond the detection of range, velocity, and AoA to classification. mmWave FMCW radars will be more robust in computer vision, visual perception, and fully autonomous ground control and traffic management cyber-physical systems as a result of the added new feature.

Description: Rayleigh waves are very useful for ultrasonic nondestructive evaluation of structural and mechanical components. Nonlinear Rayleigh waves have unique sensitivity to the early stages of material degradation because material nonlinearity causes distortion of the waveforms. The self-interaction of a sinusoidal waveform causes second harmonic generation, while the mutual interaction of waves creates disturbances at the sum and difference frequencies that can potentially be detected with minimal interaction with the nonlinearities in the sensing system. While the effect of surface roughness on attenuation and dispersion is well documented, its effects on the nonlinear aspects of Rayleigh wave propagation have not been investigated. Therefore, Rayleigh waves are sent along aluminum surfaces having small, but different, surface roughness values. The relative nonlinearity parameter increased significantly with surface roughness (average asperity heights 0.027–3.992 μm and Rayleigh wavelengths 0.29–1.9 mm). The relative nonlinearity parameter should be decreased by the presence of attenuation, but here it actually increased with roughness (which increases the attenuation). Thus, an attenuation-based correction was unsuccessful. Since the distortion from material nonlinearity and surface roughness occur over the same surface, it is necessary to make material nonlinearity measurements over surfaces having the same roughness or in the future develop a quantitative understanding of the roughness effect on wave distortion.

Description: Genetic algorithms (GA’s) are mostly used as an offline optimisation method to discover a suitable solution to a complex problem prior to implementation. In this paper, we present a different application in which a GA is used to progressively adapt the collective performance of an ad hoc collection of devices that are being integrated post-deployment. Adaptive behaviour in the context of this article refers to two dynamic aspects of the problem: (a) the availability of individual devices as well as the objective functions for the performance of the entire population. We illustrate this concept in a video surveillance scenario in which already installed cameras are being retrofitted with networking capabilities to form a coherent closed-circuit television (CCTV) system. We show that this can be conceived as a multi-objective optimisation problem which can be solved at run-time, with the added benefit that solutions can be refined or modified in response to changing priorities or even unpredictable events such as faults. We present results of a detailed simulation study, the implications of which are being discussed from both a theoretical and practical viewpoint (trade-off between saving computational resources and surveillance coverage).

Description: Understanding the factors that result in people becoming dog owners is key to developing messaging around responsible acquisition and providing appropriate support for prospective owners to ensure a strong dog–owner bond and optimise dog welfare. This qualitative study investigated factors that influence pet dog acquisition. Semi-structured interviews were conducted with 142 sets of dog owners/caretakers at 23 Dogs Trust community events. Interviews focused on the motivations and influences that impacted how people acquired their dogs. Transcribed interviews and notes were thematically analysed. Two acquisition types were reported, that each accounted for half of our interviewees’ experiences: planned and unplanned. Whilst planned acquisitions involved an intentional search for a dog, unplanned acquisitions occurred following an unexpected and unsought opportunity to acquire one. Unplanned acquisitions frequently involved a participant’s family or friends, people happening upon a dog in need, or dogs received as gifts. Motivations for deciding to take the dog included emotional attachments and a desire to help a vulnerable animal. Many reported making the decision to acquire the dog without hesitation and without conducting any pre-acquisition research. These findings present valuable insights for designers of interventions promoting responsible acquisition and ownership, because there is minimal opportunity to deliver messaging with these unplanned acquisitions. Additionally, these findings may guide future research to develop more complete understandings of the acquisition process. Further studies are required to understand the prevalence of unplanned acquisitions.

Description: Within the context of structure deterioration studies, we propose a new numerical method based on the use of fragility curves. In particular, the present work aims to theoretically study the degradation of concrete bridge structures subjected to aggressive environments. A simple probabilistic method based on fragility curves is presented which allows the forecasting of the lifetime of the considered structural system and the best monitoring time. The method was applied to investigate the degradation of a concrete bridge used as a case study. A Monte Carlo numerical procedure was used to simulate the variation over time of the residual resistant section and the ultimate bending moment of the deck of the case study. Within this context, fragility curves are used as reliable indicators of possible monitoring scenarios. In comparison with other methods, the main advantage of the proposed approach is the small amount of computing time required to obtain rapid assessment of reliability and deterioration level of the considered structure.

Description: Greywater has been identified as a valuable alternative water source over recent years. Few practices (i.e., recycling and reuse) of greywater have attracted global attention in meeting the future water demand. However, essential parameters should be analyzed for reliable reuse and treatment. The present study addresses the possibilities of the alternative source with the treated greywater. Gravity—governed flow methods through a column containing gravel, sand, and activated carbon was applied. The quality of treated greywater from the university campus, which included physical, chemical, and biological parameters, was assessed to check non-potable reuse suitability. The reduction percentage of organics in biological oxygen demand and chemical oxygen demand was 64% and 42%, respectively. Similarly, the reduction percentage was obtained at 74% and 66% for turbidity and electrical conductivity. The removal efficiency was 57%, 77%, 48%, and 44% for total dissolved solids, alkalinity, chlorides, and total hardness. The pH of treated water samples was found in the neutral range suggesting its suitability for reuse. Hence, the proposed greywater treatment method is a cost-effective and straightforward approach to reuse greywater for irrigation, watering the lawns, and car washing. The greywater collected can be disinfected immediately and reused with minimal possibility of regrowth of microorganisms.

Description: This paper proposes a novel coordinated multi-agent deep reinforcement learning (MADRL) algorithm for energy sharing among multiple unmanned aerial vehicles (UAVs) in order to conduct big-data processing in a distributed manner. For realizing UAV-assisted aerial surveillance or flexible mobile cellular services, robust wireless charging mechanisms are essential for delivering energy sources from charging towers (i.e., charging infrastructure) to their associated UAVs for seamless operations of autonomous UAVs in the sky. In order to actively and intelligently manage the energy resources in charging towers, a MADRL-based coordinated energy management system is desired and proposed for energy resource sharing among charging towers. When the required energy for charging UAVs is not enough in charging towers, the energy purchase from utility company (i.e., energy source provider in local energy market) is desired, which takes high costs. Therefore, the main objective of our proposed coordinated MADRL-based energy sharing learning algorithm is minimizing energy purchase from external utility companies to minimize system-operational costs. Finally, our performance evaluation results verify that the proposed coordinated MADRL-based algorithm achieves desired performance improvements.

Description: A quad-port antenna array operating in 3.5 GHz band (3.4–3.6 GHz) and 5 GHz band (4.8–5 GHz) for fifth-generation (5G) smartphone applications is presented in this paper. The single antenna element consists of an L-shaped strip, a parasitic rectangle strip, and a modified Z-shaped strip. To reserve space for 2G/3G/4G antennas, the quad-port antenna array is printed along the two long frames of the smartphone. The evolution design and the analysis of the optimal parameters of a single antenna element are derived to investigate the principle of the antenna. The prototype of the presented antenna is tested and the measured results agree well with the simulation. The measured total efficiency is better than 70% and the isolation is larger than 16.5 dB.

Description: It is important to understand price premiums related to certified raw wood to predict the future of forest certifications from the perspective of forestry enterprises. We focused on identifying the trading roundwood market data in the economic center of power in Tokyo. This study aimed to clarify Tama-certified raw wood prices under the local area certification scheme, forest-certified raw wood prices, and the handling volumes at the Tama Roundwood Market Center in Tokyo. Sales details of the Tama Roundwood Market Center were used to identify the handling volumes, Tama-certified raw wood prices, and FSC (Forest Stewardship Council) or SGEC (Japan-specific Sustainable Green Ecosystem Council)-certified raw wood prices. The FSC and SGEC have operated from the center since 2016. Data were collected from the 2006–2018 fiscal years. Our results showed that the volume of raw wood handled increased due to the regeneration-cutting project conducted by the Tokyo Metropolitan Government. On the other hand, there was no price premium for Tama-certified raw wood under the local certification scheme or for FSC- or SGEC-certified raw wood. Price premiums for forest certifications are necessary for ongoing sustainable forest management. There is a need to increase consumer awareness of forest certifications and to differentiate quality certifications further, and these would likely create price premiums.

Description: Using automotive smartphone applications (apps) provided by car manufacturers may offer numerous advantages to the vehicle owner, including improved safety, fuel efficiency, anytime monitoring of vehicle data, and timely over-the-air delivery of software updates. On the other hand, the continuous tracking of the vehicle data by such apps may also pose a risk to the car owner, if, say, sensitive pieces of information are leaked to third parties or the app is vulnerable to attacks. This work contributes the first to our knowledge full-fledged security assessment of all the official single-vehicle management apps offered by major car manufacturers who operate in Europe. The apps are scrutinised statically with the purpose of not only identifying surfeits, say, in terms of the permissions requested, but also from a vulnerability assessment viewpoint. On top of that, we run each app to identify possible weak security practices in the owner-to-app registration process. The results reveal a multitude of issues, ranging from an over-claim of sensitive permissions and the use of possibly privacy-invasive API calls, to numerous potentially exploitable CWE and CVE-identified weaknesses and vulnerabilities, the, in some cases, excessive employment of third-party trackers, and a number of other flaws related to the use of third-party software libraries, unsanitised input, and weak user password policies, to mention just a few.

Description: Environmental pollution is an important issue in metropolitan areas, and roadside trees are directly affected by various sources of pollution to which they exhibit numerous responses. The aim of the present study was to identify morpho-physio-biochemical attributes of maidenhair tree (Ginkgo biloba L.) and American sycamore (Platanus occidentalis L.) growing under two different air quality conditions (roadside with high air pollution, RH and roadside with low air pollution, RL) and to assess the possibility of using their physiological and biochemical parameters as biomonitoring tools in urban areas. The results showed that the photosynthetic rate, photosynthetic nitrogen-use efficiencies, and photochromic contents were generally low in RH in both G. biloba and P. occidentalis. However, water-use efficiency and leaf temperature showed high values in RH trees. Among biochemical parameters, in G. biloba, the lipid peroxide content was higher in RH than in RL trees, but in P. occidentalis, this content was lower in RH than in RL trees. In both species, physiological activities were low in trees planted in areas with high levels of air pollution, whereas their biochemical and morphological variables showed different responses to air pollution. Thus, we concluded that it is possible to determine species-specific physiological variables affected by regional differences of air pollution in urban areas, and these findings may be helpful for monitoring air quality and environmental health using trees.

Description: Suburban rural landscape multifunction has received increasing attention from scholars due to its high demand and impact on main urban areas. However, few studies have been focused on suburban rural landscape multifunction because of data constraints. The present study quantified the four landscape services based on ecological service system, i.e., regulating function (RF), provision function (PF), culture function (CF), and support function (SF), determined the interaction through the Spearman correlation coefficient, and ultimately identified the landscape multifunction hotspots and dominant functions through overlay analysis. The result indicated that suburban rural communities have exhibited the characteristics of regional multifunction, and the landscape multifunction hotspots accounted for 64.2%; it should be particularly noted that, among single-function, dual-function, and multifunction hotspots, both support function, and culture function was dominant, while only one case was found in which the regulating function was dominant. Furthermore, all landscape functions other than SF-CF exhibited certain correlations. The study suggests that planning and management should be performed in future in combination with landscape multifunction to ensure the sustainable development of suburban rural communities.

Description: Bare soil is a critical element in the urban landscape and plays an essential role in urban environments. Yet, the separation of bare soil and other land cover types using remote sensing techniques remains a significant challenge. There are several remote sensing-based spectral indices for barren detection, but their effectiveness varies depending on land cover patterns and climate conditions. Within this research, we introduced a modified bare soil index (MBI) using shortwave infrared (SWIR) and near-infrared (NIR) wavelengths derived from Landsat 8 (OLI – Operational Land Imager). The proposed bare soil index was tested in two different bare soil patterns in Thailand and Vietnam, where there are large areas of bare soil during the agricultural fallow period, obstructing the separation between bare soil and urban areas. Bare soil extracted from the MBI achieved higher overall accuracy of about 98% and a kappa coefficient over 0.96, compared to bare soil index (BSI), normalized different bare soil index (NDBaI), and dry bare soil index (DBSI). The results also revealed that MBI considerably contributes to the accuracy of land cover classification. We suggest using the MBI for bare soil detection in tropical climatic regions.

Description: Background: The use of wheat and flaxseed to produce omega-3 (ω-3) enriched poultry meat and eggs is very popular in the world. However, wheat and flaxseed contain some anti-nutritional factors (ANFs), and enzymes are usually used to alleviate the deleterious influence of ANFs. Method: A 2 × 3 two factors design was used in the experiment. A total of 540 twenty-week-old Nongda-3 laying hens were randomly allocated to six dietary treatments, two diets (corn/flaxseed and wheat/flaxseed), and three enzymes (enzyme-a contains neutral protease 10,000, xylanase 35,000, β-mannanase 1500, β-glucanase 2000, cellulose 500, amylase 100, and pectinase 10,000 (U g−1); enzyme-b contains alkaline protease 40,000 and neutral protease 10,000 (U g−1); enzyme-c contains alkaline protease 40,000, neutral protease 10,000, and cellulase 4000 (U g−1). Results: There was an interaction between dietary treatment and supplemental enzymes for liver weight and liver inflammatory cytokines of broilers. A significant increase was observed in the fat weight of birds fed a corn diet as compared with a wheat diet. A corn diet and wheat diet with the addition of enzyme-a (p 〈 0.001) showed the highest level of liver fat followed by enzyme-c (p 〈 0.01) and enzyme-b. Moreover, a high level of secretory IL-1β, IL-6, and IL-10 and comparatively higher inflammatory changes in the liver tissue were found in birds fed a corn diet as compared with a wheat diet, and enzyme-b showed more beneficial effects as compared with enzyme-a and -c. The gut microbial composition of hens fed a corn diet was significantly different than that of birds fed a wheat diet. Bacteroides were significantly (p 〈 0.05) abundant in the corn-fed birds as compared with wheat-fed birds. However, Firmicutes were less abundant in the wheat-fed birds than the corn-fed birds (16.99 vs. 31.80%, respectively). The microbial community at the genus level differed significantly in the dietary groups and we observed that Bacteroides are the predominant cecal microbiota. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of co-factors, carbohydrates, vitamins, protein, and energy were expressed at slightly higher levels in the microbiota of the wheat-fed birds, whereas, metabolic pathways for nucleotides, lipids, and glycine were expressed at higher levels in the wheat-fed birds. Furthermore, expression of the growth and cellular processes pathway and endocrine system pathway levels were predicted to be higher for the wheat-fed group as compared with the corn-fed group. Conclusions: In conclusion, our findings suggest that inflammatory changes in laying birds were mediated by a corn diet with flaxseed and enzymes instead of a wheat diet. Additionally, in the wheat-fed group, enzyme-b and -c showed more encouraging results as compared to enzyme-a.

Description: The purpose of this study was to assess the suitability of the Romanian Albanus hybrid of Sorghum bicolor as a potential functional food ingredient. Ultrasound-assisted extraction in different solvents, together with spectrophotometric and chromatographic methods, was used to monitor the variation in total phenolic and flavonoid content and the antioxidant activity of raw sorghum grains before and during short germination periods (24, 36 and 48 h). The High-Performance Liquid Chromatography with Diode-Array Detection (HPLC-DAD) investigation of the extracts revealed that the ungerminated sorghum grains extracted with methanol had the highest diversity of phenolic compounds, while the total phenolic content (TPC) was the highest after 36 h of germination in both extract types: 1853 mg GAE/100 g for the methanolic extract and 1726 mg GAE/100 g for the ethanolic extract. The findings of this study showed that the TPC of sorghum extracts is strongly correlated with their antioxidant activity and, overall, that the studied extracts presented a good radical scavenger activity, which supports the benefits of alimentary uses of Sorghum bicolor grains.

Description: Genomics comprises a set of current and valuable technologies implemented as selection tools in dairy cattle commercial breeding programs. The intensive progeny testing for production and reproductive traits based on genomic breeding values (GEBVs) has been crucial to increasing dairy cattle productivity. The knowledge of key genes and haplotypes, including their regulation mechanisms, as markers for productivity traits, may improve the strategies on the present and future for dairy cattle selection. Genome-wide association studies (GWAS) such as quantitative trait loci (QTL), single nucleotide polymorphisms (SNPs), or single-step genomic best linear unbiased prediction (ssGBLUP) methods have already been included in global dairy programs for the estimation of marker-assisted selection-derived effects. The increase in genetic progress based on genomic predicting accuracy has also contributed to the understanding of genetic effects in dairy cattle offspring. However, the crossing within inbred-lines critically increased homozygosis with accumulated negative effects of inbreeding like a decline in reproductive performance. Thus, inaccurate-biased estimations based on empirical-conventional models of dairy production systems face an increased risk of providing suboptimal results derived from errors in the selection of candidates of high genetic merit-based just on low-heritability phenotypic traits. This extends the generation intervals and increases costs due to the significant reduction of genetic gains. The remarkable progress of genomic prediction increases the accurate selection of superior candidates. The scope of the present review is to summarize and discuss the advances and challenges of genomic tools for dairy cattle selection for optimizing breeding programs and controlling negative inbreeding depression effects on productivity and consequently, achieving economic-effective advances in food production efficiency. Particular attention is given to the potential genomic selection-derived results to facilitate precision management on modern dairy farms, including an overview of novel genome editing methodologies as perspectives toward the future.

Description: Collision between rigid three-dimensional objects is a very common modelling problem in a wide spectrum of scientific disciplines, including Computer Science and Physics. It spans from realistic animation of polyhedral shapes for computer vision to the description of thermodynamic and dynamic properties in simple and complex fluids. For instance, colloidal particles of especially exotic shapes are commonly modelled as hard-core objects, whose collision test is key to correctly determine their phase and aggregation behaviour. In this work, we propose the Oriented Cuboid Sphere Intersection (OCSI) algorithm to detect collisions between prolate or oblate cuboids and spheres. We investigate OCSI’s performance by bench-marking it against a number of algorithms commonly employed in computer graphics and colloidal science: Quick Rejection First (QRI), Quick Rejection Intertwined (QRF) and a vectorized version of the OBB-sphere collision detection algorithm that explicitly uses SIMD Streaming Extension (SSE) intrinsics, here referred to as SSE-intr. We observed that QRI and QRF significantly depend on the specific cuboid anisotropy and sphere radius, while SSE-intr and OCSI maintain their speed independently of the objects’ geometry. While OCSI and SSE-intr, both based on SIMD parallelization, show excellent and very similar performance, the former provides a more accessible coding and user-friendly implementation as it exploits OpenMP directives for automatic vectorization.

Description: There is a growing demand by the modern consumer for meat containing less fat and lower levels of saturated fatty acids, which are considered to increase the risk of coronary heart disease. In southern Italy, the Gentile di Puglia breed is one of the most common on farms, and the light lambs are often consumed. The study evaluates the effect of a diet containing extruded linseed (Linum usitatissimum) on growth performances, carcass traits, and meat quality in Gentile di Puglia light lambs. Thirty-six male lambs are weaned at about 20 days of age, and divided into three groups—each group is either fed a control diet (C), a diet containing 3% extruded linseed (L), or a diet containing 3% extruded linseed and 0.6% oregano (Origanum vulgare) (L + O). The lambs’ growth performances and the slaughtering and dissection data did not differ between groups. Dietary treatments have no significant effect on the quality and chemical composition of Longissimus lumborum (Ll). The mount of linoleic acid in Ll meat is significantly higher in the L group, and this positively affected the total content of n-3 Polyunsaturated Fatty Acid, as well as the n-6/n-3 ratio. The good results obtained concerning the sensory traits meet the requirements of the market’s consumer.

Description: The onset of COVID has impacted the field of veterinary medicine and the lives of cat guardians in numerous ways, yet the subject remains largely unexplored. The purpose of this study was to better understand cat guardians’ fears and concerns pertaining to veterinary care and obtainment of pet care products and food during the initial lock down phase of the COVID pandemic to better address these concerns now and in the future. To this end, an anonymous online survey was distributed to cat guardians during the first two months of the pandemic. The survey included questions pertaining to guardians’ relationship with their cat and their veterinary related concerns and experiences as a result of the pandemic. Quantitative and qualitative data were collected from 956 participants. The results revealed that the increased amount of time guardians spent with their cat had a positive impact on their bond. Participants’ veterinary related concerns, particularly for participants between 18-29 years of age, centered around availability of their veterinarian for both emergency and non-emergency care. Other concerns included fears about the ability to afford emergency veterinary care and obtain cat food and supplies. Awareness of these concerns can help veterinarians better support cat guardians by directly communicating their continued availability and presence in the face of a pandemic as well as other challenging times.