ALBERT

Description: The Kadomtsev–Petviashvili equation is known to be the leading term of a semi-infinite hierarchy of integrable equations with evolutions given by times with positive numbers. Here, we introduce new hierarchy directed to negative numbers of times. The derivation of such systems, as well as the corresponding hierarchy, is based on the commutator identities. This approach enables introduction of linear differential equations that admit lifts up to nonlinear integrable ones by means of the special dressing procedure. Thus, one can construct not only nonlinear equations, but corresponding Lax pairs as well. The Lax operator of this evolution coincides with the Lax operator of the “positive” hierarchy. We also derive (1 + 1)-dimensional reductions of equations of this hierarchy.

Description: In the current era of a strongly competitive business environment, it is more difficult for companies to attract customers. Consumer neuroscience has growing potential here, as it reveals internal consumer preferences by using innovative methods and tools, which can effectively examine consumer behavior and attract new customers. In particular, smell has a great ability to subconsciously influence customers and, thus, support profitability. This paper examines the importance of consumer neuroscience and its modern technologies used for exploring human perceptions to influence customers and benefit from the aromatization of business spaces. We focused our analysis on various service sectors. Despite the potential of the examined issue, there are a limited number of studies in the field of service providers that use neuroscience tools to examine the effect of aromas on human emotions. Most studies took place in laboratory conditions, and the used methodological procedures varied widely. Our analysis showed that, in spite of the positive impact of aromatization in the majority of aromatized spaces, service companies still do not use the potential of consumer neuroscience and aroma marketing to a sufficient degree. Innovative methods and tools, in particular, are still very underused.

Description: The Kuroshio is the strongest warm current in the western North Pacific, which plays a crucial role in climate and human activities. In terms of this, the accurate acquisition of ocean surface current velocity and direction in the Kuroshio region is of great research value. Gaofen-3 synthetic aperture radar (SAR) provides data support for the study of ocean surface current measurements in the Kuroshio region, but no relevant experimental result has been published yet. In this paper, four available stripmap mode SARs’ data acquired by Gaofen-3 in the Kuroshio region are used for measuring the ocean surface current field. In general, the Doppler centroid anomaly (DCA) estimation is a common method to infer ocean surface currents from single-antenna stripmap data, but only the radial velocity component can be retrieved. In order to measure current vectors, a novel method combining the sub-aperture processing and the least squares (LS) technology is suggested and demonstrated by applying to the Gaofen-3 SAR data processing. The experiment’s results agree well with model-derived ocean current data, indicating that the Gaofen-3 SAR has the capability to accurately retrieve the ocean surface current field in the Kuroshio region and motivate further research by providing more data.

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: Increasing the bearing capacity of reinforced concrete structures, reducing material consumption, and ensuring quality are critical in modern construction. The article presents an experimental study of the ultimate compressive strains of short fiber basalt reinforced concrete columns and provides recommendations for increasing the bearing capacity using steel reinforcement bars with greater strength. The columns were tested in an upright position using a hydraulic press. Strains were measured with dial indicators and a strain gauge station. It was shown that the addition of 10% coarse basalt fiber increased the ultimate compressibility of concrete on ordinary crushed stone by 19.8%, and expanded clay concrete by 26.1%, which led to the strain hardening of concrete under compression by 9.0% and 12%, respectively. Ultimate compressive strains in fiber-reinforced concrete short columns with combined reinforcement increased 1.42 times in columns on a lightweight aggregate and 1.19 times on heavy aggregate. An increase in the ultimate compressibility of concrete makes it possible to use steel reinforcement with greater strength in compressed elements as the concrete crushing during compression occurs primarily due to the reaching of critical values by tensile stresses in the transverse direction. This makes it possible to manufacture structures with a higher load-bearing capacity and less material consumption. A practical example of the application of the proposed approach is given.

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: Power Factor Correction (PFC) single-phase AC/DC converters are used in several power electronics applications as full wave control rectifiers improving power quality and providing high standards of efficiency. Many papers dealing with the description or use of such topologies have been published in recent years; however, a review that describes and organizes their specific details has not been reported in the technical literature. Therefore, this paper presents an extensive review of PFC single-phase AC/DC converters operating with the Boost converter topology for low and medium voltage as well as and power appliances. A categorization of bridge, semi-bridgeless, and bridgeless, in accordance with the construction characteristics, was carried out in order to unify the technical terminology. Benefits and disadvantages are described and analyzed in detail. Furthermore, a comparison performance in terms of PFC, Total Harmonic Distortion (THD), power capacity, electromagnetic compatibility (EMC), number of elements, and efficiency is included.

Description: Evaluation and monitoring of wireless sensor networks (WSN) and the parameters defining their operations and design, such as energy consumption, latency, and stability, is a complex task due to interaction with real devices. For greater control of these variables, the use of simulators arises as an alternative. Cooja is a WSN simulator/emulator which handles the devices’ controllers and multiple communication protocol implementations, such as RPL (RPL is one of the most used protocol in IoT). However, Cooja does not consider either the implementation of an energy model (it has infinite energy consumption) nor the visual behavior of the topology construction, although these aspects are crucial for effective network analysis and decision taking. This paper presents the design and the implementation of ViTool-BC, a software built on top of Cooja, which allows the creation of different energy estimation models and also to visualize in real time the behavior of WSN topology construction. In addition, ViTool-BC offers a heat map of energy consumption traces. Therefore, this tool helps researchers to monitor in real time the topology construction, node disconnection, and battery depletion, aspects to be considered in the analysis of the available routing protocols in Cooja.

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: Mountains are reservoirs for a tremendous biodiversity which was fostered by a suite of factors acting in concert throughout evolutionary times. These factors can be climatic, geological, or biotic, but the way they combine through time to generate diversity remains unknown. Here, we investigate these factors as correlates of diversification of three closely related sections of Gentiana in the European Alpine System. Based upon phylogenetic approaches coupled with divergence dating and ancestral state reconstructions, we attempted to identify the role of bedrock preferences, chromosome numbers coupled with relative genome sizes estimates, as well as morphological features through time. We also investigated extant climatic preferences using a heavily curated set of occurrence records individually selected for superior precision, and quantified rates of climatic niche evolution in each section. We found that a number of phylogenetic incongruences derail the identification of correlates of diversification, yet a number of patterns persist regardless of the topology considered. All the studied correlates are likely to have contributed to the diversification of Gentiana in Europe, however, their respective importance varied through time and across clades. Chromosomal variation and divergence of climatic preferences appear to correlate with diversification throughout the evolution of European Gentiana (Oligocene to present), whereas shifts in bedrock preferences appear to have been more defining during recent diversification (Pliocene). Overall, a complex interaction among climatic, geological and biotic attributes appear to have supported the diversification of Gentiana across the mountains of Europe, which based upon phylogenetic as well as other evidence, was probably also bolstered by hybridization.

Description: For α∈[0,1], let Aα(Gσ)=αD(G)+(1−α)A(Gσ), where G is a simple undirected graph, D(G) is the diagonal matrix of its vertex degrees and A(Gσ) is the adjacency matrix of the signed graph Gσ whose underlying graph is G. In this paper, basic properties of Aα(Gσ) are obtained, its positive semidefiniteness is studied and some bounds on its eigenvalues are derived—in particular, lower and upper bounds on its largest eigenvalue are obtained.

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: Cervical laminoplasty is a valuable procedure for myelopathy but it is associated with complications such as increased kyphosis. The effect of ligament damage during cervical laminoplasty on biomechanics is not well understood. We developed the C2–C7 cervical spine finite element model and simulated C3–C6 double-door laminoplasty. Three models were created (a) intact, (b) laminoplasty-pre (model assuming that the ligamentum flavum (LF) between C3–C6 was preserved during surgery), and (c) laminoplasty-res (model assuming that the LF between C3–C6 was resected during surgery). The models were subjected to physiological loading, and the range of motion (ROM), intervertebral nucleus stress, and facet contact forces were analyzed under flexion/extension, lateral bending, and axial rotation. The maximum change in ROM was observed under flexion motion. Under flexion, ROM in the laminoplasty-pre model increased by 100.2%, 111.8%, and 98.6% compared to the intact model at C3–C4, C4–C5, and C5–C6, respectively. The ROM in laminoplasty-res further increased by 105.2%, 116.8%, and 101.8% compared to the intact model at C3–C4, C4–C5, and C5–C6, respectively. The maximum stress in the annulus/nucleus was observed under left bending at the C4–C5 segment where an increase of 139.5% and 229.6% compared to the intact model was observed for laminoplasty-pre and laminoplasty-res model, respectively. The highest facet contact forces were observed at C4–C5 under axial rotation, where an increase of 500.7% and 500.7% was observed compared to the intact model for laminoplasty-pre and laminoplasty-res, respectively. The posterior ligaments of the cervical spine play a vital role in restoring/stabilizing the cervical spine. When laminoplasty is performed, the surgeon needs to be careful not to injure the posterior soft tissue, including ligaments such as LF.

Description: Al2O3 and Al2O3-ZrO2 coatings were deposited on stainless steel using atmospheric plasma spraying. The influence of arc current and zirconia addition on the surface morphology of the coating, phase composition and tribological properties under dry sliding conditions were investigated. The addition of zirconia reduced the surface roughness of the coatings. The X-ray diffraction measurements indicated that the Al2O3 coatings were composed of β-Al2O3, α-Al2O3, and γ-Al2O3 phases. The addition of zirconia led to the formation of tetragonal and monoclinic phases of zirconia in the as-sprayed coatings. The friction coefficients of Al2O3 and Al2O3-ZrO2 coatings were similar and varied in the range of 0.72–0.75. The specific wear rates of the as-sprayed coatings were reduced with the increase of arc current. It was obtained that the wear rates of the Al2O3-ZrO2 coatings were at least three times lower compared to Al2O3 coatings.

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: Understanding the natural resources of native flora in a particular area is essential to be able to identify, record, and update existing records concerning the flora of that area, especially medicinal plants. Until recently, there has been very little scientific documentation on the biological diversity of Aljumum flora. The current study aimed to document medicinal plants among the flora of this region and determine the traditional usages that are documented in the literature. In the flowering season from November 2019 to May 2020, we conducted more than 80 field trips to the study area. The results reported 90 species belonging to 79 genera and 34 families in the Aljumum region, which constitute 82 species of medicinal plants from a total of 2253 known species in Saudi Arabia. The most distributed species were Calotropis procera, Panicum turgidum, and Aerva javanica (5.31%); within four endemic families, we found Fabaceae (32.35%), Poaceae (20.58%), and Asteraceae and Brassicaceae (17.64%). The present study reviews a collection of medicinal plants in Aljumum used in ethnomedicine. Additionally, these natural resources should be preserved, and therefore, conservation programs should be established to protect the natural diversity of the plant species in this region with sustainable environmental management.

Description: Ionic liquids represent a class of highly tunable organic compounds responsible for many applications in the domain of wood-based materials. It has often been emphasized that the use of ionic liquids derives from their high affinity for lignocellulose and their good penetration ability into wood structures. This paper discusses the sorption ability of different types of 1-alkyl-3-methylimidazolium ionic liquids with lateral alkyl chains, ranging from ethyl to hexyl into spruce and beech wood, as a function of their cation molecular mass, anion type (chloride, tetrafluoroborate, acetate), and intrinsic properties (surface tension and kinematic viscosity) at room temperature. All the studied ionic liquids present high relative uptake values at the equilibrium, ranging from 11.2% to 69.7%. The bulk diffusion coefficients of the ionic liquids into the wood range from 2 × 10−3 to 28 × 10−3 mm2/min, being higher in the longitudinal direction for both types of wood. The value of the diffusion coefficients for 1-ethyl-3-methylimidazolium chloride is only 25% lower than that for water, despite the obvious differences in viscosity and surface tension, demonstrating a good penetration ability and the potential for wood industry-related applications (as impregnation compound carriers and preservatives).

Description: The comprehensive intelligent development of the manufacturing industry puts forward new requirements for the quality inspection of industrial products. This paper summarizes the current research status of machine learning methods in surface defect detection, a key part in the quality inspection of industrial products. First, according to the use of surface features, the application of traditional machine vision surface defect detection methods in industrial product surface defect detection is summarized from three aspects: texture features, color features, and shape features. Secondly, the research status of industrial product surface defect detection based on deep learning technology in recent years is discussed from three aspects: supervised method, unsupervised method, and weak supervised method. Then, the common key problems and their solutions in industrial surface defect detection are systematically summarized; the key problems include real-time problem, small sample problem, small target problem, unbalanced sample problem. Lastly, the commonly used datasets of industrial surface defects in recent years are more comprehensively summarized, and the latest research methods on the MVTec AD dataset are compared, so as to provide some reference for the further research and development of industrial surface defect detection technology.

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: In hand-drawn sketch recognition, the traditional deep learning method has the problems of insufficient feature extraction and low recognition rate. To solve this problem, a new algorithm based on a dual-channel convolutional neural network is proposed. Firstly, the sketch is preprocessed to get a smooth sketch. The contour of the sketch is obtained by the contour extraction algorithm. Then, the sketch and contour are used as the input image of CNN. Finally, feature fusion is carried out in the full connection layer, and the classification results are obtained by using a softmax classifier. Experimental results show that this method can effectively improve the recognition rate of a hand-drawn sketch.

Description: The present paper deals with a fractional-order mathematical epidemic model of malaria transmission accompanied by temporary immunity and relapse. The model is revised by using Caputo fractional operator for the index of memory. We also recommend the utilization of temporary immunity and the possibility of relapse. The theory of locally bounded and Lipschitz is employed to inspect the existence and uniqueness of the solution of the malaria model. It is shown that temporary immunity has a great effect on the dynamical transmission of host and vector populations. The stability analysis of these equilibrium points for fractional-order derivative α and basic reproduction number $mathcal{R}_{0}$ R 0 is discussed. The model will exhibit a Hopf-type bifurcation. The two control variables are introduced in this model to decrease the number of populations. Mandatory conditions for the control problem are produced. Two types of numerical method via Laplace Adomian decomposition and Runge–Kutta of fourth order for simulating the proposed model with fractional-order derivative are presented. To validate the mathematical results, numerical simulations, sensitivity analysis, convergence analysis, and other important studies are given. The paper is finished with some conclusions and discussion.

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: To secure full benefits without jeopardizing project feasibility, sustainability standards in high-rise building design should be included at all phases of the decision-making process. However, there are limited empirical studies on the influence of building information modeling (BIM) implementation in high-rise buildings. Implementing BIM is a viable technique to improve high-rise building sustainability performance. Therefore, the aim of this research is to explore the influence of BIM implementation in high-rise buildings by integrating the exploratory factor analysis (EFA) and structural equation modeling (SEM) approaches. Following a detailed review of the literature to identify critical success factors (CSFs) for BIM implementation, empirical evidence was gathered through a questionnaire survey with 205 stakeholders in construction projects. The EFA revealed five components, namely, productivity, visualization, coordination, sustainability, and safety improvement, all of which have a significant impact on the long-term construction of high-rise buildings. Moreover, SEM was conducted to develop the model for high-rise buildings. However, it has been revealed that awareness and usage level of BIM technology in high-rise buildings still appears to be limited. This scenario paves the way for future researchers to develop more models in the domain of high-rise buildings in order to improve sustainable development.

Description: “Spot the Difference” is a well-known game where players must find subtle differences between two almost identical pictures. If “Spot the Difference” is designed for videos, what is the difference between videos and pictures? If the performance of videos is measured by an eye tracker, what scan paths will be conducted? In this study, we explored this game using a video to conduct a visual performance evaluation. Twenty-five subjects were recruited in a full-factorial experiment to investigate the effect of background (with background, without background), video type (animation, text), and arrangement (left-to-right, top-to-bottom) on searching, eye tracking performance, and visual fatigue. The results showed that the video type had a significant effect on the accuracy and subjective visual fatigue, with the accuracy and subjective visual fatigue for animation being better than for text. The results also indicated that the arrangement had a significant effect on the number of fixations, where top-to-bottom arrangement brought a higher number of fixations. The background had a significant effect on accuracy and subjective visual fatigue, where the accuracy and subjective visual fatigue without a background was better than with a background. For the analysis of the scan path, a denser scan path was found in text than in animation, in top-to-bottom arrangement than in left-to-right arrangement, and without a background than with a background. In the future, game manufacturers should use the results of this research to design different “Spot the Difference” videos. When designing a simple game, an animation without a background and involving a left-to-right arrangement was recommended. When designing a difficult game, the opposite settings should be used.

Description: The mathematical model of diffusion in powder oxide catalysts during the process of temperature programmed oxygen isotopic exchange is proposed. The diffusion is considered together with the homogeneous and heterogeneous oxygen isotopic exchange processes. The matrix forms of exchange rate equations of simple and complex heteroexchange, and homoexchange reactions which obtain symmetrical forms are analyzed. The quantitative values of model parameters are found from the fitting of experimental data taken from literature of temperature programmed oxygen isotopic exchange process in catalysts ZrO2 and CeO2. The fittings show a good matching of model results with experimental data. The shapes of kinetic curves registered during temperature programmed oxygen isotopic exchange process are analyzed and the influence of various process parameters such as activation energies of simple and complex heteroexchange, oxygen surface concentration of catalyst, ratio of catalysts surface and volume of reactor, diffusion activation energy is considered. The depth profiles of diffusing oxygen species in oxide catalysts powder are calculated.

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: Currently, the route planning functions in 2D/3D campus navigation systems in the market are unable to process indoor and outdoor localization information simultaneously, and the UI experiences are not optimal because they are limited by the service platforms. An ARCore-based augmented reality campus navigation system is designed in this paper in order to solve the relevant problems. Firstly, the proposed campus navigation system uses ARCore to enhance reality by presenting 3D information in real scenes. Secondly, a visual inertial ranging algorithm is proposed for real-time locating and map generating in mobile devices. Finally, rich Unity3D scripts are designed in order to enhance users’ autonomy and enjoyment during navigation experience. In this paper, indoor navigation and outdoor navigation experiments are carried out at the Lingang campus of Shanghai University of Electric Power. Compared with the AR outdoor navigation system of Gaode, the proposed AR system can achieve increased precise outdoor localization by deploying the visual inertia odometer on the mobile phone and realizes the augmented reality function of 3D information and real scene, thus enriching the user’s interactive experience. Furthermore, four groups of students have been selected for system testing and evaluation. Compared with traditional systems, such as Gaode map or Internet media, experimental results show that our system could facilitate the effectiveness and usability of learning on campus.

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: In this work, a single-layer TiO2–ZrO2 thin film is deposited on the AISI 316L austenitic stainless steel by the sol–gel process and the dip coating method to improve its corrosion resistance properties. For the sol preparation, titanium isopropoxide and zirconium butoxide are used as the precursors, yttrium acetate hydrate is used for the ZrO2 stabilization, i-propanol as the solvent, nitric acid as the catalyst, acetylacetone as the chelating agent, and the distilled water for the hydrolysis. The deposited films are annealed at 400 °C or 600 °C. Morphology and phase composition of the sol–gel TiO2–ZrO2 films and powders are analyzed by scanning electron microscopy (SEM) equipped with EDX detector and X-ray diffraction (XRD), respectively. The thickness of the sol–gel TiO2–ZrO2 films deposited on the stainless steel is determined by glow discharge optical emission spectrometry (GD-OES). The corrosion behavior of the stainless steel, coated by amorphous films, is evaluated in 3 wt% NaCl and 0.5 mol dm−3 HCl by potentiodynamic polarization and electrochemical impedance spectroscopy (EIS) techniques. It is found that the sol–gel TiO2–ZrO2 films with the amorphous structure, deposited by the sol–gel process, and calcined at 400 °C significantly enhance the corrosion properties of AISI 316L in both chloride media.

Description: This paper presents a novel architecture for detecting mathematical formulas in document images, which is an important step for reliable information extraction in several domains. Recently, Cascade Mask R-CNN networks have been introduced to solve object detection in computer vision. In this paper, we suggest a couple of modifications to the existing Cascade Mask R-CNN architecture: First, the proposed network uses deformable convolutions instead of conventional convolutions in the backbone network to spot areas of interest better. Second, it uses a dual backbone of ResNeXt-101, having composite connections at the parallel stages. Finally, our proposed network is end-to-end trainable. We evaluate the proposed approach on the ICDAR-2017 POD and Marmot datasets. The proposed approach demonstrates state-of-the-art performance on ICDAR-2017 POD at a higher IoU threshold with an f1-score of 0.917, reducing the relative error by 7.8%. Moreover, we accomplished correct detection accuracy of 81.3% on embedded formulas on the Marmot dataset, which results in a relative error reduction of 30%.

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: Collecting data allows researchers to store and analyze important information about activities, events, and situations. Gathering this information can also help us make decisions, control processes, and analyze what happens and when it happens. In fact, a scientific investigation is the way scientists use the scientific method to collect the data and evidence that they plan to analyze. Neuroscience and other related activities are set to collect their own big datasets, but to exploit their full potential, we need ways to standardize, integrate, and synthesize diverse types of data. Although the use of low-cost ElectroEncephaloGraphy (EEG) devices has increased, such as those whose price is below 300 USD, their role in neuroscience research activities has not been well supported; there are weaknesses in collecting the data and information. The primary objective of this paper was to describe a tool for data management and visualization, called MuseStudio, for low-cost devices; specifically, our tool is related to the Muse brain-sensing headband, a personal meditation assistant with additional possibilities. MuseStudio was developed in Python following the best practices in data analysis and is fully compatible with the Brain Imaging Data Structure (BIDS), which specifies how brain data must be managed. Our open-source tool can import and export data from Muse devices and allows viewing real-time brain data, and the BIDS exporting capabilities can be successfully validated following the available guidelines. Moreover, these and other functional and nonfunctional features were validated by involving five experts as validators through the DESMET method, and a latency analysis was also performed and discussed. The results of these validation activities were successful at collecting and managing electroencephalogram data.

Description: The number of people diagnosed with epilepsy as a common brain disease accounts for about 1% of the world’s total population. Seizure prediction is an important study that can improve the lives of patients with epilepsy, and, in recent years, it has attracted more and more attention. In this paper, we propose a novel hybrid deep learning model that combines a Dense Convolutional Network (DenseNet) and Long Short-Term Memory (LSTM) for epileptic seizure prediction using EEG data. The proposed method first converts the EEG data into the time-frequency domain through Discrete Wavelet Transform (DWT) for use in the input of the model. Then, we train the previously transformed image through a hybrid model combining Densenet and LSTM. To evaluate the performance of the proposed method, experiments are conducted for each preictal length of 5, 10, and 15 min using the CHB-MIT scalp EEG dataset. As a result, we obtained a prediction accuracy of 93.28%, a sensitivity of 92.92%, a specificity of 93.65%, a false positive rate of 0.063 per hour, and an F1-score of 0.923 when the preictal length was 5 min. Finally, as the proposed method is compared to previous studies, it is confirmed that the seizure prediction performance was improved significantly.

Description: In this paper, using the notions of qκ2-quantum integral and qκ2-quantum derivative, we present some new identities that enable us to obtain new quantum Simpson’s and quantum Newton’s type inequalities for quantum differentiable convex functions. This paper, in particular, generalizes and expands previous findings in the field of quantum and classical integral inequalities obtained by various authors.

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: TiAlN coatings with different Al ratios were deposited by the cathodic arc ion plating (AIP) method, and the relationship between solid particle erosion resistance and structural, mechanical properties was investigated by a micro slurry-jet erosion (MSE) test. The crystal structure of TiAlN coating changes depending on the Al ratio. The coating shows a B1 single cubic phase between the Al ratio of 0 and 0.58; above this ratio, formation of a B4 hexagonal phase is observed. The mechanical properties such as hardness and Young’s modulus of the TiAlN coating also depend on the Al ratio and the crystal structure. The erosion rate decreases by increasing the Al ratio up to 0.58, as the coating is a cubic single phase. The TiAlN coating shows the lowest erosion rate at an Al ratio of 0.58. The erosion rate increases drastically as the crystalline phase changes from the B1 cubic to B4 hexagonal phase at the Al ratio of more than 0.58. The change in erosion rate is also discussed in connection with mechanical properties such as erodent particle hardness to coating hardness ratio and coating hardness to Young’s modulus ratio.

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: This paper presents a modified NSGA-II algorithm based on the spatial density (SD) operator, combined with computer graphics-based surface parameterisation methods and computational fluid dynamics (CFD) simulations. This was done to optimise the multi-objective aerodynamic design of a centrifugal impeller for a 100-kW vehicle-mounted fuel cell and improve the multi-conditions aerodynamic performance of the centrifugal impeller of the vehicle-mounted fuel cell (FC). The optimisation objectives are to maximise the isentropic efficiency of the rated and common operating conditions. The optimisation results showed that the efficiency of rated working conditions had an increase of 1.29%, mass flow increase of 8.8%, pressure ratio increase of 0.74% and comprehensive margin increase of 6.2%. The efficiency of common working conditions had an increase of 1.2%, mass flow increase of 9.1%, pressure ratio increase of 0.24% and comprehensive margin increase of 10%. The optimisation effect is obvious under the premise of satisfying the constraints, which proves the optimisation method’s engineering effectiveness and provides technical support and methodological research for the multi-objective aerodynamic design optimisation of centrifugal impellers for vehicle-mounted FCs.

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: The aim of this study was to determine the basic chemical composition, the content of bioactive compounds and antioxidant activity in currant leaves. The leaves of black, red and white currant shrubs were collected in May, and in the beginning of June, July and August, for two years between 2018 and 2019. The proximate analysis, including dry matter, protein, fat, ash and total carbohydrates, was determined. In addition, the content of the polyphenols and the total antioxidant activity using ABTS, DPPH and FRAP assays were conducted. The highest concentration of protein was detected in the whitecurrant leaves harvested in May in both years, while the highest content of crude fat was found in the blackcurrant leaves harvested in both years, with the exception of the August harvest. Extracts from the blackcurrant leaves collected in June/July 2019 had the highest antioxidant activity that was measured by the ABTS method (about 7000 µmol Trolox/g DM) and confirmed by other methods, while extracts from the whitecurrant leaves produced from the August 2018 collection had the lowest antioxidant activity (1884 µmol Trolox/g DM). Currant leaves are a rich source of bioactive compounds and contain higher amounts of polyphenols as compared with currant fruits. These compounds may play a very important role in the risk reduction and even prevention of the most chronic non-communicable diseases. Therefore, further research is needed to identify currant leaves as a source of bioactives for functional foods and natural health products. The highest antioxidant activity was in the redcurrant leaves from all the harvest times in 2018 as measured by the ABTS and FRAP methods. On the contrary, blackcurrant leaves from all the harvest times in 2019 had the highest antioxidant activity.

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: With the wide increase in global forestry resources trade, the demand for wood is increasing day by day, especially rare wood. Finding a computer-based method that can identify wood species has strong practical value and very important significance for regulating the wood trade market and protecting the interests of all parties, which is one of the important problems to be solved by the wood industry. This article firstly studies the establishment of wood microscopic images dataset through a combination of traditional image amplification technology and Mix-up technology expansion strategy. Then with the traditional Faster Region-based Convolutional Neural Networks (Faster RCNN) model, the receptive field enhancement Spatial Pyramid Pooling (SPP) module and the multi-scale feature fusion of Feature Pyramid Networks (FPN) module are introduced to construct a microscopic image identification model based on the migration learning fusion model and analyzes the three factors (Mix-up, Enhanced SPP and FPN modules) affecting the wood microscopic image detection model. The experimental results show that the proposed approach can identify 10 kinds of wood microscopic images, and the accuracy rate has increased from 77.8% to 83.8%, which provides convenient conditions for further in-depth study of the microscopic characteristics of wood cells and is of great significance to the field of wood science.

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: With the increasing integration of non-linear electronic loads, the diagnosis and classification of power quality are becoming crucial for power grid signal management. This paper presents a novel diagnosis strategy based on unsupervised learning, namely residual denoising convolutional auto-encoder (RDCA), which extracts features from the complex power quality disturbances (PQDs) automatically. Firstly, the time–frequency analysis is applied to isolate frequency domain information. Then, the RDCA with a weight residual structure is utilized to extract the useful features in the contaminated PQD data, where the performance is improved using the residual structure. A single-layer convolutional neural network (SCNN) with an added batch normalization layer is proposed to classify the features. Furthermore, combining with RDCA and SCNN, we further propose a classification framework to classify complex PQDs. To provide a reasonable interpretation of the RDCA, visual analysis is employed to gain insight into the model, leading to a better understanding of the features from different layers. The simulation and experimental tests are conducted to verify the practicability and robustness of the RDCA.

Description: Construction projects usually involve signing various contracts with specific billing procedures. In practice, dealing with complex contract structures causes significant problems, especially with regard to timely payment and guaranteed cash flow. Furthermore, a lack of transparency leads to a loss of trust. As a result, late or non-payment is a common problem in the construction industry. This paper presents the concept of implementing smart contracts for automated, transparent, and traceable payment processing for construction projects. Automated billing is achieved by combining Building Information Modeling (BIM) approaches with blockchain-based smart contracts. Thereby, parts of traditional construction contracts are transferred to a smart contract. The smart contract is set up using digital BIM-based tender documents and contains all of the relevant data for financial transactions. Once the contracted construction work has been accepted by the client, payments can be made automatically via authorized financial institutions. This paper describes the framework, referred to as BIMcontracts, the container-based data exchange, and the digital contract management workflow. It discusses the industry-specific requirements for blockchain and data storage and explains which technical and software architectural decisions were made. A case study is used to demonstrate the current implementation of the concept.

Description: The advent of chip calorimetry has enabled an unprecedented extension of the capability of differential scanning calorimetry to explore new domains of materials behavior. In this paper, we highlight some of our recent work: the application of heating and cooling rates above 104 K/s allows for the clear determination of the glass transition temperature, Tg, in systems where Tg and the onset temperature for crystallization, Tx, overlap; the evaluation of the delay time for crystal nucleation; the discovery of new polyamorphous materials; and the in-situ formation of glass in liquid crystals. From these application examples, it is evident that chip calorimetry has the potential to reveal new reaction and transformation behavior and to develop a new understanding.

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: Optical turbulence, as determined by the widely accepted practice of profiling the temperature structure constant, CT2, via the measurement of ambient atmospheric temperature gradients, can be found to differ quite significantly when characterizing such gradients via thermal-couple differential temperature sensors as compared to doing so with acoustic probes such as those commonly used in sonic anemometry. Similar inconsistencies are observed when comparing optical turbulence strength derived via CT2 as compared to those through direct optical or imaging measurements of small fluctuations of the index of refraction of air (i.e., scintillation). These irregularities are especially apparent in stable atmospheric layers and during diurnal quiescent periods. Our research demonstrates that when care is taken to properly remove large-scale index of refraction gradients, the sonic anemometer-derived velocity structure constant, Cv2, coupled with the similarly derived turbulence-driven index of refraction and vertical wind shear gradients, provides a refractive index structure constant, Cn2, that can more closely match the optical turbulence strengths inferred by more direct means such as scintillometers or differential image motion techniques. The research also illustrates the utility and robustness of quantifying Cn2 from CT2 at a point using a single sonic anemometer and establishes a clear set of equations to calculate volumetric Cn2 data using instrumentation that measures wind velocities with more spatial/temporal fidelity than temperature.