ALBERT

Description: The lockdown was crucial to stop the COVID-19 pandemic in Italy, but it affected many aspects of social life, among which traditional live science cafés. Moreover, citizens and experts asked for a direct contact, not relying on mass-media communication. In this paper, we describe how the Florence and Rome science cafés, contacted by citizens and experts, either directly or through the Florence science shop, responded to these needs by organizing online versions of traditional face-to-face events, experiencing high levels of participation. The science café methodology was also requested by a high school that needed to conclude an engagement experience with students and their families. We also report the results of a survey about the satisfaction of this new methodology with respect to the old one.

Description: Medical disputes that result in medical compensation and losses affect the financial management and sustainable operational risks of enterprises. Employee training plays an important role in the sustainable growth of human resource management and also can help avoid any potential risks to enterprises’ operating revenue. Based on data of a company, this study’s model aims to establish a mathematical model to find the most suitable decision variables in order to provide decision-making analysis and judgment of a company’s individual economic behaviors. From the second-order differential modeling method, where the functional training time of the aesthetic medicine industry (including medical errors/dispute incidences, functional training costs, and medical benefits) links to a specific functional relationship, the optimal decision-making model and evaluation criteria for the proportion of this training time under the concept of sustainable management can be constructed. The method proposed herein reduces medical errors or disputes, strengthens risk and financial management, provides customers with the best service quality, and serves as the basis for decision-making evaluation of the maximum benefits of sustainable operations.

Description: Process-based crop models are advantageous for the identification of management strategies to cope with both temporal and spatial variability of sugarcane yield. However, global optimization of such models is often computationally expensive. Therefore, we performed global sensitivity analysis based on Gaussian process emulation to evaluate the sensitivity of cane dry weight to trait parameters implemented in the Agricultural Productions System Simulator (APSIM)-Sugar model under selected environmental and management conditions in Khon Kaen (KK), Thailand. Emulators modeled 30 years, three soil types and irrigated or rainfed conditions, and emulator performance was investigated. rue, green_leaf_no, transp_eff_cf, tt_emerg_to_begcane and cane_fraction were identified as the most influential parameters and together they explained more than 90% of total variance on the simulator output. Moreover, results indicate that the sensitivity of sugarcane yield to the most influential parameters is affected by water stress conditions and nitrogen stress. Our findings can be used to improve the efficiency and accuracy of modeling and to identify appropriate management strategies to address temporal and spatial variability of sugarcane yield in KK.

Description: This research presents a machine vision approach to detect lesions in liver ultrasound as well as resolving some issues in ultrasound such as artifacts, speckle noise, and blurring effect. The anisotropic diffusion is modified using the edge preservation conditions which found better than traditional ones in quantitative evolution. To dig for more potential information, a learnable super-resolution (SR) is embedded into the deep CNN. The feature is fused using Gabor Wavelet Transform (GWT) and Local Binary Pattern (LBP) with a pre-trained deep CNN model. Moreover, we propose a Bayes rule-based informative patch selection approach to reduce the processing time with the selective image patches and design an algorithm to mark the lesion region from identified ultrasound image patches. To train this model, standard data ensures promising resolution. The testing phase considers generalized data with a varying resolution and test the performance of the model. Exploring cross-validation, it finds that a 5-fold strategy can successfully eradicate the overfitting problem. Experiment data are collected using 298 consecutive ultrasounds comprising 15,296 image patches. This proposed feature fusion technique confirms satisfactory performance compared to the current relevant works with an accuracy of 98.40%.

Description: QR (quick response) Codes are one of the most popular types of two-dimensional (2D) matrix codes currently used in a wide variety of fields. Two-dimensional matrix codes, compared to 1D bar codes, can encode significantly more data in the same area. We have compared algorithms capable of localizing multiple QR Codes in an image using typical finder patterns, which are present in three corners of a QR Code. Finally, we present a novel approach to identify perspective distortion by analyzing the direction of horizontal and vertical edges and by maximizing the standard deviation of horizontal and vertical projections of these edges. This algorithm is computationally efficient, works well for low-resolution images, and is also suited to real-time processing.

Description: Considering the complex problem of competition and cooperation among enterprises in the process of product selling and recycling, different coopetition game models of closed-loop supply chain with the fairness preference of a manufacturer and retailers are constructed according to the actual investigation. Then, this study primarily used fairness preference theory and game theory to analyze the impact of fairness preference on the pricing decision, recycling and remanufacturing strategy, and coopetition model selection. The results show that (1) enterprises’ fairness preference would increase the wholesale price and retail price, and the stronger the degree of fairness preference is, the more obvious the trend is; (2) enterprises’ fairness preference is not conducive to recycling and remanufacturing, and the higher level of fairness preference is associated with the lower recycling rate of waste products; (3) enterprises’ fairness preference would not affect the model selection but would benefit the overall profit.

Description: Aiming at the problems of low contrast and low definition of fog degraded image, this paper proposes an image defogging algorithm based on sparse representation. Firstly, the algorithm transforms image from RGB space to HSI space and uses two-level wavelet transform extract features of image brightness components. Then, it uses the K-SVD algorithm training dictionary and learns the sparse features of the fog-free image to reconstructed I-components of the fog image. Using the nonlinear stretching approach for saturation component improves the brightness of the image. Finally, convert from HSI space to RGB color space to get the defog image. Experimental results show that the algorithm can effectively improve the contrast and visual effect of the image. Compared with several common defog algorithms, the percentage of image saturation pixels is better than the comparison algorithm.

Description: Power efficiency of two-stroke spark-ignition engine is generally low because improper amount of fuel injection leads to a lot of unburned fuel loss during the engine working process. However, parameters of the fuel injection system are hard to confirm by aviation experiments due to expensive test costs. This paper proposes a method of calibrating injection parameters of two-stroke spark-ignition engine based on thermodynamic simulation and parameter optimum algorithm. Firstly, the one-dimensional thermodynamic model is built according to the internal structure and thermodynamic process of the engine; then, the model parameters are corrected according to the operating principle of the injector; after experimental verification of the model, considering both the engine power sufficiency and fuel economy, Analytic Hierarchy Process method is applied to look for the optimal injection amount and fuel injection advance angle at different engine working speeds; finally, an aeroengine experiment station with an electronic fuel injector system is built. Through simulation and experiment studies, it can be seen that when the engine speed changes from 3000 to 3500 RPM, the oil consumption rate of the optimal results is higher than that of the previous ones; when the aeroengine speed is higher than 4000 RPM, the oil consumption rate results of the optimal method are 10% to 27% higher than the original results. This paper can be a reference in the optimization of UAV aircraft engine.

Description: The conjugate gradient projection method is one of the most effective methods for solving large-scale monotone nonlinear equations with convex constraints. In this paper, a new conjugate parameter is designed to generate the search direction, and an adaptive line search strategy is improved to yield the step size, and then, a new conjugate gradient projection method is proposed for large-scale monotone nonlinear equations with convex constraints. Under mild conditions, the proposed method is proved to be globally convergent. A large number of numerical experiments for the presented method and its comparisons are executed, which indicates that the presented method is very promising. Finally, the proposed method is applied to deal with the recovery of sparse signals.

Description: This paper establishes a two-echelon supply chain consisting of one manufacturer and one retailer. We consider the retailer buys a product from the manufacturer and sells it to consumers through a store channel and an online channel. The retailer needs to bear a fixed investment cost to running its store/online channel. We discuss the impact of the fixed investment cost, the operating cost for the product, and substitutive factor between the two channels on the optimal strategy for the retailer using complex network theory. The result shows that the ratio of net surplus and the ratio of the operating cost between the two channels play a significant role in the retailer’s optimal decisions. Moreover, finance constraint and the fixed investment cost are also two vital factors for the retailer to channel selection strategy. Numerical experiment shows the effectiveness of the conclusion, and some meaningful insights are generated.

Description: A soil tillage system adjusted to the soil type and agro-ecological conditions, together with the optimal nutrient supply, is a prerequisite for high maize (Zea mays L.) yield. However, there is little knowledge about the influence of soil tillage and nitrogen (N) rates on maize grain quality. A study was initiated in 1978 in Zemun Polje, Belgrade, Serbia, on the chernozem soil type. The effects of three N rates—0, 180 and 240 kg N ha−1—within three tillage practices—no-tillage, reduced and conventional tillage—and their influence on grain yield (GY) and the content of proteins, starch, oil, total tocopherols and carotenoids, phytic phosphorus (Phy), glutathione (GSH), phenols and inorganic P (Pi) in maize grain were analysed in the period 2016–2018. Differences in maize GY were 2.57 and 3.01 t ha−1 for reduced- or no-tillage, respectively, in comparison to conventional tillage. Conventional tillage and higher N rates contributed to the increase in maize yield and the grain nutritional quality with regard to the increased level of proteins, GSH and Phy. On the other hand reduced inputs were beneficial only for grain quality, in relation to the increase in tocopherols and phenols.

Description: ×Amarine hybrids are attractive ornamental geophytes grown for cut flower production. Their cultivation is limited due to lesser flowering percentages and lesser bulb weight gain. To optimize the growth and propagation of geophytes, plant growth regulators (PGRs) are used, but so far none have been tested in ×Amarine. We investigated the effect of gibberellic acid (GA3; 50, 100, and 200 mg dm−3) and methyl jasmonate (MeJA; 100, 500, and 1000 µmol dm−3) on growth, flowering, bulb yield, and select physiological parameters of ×A. tubergenii “Zwanenburg”. PGRs were applied as foliar sprays on the 70th and 77th day after planting. GA3 treatment at 200 mg dm−3 exhibited the greatest leaf number, leaf length, bulb weight, daughter bulb number, CO2 assimilation intensity, greenness index, total sugars, and total protein content in bulbs. GA3 application at 100 and 200 mg dm−3 accelerated flowering and at 50 and 100 mg dm−3 significantly increased the bulb flowering percentage. MeJA at all tested concentrations prolonged anthesis time and reduced the bulb flowering percentage. GA3 at all concentrations and MeJA at 500 and 1000 µmol dm−3 stimulated daughter bulbs formation. GA3, especially at 200 mg dm−3 can improve anthesis and increase ×A. tubergenii “Zwanenburg” bulb yield.

Description: Smart agriculture is an evolving trend in the agriculture industry, where sensors are embedded into plants to collect vital data and help in decision-making to ensure a higher quality of crops and prevent pests, disease, and other possible threats. One of the most critical pests of palms is the red palm weevil, which is an insect that causes much damage to palm trees and can devastate vast areas of palm trees. The most challenging problem is that the effect of the weevil is not visible by humans until the palm reaches an advanced infestation state. For this reason, there is a pressing need to use advanced technology for early detection and prevention of infestation propagation. In this project, we have developed an IoT-based smart palm monitoring prototype as a proof-of-concept that (1) allows monitoring palms remotely using smart agriculture sensors, (2) contribute to the early detection of red palm weevil infestation. Users can use web/mobile applications to interact with their palm farms and help them in getting early detection of possible infestations. We used an industrial-level IoT platform to interface between the sensor layer and the user layer. Moreover, we have collected data using accelerometer sensors, and we applied signal processing and statistical techniques to analyze collected data and determine a fingerprint of the infestation.

Description: With the background of aging, ensuring the deep integration of pension and medical services and effectively integrating pension resources and medical resources are hot issues that must be addressed in the current mode of integrating health and care services for older people. Thus, we use game theory to construct the utility model of resource allocation between pension and medical institutions. We apply this model to explore how pension institutions and medical institutions invest resources into the integration of health and care services, analysis of influencing factors, and conducting incentive mechanism research by using MATLAB 2016b software. Through theoretical deduction and experimental analysis, the following conclusions are drawn. First, the income distribution coefficient of pension institutions is positively correlated with the level of labor input, and its growth rate has a marginal diminishing effect on the level of labor. Second, in early investment, the income distribution coefficient of pension institutions is positively correlated with fixed asset investment regardless of the different effort coefficients between medical institutions and pension institutions. With a high income distribution coefficient, pension institutions are negatively correlated and marginally decrease. Third, in early investment, the income distribution coefficient of pension institutions is positively correlated with medical institutions’ labor input level. When the income distribution coefficient of pension institutions reaches a certain value, it is negatively correlated with the labor input level of medical institutions, thereby showing a marginal diminishing effect.

Description: Transportation is an example of a typical, open, fluid complex network system. Expressways are one form of complex transportation networks, and expressway service areas serve as infrastructure nodes in the expressway transportation network; hence, their construction has a significant impact on tourism development and utilization. Domestic and foreign studies on complex transportation networks have mostly been conducted from the perspective of railways, air transport, and urban transportation but seldom on expressway transportation networks. This study employed complex network theory, social network analysis, kernel density analysis, and bivariate autocorrelation to characterize the spatial structure of expressway transport networks in terms of geographical centrality. By innovating the coupling of geographical centrality and passenger flow centrality in clustering, the study also quantitatively analyzed the differences between the geographical advantage and actual passenger flow advantage of China’s Guizhou expressway transportation network to analyze the tourism utilization potential of expressway service areas. We found that (1) the geographical centrality of the Guizhou expressway transportation network ranged from −1.28 to 3.33, and its distribution shows a single-core, polyconcentric dispersed spatial structure; (2) the passenger-car flow rate ranged from 15,000 to 3.66 million, and its distribution showed a dual-core, polycentric dispersed structure that is weakly concentric; and (3) there was a positive correlation of 0.22 between the geographical centrality and passenger flow of the Guizhou expressway transportation network, which showed seven cluster types—“high-high,” “moderately high-high,” “low-high,” “moderately low-high,” “high-low,” “moderately high-low,” and “low-low”—for which seven corresponding models of tourism development were proposed. This study broadens the practical application of traffic network complexity research and provides a scientific basis for upgrading and transforming the Guizhou expressway transportation network as well as for developing composite tourism uses for expressway service areas.

Description: For face recognition systems, liveness detection can effectively avoid illegal fraud and improve the safety of face recognition systems. Common face attacks include photo printing and video replay attacks. This paper studied the differences between photos, videos, and real faces in static texture and motion information and proposed a living detection structure based on feature fusion and attention mechanism, Dynamic and Texture Fusion Attention Network (DTFA-Net). We proposed a dynamic information fusion structure of an interchannel attention block to fuse the magnitude and direction of optical flow to extract facial motion features. In addition, for the face detection failure of HOG algorithm under complex illumination, we proposed an improved Gamma image preprocessing algorithm, which effectively improved the face detection ability. We conducted experiments on the CASIA-MFSD and Replay Attack Databases. According to experiments, the DTFA-Net proposed in this paper achieved 6.9% EER on CASIA and 2.2% HTER on Replay Attack that was comparable to other methods.

Description: In this paper, three types of fractional order partial differential equations, including the fractional Cauchy–Riemann equation, fractional acoustic wave equation, and two-dimensional space partial differential equation with time-fractional-order, are considered, and these models are obtained from the standard equations by replacing an integer-order derivative with a fractional-order derivative in Caputo sense. Firstly, we discuss the fractional integral and differential properties of several functions which are derived from the Mittag-Leffler function. Secondly, by using the homotopy analysis method, the exact solutions for fractional order models mentioned above with suitable initial boundary conditions are obtained. Finally, we draw the computer graphics of the exact solutions, the approximate solutions (truncation of finite terms), and absolute errors in the limited area, which show that the effectiveness of the homotopy analysis method for solving fractional order partial differential equations.

Description: Fault identification under variable operating conditions is a task of great importance and challenge for equipment health management. However, when dealing with this kind of issue, traditional fault diagnosis methods based on the assumption of the distribution coherence of the training and testing set are no longer applicable. In this paper, a novel state identification method integrated by time-frequency decomposition, multi-information entropies, and joint distribution adaptation is proposed for rolling element bearings. At first, fast ensemble empirical mode decomposition was employed to decompose the vibration signals into a collection of intrinsic mode functions, aiming at obtaining the multiscale description of the original signals. Then, hybrid entropy features that can characterize the dynamic and complexity of time series in the local space, global space, and frequency domain were extracted from each intrinsic mode function. As for the training and testing set under different load conditions, all data was mapped into a reproducing space by joint distribution adaptation to reduce the distribution discrepancies between datasets, where the pseudolabels of the testing set and the final diagnostic results were obtained by the k-nearest neighbor algorithm. Finally, five cases with the training and testing set under variable load conditions were used to demonstrate the performance of the proposed method, and comparisons with some other diagnosis models combined with the same features and other dimensionality reduction methods were also discussed. The analysis results show that the proposed method can effectively recognize the multifaults of rolling element bearings under variable load conditions with higher accuracies and has sound practicability.

Description: Urban bus networks play an important role, when the capacity of urban public services is evaluated. With recent advancements in Internet and Communication Technologies, there is an emerging interest in building an urban bus network model through open big data. This has rarely been investigated and exposes several challenges in the provision of transportation services in urban planning. On the one hand, it is necessary to combine bus stations based on spatial distance constraints due to their ambiguous definition in open big data; on the other hand, it is difficult and time-consuming to relocate and build new stations, but the optimization of bus lines is relatively easy to implement. This study aimed to develop an explicit methodological framework for building and analyzing two different types of urban bus network model using open big data. Thereafter, the framework was applied in two case studies in China, within a county-level administration and in a region including three county-level administrations. The key result shows that there was a shortage of urban bus services across these different administrations. This paper contributes to the body of research methodologies into public transport networks and to understanding the sharing of urban public services across administrations, improving the management of urban bus networks, and highlighting the importance of examining the characteristics of urban bus network in county-level administrations rather than just in large cities in China.

Description: High-quality development is an innovative way to promote the sustainable development of the marine economy. This study uses China’s coastal areas as an example, constructing a research framework for the high-quality development of the country’s marine economy. The temporal and spatial variation in the high-quality development of China’s marine economy from 2006 to 2016 is explored utilizing the nuclear density estimation, entropy, and mean standard deviation classification methods. The results show the following: (1) the policy orientation of China’s high-quality marine economic development is obvious, and the focus of policy has changed from the pursuit of growth speed to growth quality; (2) a spatiotemporal differentiation is evident, with the coastal areas of the Yangtze River Delta continuing to be the hotspots of high-quality development. The Bohai rim and Pan Pearl River Delta areas feature a mixture of cold and hot spots, with multipolar spatial differentiation; (3) in terms of development stage, Guangdong, Shanghai, Shandong, Zhejiang, and Tianjin are at advanced levels. The intermediate-level areas consist of Fujian, Jiangsu, Hainan, and Liaoning, while Hebei and Guangxi are the primary-level areas.

Description: Single-rod electrohydraulic system is widely applied in industrial production due to high power-to-weight ratio, but it generally has a low energy efficiency and has many system states which need to be measured. Therefore, an output feedback controller with energy saving is proposed in this paper. The designed controller only needs a displacement sensor to detect the position of the single-rod cylinder; the other states of the system are estimated by extended state observer (ESO). Besides, a nonlinear disturbance observer (NDO) is introduced to estimate the external mismatched disturbance. The output feedback controller based on extended state observer and nonlinear disturbance observer has a better tracking performance compared with other controllers. In addition, a proportional relief valve (PRV) is introduced to control the supply pressure of the system. The variable supply pressure reduces the energy of throttling loss and overflow loss, which achieves energy saving of about 54% according to the simulation results. Meanwhile, the tracking error of the energy saving controller is stable at 0.1 mm. In a word, the proposed controller not only achieves energy saving but also has a satisfactory trajectory tracking performance.

Description: In order to realize the unsupervised extraction and identification of fault features in power electronic circuits, we proposed a fault diagnosis method based on sparse autoencoder (SAE) and broad learning system (BLS). Firstly, the feature is extracted by the sparse autoencoder, and the fault samples and feature vectors are combined as the input of the broad learning system. The broad learning system is trained based on the error precision step update method, and the system is used to the fault type identification. The simulation results of the thyristor fault diagnosis of the three-phase bridge rectifier circuit show that the method is effective and has better performance than other traditional methods.

Description: The aim of this experiment was to evaluate the effect of fertilizing processing tomato by coupling the green manuring of fall-winter cover crops with fertigation in spring-summer. In a two-year experiment, seven fertilization treatments were compared: green manuring of pure barley (B100) and pure vetch (V100) sown at 100% of their ordinary seeding rates, green manuring of a barley-vetch mixture at a ratio of 75:25 of their own seed rates (B75V25), fertigation with drip irrigation at a rate of 200 kg ha−1 of nitrogen (N) (Fert_N200), fertigation combined with B100 and B75V25 at a N rate complementary to 200 kg N ha−1 (B100 + Fert and B75V25 + Fert, respectively), and an unfertilized control (N0) with no cover crops for green manuring prior to tomato transplanting or fertigation. The Fert_N200 treatment resulted in maximum tomato N uptake, growth and yield, but caused high N leaching, especially during the no-cover fall-winter period, as was also the case for N0. The V100 treatment promoted quite good tomato N status and yield, but did not reduce N leaching. The B100 and B75V25 treatments reduced N leaching but decreased tomato N uptake, growth and yield. The B100 + Fert and B75V25 + Fert treatments reduced N leaching, likely increased soil N stock, and facilitated optimal tomato N nutrition and maximum yields. Combining fertigation with green manuring of cover crops composed of pure grass or grass-legume mixtures appears to be a very effective and environmentally sound practice for fertilizing high N-demanding spring-summer crops like processing tomato.

Description: In this paper, a direction of arrival (DOA) estimator is proposed to improve the cyber-physical interactions, which is based on the second-order statistics without a priori knowledge of the source number. The impact of noise will firstly be eliminated. Then the relationship between the processed covariance matrix and the steering matrix is studied. By applying the elementary column transformation, an oblique projector will be designed without the source number. At last, a rooting method will be adopted to estimate the DOAs with the constructed projector. Simulation results show that the proposed method performs as well as other methods, which requires that the source number must be known.

Description: Pesticides play a crucial role in regulating crop production by reducing crop losses and increasing crop yield and quality. However, they may threaten surface and groundwater, a phenomenon occurring at global scale, potentially causing environmental damage and prohibition of water use or high treatment costs for drinking water. Assessing spatially-defined aquifer vulnerability to pesticide is then important, as it may allow defining agricultural areas where pesticides should be used following well-defined agronomic practices/limitations. In this study, after a brief review of recent studies on aquifer vulnerability assessment to pesticide, we applied the Vulnerability Index method to the agricultural area of the Municipality of San Giuliano Terme (Pisa, Italy) in order to focus on the data needs and discuss the reliability of this method (as an example of index-overlay methods). The proposed method needs a relatively small number of parameters compared to other more complex ones. Despite a such a small number of parameters, some were not easily available in our case study. Thus, some assumptions were made. This led to vulnerability maps with reduced reliability, no validation with groundwater samples, and little practical use. This means that to produce robust but static vulnerability assessments, large datasets are needed. In turn, the cost of data gathering may be high. The value of these data may, however, be increased, and the cost better justified if the analyses are based on process-based or advanced statistical methods. While the future for vulnerability assessment methods is the use of process-based/advanced statistical methods, index-overlay methods, as a preliminary step for process-based simulation analysis, may still provide initial and relatively quick insights on potential leaching of pesticides. This in turn may support extension services in delivering timely and relevant advices on the use of such pesticides to farmers and owners of plant nurseries and greenhouses.

Description: The middle and lower reaches of the Yangtze River (MLYR) plain represent the second-largest wheat producing area in China; the winter wheat-rice system is one of the main planting systems in this region. The use of the agricultural production system simulator (APSIM)-wheat model to simulate wheat production potential and evaluate the impact of future climate change on wheat production in this region is of great importance. In this study, the adaptability of the APSIM-wheat model in the MLYR was evaluated based on observational data collected in field experiments and daily meteorological data from experimental stations in Wuhan, Jingmen, and Xiangyang in Hubei province. The results showed significant positive relationships between model-predicted wheat growth duration from sowing to anthesis and maturity and the observed values, with coefficients of determination (R2) in ranges of 0.90–0.97 and 0.93–0.96, respectively. The normalized root-mean-square error (NRMSE) of the simulated growth durations and measured values were lower than 1.6%, and the refined index of agreement (dr-values) was in the range of 0.74–0.87. The percent mean absolute relative error (PMARE) was cited here as a new index, with a value below 1.4%, indicating that the model’s rating was excellent. The model’s performance in terms of grain yield and above-ground biomass simulation was also acceptable, although it was not as good as the growth periods simulation. The R2 value was higher than 0.75 and 0.72 for the simulation of grain yield and biomass, respectively. The indices NRMSE and PMARE were lower than 19.8% and 19.9%, and the dr-value was higher than 0.71. According to our results, APSIM-wheat was an effective and accurate model for simulating the phenology and yield production processes of wheat in the MLYR, and the results also provided a theoretical basis and technical support for further research on the yield potential of wheat-rice rotation planting systems with clarification of the key factors limiting the yield gap in this region.

Description: In this paper, an ecological model described by a couple of state-dependent impulsive equations is studied analytically and numerically. The theoretical analysis suggests that there exists a semitrivial periodic solution under some conditions and it is globally orbitally asymptotically stable. Furthermore, using the successor function, we study the existence, uniqueness, and stability of order-1 periodic solution, and the boundedness of solution is also presented. The relationship between order-k successor function and order-k periodic solution is discussed as well, thereby giving the existence condition of an order-3 periodic solution. In addition, a series of numerical simulations are carried out, which not only support the theoretical results but also show the complex dynamics in the model further, for example, the coexistence of multiple periodic solutions, chaos, and period-doubling bifurcation.

Description: Exploring and investigating new chaotic systems is a popular topic in nonlinear science. Although numerous chaotic systems have been introduced in the literature, few of them focus on torus-chaotic system. The aim of our short work is to widen the current knowledge of torus chaos. In this paper, a new torus-chaotic system is proposed, which has one positive Lyapunov exponent, two zero Lyapunov exponents, and two negative Lyapunov exponents. The dynamic behavior is investigated by Lyapunov exponents, bifurcations, and stability. The analysis shows that this system has an interesting route leading to chaos. Furthermore, the pseudorandom properties of output sequence are well studied and a random number generator algorithm is proposed, which has the potential of being used in several cyber security systems such as the verification code, secure QR code, and some secure communication protocols.

Description: As a sportification form of human-computer interaction, eSports is facing great gender stereotype threat and causing female players’ withdraw. This study aims to investigate the relationship between gender-swapping and females’ continuous participation intention in eSports, the mediating effect of self-efficacy, and the moderating effect of discrimination. The results demonstrate (1) that the effect of gender-swapping on continuous participation intention in eSports was not significant, while gender-swapping had a significant association with self-efficacy, and self-efficacy had a significant association with continuous participation intention in eSports; (2) that gender-swapping had an indirect effect (via self-efficacy) on continuous participation intention in eSports; and (3) that discrimination moderated the effect of self-efficacy on continuous participation intention. Female players who had experienced discrimination displayed higher continuous participation intention in the context of self-efficacy enhanced by gender-swapping.

Description: In this paper, with the aid of symbolic computation, several kinds of exact solutions including periodic waves, cross-kink waves, and breather are proposed by using a trilinear form for the (2 + 1)-dimensional Sharmo–Tasso–Olver equation. Then, by combing the different forms, the interactions between a lump and one-kink soliton and between a lump and periodic waves are generated. Moreover, the dynamic characteristics of interaction solutions are analyzed graphically by selecting suitable parameters with the help of Maple.

Description: Certain phosphorous solubilizing (PSB) and phosphorous mineralizing (PMB) bacteria may improve plant growth by improving nutrient availability. The aim of this work was to evaluate the effect of inoculation with two Bacillus spp. strains, 12A and 25A, on wheat seedlings growth. To this aim, a durum and a bread wheat genotype were grown under controlled conditions in a low P compost medium to evaluate: (i) the effect of the bacterial isolates on plant growth and root system architecture; (ii) the expression of two key genes indicative of the P-starvation response and phosphate (Pi) uptake, TaIPS1 and TaPHT1.6-B1. The results showed that 12A Bacillus sp. significantly increased root length, surface area and biomass. Furthermore, an enhanced shoot dry weight and P content were observed. This might be explained by the capacity of strain 12A to produce indole-3-acetic acid (IAA) in addition to P mineralizing and P solubilizing capability. No effect on plant growth was observed for 25A strain. The semi-quantitative gene expression analysis showed an overall lower expression of TaIPS1 in the inoculated plants and highest expression of TaPHT1.6-B1 in 12A inoculated plants. This suggests that Pi-responsive genes might be useful molecular indicators for the effectiveness of PSB and PMB.

Description: Geophytes are an increasingly widely used group of plants. Its high biodiversity, however, significantly impacts the cultivation process. Therefore, every new insight into the behavioral aspects of each single species is crucial for production. This study, for the first time, aims at contributing to a better understanding of the interplay between the bulb size potential of Narcissus poeticus and the pattern of bulb lifting time to find the optimal combination of these factors in obtaining the best quality propagation material. Twelve bulb size groups were examined. Plants were planted in the open air in two locations of Central East Europe and lifted in three terms: immediately and two and four weeks after flowering. Bulb fresh and dry weight, as well as leaf, root and total plant dry weight, increased with growing bulb size and delayed the time of lifting. The bulb weight increase indicator was twice as high for bulbs lifted in the third date compared to those lifted earlier. Its value was the highest for the smallest bulbs. Bulb shape ratio gradually decreased together with the increase of bulb weight and later lifting term. Principal components analysis allowed singling out two principal components that accounted for 86.58% of total variability.

Description: Biochar soil amendment to agricultural systems can reduce nitrogen (N) leaching; however, application to agricultural nitrogen treatment systems has not been extensively explored. The objective of this study was to assess the impact on N leaching in soils receiving repeated N applications which may be observed in agricultural treatment systems. In this study, 400 °C, 700 °C, and oxidized 700 °C corncob biochar was amended to sandy loam soil columns at 5% (wt/wt) to assess the impacts to N cycling following repeated synthetic N applications. Columns received weekly applications of either organic N (ORG-N), ammonium (NH4+-N), or nitrate (NO3−-N) and the N effluent, gaseous emissions, and soil N retention was measured. Biochar produced at 400 °C significantly reduced N leaching compared to control columns by 19% and 15% for ORG-N and NH4+-N, respectively, with application concentrations similar to silage bunker runoff. For NO3−-N applications, 700 °C biochar significantly reduced leaching by 25% compared to the controls. The primary mechanism reducing N effluent for biochar amended columns was enhanced soil retention of ORG-N and NO3−-N. Biochar surface chemistry analysis measured an increase in oxygenated functional groups and cationic minerals on the biochar surface, which likely enhanced retention through cationic bridging or the development of an organomineral layer on the biochar surface. Results indicated biochar amendment to agricultural treatment systems receiving N runoff may reduce the risk of N leaching.

Description: Deep learning models have been applied for varied electrical applications in smart grids with a high degree of reliability and accuracy. The development of deep learning models requires the historical data collected from several electric utilities during the training of the models. The lack of historical data for training and testing of developed models, considering security and privacy policy restrictions, is considered one of the greatest challenges to machine learning-based techniques. The paper proposes the use of homomorphic encryption, which enables the possibility of training the deep learning and classical machine learning models whilst preserving the privacy and security of the data. The proposed methodology is tested for applications of fault identification and localization, and load forecasting in smart grids. The results for fault localization show that the classification accuracy of the proposed privacy-preserving deep learning model while using homomorphic encryption is 97–98%, which is close to 98–99% classification accuracy of the model on plain data. Additionally, for load forecasting application, the results show that RMSE using the homomorphic encryption model is 0.0352 MWh while RMSE without application of encryption in modeling is around 0.0248 MWh.

Description: This study uses the BEKK-GARCH model to examine the return-and-volatility spillover between the world-leading markets (USA and China) and four emerging Latin American stock markets over the global financial crisis of 2008 and the crash of the Chinese stock market of 2015. Regarding return spillover, our findings reveal a unidirectional return transmission from Mexico to the US stock market during the global financial crisis. During the crash of the Chinese stock market, the return spillover is found to be unidirectional from the US to the Brazil, Chile, Mexico, and Peru stock markets. Moreover, the results indicate a unidirectional return transmission from China to the Brazil, Chile, Mexico, and Peru stock markets during the global financial crisis and the crash of the Chinese stock market. Regarding volatility spillover, the results show the bidirectional volatility transmission between the US and the stock markets of Chile and Mexico during the global financial crisis. During the Chinese crash, the bidirectional volatility transmission is observed between the US and Mexican stock markets. Furthermore, the volatility spillover is unidirectional from China to the Brazil stock market during the global financial crisis. During the Chinese crash, the volatility spillover is bidirectional between the China and Brazil stock markets. Lastly, a portfolio analysis application has been conducted.

Description: Fifth generation (5G) is a new generation mobile communication system developed for the growing demand for mobile communication. Channel coding is an indispensable part of most modern digital communication systems, for it can improve the transmission reliability and anti-interference. In order to meet the requirements of 5G communication, a dual threshold self-corrected minimum sum (DT-SCMS) algorithm for low-density parity-check (LDPC) decoders is proposed in this paper. Besides, an architecture of LDPC decoders is designed. By setting thresholds to judge the reliability of messages, the DT-SCMS algorithm erases unreliable messages, improving the decoding performance and efficiency. Simulation results show that the performance of DT-SCMS is better than that of SCMS. When the code rate is 1/3, the performance of DT-SCMS has been improved by 0.2 dB at the bit error rate of 10 − 4 compared with SCMS. In terms of the convergence, when the code rate is 2/3, the number of iterations of DT-SCMS can be reduced by up to 20.46% compared with SCMS, and the average proportion of reduction is 18.68%.

Description: The EU’s Common Agricultural Policy (CAP 2014–2020) on soil management points to the combination of sustainable food production with environmental protection, reduction of CO2 emissions, and safeguarding of soil biodiversity. In this study, three farms (in the Emilia-Romagna region), managed with both conventional and conservation practices (the last ones with and without sub-irrigation systems), were monitored from 2014 to 2017 to highlight the impact of different crops and soil managements on soil arthropods, in terms of abundance, composition, and soil biological quality (applying QBS-ar index). To do this, linear mixed models were performed, whereas arthropods assemblages were studied through PERMANOVA and SIMPER analysis. Soil communities varied among farms, although most differences were found among crops depending on management practices. Nonetheless, conservation systems and a wider reduction in anthropogenic practices provided better conditions for soil fauna, enhancing QBS-ar. Moreover, arthropod groups responded to soil practices differently, highlighting their sensitivity to agricultural management. Community assemblages in corn and wheat differed between managements, mainly due to Acari and Collembola, respectively. In conservation management, wheat showed the overall greatest abundance of arthropods, owing to the great number of Acari, Collembola, and Hymenoptera, while the number of arthropod groups were generally higher in crop residues of forage.

Description: We report the design of a Spiking Neural Network (SNN) edge detector with biologically inspired neurons that has a conceptual similarity with both Hodgkin-Huxley (HH) model neurons and Leaky Integrate-and-Fire (LIF) neurons. The computation of the membrane potential, which is used to determine the occurrence or absence of spike events, at each time step, is carried out by using the analytical solution to a simplified version of the HH neuron model. We find that the SNN based edge detector detects more edge pixels in images than those obtained by a Sobel edge detector. We designed a pipeline for image classification with a low-exposure frame simulation layer, SNN edge detection layers as pre-processing layers and a Convolutional Neural Network (CNN) as a classification module. We tested this pipeline for the task of classification with the Digits dataset, which is available in MATLAB. We find that the SNN based edge detection layer increases the image classification accuracy at lower exposure times, that is, for 1 〈 t 〈 T /4, where t is the number of milliseconds in a simulated exposure frame and T is the total exposure time, with reference to a Sobel edge or Canny edge detection layer in the pipeline. These results pave the way for developing novel cognitive neuromorphic computing architectures for millisecond timescale detection and object classification applications using event or spike cameras.

Description: Excess of cadmium (Cd) in soil leads to a number of adverse effects which challenge agricultural production. Aims of this study were to investigate variations in Cd content in leaves (Zea mays L.) of different maize genotypes and to detect effects of Cd on photosynthesis through chlorophyll a fluorescence. Pot vegetation experiments with four maize genotypes and four different soil Cd levels were repeated for two years. Chlorophyll fluorescence, photosynthetic pigments and inductively coupled plasma (ICP) analysis for ear-leaf Cd and zinc (Zn) and soil Cd were carried out. Significant differences between genotypes were found for leaf Cd, where higher Cd soil concentrations resulted in higher Cd leaf concentrations. Cd uptake into maize leaves increased with increasing Cd levels in soil, which was genotype-dependent and higher and lower Cd accumulating groups were formed. Changes in chlorophyll fluorescence caused by elevated Cd levels in soil were mostly visible as changes in dissipation energy, yields of primary photosystem II photochemistry and electron transport. Decrease of reaction centers per antenna chlorophyll and increased variable fluorescence at J step (VJ) resulted in decrease of performance indexes in the highest Cd concentration. Decreases in chlorophyll fluorescence parameters suggest reduced functionality of reaction centers and problems in re-oxidation of primary quinone acceptor (QA).

Description: The purpose of recommendation systems is to help users find effective information quickly and conveniently and also to present the items that users are interested in. While the literature of recommendation algorithms is vast, most collaborative filtering recommendation approaches attain low recommendation accuracies and are also unable to track temporal changes of preferences. Additionally, previous differential clustering evolution processes relied on a single-layer network and used a single scalar quantity to characterise the status values of users and items. To address these limitations, this paper proposes an effective collaborative filtering recommendation algorithm based on a double-layer network. This algorithm is capable of fully exploring dynamical changes of user preference over time and integrates the user and item layers via an attention mechanism to build a double-layer network model. Experiments on Movielens, CiaoDVD, and Filmtrust datasets verify the effectiveness of our proposed algorithm. Experimental results show that our proposed algorithm can attain a better performance than other state-of-the-art algorithms.

Description: Source code similarity detection has extensive applications in computer programming teaching and software intellectual property protection. In the teaching of computer programming courses, students may utilize some complex source code obfuscation techniques, e.g., opaque predicates, loop unrolling, and function inlining and outlining, to reduce the similarity between code fragments and avoid the plagiarism detection. Existing source code similarity detection approaches only consider static features of source code, making it difficult to cope with more complex code obfuscation techniques. In this paper, we propose a novel source code similarity detection approach by considering the dynamic features at runtime of source code using process mining. More specifically, given two pieces of source code, their running logs are obtained by source code instrumentation and execution. Next, process mining is used to obtain the flow charts of the two pieces of source code by analyzing their collected running logs. Finally, similarity of the two pieces of source code is measured by computing the similarity of these two flow charts. Experimental results show that the proposed approach can deal with more complex obfuscation techniques including opaque predicates and loop unrolling as well as function inlining and outlining, which cannot be handled by existing work properly. Therefore, we argue that our approach can defeat commonly used code obfuscation techniques more effectively for source code similarity detection than the existing state-of-the-art approaches.

Description: The collection and processing of personal data offers great opportunities for technological advances, but the accumulation of vast amounts of personal data also increases the risk of misuse for malicious intentions, especially in health care. Therefore, personal data are legally protected, e.g., by the European General Data Protection Regulation (GDPR), which states that individuals must be transparently informed and have the right to take control over the processing of their personal data. In real applications privacy policies are used to fulfill these requirements which can be negotiated via user interfaces. The literature proposes privacy languages as an electronic format for privacy policies while the users privacy preferences are represented by preference languages. However, this is only the beginning of the personal data life-cycle, which also includes the processing of personal data and its transfer to various stakeholders. In this work we define a personal privacy workflow, considering the negotiation of privacy policies, privacy-preserving processing and secondary use of personal data, in context of health care data processing to survey applicable Privacy Enhancing Technologies (PETs) to ensure the individuals’ privacy. Based on a broad literature review we identify open research questions for each step of the workflow.

Description: A failure of the EPSPS-inhibiting herbicide glyphosate to control several populations of Hordeum murinum subsp. leporinum (or H. murinum) occurred in southern Spain after more than fifteen applications in both crop (olive, orchards, and citrus) and non-crop (dry areas, roadsides and ditches) areas. Eight out of 18 populations studied were resistant (R) to glyphosate with R factors higher than four based on GR50. These populations also had the highest values of LD50 and the lowest levels of shikimic acid accumulation. Two adjuvants tested increased glyphosate efficacy in both susceptible (S) and R populations thanks to better spray foliar retention. Moreover, PS I-, PS II-, and ACCase-inhibiting herbicides, in pre- or post-emergence, proved to be the best chemical alternatives with different sites of action (SoA) to control both S and glyphosate-R populations. This study represents the first report worldwide of glyphosate resistance in H. murinum found in very different crop and non-crop areas from southern Spain. To design chemical strategies to implement integrated weed management programs for glyphosate-R H. murinum, both adjuvants and herbicides with alternative SoA as well as application timings should be considered.

Description: No-tillage has been used for mitigating wind erosion on the Southern High Plains US for decades. This study investigated the effects of tillage and nitrogen (N) fertilizer timing on cotton lint yield, fiber quality, and seed N content during a three-year transition from conventional tillage (CT) to a no-tillage system both with a wheat (Triticum aestivum) cover crop (NTW) and without a cover crop (NT). Lint yield was different between tillage systems within each year with the NTW system producing greater lint yield than the CT system in the second and third year of the transition period. The concentration of cotton seed N was not different within years, although it was decreased in the no N added control in the third year. Cotton fiber strength was increased in the NTW system compared to the CT system in the second year of the study. However, the CT system produced increased fiber strength compared to the other two systems in 2018 and is likely the result of late-season weather conditions. It was determined that implementing a NTW system may increase lint yield within the first few years and has no effect on most fiber quality parameters, especially in environmentally challenging conditions.

Description: Herbicide resistance in weeds restricts control options, thereby escalating economic loss and threatening agricultural sustainability in cereal production. Field evaluation of the crop performance, competitive traits, and consequent weed suppressive potential of 13 commercial winter wheat (Triticum aestivum L.) cultivars was performed in central NSW Australia with a focus on the evaluation and modelling of above-ground interactions. In 2015 and 2016, replicated field trials were established with genetically diverse commercial wheat genotypes under moderate to low rainfall conditions in Wagga Wagga (572 mm) and Condobolin (437 mm) New South Wales, respectively. The heritage cultivar Federation and a commercial cultivar of winter cereal rye (Secale cereale L.) were included as known weed-suppressive controls. Crop and weed growth, as well as early vigour, leaf area index, and photosynthetically active radiation, were monitored at various crop phenological stages including early growth, vegetative, flowering, grain fill, and harvest. Significant differences between wheat cultivar and location were observed for crop biomass, early vigour, leaf area index, weed number, weed biomass, canopy architecture, and yield in both 2015 and 2016. Differences in weed establishment were largely impacted first by rainfall and season and secondly by crop architecture (i.e., height, size, canopy) and phenology (i.e., growth stages). Early vigour and early canopy closure were instrumental in suppressing weed establishment and growth. Cultivar performance and competition with weeds were also clearly influenced by both environmental factors and genotype, as evidenced by differences in early cultivar performance, yield, and weed suppression by season and location. Specifically, Federation, Condo, and Janz wheat cultivars were superior performers in terms of weed suppression in both locations and years; however, Federation produced up to 55% lower yield than recently introduced cultivars. Partial least squares (PLS) regression was performed to develop a predictive linear model for weed competition in commercial wheat cultivars based on weed dry biomass as the response variable and selected aboveground crop canopy traits as predictors. In 2015, the model differed in accordance with crop growth stage, but the impact of predictors on weed biomass at both locations was not significant. In 2016, under local above average rainfall conditions, the model showed a significant negative correlation (p 〈 0.001) of most predictors on weed biomass (r2 = 0.51 at Condobolin, r2 = 0.62 at Wagga Wagga), suggesting the most influential factors in reducing weed numbers and establishment as crop vigour, biomass, and height. Our results indicate the establishment of competitive wheat cultivars in the absence of post-emergent herbicides resulted in a two to five-fold increased weed suppression over less suppressive genotypes, without significant yield penalties. Therefore, cultivar choice constitutes a cost-effective and sustainable weed management tool, particularly when weed pressure is significant.

Description: Plant biostimulants have been increasingly used in agriculture. Plant biostimulants have been mostly investigated regarding their stress attenuation and only a few reports have brought information on their effects on fruit quality, which is related to appearance, chemical and physical attributes. Due to their organic nature, they have a sustainable appeal, attending the demand of consumers worried about the beneficial effects of healthier foods. This review turns evident the little information available on this matter. As fruits have differentiated metabolism, from pollination to harvest and after-harvesting, it is suggested that further research should focus separately on each stage. Thus, research should aim at quality formation, when the fruits are still attached to the plant, and shelf life extension, after harvesting.

Description: Sorghum is the world’s fifth major cereal in terms of production and acreage. It is expected that its growth will be affected by the increase in air temperature, an important component of global climate change. Our objective was to use the Agricultural Land Management Alternatives with Numerical Assessment Criteria (ALMANAC) model to (a) evaluate the impact of climate warming on forage and grain sorghum production in Argentina and (b) to analyze to what extent yield changes were associated with changes in water or nitrogen stress days. For model calibration, we used previous information related to the morpho-physiological characteristics of both sorghum types and several soil parameters. We then used multiyear field data of sorghum yields for model validation. Yield simulations were conducted under three possible climate change scenarios: 1, 2, and 4 °C increase in mean annual temperature. ALMANAC successfully simulated mean yields of forage and grain sorghum: root mean square error (RMSE): 2.6 and 1.0 Mg ha−1, respectively. Forage yield increased 0.53 Mg ha−1, and grain yield decreased 0.27 Mg ha−1 for each degree of increase in mean annual temperature. Yields of forage sorghum tended to be negatively associated with nitrogen stress (r = −0.94), while grain sorghum yield was negatively associated with water stress (r = −0.99). The information generated allows anticipating future changes in crop management and genetic improvement programs in order to reduce the yield vulnerability.

Description: Based on 243 current valid distribution records for six wild strawberry species in China and data on 20 environmental variables, the geographical distributions of and potentially suitable areas for the wild strawberry species in Yunnan Province (China) under the current climate scenario were explored using the MaxEnt model and ArcGIS software, and major environmental variables affecting their geographical distributions were evaluated. In addition, the spatio-temporal dynamic patterns of the suitable areas for the six wild strawberry species in Yunnan Province in the 2050s and 2070s under the two climate models of RCP2.6 and RCP8.5 were predicted. Under the current climate scenario, the six wild strawberry species have suitable areas in Yunnan Province, which were mainly distributed in the high-altitude and low-temperature regions in the northwest and northeast, such as Diqing and Zhaotong. In addition, the average size of the highly suitable area for diploid wild strawberry species was greater than that for tetraploid species. Under the future climate scenarios, the average size of the highly suitable area for diploid species showed a tendency to expand, while that of tetraploid species showed a tendency to shrink. Altitude was a critical variable affecting the distribution of tetraploid species. Under the two future climate models of RCP2.6 and RCP8.5, the suitable areas for wild strawberry species shifted to the regions of high latitude, high altitude, and low temperature. In addition, the average distance in the shift of the suitable area for tetraploid strawberry species was greater than that for the suitable area for diploid strawberry species. The above results provide valuable information for the management and protection of the germplasm resources of Fragaria.

Description: Soil salinization due to climate change and intensive use of water and soil is increasing exponentially. Cucurbitaceae species are cultivated worldwide and the identification of salinity tolerant genotypes to be used as rootstock or scion for securing yield stability in salt affected agricultural areas is a research priority. In the present greenhouse study, we assessed the response to salinity (0 mM a non-salt control and 150 mM NaCl dissolved in the nutrient solution) in the seedlings of 30 genotypes of cucurbits grown in a floating hydroponic system. The species tested included 16 genotypes of Cucumis melo L. (CM1-16), 6 Citrullus vulgaris Schrad. (CV1-6), 2 interspecific hybrids of Cucurbita maxima Duch. × Cucurbita moschata Duch. (CMM-R1 and 2), 4 bottle gourd (Lagenaria siceraria (Molina) Standl. (LS1-4)), 1 Cucurbita moschata Duch. (CMO51-17), and 1 luffa (Luffa cylindrica Mill. (LC1)) species. Results highlighted different morphological and physiological traits between the species and genotypes and a different response to salt stress. We identified C. maxima × C.moscata interspecific hybrid CMM-R2, melon genotypes CM6, CM7, CM10, and CM16 together with watermelon genotypes CV2 and CV6 and bottle gourd LS4 as salt tolerant genotypes and possible candidates as salt resistant rootstock to be introduced in grafting programs.

Description: The trehalose-6-phosphate synthase (TPS) gene family plays important roles in conferring plant stress resistance, but a comprehensive analysis of the gene family is lacking for sugarcane (Saccharum spp. hybrids). The objective of this study is to document functional classification, evolutionary characterization, and expression profiling of sugarcane TPS gene (ScTPS) family. Nine putative ScTPS genes were identified and assigned to two distinct classes based on gene structure and phylogeny. Phylogenetic analysis showed that 31 TPS genes from Arabidopsis, rice and sugarcane could be divided into five distinct clades, suggesting that there were at least five orthologous groups in monocot and dicot plants. Evolution analysis of TPS genes revealed that TPS family members appeared to have undergone strong negative selection. The strength of the selective pressure differed in most clades, especially Class I TPS genes, experienced significantly stronger negative selection pressure than Class II TPS genes. There were also cis-regulatory elements related to phytohormones and abiotic stresses. Additionally, ScTPS genes were found to exhibit divergent expression in response to simulated drought, salinity, and ABA stresses. Since ScTPS genes function in sugarcane adaptation to environmental stimuli, it might be used as a molecular marker in screening sugarcane germplasm for increased stress resistance.

Description: Microscopic crowd simulation can help to enhance the safety of pedestrians in situations that range from museum visits to music festivals. To obtain a useful prediction, the input parameters must be chosen carefully. In many cases, a lack of knowledge or limited measurement accuracy add uncertainty to the input. In addition, for meaningful parameter studies, we first need to identify the most influential parameters of our parametric computer models. The field of uncertainty quantification offers standardized and fully automatized methods that we believe to be beneficial for pedestrian dynamics. In addition, many methods come at a comparatively low cost, even for computationally expensive problems. This allows for their application to larger scenarios. We aim to identify and adapt fitting methods to microscopic crowd simulation in order to explore their potential in pedestrian dynamics. In this work, we first perform a variance-based sensitivity analysis using Sobol’ indices and then crosscheck the results by a derivative-based measure, the activity scores. We apply both methods to a typical scenario in crowd simulation, a bottleneck. Because constrictions can lead to high crowd densities and delays in evacuations, several experiments and simulation studies have been conducted for this setting. We show qualitative agreement between the results of both methods. Additionally, we identify a one-dimensional subspace in the input parameter space and discuss its impact on the simulation. Moreover, we analyze and interpret the sensitivity indices with respect to the bottleneck scenario.

Description: Weed management is a challenge for farmers worldwide, and the problem is exacerbated by the spread of weed herbicide resistance. Simulation models that combine population dynamics and genetics are valuable tools for predicting the impact of competing management options on weed density, allele frequency, and phenotypic resistance levels. The new R package PROSPER provides functions for the forward simulation of weed population dynamics on a field scale, the selection of individuals according to their sensitivity to herbicides, and the recombination of alleles during reproduction. Objects are provided to enter and save model parameters in a clear structure, and to save output data for further processing in R. The basic functions are extensible with R code. PROSPER combines individual-based population dynamics with monogenic or polygenic diploid inheritance and flexible selection pressure. Stochasticity can be included at all model steps. Two examples of the population dynamics of two annual weed species with herbicide resistance are presented. All parameters and the models are available in PROSPER. In addition to simulation, PROSPER is intended for sharing and publishing population dynamic parameters and models, which is easily done thanks to R.

Description: Bacterial blight is a common disease found in the rice-growing regions in the Korean peninsula. Identification of the gene network involved against Xanthomonas oryzae pv. oryzae Korean race K2 in popular japonica cultivars is essential in underpinning the molecular mechanism of resistance. A microarray of two popular Korean japonica rice cultivars, a bacterial blight susceptible Dongjin and resistant Jinbaek, was performed to investigate the transcripts of inducible genes at 48 h post-inoculation. A total of 771 differentially expressed genes were identified in Jinbaek, whereas 298 were found in Dongjin. The resistance observed in Jinbaek is likely participated by genes with predicted functions in transmembrane perception, intracellular signal transduction, and transcription activity. Moreover, the remarkable involvement of numerous WRKY proteins signifies orchestration of defense signals via robust cellular reprogramming, which leads to resistance. To discover genes essential to bacterial blight resistance in Jinbaek, 13 highly expressed genes encoding different protein classes were cloned and overexpressed in rice. Although none of the overexpression plants exhibited resistance comparable to Jinbaek, four candidate genes, including one twin-arginine translocation pathway signal (LOC_Os01g45640.1), one cytochrome p450 (LOC_Os09g10340.1), and two uncharacterized expressed protein (LOC_Os08g26230.4, LOC_Os09g04310.1) conferred partial resistance. However, of these four genes, only p450s have been reported to play an important role in the synthesis of plant defense compounds. These findings revealed the complexity of key immune signaling conduits critical to mounting a full defense against Xanthomonas. oryzae pv. oryzae race K2 in japonica rice.

Description: Prunus spinosa L. is a shrub that produces berries—sloes—with a high content of phenolic compounds and anthocyanins. Numerous consumer products are made from sloes, such as “Pacharán” liquors or jams. For these reasons, it is crucial to design accurate analytical methods for the extraction of these bioactive compounds in order to promote their consumption and to improve our health. In this study, an ultrasound-assisted extraction method was developed to extract phenolic compounds and anthocyanins from sloes. Several process variables (methanol composition, pH, temperature, cycle, amplitude, and sample–solvent ratio) were optimized based on a Box–Behnken design. The most influential parameters for the extraction of total phenolic compounds were the cycle, the percentage of methanol, and pH of the extraction solvent, with 0.2 s, 67%, and pH 7 as their optimal values, respectively. For total anthocyanins, all parameters were influential, with 44% methanol at pH 2, 48 °C, 0.2 s cycles, 30% amplitude, and 1.5 g/20 mL ratio as the optimal values for their corresponding variables. Both methods showed high recoveries, short time use, and high precision (RSD 〈 5%). In addition, the sloe liqueur “Pacharán” and sloe jams were analyzed to demonstrate the applicability of these methods for the extraction of the real matrix.

Description: The use of chatbots in news media platforms, although relatively recent, offers many advantages to journalists and media professionals and, at the same time, facilitates users’ interaction with useful and timely information. This study shows the usability of a news chatbot during a crisis situation, employing the 2020 COVID-19 pandemic as a case study. The basic targets of the research are to design and implement a chatbot in a news media platform with a two-fold aim in regard to evaluation: first, the technical effort of creating a functional and robust news chatbot in a crisis situation both from the AI perspective and interoperability with other platforms, which constitutes the novelty of the approach; and second, users’ perception regarding the appropriation of this news chatbot as an alternative means of accessing existing information during a crisis situation. The chatbot designed was evaluated in terms of effectively fulfilling the social responsibility function of crisis reporting, to deliver timely and accurate information on the COVID-19 pandemic to a wide audience. In this light, this study shows the advantages of implementing chatbots in news platforms during a crisis situation, when the audience’s needs for timely and accurate information rapidly increase.

Description: Twitter is a microblogging platform that generates large volumes of data with high velocity. This daily generation of unbounded and continuous data leads to Big Data streams that often require real-time distributed and fully automated processing. Hashtags, hyperlinked words in tweets, are widely used for tweet topic classification, retrieval, and clustering. Hashtags are used widely for analyzing tweet sentiments where emotions can be classified without contexts. However, regardless of the wide usage of hashtags, general tweet topic classification using hashtags is challenging due to its evolving nature, lack of context, slang, abbreviations, and non-standardized expression by users. Most existing approaches, which utilize hashtags for tweet topic classification, focus on extracting hashtag concepts from external lexicon resources to derive semantics. However, due to the rapid evolution and non-standardized expression of hashtags, the majority of these lexicon resources either suffer from the lack of hashtag words in their knowledge bases or use multiple resources at once to derive semantics, which make them unscalable. Along with scalable and automated techniques for tweet topic classification using hashtags, there is also a requirement for real-time analytics approaches to handle huge and dynamic flows of textual streams generated by Twitter. To address these problems, this paper first presents a novel semi-automated technique that derives semantically relevant hashtags using a domain-specific knowledge base of topic concepts and combines them with the existing tweet-based-hashtags to produce Hybrid Hashtags. Further, to deal with the speed and volume of Big Data streams of tweets, we present an online approach that updates the preprocessing and learning model incrementally in a real-time streaming environment using the distributed framework, Apache Storm. Finally, to fully exploit the batch and stream environment performance advantages, we propose a comprehensive framework (Hybrid Hashtag-based Tweet topic classification (HHTC) framework) that combines batch and online mechanisms in the most effective way. Extensive experimental evaluations on a large volume of Twitter data show that the batch and online mechanisms, along with their combination in the proposed framework, are scalable, efficient, and provide effective tweet topic classification using hashtags.

Description: Standard (Lomb-Scargle, likelihood, etc.) procedures for power-spectrum analysis provide convenient estimates of the significance of any peak in a power spectrum, based—typically—on the assumption that the measurements being analyzed have a normal (i.e., Gaussian) distribution. However, the measurement sequence provided by a real experiment or a real observational program may not meet this requirement. The RONO (rank-order normalization) procedure generates a proxy distribution that retains the rank-order of the original measurements but has a strictly normal distribution. The proxy distribution may then be analyzed by standard power-spectrum analysis. We show by an example that the resulting power spectrum may prove to be quite close to the power spectrum obtained from the original data by a standard procedure, even if the distribution of the original measurements is far from normal. Such a comparison would tend to validate the original analysis.

Description: Zinc (Zn) is a nutrient that regulates many vital functions of the human body, hence the demand for Zn in the balanced daily nutrition has to be covered. Priming of seeds with Zn for Zn-enriched sprout production can be considered a good alternative to artificial supplementation in a modern diet. Hence, the aim of our study was to determine the exposure level of Zn bringing neither risk for humans nor growth inhibition of enriched broccoli, pea and sunflower sprouts. Seeds treated with 0, 10, 20 and 30 μg mL−1 ZnSO4 responded in a differentiated way to Zn. Pea seed germination and sprout growth was diminished by 30 μg mL−1 ZnSO4, but for sunflower sprouts this Zn level resulted in the highest fresh mass and largest hypocotyls. Zn content in sprouts greatly increased in a dose-dependent manner, mostly in broccoli (up to 25 times) and peas (up to 4 times), and to a lesser extent (up to 120%) for sunflowers. Free radical scavenging activity was usually decreased. Considering the potential non-carcinogenic risk of sprouts consumption estimated by the hazard analysis, the safest exposure level for seed priming will be 10 μg mL−1 ZnSO4. It was confirmed by random amplification of polymorphic DNA analyses indicating no DNA variations in sprouts treated with 10 μg mL−1 ZnSO4 compared to the control.

Description: Wild blueberry is a native plant managed as a crop in northeastern North America. Fruit drop in wild blueberry, Vaccinium angustifolium Aiton, was studied over 6 years (1996–1999, 2008 and 2011). Three hypotheses were tested regarding fruit drop mechanisms from plants (clones) sampled in the field: (1) proportional fruit drop; (2) constant or uniform fruit drop; and (3) pollen or resource limitation. In addition, the temporal rate of fruit drop was measured between the end of pollination (petal fall) and fruit maturity just prior to harvest (2008 and 2011). The dynamics of fruit drop were different in the two years. The role of pollination intensity in fruit drop was also assessed. Evidence for both proportional fruit drop and resource limitation at the clone level was found but only proportional drop at the field level. Low levels of seed set are associated with fruit drop. In a four-year study (1996–1999), fruit drop ranged from 23.3 to 49.4% of the total set fruit. Partitioning the variance in fruit drop between years, fields and clones (plant genotypes) showed that the highest variation is between stems within clones (37.4%) and clones (plant genotype) within fields (35.4%), followed by between fields (19.8%) and between years (7.4%).

Description: Nitrogen fertilization is indispensable in increasing wheat crop productivity but, in order to achieve maximum profitable production and minimum negative environmental impact, improving nitrogen use efficiency (NUE) should be considered. The aim of this study was to evaluate the nitrogen use efficiency (NUE) in a long-term wheat–maize–soybean rotation system with the final purpose of increasing the overall performance of the wheat cropping system. Research was undertaken at the Agricultural Research Development Station Turda (ARDS Turda), located in Western Transylvania Plain, Romania. The experimental field was carried out at a fixed place during seven wheat vegetation seasons. The plant material consisted of a wheat variety created by the ARDS Turda (Andrada), one variety of maize (Turda 332) and one variety of soybean (Felix). The experiment covered two planting patterns: wheat after maize and wheat after soybean and five levels of nitrogen fertilization (control-unfertilized, fertilization with 0—control plot, 30, 60, 90 and 120 kg N ha−1 y−1). The following indices were assessed: NUE (nitrogen use efficiency), N uptake and PFP (partial factor productivity). The results of the present study suggest that reduced N-fertilization doses could improve N uptake and utilization for both planting patterns.

Description: Toward strong demand for very high-speed I/O for processors, physical performance growth of hardware I/O speed was drastically increased in this decade. However, the recent Big Data applications still demand the larger I/O bandwidth and the lower latency for the speed. Because the current I/O performance does not improve so drastically, it is the time to consider another way to increase it. To overcome this challenge, we focus on lossless data compression technology to decrease the amount of data itself in the data communication path. The recent Big Data applications treat data stream that flows continuously and never allow stalling processing due to the high speed. Therefore, an elegant hardware-based data compression technology is demanded. This paper proposes a novel lossless data compression, called ASE coding. It encodes streaming data by applying the entropy coding approach. ASE coding instantly assigns the fewest bits to the corresponding compressed data according to the number of occupied entries in a look-up table. This paper describes the detailed mechanism of ASE coding. Furthermore, the paper demonstrates performance evaluations to promise that ASE coding adaptively shrinks streaming data and also works on a small amount of hardware resources without stalling or buffering any part of data stream.

Description: When highly automated driving is realized, the role of the driver will change dramatically. Drivers will even be able to sleep during the drive. However, when awaking from sleep, drivers often experience sleep inertia, meaning they are feeling groggy and are impaired in their driving performance―which can be an issue with the concept of dual-mode vehicles that allow both manual and automated driving. Proactive methods to avoid sleep inertia like the widely applied ‘NASA nap’ are not immediately practicable in automated driving. Therefore, a reactive countermeasure, the sleep inertia counter-procedure for drivers (SICD), has been developed with the aim to activate and motivate the driver as well as to measure the driver’s alertness level. The SICD is evaluated in a study with N = 21 drivers in a level highly automation driving simulator. The SICD was able to activate the driver after sleep and was perceived as “assisting” by the drivers. It was not capable of measuring the driver’s alertness level. The interpretation of the findings is limited due to a lack of a comparative baseline condition. Future research is needed on direct comparisons of different countermeasures to sleep inertia that are effective and accepted by drivers.

Description: With the rapid development of Internet and big data, place retrieval has become an indispensable part of daily life. However, traditional retrieval technology cannot meet the semantic needs of users. Knowledge graph has been introduced into the new-generation retrieval systems to improve retrieval performance. Knowledge graph abstracts things into entities and establishes relationships among entities, which are expressed in the form of triples. However, with the expansion of knowledge graph and the rapid increase of data volume, traditional place retrieval methods on knowledge graph have low performance. This paper designs a place retrieval method in order to improve the efficiency of place retrieval. Firstly, perform data preprocessing and problem model building in the offline stage. Meanwhile, build semantic distance index, spatial quadtree index, and spatial semantic hybrid index according to semantic and spatial information. At the same time, in the online retrieval stage, this paper designs an efficient query algorithm and ranking model based on the index information constructed in the offline stage, aiming at improving the overall performance of the retrieval system. Finally, we use experiment to verify the effectiveness and feasibility of the place retrieval method based on knowledge graph in terms of retrieval accuracy and retrieval efficiency under the real data.

Description: The motivation for the research is the need to develop an integrated and holistic approach to fostering students’ scientific inquiry based on scientific programming education by conducting computational experiments and simulations. At the same time, the implementation of the learner-centred approaches to scientific programming education and the related development of science, technology, engineering, and mathematics (STEM) learner-centred educational environment are of primary importance for K-16 education. The key interest is how to design and integrate learning resources which include software learning objects for making simulations. The research investigates educational aspects of the technological, pedagogical, and content knowledge (TPACK) framework applied to scientific computing and scientific programming educational domain and provides methodological guidelines and design principles of practical implementation of educational resources. These include design principles for the development of the model-based scientific inquiry-centred learning resources, generic design templates for designing educational aspects of scientific programming education, generic use case models for learning resources for scientific programming education, and supportive methodological considerations.

Description: Industrial ecology is an advanced form and ideal model of modern industrial development, in which the industrial ecosystem is the core. Based on the PSR model, this paper builds a comprehensive evaluation index system for urban industrial ecosystem development and selects 14 prefecture-level cities in Liaoning Province of the traditional industrial area in Northeastern China as cases to calculate the development level of its industrial ecosystem during 2000–2018 using an improved Topsis method and then to conduct a spatial visualization analysis. Finally, based on the “stress-state-response” subsystem, this paper diagnoses the constraints for industrial ecosystem development, which can provide a reference basis for decision-making in industrial ecology of traditional industrial area represented by those in Northeast China. The results show the following: (1) From 2000 to 2018, the industrial ecology of the 14 cities in Liaoning Province was at a medium level. Except for Shenyang and Dalian with the rapid development, the difference of industrial ecosystem development for other cities was relatively small. (2) From 2000 to 2018, the industrial ecosystem development of each city was in a status of “either increasing, or decreasing, or fluctuating,” which generally raised first and then decreased. Regarding spatial difference, the development exhibited a “center-periphery” pattern, with Shenyang and Dalian as the “dual-core” that were increasingly strengthened with significantly high-level industrial ecology. (3) At system level, PSR constraint grades for the industrial ecosystem development in the 14 cities of Liaoning Province were different. Constraint grades in the pressure subsystem, the state subsystem, and the response subsystem for the industrial ecosystem of Liaoning were 45.73%, 20.01%, and 34.34%, respectively, indicating that the lack of human response to the ecological environment and the pressure of human activities on the ecological environment during the industrial economy development were the main constraints affecting the process of industrial ecology in these cities. (4) Due to the differences in geographical environments, economic bases, industrial structures, and local development contexts, the major constraint factors of industrial ecosystem development in different cities are significantly different and complicated; however, there are five factors that are generally considered as major constraint factors in all cities, i.e., regional GDP, number of labor force employed in the secondary industrial sector, gross investment in fixed assets, amount of industrial sulfur dioxide removal, and production value from “three-wastes” comprehensive utilization. At last, this paper puts forward some recommendations and suggestions for providing scientific support for industrial ecosystem construction in the traditional industrial area of Northeastern China.

Description: An essential input of annuity pricing is the future retiree mortality. From observed age-specific mortality data, modeling and forecasting can take place in two routes. On the one hand, we can first truncate the available data to retiree ages and then produce mortality forecasts based on a partial age-range model. On the other hand, with all available data, we can first apply a full age-range model to produce forecasts and then truncate the mortality forecasts to retiree ages. We investigate the difference in modeling the logarithmic transformation of the central mortality rates between a partial age-range and a full age-range model, using data from mainly developed countries in the Human Mortality Database (2020). By evaluating and comparing the short-term point and interval forecast accuracies, we recommend the first strategy by truncating all available data to retiree ages and then produce mortality forecasts. However, when considering the long-term forecasts, it is unclear which strategy is better since it is more difficult to find a model and parameters that are optimal. This is a disadvantage of using methods based on time-series extrapolation for long-term forecasting. Instead, an expectation approach, in which experts set a future target, could be considered, noting that this method has also had limited success in the past.

Description: Pre-harvest application of prohydrojasmon (PDJ) or abscisic acid (ABA) induces the red color in fruits that were exposed to sunlight at the orchard. In this large-scale work, we evaluated the effect of two different pruning techniques of ‘Kent’ mango orchards, one leading to opening the orchard canopy to expose as much fruit as possible to sunlight, while the second pruning leads to square-shaped trees and subsequently reduces the amount of sunlight reaching the fruit. These two pruning methods were combined with preharvest spraying with prohydrojasmon (PDJ) or abscisic acid (ABA) using two different types of sprayers, i.e., regular and air-jet sprayer. Pruning the canopy of the orchards to open and closed trees exposed 80% or 30% of fruits to sunlight, respectively. Both of the application with air-jet and regular sprayers effectively covered the fruit without causing fruit detachment and damage to yield. Both the phytohormones (PDJ and ABA) application treatments induced red blush skin, red intensity, anthocyanin, and flavonoids, particularly in fruit grown outside the tree canopy in both open and closed trees. PDJ and ABA treatments exhibited marginally reduced acidity than the untreated control, while the brix was not affected much by any of the treatments. Besides these, exposure to sunlight and PDJ treatment also reduced postharvest decay and increased chlorophyll degradation and yellowing in comparison to the controls. This study promoted applicative evidence about the positive effects of exposure to sunlight, prohydrojasmon (PDJ), and abscisic acid (ABA) on red color development without compromising the mango fruit’s quality.

Description: Among the rust diseases, leaf rust of wheat caused by Puccinia triticina, is the most prevalent worldwide and causes significant yield losses. This study aimed to determine the genomic location of loci that control adult plant resistance (APR) to leaf rust in the pre-Green Revolution landrace accession, Aus27506, from the “Watkins Collection”. An Aus27506/Aus27229-derived F7 recombinant inbred line (RIL) population was screened under field conditions across three cropping seasons and genotyped with the iSelect 90K Infinium SNP bead chip array. One quantitative trait loci (QTL) on each of the chromosomes 1BL, 2B and 2DL explained most of the leaf rust response variation in the RIL population, and these were named QLr.sun-1BL, QLr.sun-2B and QLr.sun-2DL, respectively. QLr.sun-1BL and QLr.sun-2DL were contributed by Aus27506. QLr.sun-1BL is likely Lr46, while QLr.sun-2DL appeared to be a new APR locus. The alternate parent, Aus27229, carried the putatively new APR locus QLr.sun-2B. The comparison of average severities among RILs carrying these QTL in different combinations indicated that QLr.sun-2B does not interact with either of the other two QTL; however, the combination of QLr.sun-1BL and QLr.sun-2DL reduced disease severity significantly. In planta fungal quantification assays validated these results. The RILs carrying QLr.sun-1BL and QLr.sun-2DL did not differ significantly from the parent Aus27506 in terms of resistance. Aus27506 can be used as a source of adult plant leaf rust resistance in breeding programs.

Description: Text annotation is the process of identifying the sense of a textual segment within a given context to a corresponding entity on a concept ontology. As the bag of words paradigm’s limitations become increasingly discernible in modern applications, several information retrieval and artificial intelligence tasks are shifting to semantic representations for addressing the inherent natural language polysemy and homonymy challenges. With extensive application in a broad range of scientific fields, such as digital marketing, bioinformatics, chemical engineering, neuroscience, and social sciences, community detection has attracted great scientific interest. Focusing on linguistics, by aiming to identify groups of densely interconnected subgroups of semantic ontologies, community detection application has proven beneficial in terms of disambiguation improvement and ontology enhancement. In this paper we introduce a novel distributed supervised knowledge-based methodology employing community detection algorithms for text annotation with Wikipedia Entities, establishing the unprecedented concept of community Coherence as a metric for local contextual coherence compatibility. Our experimental evaluation revealed that deeper inference of relatedness and local entity community coherence in the Wikipedia graph bears substantial improvements overall via a focus on accuracy amelioration of less common annotations. The proposed methodology is propitious for wider adoption, attaining robust disambiguation performance.

Description: The problem posed by complex, articulated or deformable objects has been at the focus of much tracking research for a considerable length of time. However, it remains a major challenge, fraught with numerous difficulties. The increased ubiquity of technology in all realms of our society has made the need for effective solutions all the more urgent. In this article, we describe a novel method which systematically addresses the aforementioned difficulties and in practice outperforms the state of the art. Global spatial flexibility and robustness to deformations are achieved by adopting a pictorial structure based geometric model, and localized appearance changes by a subspace based model of part appearance underlain by a gradient based representation. In addition to one-off learning of both the geometric constraints and part appearances, we introduce a continuing learning framework which implements information discounting i.e., the discarding of historical appearances in favour of the more recent ones. Moreover, as a means of ensuring robustness to transient occlusions (including self-occlusions), we propose a solution for detecting unlikely appearance changes which allows for unreliable data to be rejected. A comprehensive evaluation of the proposed method, the analysis and discussing of findings, and a comparison with several state-of-the-art methods demonstrates the major superiority of our algorithm.

Description: Bioremediation and phytoremediation of soil polluted with petroleum hydrocarbons (PHs) are an effective and eco-friendly alternative to physicochemical methods of soil decontamination. These techniques can be supported by the addition of effective strains and/or surface-active compounds. However, to obtain maximum efficacy of bioremediation, the interactions that occur between the microorganisms, enhancement factors and plants need to be studied. Our study aimed to investigate the removal of petroleum hydrocarbons from an aged and highly polluted soil (hydrocarbon content about 2.5%) using multiway enhanced bio- and phytoremediation. For this purpose, 10 enhanced experimental groups were compared to two untreated controls. Among the enhanced experimental groups, the bio- and phytoremediation processes were supported by the endophytic strain Rhodococcus erythropolis CDEL254. This bacterial strain has several plant growth-promoting traits and can degrade petroleum hydrocarbons and produce biosurfactants. Additionally, a rhamnolipid solution produced by Pseudomonas aeruginosa was used to support the total petroleum hydrocarbon loss from soil. After 112 days of incubation, the highest PH removal (31.1%) was observed in soil planted with ryegrass (Lolium perenne L. cv. Pearlgreen) treated with living cells of the CDEL254 strain and rhamnolipid solution. For non-planted experimental groups, the highest PH loss (26.1%) was detected for soil treated with heat-inactivated CDEL254 cells and a rhamnolipid solution. In general, the differences in the efficacy of the 10 experimental groups supported by plants, live/dead cells of the strain tested and rhamnolipid were not statistically significant. However, each of these groups was significantly more effective than the appropriate control groups. The PH loss in untreated (natural attenuation) and soils that underwent phytoremediation reached a value of 14.2% and 17.4%, respectively. Even though the CDEL254 strain colonized plant tissues and showed high survival in soil, its introduction did not significantly increase PH loss compared to systems treated with dead biomass. These results indicate that the development of effective biological techniques requires a customized approach to the polluted site and effective optimization of the methods used.

Description: Presumably efficient computing models are characterized by their capability to provide polynomial-time solutions for NP-complete problems. Given a class ℛ of recognizer membrane systems, ℛ denotes the set of decision problems solvable by families from ℛ in polynomial time and in a uniform way. PMCℛ is closed under complement and under polynomial-time reduction. Therefore, if ℛ is a presumably efficient computing model of recognizer membrane systems, then NP ∪ co-NP ⊆ PMCℛ. In this paper, the lower bound NP ∪ co-NP for the time complexity class PMCℛ is improved for any presumably efficient computing model ℛ of recognizer membrane systems verifying some simple requirements. Specifically, it is shown that DP ∪ co-DP is a lower bound for such PMCℛ, where DP is the class of differences of any two languages in NP. Since NP ∪ co-NP ⊆ DP ∩ co-DP, this lower bound for PMCℛ delimits a thinner frontier than that with NP ∪ co-NP.

Description: Repeated applications of herbicides at the labelled rates have often resulted in the selection and evolution of herbicide-resistant weeds capable of surviving the labelled and higher rates in subsequent generations. However, the evolutionary outcomes of recurrent herbicide selection at low rates are far less understood. In this study of a herbicide-susceptible population of Lolium perenne ssp. multiflorum, we assessed the potential for low glufosinate rates to select for reduced susceptibility to the herbicide, and cross-resistance to herbicides with other modes of action. Reduced susceptibility to glufosinate was detected in progeny in comparison with the parental population following three rounds of selection at low glufosinate rates. Differences were mainly observed at the 0.5X, 0.75X, and 1X rates. Comparing the parental susceptible population and progeny from the second and third selection cycle, the percentage of surviving plants increased to values of LD50 (1.31 and 1.16, respectively) and LD90 (1.36 and 1.26, respectively). When treated with three alternative herbicides (glyphosate, paraquat, and sethoxydim), no plants of either the parental or successive progeny populations survived treatment with 0.75X or higher rates of these herbicides. The results of this study provide clear evidence that reduced susceptibility to glufosinate can evolve in weed populations following repeated applications of glufosinate at low herbicide rates. However, the magnitude of increases in resistance levels over three generations of recurrent low-rate glufosinate selection observed is relatively low compared with higher levels of resistance observed in response to low-rate selection with other herbicides (three fold and more).

Description: Trajectory planning is the foundation of locomotion control for quadruped robots. This paper proposes a bionic foot-end trajectory which can adapt to many kinds of terrains and gaits based on the idea of trajectory planning combining Cartesian space with joint space. Trajectory points are picked for inverse kinematics solution, and then quintic polynomials are used to plan joint space trajectories. In order to ensure that the foot-end trajectory generated by the joint trajectory planning is closer to the original Cartesian trajectory, the distributions of the interpolation point are analyzed from the spatial domain to temporal domain. An evaluation function was established to assess the closeness degree between the actual trajectory and the original curve. Subsequently, the particle swarm optimization (PSO) algorithm and genetic algorithm (GA) for the points selection are used to obtain a more precise trajectory. Simulation and physical prototype experiments were included to support the correctness and effectiveness of the algorithms and the conclusions.

Description: This paper focuses on the bargaining behavior of supply chain members and studies the stability of the bargaining system. There are two forms of bargaining in the process of negotiation. One is separate bargaining, and the other is that the automobile manufacturers form an alliance and bargain with the supplier collectively. We explore the influence of bargaining power and adjustment speed on the stability of the dynamic system and find that both of the factors need to be small to maintain the stability of the supply chain. After comparing the two forms of bargaining in terms of profits and stable regions, we find that the collective bargaining is a pattern with the existence of risk and benefit simultaneously. In order to control chaos in collective bargaining to lower the risk, we adopt the delay feedback control method. With the introduction of the control factor, the system tends to be stable finally.

Description: Although the EU structural funds aim to alleviate disparities through supporting regional development, their impact on local economies and societies is considered as uneven. As existing studies explore the absorption rate of the EU share of contribution as a point-in-time indicator at the end of the policy cycle, evidence about regional co-finance and the factors dynamically affecting absorption performance is lacking. To that end, this paper aims to provide a new longitudinal investigation of the absorption time series and develop an original indicator, supported by a statistical error analysis, for offering a transparent view of the total funds’ absorption. The analysis highlights that undesired regional strategies due to low administrative capacity may increase the absorption rate, though without supporting regional growth. The proposed approach could further facilitate the equitable allocation of political accountability regarding the structural funds’ absorption to the EU and the regions. Overall, it is anticipated that this research will support the EU in monitoring actual regional performance for prompting local managing authorities to improve their administrative capacity.

Description: Image fusion is a process that integrates similar types of images collected from heterogeneous sources into one image in which the information is more definite and certain. Hence, the resultant image is anticipated as more explanatory and enlightening both for human and machine perception. Different image combination methods have been presented to consolidate significant data from a collection of images into one image. As a result of its applications and advantages in variety of fields such as remote sensing, surveillance, and medical imaging, it is significant to comprehend image fusion algorithms and have a comparative study on them. This paper presents a review of the present state-of-the-art and well-known image fusion techniques. The performance of each algorithm is assessed qualitatively and quantitatively on two benchmark multi-focus image datasets. We also produce a multi-focus image fusion dataset by collecting the widely used test images in different studies. The quantitative evaluation of fusion results is performed using a set of image fusion quality assessment metrics. The performance is also evaluated using different statistical measures. Another contribution of this paper is the proposal of a multi-focus image fusion library, to the best of our knowledge, no such library exists so far. The library provides implementation of numerous state-of-the-art image fusion algorithms and is made available publicly at project website.

Description: This paper provides an econometric analysis aiming at evidencing the dynamics showed by the S&P 500 market index during the period of 4 January 2001–28 April 2020, in which the subprime crisis has taken place and the COVID-19 crisis has begun. In particular, we fit a three-regime switching model that allows market parameters to behave differently during economic downturns, with the regimes representative of the tranquil, volatile, and turbulent states. We document that the tranquil regime is the most frequent for the whole period, while the dominant regime is the volatile one for the crisis of 2008 and the turbulent one for the first four months of 2020. We fit the same model to the returns of the Dow Jones Industrial Average index and find that during the same period of investigation, the most frequent regime has been the tranquil one, while the volatile and turbulent regimes share the same frequencies. Additionally, we use a multinomial logit model to describe the probabilities of volatile or turbulent regimes. We show that, in the case of the S&P 500 index, the returns from the Volatility Index (VIX) index are significant for both the volatile and the turbulent regimes, while the gold, WTI oil, and the dollar indices have some explanatory power only for the turbulent regime.

Description: When using the Focused Information Criterion (FIC) for assessing and ranking candidate models with respect to how well they do for a given estimation task, it is customary to produce a so-called FIC plot. This plot has the different point estimates along the y-axis and the root-FIC scores on the x-axis, these being the estimated root-mean-square scores. In this paper we address the estimation uncertainty involved in each of the points of such a FIC plot. This needs careful assessment of each of the estimators from the candidate models, taking also modelling bias into account, along with the relative precision of the associated estimated mean squared error quantities. We use confidence distributions for these tasks. This leads to fruitful CD–FIC plots, helping the statistician to judge to what extent the seemingly best models really are better than other models, etc. These efforts also lead to two further developments. The first is a new tool for model selection, which we call the quantile-FIC, which helps overcome certain difficulties associated with the usual FIC procedures, related to somewhat arbitrary schemes for handling estimated squared biases. A particular case is the median-FIC. The second development is to form model averaged estimators with weights determined by the relative sizes of the median- and quantile-FIC scores.

Description: Fitness and physical exercise are preferred in the pursuit of healthier and active lifestyles. The number of mobile applications aiming to replace or complement a personal trainer is increasing. However, this also raises questions about the reliability, integrity, and even safety of the information provided by such applications. In this study, we review mobile applications that serve as virtual personal trainers. We present a systematic review of 36 related mobile applications, updated between 2017 and 2020, classifying them according to their characteristics. The selection criteria considers the following combination of keywords: “workout”, “personal trainer”, “physical activity”, “fitness”, “gymnasium”, and “daily plan”. Based on the analysis of the identified mobile applications, we propose a new taxonomy and present detailed guidelines on creating mobile applications for personalised workouts. Finally, we investigated how can mobile applications promote health and well-being of users and whether the identified applications are used in any scientific studies.

Description: Wheat was one of the first grain crops domesticated by humans and remains among the major contributors to the global calorie and protein budget. The rapidly expanding world population demands further enhancement of yield and performance of wheat. Phenotypic information has historically been instrumental in wheat breeding for improved traits. In the last two decades, a steadily growing collection of tools and imaging software have given us the ability to quantify shoot, root, and seed traits with progressively increasing accuracy and throughput. This review discusses challenges and advancements in image analysis platforms for wheat phenotyping at the organ level. Perspectives on how these collective phenotypes can inform basic research on understanding wheat physiology and breeding for wheat improvement are also provided.

Description: Text similarity measurement is the basis of natural language processing tasks, which play an important role in information retrieval, automatic question answering, machine translation, dialogue systems, and document matching. This paper systematically combs the research status of similarity measurement, analyzes the advantages and disadvantages of current methods, develops a more comprehensive classification description system of text similarity measurement algorithms, and summarizes the future development direction. With the aim of providing reference for related research and application, the text similarity measurement method is described by two aspects: text distance and text representation. The text distance can be divided into length distance, distribution distance, and semantic distance; text representation is divided into string-based, corpus-based, single-semantic text, multi-semantic text, and graph-structure-based representation. Finally, the development of text similarity is also summarized in the discussion section.

Description: Global change modifies vegetation composition in grasslands with shifts in plant functional types (PFT). Although changes in plant community composition imply changes in soil function, this relationship is not well understood. We investigated the relative importance of environmental (climatic, management and soil) variables and plant functional diversity (PFT composition and interactions) on soil activity and fertility along a climatic gradient. We collected samples of soil and PFT biomass (grasses, legumes, and non-legume forbs) in six extensively managed grasslands along a climatic gradient in the Northern Iberian Peninsula. Variation Partitioning Analysis showed that abiotic and management variables explained most of the global variability (96.5%) in soil activity and fertility; soil moisture and grazer type being the best predictors. PFT diversity accounted for 27% of the total variability, mostly in interaction with environmental factors. Diversity-Interaction models applied on each response variable revealed that PFT-evenness and pairwise interactions affected particularly the nitrogen cycle, enhancing microbial biomass nitrogen, dissolved organic nitrogen, total nitrogen, urease, phosphatase, and nitrification potential. Thus, soil activity and fertility were not only regulated by environmental variables, but also enhanced by PFT diversity. We underline that climate change-induced shifts in vegetation composition can alter greenhouse gas—related soil processes and eventually the feedback of the soil to the atmosphere.

Description: Pigeonpea is one of the important pulse crops grown in many states of India and plays a major role in sustainable food and nutritional security for the smallholder farmers. In order to overcome the productivity barrier the Translational Pigeonpea Genomics Consortium (TPGC) was established, representing research institutes from six different states (Andhra Pradesh, Karnataka, Madhya Pradesh, Maharashtra, Telangana, and Uttar Pradesh) of India. To enhance pigeonpea productivity and production the team has been engaged in deploying modern genomics approaches in breeding and popularizing modern varieties in farmers’ fields. For instance, new genetic stock has been developed for trait mapping and molecular breeding initiated for enhancing resistance to fusarium wilt and sterility mosaic disease in 11 mega varieties of pigeonpea. In parallel, genomic segments associated with cleistogamous flower, shriveled seed, pods per plant, seeds per pod, 100 seed weight, and seed protein content have been identified. Furthermore, 100 improved lines were evaluated for yield and desirable traits in multi-location trials in different states. Furthermore, a total of 303 farmers’ participatory varietal selection (FPVS) trials have been conducted in 129 villages from 15 districts of six states with 16 released varieties/hybrids. Additionally, one line (GRG 152 or Bheema) from multi-location trials has been identified by the All India Coordinated Research Project on Pigeonpea (AICRP-Pigeonpea) and released for cultivation by the Central Variety Release Committee (CVRC). In summary, the collaborative efforts of several research groups through TPGC is accelerating genetics gains in breeding plots and is expected to deliver them to pigeonpea farmers to enhance their income and improve livelihood.

Description: Partially automated driving (PAD, Society of Automotive Engineers (SAE) level 2) features provide steering and brake/acceleration support, while the driver must constantly supervise the support feature and intervene if needed to maintain safety. PAD could potentially increase comfort, road safety, and traffic efficiency. As during manual driving, users might engage in non-driving related tasks (NDRTs). However, studies systematically examining NDRT execution during PAD are rare and most importantly, no established methodologies to systematically evaluate driver distraction during PAD currently exist. The current project’s goal was to take the initial steps towards developing a test protocol for systematically evaluating NDRT’s effects during PAD. The methodologies used for manual driving were extended to PAD. Two generic take-over situations addressing system limits of a given PAD regarding longitudinal and lateral control were implemented to evaluate drivers’ supervisory and take-over capabilities while engaging in different NDRTs (e.g., manual radio tuning task). The test protocol was evaluated and refined across the three studies (two simulator and one test track). The results indicate that the methodology could sensitively detect differences between the NDRTs’ influences on drivers’ take-over and especially supervisory capabilities. Recommendations were formulated regarding the test protocol’s use in future studies examining the effects of NDRTs during PAD.

Description: This research concerns the application of micro X-ray fluorescence (µXRF) mapping to the investigation of a group of selected metal objects from the archaeological site of Ferento, a Roman and then medieval town in Central Italy. Specifically, attention was focused on two test pits, named IV and V, in which metal objects were found, mainly pertaining to the medieval period and never investigated before the present work from a compositional point of view. The potentiality of µXRF mapping was tested through a Bruker Tornado M4 equipped with an Rh tube, operating at 50 kV, 500 μA, and spot 25 μm obtained with polycapillary optics. Principal component analysis (PCA) and multivariate curve resolution (MCR) were used for processing the X-ray fluorescence spectra. The results showed that the investigated items are characterized by different compositions in terms of chemical elements. Three little wheels are made of lead, while the fibulae are made of copper-based alloys with varying amounts of tin, zinc, and lead. Only one ring is iron-based, and the other objects, namely a spatula and an applique, are also made of copper-based alloys, but with different relative amounts of the main elements. In two objects, traces of gold were found, suggesting the precious character of these pieces. MCR analysis was demonstrated to be particularly useful to confirm the presence of trace elements, such as gold, as it could differentiate the signals related to minor elements from those due to major chemical elements.

Description: This paper proposes a new classification of correlated data types based upon the relative number of direct connections among observations, producing a family of correlated observations embracing seven categories, one whose empirical counterpart currently is unknown, and ranging from independent (i.e., no links) to approaching near-complete linkage (i.e., n(n − 1)/2 links). Analysis of specimen datasets from publicly available data sources furnishes empirical illustrations for these various categories. Their descriptions also include their historical context and calculation of their effective sample sizes (i.e., an equivalent number of independent observations). Concluding comments contain some state-of-the-art future research topics.

Description: Geomechanical modelling of the processes associated to the exploitation of subsurface resources, such as land subsidence or triggered/induced seismicity, is a common practice of major interest. The prediction reliability depends on different sources of uncertainty, such as the parameterization of the constitutive model characterizing the deep rock behaviour. In this study, we focus on a Sobol’-based sensitivity analysis and uncertainty reduction via assimilation of land deformations. A synthetic test case application on a deep hydrocarbon reservoir is considered, where land settlements are predicted with the aid of a 3-D Finite Element (FE) model. Data assimilation is performed via the Ensemble Smoother (ES) technique and its variation in the form of Multiple Data Assimilation (ES-MDA). However, the ES convergence is guaranteed with a large number of Monte Carlo (MC) simulations, that may be computationally infeasible in large scale and complex systems. For this reason, a surrogate model based on the generalized Polynomial Chaos Expansion (gPCE) is proposed as an approximation of the forward problem. This approach allows to efficiently compute the Sobol’ indices for the sensitivity analysis and greatly reduce the computational cost of the original ES and MDA formulations, also enhancing the accuracy of the overall prediction process.

Description: New economic geography (NEG) raises the question why the “agglomeration shadow” effect is significant in some urban regions but not others. This study attempts to answer this question in the context of China by examining the impact of industrial composition of core cities on regional urban systems. Based on the model of urban location from NEG, this study attempts to examine the population growth of six core cities and their urban systems in China. We find services exert a significantly negative effect on market potential, while that of manufacturing is positive. The results are robust in different spatial scales and time spans. This suggests that strong centralizing and agglomerative effects of higher order service sector are more likely to cast an agglomeration shadow on noncore cities in China’s regional urban systems. The differences in industrial composition can explain why an urban region centered on a large core city like Beijing is more likely to cast an agglomeration shadow on its surrounding cities compared to the Shanghai-centered urban region. The findings hold important implications for uneven development of regional urban systems and construction of metropolitan areas in China.

Description: This study presents the fundamental concepts and technical details of a U-model-based control (U-control for short) system design framework, including U-model realisation from classic model sets, control system design procedures, and simulated showcase examples. Consequently, the framework provides readers with clear understandings and practical skills for further research expansion and applications. In contrast to the classic model-based design and model-free design methodologies, this model-independent design takes two parallel formations: (1) it designs an invariant virtual controller with a specified closed-loop transfer function in a feedback control loop and (2) it determines the real controller output by resolving the inverse of the plant U-model. It should be noted that (1) this U-control provides a universal control system design platform for many existing linear/nonlinear and polynomial/state-space models and (2) it complements many existing design approaches. Simulation studies are used as examples to demonstrate the analytically developed formulations and guideline for potential applications.

Description: Pseudostellaria heterophylla, in the family Caryophyllaceae, is an important Chinese medicinal plant commonly used to treat various diseases in children and valued for its ornamental properties. In this study, nodal segments were obtained from wild plants and used as explants to develop an efficient micropropagation protocol for this species. Murashige and Skoog (MS) medium supplemented with 1.5 mg·L−1 6-benzyladenine (6-BA) was the most suitable medium for inducing axillary buds and enhancing their growth, and MS medium containing 0.1 mg·L−1 indole-3-butyric acid (IBA) was the most effective for inducing in vitro rooting. To reduce labor, time, and cost, microshoots were rooted under ex vitro conditions. Pretreatments of the shoots with 100 mg·L−1 naphthaleneacetic acid (NAA) for 1 min ensured successful rooting in 86.7% of shoots. Comparison of the leaf microstructure between in vitro- and ex vitro-rooted plantlets revealed abnormal stomatal apparatus in the former. The stomatal apparatus of ex vitro plantlets were normal, although the stomatal density was reduced, which indicated that these plantlets were more likely to be able to adapt to environmental conditions in the field. We identified the optimal medium for P. heterophylla multiplication with respect to increased rooting efficiency of micropropagated shoots under ex vitro conditions. This results presented here will be helpful for agricultural cultivation of P. heterophylla.

Description: Tomato and cucumber seedlings were grown in a growth chamber to evaluate the effects of different cycles of light–dark exposure conditions (T0 (control treatment) (1 cycle of 24 h distributed in 18 h of light exposure and six hours of dark), T1 (two cycles of 12 h distributed in nine hours of light exposure and three hours of dark) and T2 (three cycles of eight hours distributed in six hours of light exposure and two hours of dark) on growth, nutrient status, pigment concentration and physiological changes. Total dry weight showed different behaviors in both species, since in tomato the total dry weight remained unchanged under varying light–dark cycles, whereas in cucumber seedlings there was a clear decrease compared to the control treatment. In both species, plants grown under T2 showed the best water content. Nitrogen, P and K content—as well as partitioning in the different organs of the plants—displayed different patterns under varying cycles of light–dark conditions in both species. Chlorophyll (b and a + b) concentration decreased significantly in both species in T1 and T2 compared to the control treatment (T0). At physiological level, the concentration of total soluble sugars and proline in leaf showed the highest value in the control treatment with 18 h of light and six hours of dark.

Description: This study aims to evaluate the bio-morphological and biochemical variability of three Tunisian wild populations and one growing cultivar of purslane (Portulaca oleracea L.). The studied varieties should be easily distinguished by the color and the habitus of the plant as mentioned in literature, but the various analyses have shown a strong morphological heterogeneity within and among the wild and cultivated accessions as presented by the variance analysis test (ANOVA) and the PCA (Principal component analysis). We found high intrapopulation variability through the wild populations that make it hard to differentiate them only on the base of morphology. We analyzed the biochemical profile of those populations based on the analysis of freeze-dried samples of leaves and stems. We identified and quantified twelve different phenolic compounds by the HPLC-diode array detector (DAD) technique. Six phenolic acids and flavonoids were identified in the leaves and stems of the wild and cultivated populations. Sinapic acid and myricetin are the majors identified compounds through our samples. The results were significantly different in relation to the plant organs and to the geographic origin for most of the compounds. The obtained results highlighted the importance of Portulaca as a medicinal plant by showing its richness in phenols and flavonoids that have multi-medicinal effects besides their antioxidant power.

Description: The purpose of this paper is to assess the impact of ambiguity on financial analyst forecast incentives and the associated abnormal stock returns. I present a model incorporating ambiguity aversion into a two-period Lucas tree model. The resulting model confirms the role of ambiguity in the determination of asset returns. In particular, the model with ambiguity aversion generates a lower price and a higher required rate of returns compared to the classical model without ambiguity concern. I construct a measure of ambiguity and provide empirical evidence showing that the incentive of analysts to misrepresent information is a function of ambiguity. Analysts are more likely to bias their forecasts when it is more difficult for investors to detect their misrepresentation. Under ambiguity, analysts’ optimistic forecasts for good/bad news tend to deteriorate. Moreover, stock returns are positively related with ambiguity. Under ambiguity neither good nor bad news is credible. Investors systematically underreact to good news forecast and overreact to bad news forecast when ambiguity exists.

Description: The production of a biomass as a feedstock for biorefinery is gaining attention in many agricultural areas. The adoption of biorefinery crops (i.e., perennial cardoon) can represent an interesting option for farmers and can contribute to increase soil organic carbon stock (SOCS). The study aimed to assess the potential effect on long-term SOCS change by the introduction of cardoon in a Mediterranean marginal area (Sassari, Italy). To this end, three process-oriented models, namely the Intergovernmental Panel on Climate Change (IPCC) guidelines for national greenhouse gas inventories (Tier 2), a humus-balance model (SOMBIT) and Rothamsted carbon model (RothC), were used to compare two scenarios over 20 years. The traditional cropping system’s faba bean–durum wheat biennial rotation was compared with the same scenario alternating seven years of cardoon cultivation. The model’s calibration was performed using climate, soil and crop data measured in three cardoon trials between 2011 and 2019. SOMBIT and Roth C models showed the best values of model performance metrics. By the insertion of cardoon, IPCC tool, SOMBIT and RothC models predicted an average annual SOCS increase, whereas, in the baseline scenario, the models predicted a steady state or a slight SOCS decrease. This increase can be attributed to a higher input of above- and belowground plant residues and a lower number of bare soil days (41 vs. 146 days year−1).