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In this article, the observer-based guaranteed cost control method is investigated for the networked control systems (NCSs) with packet dropout and nonlinear disturbance involving event-triggered mechanism (ETM). The packet dropout processes, which appear in both the sensor-to-controller and the controller-to-actuator links, are represented by two mutually independent Bernoulli distributions, and the nonlinear disturbance is supposed to satisfy the Lipschitz condition. Since the systems states are unmeasurable, a state observer is designed to estimate the values of system state. The sufficient condition for the stability of the closed-loop system is addressed by applying the Lyapunov theorem, and a guaranteed cost controller is obtained based on the stability condition with the satisfaction of some given specified value of cost function. Furthermore, the controller design issue can be formulated as a convex optimization problem that is addressed by the linear matrix inequality (LMI) technique using the cone complementarity linearization algorithm. Finally, the availability of above method is proved by a simulation example. [ABSTRACT FROM AUTHOR]

• A fast and accurate yield prediction method for wild blueberry using drone imagery. • Higher altitude (30 m) images perform better than lower altitude images. • Systematic sampling reduces 70% of labor cost in training image collection. • Combining genotype information enables more accurate yield prediction. Precise yield estimation is important for phenotyping and crop management but is challenging for large-scale and high-density crops, such as wild blueberries. Machine learning (ML) technology and unmanned aerial vehicle (UAV) platforms created opportunities for more accurate and efficient yield estimation. RGB cameras are widely used in image processing due to their low cost, easy operation, and data analysis advantages. Here, we aimed to identify an effective, accurate, low-cost, and practical yield prediction method that can be applied to wild blueberry fields and potentially other high-density crops. Deep learning (DL) was overlooked due to its data-intensive training requirements, and the acquisition of image and yield data for wild blueberries is time-consuming and difficult. We evaluated the feasibility of using RF (Random Forest) and XGBoost (Extreme Gradient Boosting) models based on color and texture feature data obtained from UAV RGB images to predict wild blueberry yield, and tested the effect of flight altitude, sampling method, and ML model. We found that the XGBoost model trained on a systematically sampled dataset built from high-altitude color and texture features achieved the best prediction performance (R2 = 0.89, RMSE = 542.01, and MAE = 379.94). Interestingly, images captured at a higher altitude (30 m) performed better compared to that at lower altitudes (5 and 15 m), which provides clear evidence supporting higher and more efficient UAV cruise operations. Our study provides a non-destructive, efficient, fast, and easy-to-use yield prediction method for large-scale and high-density crops, confirming its feasibility with wild blueberries. [ABSTRACT FROM AUTHOR]

• Deceiving flowers attract exploring pollinators without rewarding them with nectar. • Mimics resemble a specific nectar-secreting plant, general food deceivers don't. • We studied what drives deception strategies using an evolutionary game. • We found that each deception strategy is stable under different conditions. • Mimicry efficiency/nectar provision costs determine the stable deception strategy. Floral food deception is a well-known phenomenon which is not thoroughly understood. Particularly, it is unclear what drives a plant towards Batesian mimicry or towards generalized food deception. We analysed the evolutionary game between a Model species with nectar-secreting flowers and a Deceiver species that provides no nectar who share pollinators for reproduction. We focused our analysis on the effect of similarity of floral signals between participating plants and on costs of nectar production. We defined payoffs in the game between Models and Deceivers as the stationary visitation frequencies to participating species with different signal similarities and nectar costs. Therefore, fitness payoff of each strategy was a product of complex interactions between plant species composing the community and the pollinators visiting them. Our model provides a unified framework in which consequences of Model species interaction with different deception modes can be compared. Our findings suggest that plant-pollinator systems, like other mutualistic systems, are prone to exploitation, and that exploitation may persist at a large range of conditions. We showed that floral similarity, and thus, pollinators' ability to discriminate between Model and deceptive species, governs the stability of Batesian mimicry, while pollinator switching and sampling behaviour enables the persistence of general food deception. [ABSTRACT FROM AUTHOR]

The high variability of wild (lowbush) blueberry plants in spatial and genetic structure, in combination with bee foraging behavior varying between species, and the complexity of these factors interacting over time and space, are major obstacles to understanding of pollination dynamics subject to environmental change. The bottom-up modeling paradigm provides an ideal approach to bridging the gap between known mechanisms of individual organisms and unknown spatial–temporal dynamics of pollination at the field scale. By linking empirical data to stochastically-based ecological process modeling, we present a spatially-explicit agent-based simulation model that enables exploration of how various factors, including plant spatial arrangements, outcrossing and self-pollination, bee species compositions and weather conditions, in isolation and combination, affect pollination efficiency throughout a blueberry bloom season. The firmly validated open-source model is a useful tool for hypothesis testing and theory development for wild blueberry pollination researches. Sensitivity analysis suggested that fruit set and resulting measures of productivity such as fruit mass and viable seeds per fruit were sensitive to parameterization of blueberry genotype or clone size and the amount of blueberry plant cover in a field. Fruit set due to pollen compatibility was sensitive to ovule number per flower and foraging bee density. Simulation experiments allowed us to compare bee pollination efficiencies at the bee taxon population level (honey bees, bumble bees, and native solitary bees), the effect of foraging distance from bee nest or colony site on fruit set, and test whether the mechanism of gametophytic self-incompatibility (pre- vs. post-zygotic decision making by the plant) in wild blueberry pollination at the field level matters in estimating yield. [ABSTRACT FROM AUTHOR]

This article is concerned with the adaptive event‐triggered (AET) efficient output feedback model predictive control (EOFMPC) for a set of nonlinear networked interval type‐2 Takagi–Sugeno (IT2 T–S) fuzzy control systems with data loss. For the reduction in data transmission burden and required control performance, the AET scheme and a novel dynamic triggering law are introduced jointly in this article. The efficient model predictive control approach as in Kouvaritakis et al. (IEEE Trans Automat Contr. 2000;45(8):1545–1549.), which transfers the main part of online optimization work to offline stage by incorporating an extra perturbation into the fixed feedback law, is extended to networked control systems to reduce their online computational burden. This article proposes the following: (1) an unified closed‐loop model representation of data loss process and IT2 T–S fuzzy nonlinearities is established under the framework of EOFMPC, which explicitly considers the AET condition; (2) a new design method for the state observer gain is presented based on an ellipsoidal invariant set, so that a smaller bound of estimation error can be obtained through an extra optimization problem; (3) some optimization problems are formulated to design the proposed controllers, and the corresponding EOFMPC algorithm is summarized; (4) by means of the extra matrix partition technique, the online optimization problem subject to one ellipsoidal constraint is transformed into a novel convex form to solve the uncertainty of the estimation error. A simulation example and a few comparative simulation experiments illustrate the effectiveness and availability of the aforementioned approach. [ABSTRACT FROM AUTHOR]

Model-mimic plant systems are well known. However, the conditions promoting the existence of such systems are still an enigma. We suggest that by focusing on floral similarity between model and mimic, reward levels offered by models, and pollinators' ability to adjust foraging accordingly, the conditions can be better understood. Using spatially-explicit modelling, we examined trait combinations that lead to the survival of deceptive species under a large range of mimic strategies, from Batesian mimicry to general food deception. Unlike previous models studying such systems, we examined model-mimic interactions in the presence of a third, dissimilar species, thus generating a more realistic scenario where pollinators may avoid the model-mimic system altogether. Results showed that overall survival and abundance of species in food deceptive systems depend on the relative reward provided by the participating species and the potential alternatives available. Specifically, the success of a mimic in a Batesian mimicry system depends on high levels of reward provided by its model species relative to potential alternatives in the flower community. On the other hand, the success of a mimic in a general food deception system was higher when the reward offered was lower. Our study suggests that the ability of pollinators to utilize their experience as part of decision-making is highly relevant in promoting mimic survival, thus shedding light on the conditions under which food deception is expected. [ABSTRACT FROM AUTHOR]

• Manifold distance metric better reflects similarities between ecological states in Empirical Dynamic Model. • Prediction accuracy improved by incorporating manifold distance into simplex projection and S-map. • Multivariate embedding and S-map amplify the advantage of manifold distance for prediction accuracy. • Effectiveness of manifold distance metric demonstrated by both synthetic and empirical data. Empirical Dynamic Modeling (EDM) has been a powerful tool for complex ecosystem prediction by providing an equation-free modelling framework. Theoretically, it allows future ecosystem behavior to be predicted by connecting current state to the similar, adjacent and future state on the attractor manifold which is reconstructed by single or multiple time series observed from natural systems. However, the Euclidean distance metric used in these algorithms could bias the true distance on the attractor manifold and consequently decrease the prediction performance. This could become worse if the dimension of the ecosystem is much higher and the system behavior is much complicated so that the reconstructed attractor manifold is more intricate. Therefore, manifold distance metric for both Simplex Projection and S-map was proposed. Our results clearly showed that the prediction accuracy of EDM had a general improvement after manifold distance metric was adopted. Experiments conducted on both synthetic and empirical data proved this advancement. Interestingly, these improvements were unequal for different implementations and the number of variables for embedding. Analysis demonstrated that S-map under multivariate embedding achieved the best prediction performance when manifold distance metric was applied. This suggested that the proposed manifold distance metric can work particularly well for predicting high dimensional ecosystem with complex behaviors. The main contribution of this research is that a new ecological indicator has been developed to more accurately estimate the similarity between ecological states in a reconstructed manifold and therefore provide higher prediction accuracy for EDM framework. [ABSTRACT FROM AUTHOR]

Learning an efficient discriminative projection to extract meaningful features from high-dimensional data is a challenging task in machine learning. However, most of the existing discriminative projection literatures have the following two issues: (1) the performance of projection would largely degenerate when handling the data corrupted by complex outlier, e.g., noisy and outlier mixed data; and (2) fixed graph in graph regularization can be negatively affected by outliers or noisy features. To deal with above issues, we design a new feature extraction method called robust discriminative projection with dynamic graph regularization (RDPDG) to handle above problems. Specifically, RDPDG simulates complex outlier features, i.e., low rank and sparse structured outlier features, using two matrices constrained by the nuclear and adaptive loss norm. RDPDG is able to boost the performance of classification, which primarily due to two key factors: (1) learns reliable projection through a unified framework that integrates discriminant analysis, reconstituted data and manifold learning; and (2) can exploit the local intrinsic structure among data effectively by dynamic graph regularization. Furthermore, an alternating iteration algorithm is designed to solve the proposed nonconvex model, which seamlessly connects the update process of all components in the RDPDG framework, offering a powerful discriminative capability for projection. Comprehensive experiments on a synthetic and several real-world data sets demonstrate that the RDPDG outperforms the precious related methods for classification. • A new feature extraction method named RDPDG is proposed. • RDPDG is found to be more robust to complex outliers than other related methods. • Dynamic graph regularization is used to explore the local structure of outlier data. • An iterative algorithm is developed to solve RDPDG formulations efficiently. [ABSTRACT FROM AUTHOR]

• Proper loss function selection improves smoothness better than the reduction of data sparsity. • The less sensitive a deep model is to data sparsity, the smoother the extracted manifold is. • Simply stacking hidden layers in deep model does not significantly improve smoothness. Deep model is a useful tool that can extract smooth manifold from data. As a computationally intensive method, however, model parameters and data sparsity are common factors that are likely to cause uncertainty and consequently affect the result. Thus, it is a challenge to design an effective deep model for smooth manifold extraction without prior knowledge to the target datasets, which is usually sparse in many real-world applications. In this paper, we proposed a deep model based on Brenier theorem to extract smooth manifold. Beyond the model design, several experiments on both simulated and real-world datasets were conducted to explore three scientific questions: 1) How to design or select an appropriate loss function for a deep neural network to better fit the data, which will be helpful to extract smooth manifold? 2) How deep model's sensitivity to data sparsity affect the smoothness of manifold extraction? And 3) What are the relative importance of these factors in improving the smoothness of manifold extraction? Results showed that our model outperformed the state-of-the-art models in many metrics, such as manifold smoothness and computational error. The experiments demonstrated that the less sensitivity a model is to data sparsity, the smoother manifold it can extract. Our finding indicated that the smoothness of a manifold extracted by deep models is significantly dependent on the layout of loss functions, which means that simply stack hidden layers in deep models is a less effective way to improve the smoothness of manifold extraction. In addition, our exploration also suggested that the selection of loss functions carries more weights in improving manifold smoothness than investing the effort of overcoming data sparsity. [ABSTRACT FROM AUTHOR]

Abstract: The alarming reports from around the world on pollinator population declines made the understanding of the effects of shared pollination service on biodiversity into one of the most urgent goals in nature conservation, both for the scientists and managers. The classic field-based methodology which is commonly used in such studies, has three major problems which limit the researchers’ ability to further understand the nature of plant-pollinator dynamics: (1) Natural systems do not allow for a full factorial controlled studies of specific characteristics and traits of both plants and pollinators, because of many confounding effects which are usually unknown. Furthermore, (2) Many of the interactions between plants and pollinators are indirect, via their reciprocal effect on shared pollination services and therefore difficult to detect in the field. Finally, and (3) though plant composition and abundance may be manipulated in the field, it is almost impossible to manipulate pollinator populations, strongly restricting researchers’ ability to thoroughly understand the specific pollinator characteristics which created the observed effects. Therefore, simulation tools are needed that can address this complexity on one hand, and allow to identify potential research directions for targeted experiments on the other hand. Here, we present EcoSimInGrid, a spatially explicit agent-based simulator for investigating effects of shared pollination services on plant communities. EcoSimInGrid can be used to represent complex spatio-temporal interactions among ecological entities of different trophic levels, to investigate effects of plant traits, spatial distribution and pollinator behavior on shared pollination services and to analyze the relative effects of shared pollination and habitat productivity in shaping community diversity. Features like capability to model large ecosystems with tens of thousands of plants and pollinators, graphical user interface, flexible parameter configuration, comprehensive data output and fast speed parallel computing make EcoSimInGrid a welcome addition to the ecological modeling world. [Copyright &y& Elsevier]

Abstract: This paper presented an agent-based functional–structural model ORASIM for orange tree growth simulation. In ORASIM, detailed geometry, carbon/water acquisitions and expenses, as well as their dynamics are integrated into individual metamer/root agents. The nested-list of metamer/root agents forms a growing, three-dimensional orange tree structure. After model parameterization and validation using field data of orange tree growth, main features of tree functioning, i.e., morphological and physiological responses to environmental heterogeneity on different time scales have been investigated. It demonstrated that, using ORASIM, the phenotypic plasticity can be fully resulted from interactions between agents. Meanwhile, the output of ORASIM shows a good agreement for the characters of shape, branch pattern and other physiological features between the simulation and the real growth orange trees. [Copyright &y& Elsevier]

Abstract: This research presented a teleonomic-based simulation approach to virtual plants integrating the technology of intelligent agent as well as the knowledge of plant physiology and morphology. Plant is represented as the individual metamers and root agents with both functional and geometrical structure. The development of plant is achieved by the flush growth of metamer and root agents controlled by their internal physiological status and external environment. The eggplant based simulation results show that simple rules and actions (internal carbon allocation among organs, dynamic carbon reserve/mobilization, carbon transport in parallel using a discrete pressure-flow paradigm and child agent position choosing for maximum light interception, etc.) executed by agents can cause the complex adaptive behaviors on the whole plant level: carbon partitioning among metamers and roots, carbon reserve dynamics, architecture and biomass adaptation to environmental heterogeneity and the phototropism, etc. This phenomenon manifest that the virtual plant simulated in presented approach can be viewed as a complex adaptive system. [Copyright &y& Elsevier]

In this paper, we propose an automatic analyzing and transforming approach to L-system grammar extraction from real plants. Instead of using manually designed rules and cumbersome parameters, our method establishes the relationship between L-system grammars and the iterative trend of botanical entities, which reflect the endogenous factors that caused the plant branching process. To realize this goal, we use a digital camera to take multiple images of unfoliaged (leafless) plants and capture the topological and geometrical data of plant entities using image processing methods. The data then stored into specific data structures. A Hidden Markov based statistical model is then employed to reveal the hidden relations of plant entities which have been classified into categories based on their statistical properties extracted by a classic EM algorithm, the hidden relations have been integrated into the target L-system as grammars. Results show that our method is capable of automatically generating L-grammars for a given unfoliaged plant no matter what branching type it is belongs to. [ABSTRACT FROM AUTHOR]

[Display omitted] • Two novel DR methods are proposed to extract critical discriminative information. • Neighborhood size parameters of DR models are decided adaptively via data similarity. • Outside data can be projected directly through SD-IsoP that prevents over-fitting. • Margins between classes in reduced space can be maximized by proposed methods. As one of the most popular nonlinear dimensionality reduction methods, Isomap has been widely used in pattern recognition and machine learning. However, Isomap has the following problems: (1) Isomap is an unsupervised dimensionality reduction method, it cannot use class label information to obtain discriminative low dimensional embedding for classification; (2) The embedding performance of Isomap is sensitive to neighborhood size parameter; (3) Isomap cannot deal with outside new data by direct embedding. In this paper, a novel dimensionality reduction method called supervised discriminant Isomap is proposed to solve the first two problems mentioned above. Specifically, first, raw data points are partitioned into different manifolds by using their class label information. Then, supervised discriminant Isomap aims at seeking an optimal nonlinear subspace to preserve the geometrical structure of each manifold according to the Isomap criterion, and to enhance the discriminating capability by maximizing the distances between data points of different classes and the maximum margin graph regularization term. Finally, the corresponding optimization problems are solved by using eigen-decomposition algorithm. Further, we extend supervised discriminant Isomap to a linear dimensionality reduction method called supervised discriminant Isomap projection for handling the above three problems. Moreover, our approaches have three important characteristics: (1) Proposed methods adaptively estimate the local neighborhood surrounding each sample based on data density and similarity; (2) The objective functions of proposed methods can maximize margins between the each classes in the dimension-reduced feature space; (3) The objective functions of proposed methods have closed-form solutions. Furthermore, our methods can capture more discriminative information from raw data than other Isomap based methods. Extensive experiments on nine data sets demonstrate that the proposed methods are superior to the related state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Data deficiency prevents the development of reliable machine learning models for many agroecosystems, especially those characterized by a dearth of knowledge derived from field data. However, other similar agroecosystems with extensive data resources can be of use. We propose a new predictive modeling approach based upon the concept of transfer learning to solve the problem of data deficiency in predicting productivity of agroecosystems, where productivity is a nonlinear function of various interacting biotic and abiotic factors. We describe the process of building metamodels (machine learning models built and trained on simulation data) from simulations built for one agroecosystem (US wild blueberry) as the source domain, where the data resource is abundant. Metamodels are evaluated and the best metamodel representing the system dynamics is selected. The best metamodel is re-parameterized and calibrated to another agroecosystem (Northeast China bog blueberry) as the target domain where field collected data are lacking. Experimental results showed that our metamodel developed for wild blueberry achieved 78% accuracy in fruit-set prediction for bog blueberry. To demonstrate its usefulness, we applied this calibrated metamodel to investigate the response of bog blueberry to various weather conditions. We found that an 8% reduction in fruit-set of bog blueberry is likely to happen if weather becomes warmer and wetter as predicted by climate models. In addition, southern and eastern production regions will suffer more severe fruit-set decline than the other growing regions. Predictions also suggest that increasing commercially available honeybee densities to 18 bees/m2/min, or bumble bee densities to 0.6 bees/m2/min, is a viable way to compensate for the predicted 8% climate induced fruit-set decline in the future. [ABSTRACT FROM AUTHOR]

• The study provides inspiration to use simulation data for yield predictive modeling. • The proposed machine learning algorithm approach has achieved 93.8% accuracy. • Interactions between bee species composition and weather are significant in yield variability. • Prediction showed that wet rainy springs will greatly reduce blueberry yield in the future. The most challenging task in the agricultural sector is to accurately predict crop yield. A typical machine learning algorithm often uses real data to predict crop yield. In this study, we used data generated by the Wild Blueberry Pollination Model, a spatially explicit simulation model validated by field observation and experimental data collected in Maine USA during the last 30 years. The main aim of this study is to evaluate the relative importance of bee species composition and weather factors in regulating wild blueberry agroecosystems. Specifically, we sought to reveal how bee species composition and weather affect yield and to predict optimal bee species composition and weather conditions that achieve the best yield using computer simulation and machine learning algorithms. Multiple linear regression (MLR), boosted decision trees (BDT), random forest (RF), and extreme gradient boosting (XGBoost) were evaluated as predictive tools. We also performed a predictor selection before submitting our data to the learning algorithms. In this way, we are able to reduce the dimension of the input without a significant drop in prediction accuracy. As a result, clone size, honeybee, bumblebee, Andrena bee species, Osmia bee species, maximum of upper-temperature ranges, and the number of days with precipitation were chosen as the best predictor variable subset. The results showed that the XGBoost outperformed other algorithms in all measures of model performance for predicting the yield of wild blueberry by achieving a coefficient of determination (R2) of 0.938, root mean square error (RMSE) of 343.026, mean absolute error (MAE) of 206 and relative root mean square error (RRMSE) of 5.444%. The results are consistent with previous work on predicting wild blueberry fruit yield using digital color photography by (Zaman et al., 2008). This study showed that crop yield predictions can be based on computer simulation modeling datasets. Therefore, if a reasonable prediction can be reached, this study should have a significant impact, especially when data collection in the field is challenging. [ABSTRACT FROM AUTHOR]

This paper studies cluster dependence and synchronization in multilayer directed networks. Based on network quotient graphs, we provide a more efficient way to identify dependencies between clusters. By considering the relationships and interactions between clusters at a higher level of abstraction, this analysis can reduce the computational effort without requiring extensive computation on the original network. Furthermore, this paper investigates the impact of structural perturbations on network synchronization capabilities within a multilayer framework. Specifically, for the case where perturbations are applied to the dominant group, we study how changes in the network structure affect its cluster synchronization (CS) behavior. Finally, we employe a multilayer neural network model to validate the findings presented in this paper. [ABSTRACT FROM AUTHOR]

The detection and characterization of somatic mutations have become the important means to analyze the occurrence and development of cancer and, ultimately, will help to select effective and precise treatment for specific cancer patients. It is very difficult to detect somatic mutations accurately from the massive sequencing data. In this paper, a forest-graph-embedded deep feed-forward network (forgeNet) is utilized to detect somatic mutations from the sequencing data. In forgeNet, the random forest (RF) or Gradient Boosting Machine (GBM) and graph-embedded deep feed-forward network (GEDFN) are utilized to extract features and implement classification, respectively. Three real somatic mutation datasets collected from 48 triple-negative breast cancers are utilized to test the somatic mutation detection performances of forgeNet. The detection results show that forgeNet could make the 0.05%–0.424% improvements in terms of area under the curve (AUC) compared with support vector machines and random forest. [ABSTRACT FROM AUTHOR]

The detection and characterization of somatic mutations have become the important means to analyze the occurrence and development of cancer and, ultimately, will help to select effective and precise treatment for specific cancer patients. It is very difficult to detect somatic mutations accurately from the massive sequencing data. In this paper, a forest-graph-embedded deep feed-forward network (forgeNet) is utilized to detect somatic mutations from the sequencing data. In forgeNet, the random forest (RF) or Gradient Boosting Machine (GBM) and graph-embedded deep feed-forward network (GEDFN) are utilized to extract features and implement classification, respectively. Three real somatic mutation datasets collected from 48 triple-negative breast cancers are utilized to test the somatic mutation detection performances of forgeNet. The detection results show that forgeNet could make the 0.05%–0.424% improvements in terms of area under the curve (AUC) compared with support vector machines and random forest. [ABSTRACT FROM AUTHOR]

The cultivated strawberry Fragaria × ananassa Duch. is widely planted in greenhouses around the world. Its production heavily depends on pollination services. Bee pollination can significantly improve fruit quality and save considerable labor requirements. In this paper, we used a simulation model to study the effects of bee density and hive distribution on pollination efficiency for greenhouse strawberries. Simulation experiments allowed us to obtain the pollination efficiency under different conditions and track every bee, flower, pollen and fruit in detail without great planting cost. In particular, we found that fruit quality cannot be significantly improved once bee density is higher than one bee/plant due to a saturation effect. Distributing bee hives and strawberry interplanting spatially can improve fruit quality. In addition, the simulation results suggested that a continuous bee pollination process can significantly reduce the influence of stigma receptivity. This effect and the even distribution of pollen are the top two reasons explaining how bee pollination outperforms artificial pollination. [ABSTRACT FROM AUTHOR]

In addition to cognitive impairments, deficits in non-cognitive behaviors are also common neurological sequelae in Alzheimer's disease and its animal models. Hesperidin, a flavanone glycoside found abundantly in citrus fruits, was orally given (100 mg/kg body weight) to 5-month-old transgenic APP/PS1 mice, a mouse model of cerebral amyloidosis for Alzheimer's disease. After a relatively short-term treatment of 10 days, hesperidin significantly restored deficits in non-cognitive nesting ability and social interaction. Further immunohistochemical analysis showed significantly attenuated β-amyloid deposition, plaque associated APP expression, microglial activation and TGF-β immunoreactivity in brains of APP/PS1 mice, which suggests that ameliorated behavioral impairments might be attributable to reduced Aβ deposition and attenuated neuro-inflammatory reaction. Additionally, efficient anti-inflammatory effects of hesperidin were confirmed in vitro. Our findings suggest that hesperidin might be a potential candidate for the treatment of AD or even other neurodegenerative diseases. [ABSTRACT FROM AUTHOR]

Due to the COVID‐19 pandemic at present, it is necessary to detect whether pedestrians in public places wear face masks or not for preventing the spread of novel coronavirus. The pedestrian flow in public places is large, and it puts forward higher requirements for the accuracy and speed of real‐time mask detection. Improving the face mask detection effect especially in the night environment is a challenging problem. A novel object detector namely MaskHunter is proposed in this study for the real‐time mask detection. Specifically, the authors propose novel effective structures of backbone, neck and prediction head based on YOLOv4 series, which achieves the state‐of‐the‐art performance and a novel improved Mosaic data augmentation method. Moreover, they propose a novel mask‐guided module to enhance the discrimination ability of face mask especially in the night environment. As a consequent, experiments show that MaskHunter achieves better detection performance for real‐time mask detection compared with other obtained models in this scenario. [ABSTRACT FROM AUTHOR]

Due to the COVID-19 pandemic at present, it is necessary to detect whether pedestrians in public places wear face masks or not for preventing the spread of novel coronavirus. The pedestrian flow in public places is large, and it puts forward higher requirements for the accuracy and speed of real-time mask detection. Improving the face mask detection effect especially in the night environment is a challenging problem. A novel object detector namely MaskHunter is proposed in this study for the real-time mask detection. Specifically, the authors propose novel effective structures of backbone, neck and prediction head based on YOLOv4 series, which achieves the state-of-the-art performance and a novel improved Mosaic data augmentation method. Moreover, they propose a novel mask-guided module to enhance the discrimination ability of face mask especially in the night environment. As a consequent, experiments show that MaskHunter achieves better detection performance for real-time mask detection compared with other obtained models in this scenario. [ABSTRACT FROM AUTHOR]

Hypergraph learning is largely used to handle dimensional disaster problem of high-dimensional data in view of its flexible ability to model complex data correlations. However, the following three problems limit the performance of unsupervised hypergraph-based dimensionality reduction methods: (1) most of them are unable to obtain consistently good performance for different data sets; (2) noise or outliers in the data can lead to unreliability of the fixed hypergraph; and (3) the simple K-nearest neighbor strategy generates hypergraph with a fixed number of vertices in each hyperedge. To solve the above problems, an unsupervised dimensionality reduction method named joint dynamic hypergraph and low-rank embedding (DHLRE) is proposed. Specifically, DHLRE unifies hypergraph learning and low-rank learning into a single objective function. By doing so, both global and higher-order local structural information of the data can be extracted by DHLRE, and the two structural information complement each other to make the projection matrix have stronger discriminative power. In addition, the weight of hyperedge and hypergraph in DHLRE are dynamically generated in dimension-reduced space, which suppresses the ability of noise or outlier to affect the projection matrix. To divide hyperedge in a flexible way, a new approach without neighborhood parameter is proposed. By using this approach, the number of vertices of the hyperedge can be adaptively decided for different data sets. Further, an iterative algorithm is designed to solve the objective function of DHLRE. Finally, experimental results on several real-world data sets show that DHLRE outperforms others related methods for both classification and clustering tasks. • An unsupervised dimensionality reduction method called DHLRE is proposed. • DHLRE is found to be able to extract features with robustness and discrimination. • A new hypergraph construction method without neighborhood parameter is proposed. • An efficient optimization algorithm is developed to solve DHLRE model. [ABSTRACT FROM AUTHOR]