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This article focuses on constrained high-order multicoalition games (MCGs), which differ from most MCGs as they contain the high-order dynamics of players. Furthermore, all players are subject to inequality constraints. Existing distributed strategies for Nash equilibrium (NE) seeking are inapplicable to our problem, due to the presence of high-order dynamics and/or inequality constraints. The coexistence of high-order dynamics and inequality constraints poses significant challenges for distributed strategy design and analysis, since these players’ actions cannot be directly controlled by their inputs while NE must satisfy the constraints. To ensure that the high-order players fulfill the MCG tasks autonomously, we propose a distributed strategy by employing state feedback, gradient descents, and primal–dual methods. Further, we prove the stabilization of high-order players to the NE through our strategy. Additionally, we apply our method to electricity market games (EMGs). Based on our method, boiler–turbine generation systems (BTGSs) can engage in EMGs autonomously, which contributes to enhancing the intellectualization of smart grids.

This article investigates the constrained nonsmooth distributed optimization problems (DOPs) of general linear multiagent systems. Our problem involves the general linear dynamics of agents, and the cost functions are nondifferentiable. Moreover, the decisions of agents are constrained by both local and coupled inequalities. Without considering the general linear dynamics, the nonsmooth cost functions, and/or the inequality constraints, existing distributed algorithms are ineffective for our problem. Besides, due to the general linear dynamics, the decisions of agents cannot be determined directly by their control inputs, while the optimal solution must satisfy the constraints. Therefore, it is not easy to design algorithms for our problem. In addition, noteworthily, the global Lipschitz continuity of gradients is an indispensable condition for existing results of DOPs with physical systems. However, the subgradients of nonsmooth cost functions are not Lipschitz continuous, which consequently puts up barriers to algorithm analysis. Based on state/output feedback, subgradient descents, and primal-dual methods, we develop two distributed algorithms. Compared with existing algorithms for DOPs with physical systems, our algorithms are fully distributed. By set-valued Lasalle invariance principle and convex analysis, we analyze the two algorithms rigorously. The first algorithm can solve strictly convex DOPs, in contrast to most results of DOPs with physical systems. The second algorithm only depends on the output information, different from many related algorithms. Finally, the proposed methods are applied to smart grids. With the methods, doubly-fed induction generators can achieve the optimal economic dispatch autonomously.

Age estimation is a significant and active research field in radiological medicine. Recently, researchers have turned their attention to magnetic resonance imaging (MRI) for age estimation, primarily due to concerns regarding ionizing radiation in traditional imaging techniques. In this article, based on the knee MRI dataset, we present a novel end-to-end network that combines convolutional neural network (CNN) and visual transformer network to extract complementary features. In addition, we introduce a feature aggregation module (FAM) that aggregates features from different local knee MRI slices. Our proposed method integrates the feature maps from CNN with the patch embeddings of visual transformer branches, allowing for the acquisition of both local and global information necessary for extracting age-related features. Moreover, we introduce an FAM utilizing the graph attention network (GAT) to achieve interaction between multiple slice features at the feature level. Through extensive experiments conducted on a dataset of 80 knee MRI samples spanning ages 12.0–25.9 years, our method has demonstrated state-of-the-art performance in knee MRI age estimation. Specifically, in a fivefold cross-validation setup, we achieved a mean absolute error (MAE) of 1.52 ± 1.27 years in age regression.

In this article, we study the multicluster games over weight-balanced digraphs, where the cost functions of all players are nonsmooth. Besides, in the problem, not only are the decisions of all players constrained by heterogeneous local constraints but also the decisions of players in the same cluster are constrained by coupling constraints. Due to the nonsmooth cost functions, the coupling constraints, the general local convex constraints, and the weight-balanced digraphs, existing Nash equilibrium seeking algorithms cannot solve our problem. In order to seek the Nash equilibrium of the game, we design a distributed algorithm based on subgradient descent, differential inclusions, and projection operations. In the algorithm, a distributed learning strategy is embedded for the players to estimate the decisions of other players. Moreover, we analyze the asymptotical convergence of the algorithm via set-valued LaSalle invariance principle. Finally, a numerical simulation about electricity market games is presented to illustrate the effectiveness of our result.

In this work, a novel semisupervised framework is proposed to tackle the small-sample problem of dental-based human identification (DHI), achieving enhanced performance via a “classifying while generating” paradigm. A generative adversarial network (GAN), called the DHI-GAN, is presented to implement this idea, in which an extra classifier is also dedicatedly proposed to achieve an efficient training procedure. Considering the complex specificities of this problem, except for the noise input of the generator, an identity embedding-guided architecture is proposed to retain informative features for each individual. A parallel spatial and channel fusion attention block is innovatively designed to encourage the model to learn discriminative and informative features by focusing on different regional details and abstract concepts. The attention block is also widely applied to the overall classifier to learn identity-dependent information. A loss combination of the ArcFace and focal loss is utilized to address the small-sample problem. Two parameters are proposed to control the generated samples that are fed into the classifier during the optimization procedure. The proposed DHI-GAN framework is finally validated on a real-world dataset, and the experimental results demonstrate that it outperforms other baselines, achieving a 92.5% top-one accuracy rate. Most importantly, the proposed GAN-based semisupervised training strategy is able to reduce the required number of training samples (individuals) and can also be incorporated into other classification models. Our code will be available at https://github.com/sculyi/MedicalImages/.

In this article, the formation problems of multiple quadrotor unmanned aerial vehicles (QUAVs) are studied. In contrast to existing results, the problems are investigated from the perspective of aggregative games (AGs). An interesting discovery is that the desired formation can be constituted by the Nash equilibrium of a quadratic AG. To realize the desired formation for QUAVs, we design two distributed game-based formation algorithms. With the developed algorithms, the QUAVs do not need to measure the (relative) positions and (relative) velocities of neighboring QUAVs, which differs from existing algorithms. By constructing suitable Lyapunov functions, we rigorously analyze the two formation algorithms. The first formation algorithm converges exponentially to the desired formation for the QUAVs without parameter uncertainties. The other algorithm converges asymptotically to the desired formation, even if the QUAVs contain uncertain parameters. Finally, we provide illustrative examples.

This paper investigates the nonsmooth noncooperative games (NGs) over unbalanced digraphs, where each player has a nondifferentiable payoff function. Meanwhile, these players are subject to local nonsmooth inequality constraints, local convex set constraints and coupled equality constraints. Due to the coexistence of nonsmooth payoff functions, unbalanced digraphs and constraints, existing generalized Nash equilibrium (GNE) seeking strategies are fail to address our problem. Based on subgradient descents, differential inclusions, projection and primal-dual methods, we develop a distributed seeking strategy. Besides, the convergence of the strategy is proved. Finally, the proposed method is applied to electricity market games (EMGs) of smart grids. [ABSTRACT FROM AUTHOR]

An aggregative game of disturbed Euler-Lagrange systems is studied in this paper. The cost function of each player depends on its own decision and the aggregate of all decisions. Different from the well-known aggregative games, the second-order nonlinear dynamic of every player is considered in our problem, and every player is influenced by exogenous disturbances. To seek the Nash equilibrium, a distributed algorithm is developed via state feedback, gradient descent, and internal model. The convergence of the algorithm is analyzed with the help of variational analysis and Lyapunov stability theory. It shows that the Euler-Lagrange players with the proposed algorithm, can asymptotically converge to the Nash equilibrium, even though exogenous disturbances have impact on the behaviors of the players. Finally, a numerical simulation is given to illustrate the effectiveness of our algorithm.

In this article, we investigate multi-cluster games, where the cost functions of every player depends on its own decision as well as the decisions of players in other clusters. The decisions of players is constrained by not only local convex set constraints but also coupling constraints. For the purpose of seeking the variational generalized Nash equilibrium (GNE) of the multi-cluster game, we design a distributed algorithm by virtue of gradient descent and projections. Moreover, the asymptotical convergence of the algorithm to the variational GNE is analyzed with the help of Lyapunov stability theory and variational analysis. Finally, a simulation example is proposed to verify the validity of the algorithm. [ABSTRACT FROM AUTHOR]

In this article, we study nondifferentiable resource allocation problems (RAPs). In our problem, the decisions of agents are subject to coupling inequality constraints, local inequality constraints, and local convex set constraints. In contrast to existing RAPs, the cost functions are nonsmooth and the inequality constraints are nonsmooth and nonlinear in our problem. Based on differential inclusions and projection methods, we exploit a fully distributed subgradient-based resource allocation algorithm to optimally allocate the network resources. With the help of the set-valued LaSalle invariance principle, we prove the global convergence of the algorithm to the optimal resource allocation of our problem. Finally, our method is applied to the economic dispatch problems of smart grids. With our method, the generations of generating units converge to the optimal power generation.

This article investigates the N-coalition games of Euler–Lagrange (EL) systems. In the games, every coalition consists of a group of players, whose dynamics are formulated as EL equations. The objective of each coalition is to minimize its cost function, which is the sum of local cost functions of the players in the corresponding coalition. That is, the players within the coalition collaboratively minimize their coalition’s cost function. The cost function of each player is only available to itself and depends on not only its decision, but also the aggregate of all decisions. Based on gradient descent, consensus technique, and state feedback, a fully distributed Nash equilibrium seeking algorithm for N-coalition games is proposed. The exponential convergence of the algorithm is analyzed by the Lyapunov stability theorem. A numerical example in smart grids is given to illustrate the effectiveness of the proposed algorithm.

Diabetes mellitus (DM) is one of the largest epidemics the world has faced. Given the morbidity and mortality burden of DM, it is important to identify modifiable factors that affect the natural course of the disease. We carried out this meta-analysis of published studies to estimate the overall DM risk of intake of fermented dairy foods, and to reveal the dose-dependent effect on DM risk. We searched the PubMed, Embase and China National Knowledge Infrastructure databases for all articles published between the years 1980 to 2020 on the association between fermented dairy foods intake and DM risk. Fifteen studies met the inclusion criteria for our study, including a total of 485,992 participants and 20,207 incidences of diabetes. Overall, statistical evidence of significantly decreased diabetes risk was found to be associated with higher intake of fermented dairy foods (odds ratio [OR], 0.925; 95% confidence interval [CI], 0.856 to 0.999). In a subgroup analysis, higher yogurt consumption was significantly associated with decreased DM risk (OR, 0.828; 95% CI, 0.729 to 0.941). Our meta-analysis shows that intake of fermented dairy foods is associated with decreased DM risk, and the effect may be dose-dependent.

In this article, we study the event-triggered distributed optimization problem of second-order nonlinear multiagent systems under undirected and connected communication topologies. To reduce the cost of information exchange in an optimization problem, a distributed event-triggered communication-based algorithm is proposed. In an event-triggered mechanism, the information interaction process of the network is controlled by a set of trigger conditions. The trigger condition of each agent only requires its own state information and its neighboring agents’ state information. Each agent updates its own states only at the triggering time. In order to achieve the optimal result of the optimization problem and reduce the cost of communicatio, we propose a distributed event-triggered algorithm based on gradient descent. Moreover, we analyze the convergence of the algorithm by constructing a suitable Lyapunov function. Furthermore, we prove that the Zeno behavior can be avoid. Finally, we provide examples to validate the effectiveness of the obtained result.

As the most fundamental economic sector of human societies, farming has played a significant role in interactions between humans and the environment. The study of crop patterns in various regions could not only clarify the economic foundations of early societies, but also greatly improve our understanding of interactions among human beings, the environment, and other creatures. In this regard, this study focuses on early agriculture practices at the Haojiatai site, situated in the cultural and natural transitional region between North and South China. Macro plant remains from this site reveal that foxtail millet was the most important staple crop here during the Longshan and Xinzhai periods (4300-3800 cal. yr BP), along with common millet and a small amount of rice and soybean. This crop pattern is in contrast to the ancient environmental conditions of this region, and cultural factors such as agriculture traditions and food preferences may have been more important in the formation process of this crop pattern.

A distributed resource allocation problem of high-order systems is studied in this article, where the decisions of agents are constrained by network resources. In contrast to well-known resource allocation problems, this problem involves the high-order dynamics of agents, which results in the ineffectiveness of existing distributed resource allocation algorithms. In order to optimally allocate the network resource, a distributed algorithm is developed via state feedback and gradient descent. Moreover, the convergence of the algorithm is analyzed with the help of convex analysis and Lyapunov stability theory. By the algorithm, the high-order agents globally exponentially converge to the optimal solution. Finally, the algorithm is illustrated by numerical examples.

This paper studies the constrained distributed resource allocation problems (DRAPs) of autonomous multi-agent systems (MASs). Unlike well-defined DRAPs, the disturbed second-order agents are taken into account. Moreover, all agents' decisions are subject to both coupling and local inequalities. Because of the coexistence of disturbed second-order nonlinear dynamics and inequality constraints, existing strategies are not applicable to tackle our problem. Also, they create challenges for the strategy design, because the inequality constraints must hold at the optimal allocation (OA), while these agents are not able to control their decisions directly. By gradient descent, state feedback and internal model (IM), we exploit a distributed strategy to control all agents to carry out the distributed resource allocation tasks (DRATs). Furthermore, the strategy is rigorously analyzed. Finally, our strategy is applied to the economic dispatch problems (EDPs). Under our approach, turbine-generators can autonomously achieve the optimal generation allocation by regulating their powers, in accordance with the load demand of smart grids. [ABSTRACT FROM AUTHOR]

Context Bacillus subtilis (BS) is a widely used microbial agent that could improve soil fertility and soil microenvironment. There is still uncertainty about the suitability of BS for cultivating crops with high demand for nitrogen fertiliser. Aims To evaluate the effects of BS agent on microbial community diversity and nitrogen-cycling genes in mulberry rhizosphere soil. Methods Pot experiments were conducted. Different dosages (CK, 0; T1, 0.5×106 CFUg−1 soil; T2, 1×106 CFUg−1 soil; T3, 2×106 CFUg−1 soil) of BS agent were applied to irrigate the mulberry soil. The soil nutrient content, enzyme activity, bacterial community, and nitrogen-cycling genes were determined. Key results T1 had the highest Chao1 and Shannon index, while T3 had the lowest. BS-treated samples had higher relative abundance of Actinobacteria and Chloroflexi than that of CK. Specially, BS-treated samples had higher relative abundance of Sphingomonas , Reyranella , and Hyphomicrobium , which was significantly positively correlated with the content of organic matter, total soluble nitrogen, ammonium nitrogen, and the activity of sucrase. The abundance of genes involved in amino acid metabolism, energy metabolism, metabolism of cofactors, and vitamin functions also increased in the BS-treated samples. BS treatment significantly increased the abundance of AOA-amoA and nirK genes, but decreased the abundance of nirS and nifH genes. Conclusions An appropriate amount of BS agent could improve soil fertility, regulate the dominant bacterium communities, and affect the abundance of functional genes involved in nitrogen cycling. Implications BS is probably a good choice for mulberry cultivation to improve nitrogen fertiliser utilisation efficiency.

In this paper, two aggregative games over weight-balanced digraphs are studied, where the cost functions of all players depend on not only their own decisions but also the aggregate of all decisions. In the first problem, the cost functions of players are differentiable with Lipschitz gradients, and the decisions of all players are coupled by linear coupling constraints. In the second problem, the cost functions are nonsmooth, and the decisions of all players are constrained by local feasibility constraints as well as linear coupling constraints. In order to seek the variational generalized Nash equilibrium (GNE) of the differentiable aggregative games, a continuous-time distributed algorithm is developed via gradient descent and dynamic average consensus, and its exponential convergence to the variational GNE is proven with the help of Lyapunov stability theory. Then, another continuous-time distributed projection-based algorithm is proposed for the nonsmooth aggregative games based on differential inclusions and differentiated projection operations. Moreover, the convergence of the algorithm to the variational GNE is analyzed by utilizing singular perturbation analysis. Finally, simulation examples are presented to illustrate the effectiveness of our methods. [ABSTRACT FROM AUTHOR]

In this paper, distributed optimization control for a group of autonomous Lagrangian systems is studied to achieve an optimization task with local cost functions. To solve the problem, two continuous-time distributed optimization algorithms are designed for multiple heterogeneous Lagrangian agents with uncertain parameters. The proposed algorithms are proved to be effective for those heterogeneous nonlinear agents to achieve the optimization solution in the semi-global sense, even with the exponential convergence rate. Moreover, simulation adequately illustrates the effectiveness of our optimization algorithms.

Baligang is a Neolithic site with a long occupation, from before 6300BC up to the first millennium BC, although the bulk of excavated finds and archaeobotanical evidence from the site comes from the Yangshao, Qujialing, Shijiahe and Longshan (4300–1800BC). The cultural group affiliation of the site varies between northern (Yangshao and Longshan) and southern (Qujialing and Shijiahe) cultural connections. The earliest occupation of the site represents a pre-Yangshao society with early cultivation of rice (Oryza). In later periods Baligang has evidence for mixed farming of both rice and millets (Setaria italicaand Panicum miliaceum), although rice is the most prominent crop in the phytolith record throughout the occupation. Wetland rice cultivation is indicated throughout the Yangshao, Qujialing, Shijiahe and Late Longshan periods. However, there is a shift towards better watered rice in the Qujialing and Shijiahe phytolith assemblages, indicated by a decline in sedges (Cyperaceae) alongside occurrence of sponge spicules and diatoms. These data suggest deeper flooding of rice fields in order to suppress weeds and increase productivity, indicating that the ecology of rice cultivation changed over time. In the Late Longshan period, when millet became more prominent and the cultural influence shifted northwards, it appears that more sedge-infested and weedy rice fields became the norm, suggesting a decline in rice cultivation intensity, perhaps connected to influences of cultivation practices from the north. In addition, we can infer aspects of the organisation of crop-processing from the phytolith evidence. In the Yangshao period the remains consist of mostly dehusking waste from the final processing, suggesting storage of a more processed crop and therefore larger scale, more communal post-harvest processing. By contrast this declined in the subsequent period with more evidence for primary winnowing waste indicating a shift towards smaller social scales of harvesting and processing, such as smaller household groups replacing a more communal approach. The household-level of processing is most evident in the Late Longshan period.

In this paper, permanent magnet synchronous motors (PMSMs) are investigated. According to the feature of PMSMs, a novel state equation of PMSMs is obtained by choosing suitable state variables. Based on the state equation, robust controllers are designed via interval matrix and PI control idea. In terms of bilinear matrix inequations, sufficient conditions for the existence of the robust controller are derived. In order to reduce the conservation and the dependence on parameter, the control inputs of PMSMs are divided into two parts, a feedforward control input and a feedback control input, and relevant sufficient conditions for the existence of the controller are obtained. Because of the suitable choice of state variables, the proposed control strategies can cope with the load uncertainty and have robustness for disturbance. Finally, simulations are carried out via Matlab/Simulink soft to verify the effectiveness of the proposed control strategies. The performance of the proposed control strategies are demonstrated by the simulation results.

When accidents occur, panoramic dental images play a significant role in identifying unknown bodies. In recent years, deep neural networks have been applied to address this task. However, while tooth contours are significant in classical methods, few studies using deep learning methods devise an architecture specifically to introduce tooth contours into their models. Since fine-grained image identification aims to distinguish subordinate categories by specific parts, we devise a fine-grained human identification model that leverages the distribution of tooth masks to distinguish different individuals with local and subtle differences in their teeth. First, a bilateral branched architecture is designed, of which one branch was designed as the image feature extractor, while the other was the mask feature extractor. In this step, the mask feature interacts with the extracted image feature to perform elementwise reweighting. Additionally, an improved attention mechanism was used to make our model concentrate more on informative positions. Furthermore, we improved the ArcFace loss by adding a learnable parameter to increase the loss of those hard samples, thereby exploiting the potential of our loss function. Our model was tested on a large dataset consisting of 23,715 panoramic X-ray dental images with tooth masks from 10,113 patients, achieving an average rank-1 accuracy of 88.62% and rank-10 accuracy of 96.16%.

In this paper, we consider the predefined-time distributed optimization problems of homogeneous and heterogeneous linear multi-agent systems under undirected and connected communication topologies, in which, all agents share coupled equality constraint. In order to make all agents converge to the optimal output at predefined-time cooperatively, we propose two distributed algorithms for homogeneous and heterogeneous multi-agent systems according to time-base generator technology and output feedback, therein, the optimal output can make the global cost function reach minimum. In the design of the algorithms, the control gains are not required, which can avoid the requirement of some global information in advance. Furthermore, all agents converge to the optimal output with exponential speed, and we can set the convergence time arbitrarily. Finally, we provide examples to illustrate the effectiveness of the proposed distributed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we consider the distributed optimization problems with linear coupling constraint of general homogeneous and heterogeneous linear multi-agent systems under weighted-balanced and strongly connected digraphs. In order to control all agents converge to the optimal output, we propose distributed control laws, therein, the optimal output can make the global cost function reach minimum. Then we guarantee the convergence of the proposed algorithms by the properties of Laplacian matrix and Lyapunov stability theorem. Furthermore, we extend the result of heterogeneous linear multi-agent system to the case that dynamics of agents are subject to external disturbances, and prove that the algorithm designed by internal model principle can make all agents reach the optimal output exactly. Finally, we provide examples to illustrate the effectiveness of the proposed distributed algorithms. [ABSTRACT FROM AUTHOR]