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Brain decoding is a process of decoding human cognitive contents from brain activities. However, improving the accuracy of brain decoding remains difficult due to the unique characteristics of the brain, such as the small sample size and high dimensionality of brain activities. Therefore, this paper proposes a method that effectively uses multi-subject brain activities to improve brain decoding accuracy. Specifically, we distinguish between the shared information common to multi-subject brain activities and the individual information based on each subject’s brain activities, and both types of information are used to decode human visual cognition. Both types of information are extracted as features belonging to a latent space using a probabilistic generative model. In the experiment, an publicly available dataset and five subjects were used, and the estimation accuracy was validated on the basis of a confidence score ranging from 0 to 1, and a large value indicates superiority. The proposed method achieved a confidence score of 0.867 for the best subject and an average of 0.813 for the five subjects, which was the best compared to other methods. The experimental results show that the proposed method can accurately decode visual cognition compared with other existing methods in which the shared information is not distinguished from the individual information.

The integration of wind power into power grid will bring some impacts on the multiple subjects of electric power system. Economic impacts of wind power integration on multiple subjects of China’s electric power system were quantitatively assessed from Quasi-public goods property perspective in this paper. Firstly, the Quasi-public goods property of transmission services provided by power grid corporations was elaborated. Secondly, the multiple subjects of China’s electric power system, which include electricity generation enterprises (EGEs), power grid corporations (PGCs), electricity consumers (ECs), and environment, were detailed analyzed. Thirdly, based on the OPF-based nodal price model and transmission service cost allocation model, the economic impact assessment model of wind power integration was built from Quasi-public goods property perspective. Then, the IEEE-24 bus system employed in this paper was introduced according to current status of China’s electric power system, and the modeling of wind turbine was also introduced. Finally, the simulation analysis was performed, and the economic impacts of wind power integration on EGEs, PGCs, ECs and Environment were calculated. The results indicate, from Quasi-public goods property perspective, the wind power integration will bring positive impacts on EGEs, PGCs and Environment, while negative impacts on ECs. The findings can provide references for power system managers, energy planners, and policy makers.

Community sports governance is a complex process involving the participation of multiple governance subjects, and the interests of different subjects are often very different, so this paper constructs an evolutionary game model to analyze the process of community governance. The game payoff matrix and payoff tree model are constructed with the three parties of regulation, supply and demand of sports services, and the replicated dynamic equations are used to represent the payoff model of each party, and then the payoff status of each governance subject under different combinations of governance strategies is analyzed based on the Jacobian matrix. When the reputation loss of the regulator < regulatory cost, the probability of strict regulation tends to be close to 0. When the social reputation loss of the sport service provider > governance cost-governance gain, the enterprise tends to actively govern. And community residents are more likely to remain active in participating in community sports governance when they obtain sufficient compensation and rewards. It indicates that multiple subjects should balance governance regulation and reward and punishment when participating in community sports governance in order to improve governance efficiency.

[Purpose/Significance] Information literacy (IL) training for farmers has become one of the main contents for farmers in the new era. However, the current implementation of rural revitalization still does not pay enough attention to farmers. At the same time, farmers' IL ability is an important embodiment of farmers' integration into the digital countryside, which can give a strong boost to the modernization of agriculture and rural areas. Therefore, it is of great practical significance for the rural revitalization strategy in the new era to make full use of multiple social subjects and improve farmers' IL. [Method/Process] This paper reviews the concept and definition of IL, and analyzes the research on farmers' IL in recent years. The results show that most of the current research on the cultivation of farmers' IL focuses on a specific topic and lacks holistic research. Therefore, it is necessary to systematically understand the cultivation process of farmers' IL, and guide the cultivation behavior of IL by the all-round cultivation concept. [Results/Conclusions] At present, although the local governments have initially built an IL training model of the new era, with schools and social organizations as participants in the model, farmers still lack information knowledge, information awareness, and IL skills. Several proposals are put forward here to address the above issue. First of all,it is necessary to strengthen the construction of IL education system and improve farmers' information knowledge. The government should give full play to the local government agencies in resource integration, schools and scientific research institutions in professional advantages, and social organizations in providing information services, so as to provide farmers with more systematic IL training. Second, efforts should be made to jointly build IL education space to raise farmers' information awareness: the government should build farmers' IL training base, the schools should promote the transformation of the education model, and social organizations continue to carry out IL training project. The three parties join hands to build a three-dimensional integrated IL education space of \"material space, spiritual space and social space\", and a new way of the cultivating farmers' information awareness. Finally, IL teachers should be trained to improve farmers' information literacy. The government will attract and retain information talent in rural areas through positive talent polices. Schools will play an educational role in developing farmers' information literacy skills.

A novel non-contact vital-sign sensing algorithm for use in cases of multiple subjects is proposed. The approach uses a 24 GHz frequency-modulated continuous-wave Doppler radar with the parametric spectral estimation method. Doppler processing and spectral estimation are concurrently implemented to detect vital signs from more than one subject, revealing excellent results. The parametric spectral estimation method is utilized to clearly identify multiple targets, making it possible to distinguish multiple targets located less than 40 cm apart, which is beyond the limit of the theoretical range resolution. Fourier transformation is used to extract phase information, and the result is combined with the spectral estimation result. To eliminate mutual interference, the range integration is performed when combining the range and phase information. By considering breathing and heartbeat periodicity, the proposed algorithm can accurately extract vital signs in real time by applying an auto-regressive algorithm. The capability of a contactless and unobtrusive vital sign measurement with a millimeter wave radar system has innumerable applications, such as remote patient monitoring, emergency surveillance, and personal health care.

Time-oriented domains with large volumes of time-stamped information, such as medicine, security information and finance, require useful, intuitive intelligent tools to process large amounts of time-oriented multiple-subject data from multiple sources. We designed and developed a new architecture, the VISualizatIon of Time-Oriented RecordS (VISITORS) system, which combines intelligent temporal analysis and information visualization techniques. The VISITORS system includes tools for intelligent selection, visualization, exploration, and analysis of raw time-oriented data and of derived (abstracted) concepts for multiple subject records. To derive meaningful interpretations from raw time-oriented data (known as temporal abstractions ), we use the knowledge-based temporal-abstraction method. A major task in the VISITORS system is the selection of the appropriate subset of the subject population on which to focus during the analysis. Underlying the VISITORS population-selection module is our ontology-based temporal-aggregation (OBTAIN) expression-specification language which we introduce in this study. The OBTAIN language was implemented by a graphical expression-specification module integrated within the VISITORS system. The module enables construction of three types of expressions supported by the language: Select Subjects, Select Time Intervals , and Get Subjects Data . These expressions retrieve a list of subjects, a list of relevant time intervals, and a list of time-oriented subjects’ data sets, respectively. In particular, the OBTAIN language enables population-specification, through the Select Subjects expression, by using an expressive set of time and value constraints. We describe the syntax and semantics of the OBTAIN language and of the expression-specification module. The OBTAIN expressions constructed by the expression-specification module, are computed by a temporal abstraction mediation framework that we have previously developed. To evaluate the expression-specification module, five clinicians and five medical informaticians defined ten expressions, using the expression-specification module, on a database of more than 1,000 oncology patients. After a brief training session, both user groups were able in a short time (mean = 3.3 ± 0.53 min) to construct ten complex expressions using the expression-specification module, with high accuracy (mean = 95.3 ± 4.5 on a predefined scale of 0 to 100). When grouped by time and value constraint subtypes, five groups of expressions emerged. Only one of the five groups (expressions using time-range constraints), led to a significantly lower accuracy of constructed expressions. The five groups of expressions could be clustered into four homogenous groups, ordered by increasing construction time of the expressions. A system usability scale questionnaire filled by the users demonstrated the expression-specification module to be usable (mean score for the overall group = 68), but the clinicians’ usability assessment (60.0) was significantly lower than that of the medical informaticians (76.1).

Gait recognition is being employed as an effective approach to identify people without requiring subject collaboration. Nowadays, developed techniques for this task are obtaining high performance on current datasets (usually more than 90 % of accuracy). However, those datasets are simple as they only contain one subject in the scene at the same time. This fact limits the extrapolation of the results to real world conditions where, usually, multiple subjects are simultaneously present at the scene, generating different types of occlusions and requiring better tracking methods and models trained to deal with those situations. Thus, with the aim of evaluating more realistic and challenging situations appearing in scenarios with multiple subjects, we release a new framework (MuPeG) that generates augmented datasets with multiple subjects using existing datasets as input. By this way, it is not necessary to record and label new videos, since it is automatically done by our framework. In addition, based on the use of datasets generated by our framework, we propose an experimental methodology that describes how to use datasets with multiple subjects and the recommended experiments that are necessary to perform. Moreover, we release the first experimental results using datasets with multiple subjects. In our case, we use an augmented version of TUM-GAID and CASIA-B datasets obtained with our framework. In these augmented datasets the obtained accuracies are 54.8 % and 42.3 % whereas in the original datasets (single subject), the same model achieved 99.7 % and 98.0 % for TUM-GAID and CASIA-B, respectively. The performance drop shows clearly that the difficulty of datasets with multiple subjects in the scene is much higher than the ones reported in the literature for a single subject. Thus, our proposed framework is able to generate useful datasets with multiple subjects which are more similar to real life situations.

Open burning of straw is the most significant problem of environmental pollution in rural areas. Returning straw to the fields is beneficial to rural environmental management and rural development. Comprehensive utilization of straw in the field not only reduces environmental pollution, but also benefits food production and farmers’ income. Because planting farmers, enterprises, and local governments have different interests, it is difficult for the straw return system to operate soundly. In this study, a three-party evolutionary game model of farmers, enterprises, and local governments was constructed to analyze the evolutionary stability of the strategic choices of the three subjects, explore the influence of each element on the strategic choices of the three parties, and use Matlab2022b simulation to further analyze the dynamic evolution of the game behavior of the system subjects under the given benefits and the given subjects, respectively. The study findings showed that the higher the preferences given by the local government, the higher the probability of farmers and enterprises participating in the straw return system. Only with the participation of local governments can the straw return system be operated robustly. Our study findings also revealed that the interests of farmers must be fully protected in order to mobilize the main body and stimulate market dynamics. The overall findings of this study provide useful insights for promoting government agencies to govern the local environment, increase local revenues, and build integrated waste utilization systems.

Low-value recyclable waste accounts for a large portion of urban waste output in many modern cities. The improper management and disposal of LVRW result in environmental pollution and a waste of resources. Given the characteristics of a high recovery cost and low recovery income of low-value recyclables, it is difficult to obtain a satisfactory waste disposal effect by completely relying on the market mechanism. It is thus necessary for the government to implement effective subsidies for multiple subjects in the urban waste recycling system (UWRS). This study examines the independent roles of four subsidy policies—subsidy to the third-party waste disposal institutions, subsidy to a state-owned waste disposal institution, R&D subsidy for green technology, and subsidy for government publicity—and develops a system dynamics model to verify the performance of the UWRS under different combinations of subsidy-based policies under multiple scenarios. Data on urban waste disposal for Guangzhou from 2019 and 2020 were used to validate and simulate the model. A sensitivity analysis of the main exogenous variables was carried out, and the conclusions are as follows: (1) On the premise of a fixed subsidy capital pool, a mixed subsidy policy produced the best impact on the UWRS. (2) The total subsidy needed to reach a certain threshold; otherwise, the mixed subsidy policy did not improve the UWRS. The total subsidy produced diminishing returns once it had exceeded the threshold. (3) Appropriately reducing subsidies for the third-party waste disposal institutions within a reasonable range does not affect the performance of the UWRS. (4) The effect of government publicity has short-term advantages, while the long-term potential of green technology is greater. Multi-agent coordination and the guidance of the market mechanism are important priorities in the design of subsidy-based policies. In addition, the trade-off between subjects needs attention, and a plan for mixed subsidy policies needs to be designed and implemented according to the response periods of different policies. The research here provides theoretical support for the government for designing subsidy-based policies.

Tourism-based poverty alleviation strategies have played an important role in China’s efforts to eliminate poverty. In the post-poverty-alleviation era, the mechanism by which the government, market and society jointly promote the sustainable development of the tourism-based poverty alleviation industry requires further research. Based on data from 33 tourism-based poverty alleviation demonstration counties in China, this paper uses the fsQCA method to explore the role positioning and function of the three major entities of government, market and society. The results show that no single condition can promote the long-term development of tourism-based poverty alleviation. Four modes are proposed: government financial support, resource endowment, ecosystem tourism and multisubject coordination. The government provides a diverse and sustainable “haematopoietic” mechanism by focusing on different elements in each path. In addition, public tourism services are crucial to the long-term mechanism of tourism-based poverty alleviation.