China＇s Decision-making System from Centralization to Democracy

Established during the long years of revolutionary struggle and the founding of the People＇s Republic of China （PRC）, the Communist Party of China（CPC）-led decision-making system has a historical rationality. Before China＇s reform and opening-up was implemented in 1978, the decision-making system highlighted centralization which led to disadvantages such as non-existent specialized division of functions, a low level of institutionalization, heavy reliance on experience, an enclosed decision-making mode and non-existent self-correction mechanisms. These disadvantages were institutional factors which ultimately caused the historic tragedy of ＂ Cultural Revolution. ＂After the reform and opening-up, the decision-making structure, mode and mechanism were reformed in order to promote scientific, democratic and law-based decisions. History has shown that the reform has successfully overcome various challenges brought about by China＇s rapidly changing economy and society. From the perspective of political development, the reform has promoted several transitions： from individual controlled decision-making system to a more democratic decision-making process, from being empirical to scientifically-based, from highly centralized decision-making organizations to decentralized one, from an enclosed mode to an open mode, from passive to active participation in the decision-making process and from a non-institutionalized to an institutionalized system. Therefore, the CPC-led open decision-making model based on science and laws and participated by other parties has taken an initial shape.

Evolutionary Decision-Making and Planning for Autonomous Driving Based on Safe and Rational Exploration and Exploitation

Decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment.This study proposes an online evolutionary decision-making and motion planning framework for autonomous driving based on a hybrid data-and model-driven method.First,a data-driven decision-making module based on deep reinforcement learning(DRL)is developed to pursue a rational driving performance as much as possible.Then,model predictive control(MPC)is employed to execute both longitudinal and lateral motion planning tasks.Multiple constraints are defined according to the vehicle’s physical limit to meet the driving task requirements.Finally,two principles of safety and rationality for the self-evolution of autonomous driving are proposed.A motion envelope is established and embedded into a rational exploration and exploitation scheme,which filters out unreasonable experiences by masking unsafe actions so as to collect high-quality training data for the DRL agent.Experiments with a high-fidelity vehicle model and MATLAB/Simulink co-simulation environment are conducted,and the results show that the proposed online-evolution framework is able to generate safer,more rational,and more efficient driving action in a real-world environment.

Toward Trustworthy Decision-Making for Autonomous Vehicles:A Robust Reinforcement Learning Approach with Safety Guarantees

While autonomous vehicles are vital components of intelligent transportation systems,ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving.Therefore,we present a novel robust reinforcement learning approach with safety guarantees to attain trustworthy decision-making for autonomous vehicles.The proposed technique ensures decision trustworthiness in terms of policy robustness and collision safety.Specifically,an adversary model is learned online to simulate the worst-case uncertainty by approximating the optimal adversarial perturbations on the observed states and environmental dynamics.In addition,an adversarial robust actor-critic algorithm is developed to enable the agent to learn robust policies against perturbations in observations and dynamics.Moreover,we devise a safety mask to guarantee the collision safety of the autonomous driving agent during both the training and testing processes using an interpretable knowledge model known as the Responsibility-Sensitive Safety Model.Finally,the proposed approach is evaluated through both simulations and experiments.These results indicate that the autonomous driving agent can make trustworthy decisions and drastically reduce the number of collisions through robust safety policies.

Orientation and Decision-Making for Soccer Based on Sports Analytics and AI:A Systematic Review

Due to ever-growing soccer data collection approaches and progressing artificial intelligence(AI) methods, soccer analysis, evaluation, and decision-making have received increasing interest from not only the professional sports analytics realm but also the academic AI research community. AI brings gamechanging approaches for soccer analytics where soccer has been a typical benchmark for AI research. The combination has been an emerging topic. In this paper, soccer match analytics are taken as a complete observation-orientation-decision-action(OODA) loop.In addition, as in AI frameworks such as that for reinforcement learning, interacting with a virtual environment enables an evolving model. Therefore, both soccer analytics in the real world and virtual domains are discussed. With the intersection of the OODA loop and the real-virtual domains, available soccer data, including event and tracking data, and diverse orientation and decisionmaking models for both real-world and virtual soccer matches are comprehensively reviewed. Finally, some promising directions in this interdisciplinary area are pointed out. It is claimed that paradigms for both professional sports analytics and AI research could be combined. Moreover, it is quite promising to bridge the gap between the real and virtual domains for soccer match analysis and decision-making.

Ethical Decision-Making Framework Based on Incremental ILP Considering Conflicts

Humans are experiencing the inclusion of artificial agents in their lives,such as unmanned vehicles,service robots,voice assistants,and intelligent medical care.If the artificial agents cannot align with social values or make ethical decisions,they may not meet the expectations of humans.Traditionally,an ethical decision-making framework is constructed by rule-based or statistical approaches.In this paper,we propose an ethical decision-making framework based on incremental ILP(Inductive Logic Programming),which can overcome the brittleness of rule-based approaches and little interpretability of statistical approaches.As the current incremental ILP makes it difficult to solve conflicts,we propose a novel ethical decision-making framework considering conflicts in this paper,which adopts our proposed incremental ILP system.The framework consists of two processes:the learning process and the deduction process.The first process records bottom clauses with their score functions and learns rules guided by the entailment and the score function.The second process obtains an ethical decision based on the rules.In an ethical scenario about chatbots for teenagers’mental health,we verify that our framework can learn ethical rules and make ethical decisions.Besides,we extract incremental ILP from the framework and compare it with the state-of-the-art ILP systems based on ASP(Answer Set Programming)focusing on conflict resolution.The results of comparisons show that our proposed system can generate better-quality rules than most other systems.

Stroke Risk Assessment Decision-Making Using a Machine Learning Model:Logistic-AdaBoost

Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to objectively predict and identify strokes,this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost(Logistic-AB)based on machine learning.First,the categorical boosting(CatBoost)method is used to perform feature selection for all features of stroke,and 8 main features are selected to form a new index evaluation system to predict the risk of stroke.Second,the borderline synthetic minority oversampling technique(SMOTE)algorithm is applied to transform the unbalanced stroke dataset into a balanced dataset.Finally,the stroke risk assessment decision-makingmodel Logistic-AB is constructed,and the overall prediction performance of this new model is evaluated by comparing it with ten other similar models.The comparison results show that the new model proposed in this paper performs better than the two single algorithms(logistic regression and AdaBoost)on the four indicators of recall,precision,F1 score,and accuracy,and the overall performance of the proposed model is better than that of common machine learning algorithms.The Logistic-AB model presented in this paper can more accurately predict patients’stroke risk.

Controlling the dynamic behavior of decentralized cluster through centralized approaches

How to control the dynamic behavior of large-scale artificial active matter is a critical concern in experimental research on soft matter, particularly regarding the emergence of collective behaviors and the formation of group patterns. Centralized systems excel in precise control over individual behavior within a group, ensuring high accuracy and controllability in task execution. Nevertheless, their sensitivity to group size may limit their adaptability to diverse tasks. In contrast, decentralized systems empower individuals with autonomous decision-making, enhancing adaptability and system robustness. Yet, this flexibility comes at the cost of reduced accuracy and efficiency in task execution. In this work, we present a unique method for regulating the centralized dynamic behavior of self-organizing clusters based on environmental interactions. Within this environment-coupled robot system, each robot possesses similar dynamic characteristics, and their internal programs are entirely identical. However, their behaviors can be guided by the centralized control of the environment, facilitating the accomplishment of diverse cluster tasks. This approach aims to balance the accuracy and flexibility of centralized control with the robustness and task adaptability of decentralized control. The proactive regulation of dynamic behavioral characteristics in active matter groups, demonstrated in this work through environmental interactions, holds the potential to introduce a novel technological approach and provide experimental references for studying the dynamic behavior control of large-scale artificial active matter systems.

Determination of the best materials for development and designing product using a multi-criteria decision-making

Purpose–Material selection,driven by wide and often conflicting objectives,is an important,sometimes difficult problem in material engineering.In this context,multi-criteria decision-making(MCDM)methodologies are effective.An approach of MCDM is needed to cater to criteria of material assortment simultaneously.More firms are now concerned about increasing their productivity using mathematical tools.To occupy a gap in the previous literature this research recommends an integrated MCDM and mathematical Bi-objective model for the selection of material.In addition,by using the Technique for Order Preference by Similarity to Ideal Solution(TOPSIS),the inherent ambiguities of decision-makers in paired evaluations are considered in this research.It goes on to construct a mathematical bi-objective model for determining the best item to purchase.Design/methodology/approach–The entropy perspective is implemented in this paper to evaluate the weight parameters,while the TOPSIS technique is used to determine the best and worst intermediate pipe materials for automotive exhaust system.The intermediate pipes are used to join the components of the exhaust systems.The materials usually used to manufacture intermediate pipe are SUS 436LM,SUS 430,SUS 304,SUS 436L,SUH 409 L,SUS 441 L and SUS 439L.These seven materials are evaluated based on tensile strength(TS),hardness(H),elongation(E),yield strength(YS)and cost(C).A hybrid methodology combining entropy-based criteria weighting,with the TOPSIS for alternative ranking,is pursued to identify the optimal design material for an engineered application in this paper.This study aims to help while filling the information gap in selecting the most suitable material for use in the exhaust intermediate pipes.After that,the authors searched for and considered eight materials and evaluated them on the following five criteria:(1)TS,(2)YS,(3)H,(4)E and(5)C.The first two criteria have been chosen because they can have a lot of influence on the behavior of the exhaust intermediate pipes,on their performance and on the cost.In this structure,the weights of the criteria are calculated objectively through the entropy method in order to have an unbiased assessment.This essentially measures the quantity of information each criterion contribution,indicating the relative importance of these criteria better.Subsequently,the materials were ranked using the TOPSIS method in terms of their relative performance by measuring each material from an ideal solution to determine the best alternative.The results show that SUS 309,SUS 432L and SUS 436 LM are the first three materials that the exhaust intermediate pipe optimal design should consider.Findings–The material matrix of the decision presented in Table 3 was normalized through Equation 5,as shown in Table 5,and the matrix was multiplied with weighting criteriaß_j.The obtained weighted normalized matrix V_ij is presented in Table 6.However,the ideal,worst and best value was ascertained by employing Equation 7.This study is based on the selection of material for the development of intermediate pipe using MCDM,and it involves four basic stages,i.e.method of translation criteria,screening process,method of ranking and search for methods.The selection was done through the TOPSIS method,and the criteria weight was obtained by the entropy method.The result showed that the top three materials are SUS 309,SUS 432L and SUS 436 LM,respectively.For the future work,it is suggested to select more alternatives and criteria.The comparison can also be done by using different MCDM techniques like and Choice Expressing Reality(ELECTRE),Decision-Making Trial and Evaluation Laboratory(DEMATEL)and Preference Ranking Organization Method for Enrichment Evaluation(PROMETHEE).Originality/value–The results provide important conclusions for material selection in this targeted application,verifying the employment of mutual entropy-TOPSIS methodology for a series of difficult engineering decisions in material engineering concepts that combine superior capacity with better performance as well as cost-efficiency in various engineering design.

The Spherical q-Linear Diophantine Fuzzy Multiple-Criteria Group Decision-Making Based on Differential Measure

Spherical q-linearDiophantine fuzzy sets(Sq-LDFSs)provedmore effective for handling uncertainty and vagueness in multi-criteria decision-making(MADM).It does not only cover the data in two variable parameters but is also beneficial for three parametric data.By Pythagorean fuzzy sets,the difference is calculated only between two parameters(membership and non-membership).According to human thoughts,fuzzy data can be found in three parameters(membership uncertainty,and non-membership).So,to make a compromise decision,comparing Sq-LDFSs is essential.Existing measures of different fuzzy sets do,however,can have several flaws that can lead to counterintuitive results.For instance,they treat any increase or decrease in the membership degree as the same as the non-membership degree because the uncertainty does not change,even though each parameter has a different implication.In the Sq-LDFSs comparison,this research develops the differentialmeasure(DFM).Themain goal of the DFM is to cover the unfair arguments that come from treating different types of FSs opposing criteria equally.Due to their relative positions in the attribute space and the similarity of their membership and non-membership degrees,two Sq-LDFSs formthis preference connectionwhen the uncertainty remains same in both sets.According to the degree of superiority or inferiority,two Sq-LDFSs are shown as identical,equivalent,superior,or inferior over one another.The suggested DFM’s fundamental characteristics are provided.Based on the newly developed DFM,a unique approach tomultiple criterion group decision-making is offered.Our suggestedmethod verifies the novel way of calculating the expert weights for Sq-LDFSS as in PFSs.Our proposed technique in three parameters is applied to evaluate solid-state drives and choose the optimum photovoltaic cell in two applications by taking uncertainty parameter zero.The method’s applicability and validity shown by the findings are contrasted with those obtained using various other existing approaches.To assess its stability and usefulness,a sensitivity analysis is done.

A NovelMethod for Determining Tourism Carrying Capacity in a Decision-Making Context Using q−Rung Orthopair Fuzzy Hypersoft Environment

Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons,including leisure,pleasure,or business.A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set(q−ROFHS)to enhance the formal representation of human thought processes and evaluate tourism carrying capacity.This approach can capture the imprecision and ambiguity often present in human perception.With the advanced mathematical tools in this field,the study has also incorporated the Einstein aggregation operator and score function into the q−ROFHS values to supportmultiattribute decision-making algorithms.By implementing this technique,effective plans can be developed for social and economic development while avoiding detrimental effects such as overcrowding or environmental damage caused by tourism.A case study of selected tourism carrying capacity will demonstrate the proposed methodology.

UAV maneuvering decision-making algorithm based on deep reinforcement learning under the guidance of expert experience

Autonomous umanned aerial vehicle(UAV) manipulation is necessary for the defense department to execute tactical missions given by commanders in the future unmanned battlefield. A large amount of research has been devoted to improving the autonomous decision-making ability of UAV in an interactive environment, where finding the optimal maneuvering decisionmaking policy became one of the key issues for enabling the intelligence of UAV. In this paper, we propose a maneuvering decision-making algorithm for autonomous air-delivery based on deep reinforcement learning under the guidance of expert experience. Specifically, we refine the guidance towards area and guidance towards specific point tasks for the air-delivery process based on the traditional air-to-surface fire control methods.Moreover, we construct the UAV maneuvering decision-making model based on Markov decision processes(MDPs). Specifically, we present a reward shaping method for the guidance towards area and guidance towards specific point tasks using potential-based function and expert-guided advice. The proposed algorithm could accelerate the convergence of the maneuvering decision-making policy and increase the stability of the policy in terms of the output during the later stage of training process. The effectiveness of the proposed maneuvering decision-making policy is illustrated by the curves of training parameters and extensive experimental results for testing the trained policy.

Influence of social media on maternal decision-making and breastfeeding practices

Breastfeeding practices are influenced by multifactorial determinants including individual characteristics,external support systems,and media influences.This commentary emphasizes such complex factors influencing breastfeeding practices.Potential methodological limitations and the need for diverse sampling in studying breastfeeding practices are highlighted.Further research must explore the interplay between social influences,cultural norms,government policies,and individual factors in shaping maternal breastfeeding decisions.

Decision-Making and Management of Self-Care in Persons with Traumatic Spinal Cord Injuries: A Preliminary Study

Patients and physicians understand the importance of self-care following spinal cord injury (SCI), yet many individuals with SCI do not adhere to recommended self-care activities despite logistical supports. Neurobehavioral determinants of SCI self-care behavior, such as impulsivity, are not widely studied, yet understanding them could inform efforts to improve SCI self-care. We explored associations between impulsivity and self-care in an observational study of 35 US adults age 18 - 50 who had traumatic SCI with paraplegia at least six months before assessment. The primary outcome measure was self-reported self-care. In LASSO regression models that included all neurobehavioral measures and demographics as predictors of self-care, dispositional measures of greater impulsivity (negative urgency, lack of premeditation, lack of perseverance), and reduced mindfulness were associated with reduced self-care. Outcome (magnitude) sensitivity, a latent decision-making parameter derived from computationally modeling successive choices in a gambling task, was also associated with self-care behavior. These results are preliminary;more research is needed to demonstrate the utility of these findings in clinical settings. Information about associations between impulsivity and poor self-care in people with SCI could guide the development of interventions to improve SCI self-care and help patients with elevated risks related to self-care and secondary health conditions.

Research on Public Engineering Emergency Decision-Making Based on Multi-Granularity Language Information

To effectively deal with fuzzy and uncertain information in public engineering emergencies,an emergency decision-making method based on multi-granularity language information is proposed.Firstly,decision makers select the appropriate language phrase set according to their own situation,give the preference information of the weight of each key indicator,and then transform the multi-granularity language information through consistency.On this basis,the sequential optimization technology of the approximately ideal scheme is introduced to obtain the weight coefficient of each key indicator.Subsequently,the weighted average operator is used to aggregate the preference information of each alternative scheme with the relative importance of decision-makers and the weight of key indicators in sequence,and the comprehensive evaluation value of each scheme is obtained to determine the optimal scheme.Lastly,the effectiveness and practicability of the method are verified by taking the earthwork collapse accident in the construction of a reservoir as an example.

A Blind Spot in the Reframing of a Universe of Possibles: Towards a Suitable Model for Decision-Making Theory and A.I.

Bayesian inference model is an optimal processing of incomplete information that, more than other models, better captures the way in which any decision-maker learns and updates his degree of rational beliefs about possible states of nature, in order to make a better judgment while taking new evidence into account. Such a scientific model proposed for the general theory of decision-making, like all others in general, whether in statistics, economics, operations research, A.I., data science or applied mathematics, regardless of whether they are time-dependent, have in common a theoretical basis that is axiomatized by relying on related concepts of a universe of possibles, especially the so-called universe (or the world), the state of nature (or the state of the world), when formulated explicitly. The issue of where to stand as an observer or a decision-maker to reframe such a universe of possibles together with a partition structure of knowledge (i.e. semantic formalisms), including a copy of itself as it was initially while generalizing it, is not addressed. Memory being the substratum, whether human or artificial, wherein everything stands, to date, even the theoretical possibility of such an operation of self-inclusion is prohibited by pure mathematics. We make this blind spot come to light through a counter-example (namely Archimedes’ Eureka experiment) and explore novel theoretical foundations, fitting better with a quantum form than with fuzzy modeling, to deal with more than a reference universe of possibles. This could open up a new path of investigation for the general theory of decision-making, as well as for Artificial Intelligence, often considered as the science of the imitation of human abilities, while being also the science of knowledge representation and the science of concept formation and reasoning.

Multilevel analysis of the central-peripheral-target organ pathway:contributing to recovery after peripheral nerve injury

Peripheral nerve injury is a common neurological condition that often leads to severe functional limitations and disabilities.Research on the pathogenesis of peripheral nerve injury has focused on pathological changes at individual injury sites,neglecting multilevel pathological analysis of the overall nervous system and target organs.This has led to restrictions on current therapeutic approaches.In this paper,we first summarize the potential mechanisms of peripheral nerve injury from a holistic perspective,covering the central nervous system,peripheral nervous system,and target organs.After peripheral nerve injury,the cortical plasticity of the brain is altered due to damage to and regeneration of peripheral nerves;changes such as neuronal apoptosis and axonal demyelination occur in the spinal cord.The nerve will undergo axonal regeneration,activation of Schwann cells,inflammatory response,and vascular system regeneration at the injury site.Corresponding damage to target organs can occur,including skeletal muscle atrophy and sensory receptor disruption.We then provide a brief review of the research advances in therapeutic approaches to peripheral nerve injury.The main current treatments are conducted passively and include physical factor rehabilitation,pharmacological treatments,cell-based therapies,and physical exercise.However,most treatments only partially address the problem and cannot complete the systematic recovery of the entire central nervous system-peripheral nervous system-target organ pathway.Therefore,we should further explore multilevel treatment options that produce effective,long-lasting results,perhaps requiring a combination of passive(traditional)and active(novel)treatment methods to stimulate rehabilitation at the central-peripheral-target organ levels to achieve better functional recovery.

Crossing the Achilles Heel of Algorithms:Identifying the Developmental Dilemma of Artificial Intelligence-Assisted Judicial Decision-Making

In the developmental dilemma of artificial intelligence(AI)-assisted judicial decision-making,the technical architecture of AI determines its inherent lack of transparency and interpretability,which is challenging to fundamentally improve.This can be considered a true challenge in the realm of AI-assisted judicial decision-making.By examining the court’s acceptance,integration,and trade-offs of AI technology embedded in the judicial field,the exploration of potential conflicts,interactions,and even mutual shaping between the two will not only reshape their conceptual connotations and intellectual boundaries but also strengthen the cognition and re-interpretation of the basic principles and core values of the judicial trial system.

Role of copper in central nervous system physiology and pathology

Copper is a transition metal and an essential element for the organism,as alterations in its homeostasis leading to metal accumulation or deficiency have pathological effects in several organs,including the central nervous system.Central copper dysregulations have been evidenced in two genetic disorders characterized by mutations in the copper-ATPases ATP7A and ATP7B,Menkes disease and Wilson’s disease,respectively,and also in multifactorial neurological disorders such as Alzheimer’s disease,Parkinson’s disease,amyotrophic lateral sclerosis,and multiple sclerosis.This review summarizes current knowledge about the role of copper in central nervous system physiology and pathology,reports about unbalances in copper levels and/or distribution under disease,describes relevant animal models for human disorders where copper metabolism genes are dysregulated,and discusses relevant therapeutic approaches modulating copper availability.Overall,alterations in copper metabolism may contribute to the etiology of central nervous system disorders and represent relevant therapeutic targets to restore tissue homeostasis.

Using multi-criteria decision-making and machine learning for football player selection and performance prediction:a systematic review

Evaluating and selecting players to suit football clubs and decision-makers (coaches, managers, technical, and medical staff) is a difficult process from a managerial-financial and sporting perspective. Football is a highly competitive sport where sponsors and fans are attracted by success. The most successful players, based on their characteristics (criteria and sub-criteria), can influence the outcome of a football game at any given time. Consequently, the D-day of selection should employ a more appropriate approach to human resource management. To effectively address this issue, a detailed study and analysis of the available literature are needed to assist practitioners and professionals in making decisions about football player selection and hiring. Peer-reviewed journals were selected for collecting published papers between 2018 and 2023. A total of 66 relevant articles (journal articles, conference articles, book sections, and review articles) were selected for evaluation and analysis. The purpose of the study is to present a systematic literature review (SLR) on how to solve this problem and organize the published research papers that answer our four research questions.

Meningeal lymphatic vessel crosstalk with central nervous system immune cells in aging and neurodegenerative diseases

Meningeal lymphatic vessels form a relationship between the nervous system and periphery, which is relevant in both health and disease. Meningeal lymphatic vessels not only play a key role in the drainage of brain metabolites but also contribute to antigen delivery and immune cell activation. The advent of novel genomic technologies has enabled rapid progress in the characterization of myeloid and lymphoid cells and their interactions with meningeal lymphatic vessels within the central nervous system. In this review, we provide an overview of the multifaceted roles of meningeal lymphatic vessels within the context of the central nervous system immune network, highlighting recent discoveries on the immunological niche provided by meningeal lymphatic vessels. Furthermore, we delve into the mechanisms of crosstalk between meningeal lymphatic vessels and immune cells in the central nervous system under both homeostatic conditions and neurodegenerative diseases, discussing how these interactions shape the pathological outcomes. Regulation of meningeal lymphatic vessel function and structure can influence lymphatic drainage, cerebrospinal fluid-borne immune modulators, and immune cell populations in aging and neurodegenerative disorders, thereby playing a key role in shaping meningeal and brain parenchyma immunity.