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.

Impact of Artificial Intelligence on Corporate Leadership

Artificial Intelligence (AI) is transforming organizational dynamics, and revolutionizing corporate leadership practices. This research paper delves into the question of how AI influences corporate leadership, examining both its advantages and disadvantages. Positive impacts of AI are evident in communication, feedback systems, tracking mechanisms, and decision-making processes within organizations. AI-powered communication tools, as exemplified by Slack, facilitate seamless collaboration, transcending geographical barriers. Feedback systems, like Adobe’s Performance Management System, employ AI algorithms to provide personalized development opportunities, enhancing employee growth. AI-based tracking systems optimize resource allocation, as exemplified by studies like “AI-Based Tracking Systems: Enhancing Efficiency and Accountability.” Additionally, AI-powered decision support, demonstrated during the COVID-19 pandemic, showcases the capability to navigate complex challenges and maintain resilience. However, AI adoption poses challenges in human resources, potentially leading to job displacement and necessitating upskilling efforts. Managing AI errors becomes crucial, as illustrated by instances like Amazon’s biased recruiting tool. Data privacy concerns also arise, emphasizing the need for robust security measures. The proposed solution suggests leveraging Local Machine Learning Models (LLMs) to address data privacy issues. Approaches such as federated learning, on-device learning, differential privacy, and homomorphic encryption offer promising strategies. By exploring the evolving dynamics of AI and leadership, this research advocates for responsible AI adoption and proposes LLMs as a potential solution, fostering a balanced integration of AI benefits while mitigating associated risks in corporate settings.

Behavioral Decision-Making of Key Stakeholders in Public-Private Partnerships:A Hybrid Method and Benefit Distribution Study

Public-private partnerships(PPPs)have been used by governments around the world to procure and construct infrastructural amenities.It relies on private sector expertise and funding to achieve this lofty objective.However,given the uncertainties of project management,transparency,accountability,and expropriation,this phenomenon has gained tremendous attention in recent years due to the important role it plays in curbing infrastructural deficits globally.Interestingly,the reasonable benefit distribution scheme in a PPP project is related to the behavior decisionmaking of the government and social capital,aswell as the performance of the project.In this paper,the government and social capital which are the key stakeholders of PPP projects were selected as the research objects.Based on the fuzzy expected value model and game theory,a hybrid method was adopted in this research taking into account the different risk preferences of both public entities and private parties under the fuzzy demand environment.To alleviate the problem of insufficient utilization of social capital in a PPP project,this paper seeks to grasp the relationship that exists between the benefit distribution of stakeholders,their behavioral decision-making,and project performance,given that they impact the performance of both public entities and private parties,as well as assist in maximizing the overall utility of the project.Furthermore,four game models were constructed in this study,while the expected value and opportunity-constrained programming model for optimal decision-making were derived using alternate perspectives of both centralized decision-making and decentralized decision-making.Afterward,the optimal behavioral decision-making of public entities and private parties in four scenarios was discussed and thereafter compared,which led to an ensuing discussion on the benefit distribution system under centralized decision-making.Lastly,based on an example case,the influence of different confidence levels,price,and fuzzy uncertainties of PPP projects on the equilibrium strategy results of both parties were discussed,giving credence to the effectiveness of the hybrid method.The results indicate that adjusting different confidence levels yields different equilibriumpoints,and therefore signposts that social capital has a fair perception of opportunities,as well as identifies reciprocal preferences.Nevertheless,we find that an increase in the cost coefficient of the government and social capital does not inhibit the effort of both parties.Our results also indicate that a reasonable benefit distribution of PPP projects can assist them in realizing optimum Pareto improvements over time.The results provide us with very useful strategies and recommendations to improve the overall performance of PPP projects in China.

Human-Like Decision-Making of Autonomous Vehicles in Dynamic Traffic Scenarios

With the maturation of autonomous driving technology, the use of autonomous vehicles in a socially acceptable manner has become a growing demand of the public. Human-like autonomous driving is expected due to the impact of the differences between autonomous vehicles and human drivers on safety.Although human-like decision-making has become a research hotspot, a unified theory has not yet been formed, and there are significant differences in the implementation and performance of existing methods. This paper provides a comprehensive overview of human-like decision-making for autonomous vehicles. The following issues are discussed: 1) The intelligence level of most autonomous driving decision-making algorithms;2) The driving datasets and simulation platforms for testing and verifying human-like decision-making;3) The evaluation metrics of human-likeness;personalized driving;the application of decisionmaking in real traffic scenarios;and 4) The potential research direction of human-like driving. These research results are significant for creating interpretable human-like driving models and applying them in dynamic traffic scenarios. In the future, the combination of intuitive logical reasoning and hierarchical structure will be an important topic for further research. It is expected to meet the needs of human-like driving.

On the role of impulsivity and decision-making in suicidal behavior

Suicide risk constitutes a complex set of interacting demographic, clinical, psychobiological and environmental variables. Impulsivity is a long-known risk factor for suicide attempts. However, research based on clearer conceptual refinement in this area is imperative. One emerging field of study is that of decision-making. Impulsivity involves a failure of higher-order control, including decision-making. Using standardized operational definitions that take into consideration relevant aspects of impulsivity, including state- and trait-components and a deeper understanding of the process of decision-making in the suicidal mind, we may come a step closer to understanding suicidality and winning the fight in this scourge of human suffering.

Modeling and TOPSIS-GRA Algorithm for Autonomous Driving Decision-Making Under 5G-V2X Infrastructure

This paper is to explore the problems of intelligent connected vehicles(ICVs)autonomous driving decision-making under a 5G-V2X structured road environment.Through literature review and interviews with autonomous driving practitioners,this paper firstly puts forward a logical framework for designing a cerebrum-like autonomous driving system.Secondly,situated on this framework,it builds a hierarchical finite state machine(HFSM)model as well as a TOPSIS-GRA algorithm for making ICV autonomous driving decisions by employing a data fusion approach between the entropy weight method(EWM)and analytic hierarchy process method(AHP)and by employing a model fusion approach between the technique for order preference by similarity to an ideal solution(TOPSIS)and grey relational analysis(GRA).The HFSM model is composed of two layers:the global FSM model and the local FSM model.The decision of the former acts as partial input information of the latter and the result of the latter is sent forward to the local pathplanning module,meanwhile pulsating feedback to the former as real-time refresh data.To identify different traffic scenarios in a cerebrum-like way,the global FSM model is designed as 7 driving behavior states and 17 driving characteristic events,and the local FSM model is designed as 16 states and 8 characteristic events.In respect to designing a cerebrum-like algorithm for state transition,this paper firstly fuses AHP weight and EWM weight at their output layer to generate a synthetic weight coefficient for each characteristic event;then,it further fuses TOPSIS method and GRA method at the model building layer to obtain the implementable order of state transition.To verify the feasibility,reliability,and safety of theHFSMmodel aswell as its TOPSISGRA state transition algorithm,this paper elaborates on a series of simulative experiments conducted on the PreScan8.50 platform.The results display that the accuracy of obstacle detection gets 98%,lane line prediction is beyond 70 m,the speed of collision avoidance is higher than 45 km/h,the distance of collision avoidance is less than 5 m,path planning time for obstacle avoidance is averagely less than 50 ms,and brake deceleration is controlled under 6 m/s2.These technical indexes support that the driving states set and characteristic events set for the HFSM model as well as its TOPSIS-GRA algorithm may bring about cerebrum-like decision-making effectiveness for ICV autonomous driving under 5G-V2X intelligent road infrastructure.

Summarizing vehicle driving decision-making methods on vulnerable road user collision avoidance

With the development of intelligent vehicles and autonomous driving technology,the safety of vulnerable road user(VRU)in traffic has been more guaranteed,and many research achievements have been made in the key area of collision avoidance decision-making methods.In this paper,the knowledge mapping method is used to mine the available literature in depth,and it is found that the research focus has shifted from the traditional accident cause analysis to emerging deep learning and virtual reality technology.This paper summarizes research on the three core dimensions of environmental perception,behavior cognition and collision avoidance decision-making in intelligent vehicle systems.In terms of perception,accurate identification of pedestrians and cyclists in complex environments is a major demand for VRU perception;in terms of behavior cognition,the coupling of VRU intention identification and motion trajectory prediction and other multiple factors needs further research;in terms of decision-making,the intention identification and trajectory prediction of collision objects are not included in the risk assessment model,and there is a lack of exploration specifically for cyclists'collision risk.On this basis,this paper provides guidance for the improvement of traffic safety of contemporary VRU under the conditions of intelligent and connected transportation.

Unlocking the Network Traffic Password of the Sudden Popularity of Oriental Selection:Consumer Behavior Decision-Making Mechanism Based on Mental Accounting Theory

This paper explores the decision-making mechanism of the consuming behavior hidden behind the sudden popularity of the Oriental Selection Company in terms of the mental accounting theory.Firstly,according to the“Non-alternative”characteristics of mental accounting,this paper expounds how the strategy of the bilingual live-streaming of Oriental Selection promoters stimulates consumers’desire to buy the advertised products and services whilst using the utility theory of mental accounting to analyze how Oriental Selection promoters improve consumers’acquisition utility and total utility.Secondly,we sum up the successful experiences of Oriental Selection:The live-streaming industry should apply the theory of mental accounting in effectively overcoming the shortcomings of the live-streamed marketing by stimulating consumers’desire and influencing their decision-making behavior through the streaming of content that triggers them to make purchases.This is achievable by abandoning the traditional ways of loudly urging consumers to buy goods.Finally,this paper puts forward some suggestions on how to use the mental accounting theory in promoting sustainable consumption and points out the prospects for Oriental Selection.

Analysis of Influencing Factors Tourists1 Purchase Decision-making Behavior in Kunming

Based on the theory of consumer behavior,this paper analyzes the current situation of tourism shopping market in Kunming,and analyzes the decision-making behavior of tourists shopping in Kunming with the questionnaire survey,and clarifies the influencing factors of the decision-making behavior of visitors to Kunming.In the future,the influencing factors of Kunming tourists'shopping decision-making behavior are combined with the current situation of Kunming's tourism shopping market.The problems of cheating-induced shopping,the high price of shopping products,the low level of tourism shopping experience and the imperfect after-sales service are analyzed.Finally,the corresponding countermeasures and suggestions are proposed from four aspects:rectifying the tourism shopping market,establishing a sound price supervision mechanism,strengthening the tourism shopping experience,and improving after-sales service.

COLLISION AVOIDANCE DECISION-MAKING MODEL OF MULTI-AGENTS IN VIRTUAL DRIVING ENVIRONMENT WITH ANALYTIC HIERARCHY PROCESS

Collision avoidance decision-making models of multiple agents in virtual driving environment are studied. Based on the behavioral characteristics and hierarchical structure of the collision avoidance decision-making in real life driving, delphi approach and mathematical statistics method are introduced to construct pair-wise comparison judgment matrix of collision avoidance decision choices to each collision situation. Analytic hierarchy process （AHP） is adopted to establish the agents＇ collision avoidance decision-making model. To simulate drivers＇ characteristics, driver factors are added to categorize driving modes into impatient mode, normal mode, and the cautious mode. The results show that this model can simulate human＇s thinking process, and the agents in the virtual environment can deal with collision situations and make decisions to avoid collisions without intervention. The model can also reflect diversity and uncertainly of real life driving behaviors, and solves the multi-objective, multi-choice ranking priority problem in multi-vehicle collision scenarios. This collision avoidance model of multi-agents model is feasible and effective, and can provide richer and closer-to-life virtual scene for driving simulator, reflecting real-life traffic environment more truly, this model can also promote the practicality of driving simulator.

Neural mechanism of proposer's decision-making in the ultimatum and dictator games

Previous studies have demonstrated that reactions to unfair offers in the ultimatum game are correlated with negative emotion. However, little is known about the difference in neural activity between a proposer＇s decision-making in the ultimatum game compared with the dictator game. The present functional magnetic resonance imaging study revealed that proposing fair offers in the dictator game elicited greater activation in the right supramarginal gyrus, right medial frontal gyrus and left anterior cingulate cortex compared with proposing fair offers in the ultimatum game in 23 Chinese undergraduate and graduate students from Beijing Normal University in China. However, greater activation was found in the right superior temporal gyrus and left cingulate gyrus for the reverse contrast. ＂The results indicate that proposing fair offers in the dictator game is more strongly associated with cognitive control and conflicting information processing compared with proposing fair offers in the ultimatum game.

Empirical Analysis of Impact of Conversion ofConvertible Bonds on Corporate Performance of Different Industries in China

We apply the methods of principle component analysis (PCA) and Wilcoxon’s signed rank test (WSRT) to study influence of conversion of convertible bonds on corporate performance of different industries in China. In this paper, 33 convertible bonds that were issued by non-financial sectors from 2002 to 2009 in China with the convertible rate more than 50% within a year were selected as a research sample. In order to provide a reference for the parties concerned, the influence of conversion of convertible bonds on corporate performance in different industries was studied from the industrial properties point of review.

A Theoretical Approach to the Effective Decision-Making Process

This research develops and elaborates studies done for a contribution to the 2019 PIC International Conference 2019 in Malta, about the decision-making process. Decision-making is the act of choosing between two or more courses of action. In the wider process of problem-solving, decision-making involves choosing between possible solutions to a problem, and these decisions can be made through either an intuitive or reasoned process, or a combination of the two. The study of decision-making processes, to be understood as the role of human factors, becomes particularly interesting in complex organizations. This research aims to analyze how an effective team, within organizations, can develop a more correct and effective decision-making, in order to get an optimal solution, overcoming the typical uncertainty. The paper describes the point of departure of decision in complex, time-pressured, uncertain, ambiguous and changing environments. The use of a leading case (the Tenerife air accident, 1977), will lead us to the desired results, i.e. to demonstrate how an effective decisional process, including team dynamics, can be useful to reduce the risk, present in all decisions, and reduce errors. The case of Tenerife air disaster, confirm our research. In that case, in fact, the group dynamics prove not to have worked. Thus, we can state that if a team approach had been followed instead of a more individual one, the results would probably have been different. The central belief of the research, is that classic decision theory could benefit from a team approach, which reduces the risk that a decision may lead to undesirable consequences. As demonstrated with the case study, within organizations, the decision-making is not a solitary action. Decisions, in fact, are made within a team and in order to be able to function effectively in a group, and manage group situations, there are essential skills. The team can then become a resource for the decisional process and problem solving, but it is necessary to understand the dynamics.

Regional Dialect Diversity and Corporate Strategic Aggressiveness

As an important role of the regional culture,dialects have a significant influence on the corporate strategy.Diversity of dialects appears to move firms towards strategic radicalization by weakening the“hometown effect”,enhancing innovation awareness,and increasing the diversification of business.Using A-share listed companies from 2006 to 2018 as sample,based on information from the annual financial statements,this paper argues that higher dialect diversity in the city where enterprise is located leads the corporate strategy to be more aggressive;and the mismatch between strategy and dialectal environment will have a negative effect on corporate performance.After a series of robustness tests and using instrumental variables to solve endogenous problems,the conclusions remain consistent.Further study indicates that the influence of dialect diversity on strategy is enhanced as managers’awareness of the dialectal environment increases,but is weaken if the enterprise is a state-owned enterprise or has larger scale.This research reveals the connection between dialect culture and corporate behavior form a strategic level,provides empirical evidence and enriches the literature for the traditional view of“culture influences strategy”,and has significance on promoting the corporate strategic management practices.

机构投资者持股会助推企业“漂绿”吗--基于重污染企业社会责任报告披露的实证研究

以中国A股上市的455家重污染企业为样本,探究机构投资者与企业“漂绿”的关系。实证结果表明,机构投资者持股比例越高,企业发布社会责任报告的可能性越大,但报告内容披露的完整程度会降低,即机构投资者持股助推企业“漂绿”。机制分析表明,机构投资者持股可以通过提升公司盈利能力,助推企业“漂绿”,但通过降低公司负债水平来抑制企业“漂绿”。进一步分析发现,2016年绿色金融政策发布后,机构投资者持股对企业“漂绿”的助推效果减弱;机构投资者持股对国企“漂绿”行为的助推效果不明显。建议提升对公司绿色治理的监督力度,促使公司减少“漂绿”行为,更好地维护投资者的利益和社会效益。

明星分析师关注、企业投资行为与公司价值

企业投资行为对于中国经济高质量发展与“双循环”国家战略实施发挥重要作用。通过手工搜集2016—2021年明星分析师研究报告数据,研究明星分析师对公司投资行为的影响。研究发现,明星分析师通过缓解融资约束、增强股票流动性、解读研发行为,促进企业投资行为,而且明星分析师在管理层持股比例较低、大股东持股比例低、成长性水平较低、公司透明度较低的样本中,结果更加显著,体现其较好的专业胜任能力。进一步分析发现,明星分析师能够解读不同盈利分组的投资行为,不存在明显的选择性偏好;与没有明星分析师跟踪的样本相比,明星分析师跟踪对投资行为具有更强的影响;明星分析师通过正向解读投资行为途径,对公司价值挖掘,发挥积极作用。研究结论对发挥明星分析师信息中介作用,优化投资结构与提升投资绩效,提供经验参考。

感激导向人力资源管理实践的驱动机制及其作用机理

目前关于战略人力资源管理实践的研究过度关注结果导向而忽视了员工福祉,员工作为企业最重要的资源,其付出需要组织给予认可与肯定。然而,感激导向人力资源管理实践是由何种因素形成,又会给组织、部门及个体带来何种影响,既有文献缺乏系统思考和实证研究。鉴于此,本文将探究中国情境下感激导向人力资源管理实践的驱动机制与作用机理。首先,探讨感激导向人力资源管理实践的形成因素。其次,探讨感激导向人力资源管理实践对组织韧性与企业社会责任的影响机制。再次,探讨感激导向人力资源管理实践对领导感激表达的影响机制。最后,探讨感激导向人力资源管理实践对员工帮助行为的影响机制。本文拓展了工作场所组织感激的研究视角,为组织优化人力资源管理实践提供参考和借鉴。