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.

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.

Factors of Marketing Toward Customer Decision-Making in Cross-Border E-Commerce:Case Study of QD Company

Taking small cross-border e-commerce enterprise QD company as an example,this study targets customers consuming on the platform of cross-border e-commerce enterprise QD company,and the purpose is to study the marketing factors affecting customer decision-making.By combining with relevant literature,this paper preliminarily confirmed that 7Ps,Technology Acceptance Model(TAM),and customer decision-making theory as the theoretical basis,and established the theoretical model.According to the mature scale of domestic and foreign scholars to measure the research variables,with 7Ps and TAM as the independent variable,TAM perceived usefulness and ease of use as the core of the independent variable,customer decision-making as the dependent variable,in the test before the project,forming a formal questionnaire,the main data are obtained through the design and distribution of the questionnaire.Based on a large number of relevant literature,this paper examines the marketing factors influencing customer decisions in cross-border e-commerce,then identifies goals,builds models,and proposes hypotheses.According to the technical acceptance model in consumer behavior theory,select the cross-border e-commerce enterprise quantum dot platform of consumers as the research object,finally through SPSS software on cross-border consumer feedback,including descriptive statistical analysis,factor analysis,correlation analysis,regression analysis test customer satisfaction,finally found a significant positive relationship between customer decision-making and marketing mix and TAM variables.

Marketing Factors Influencing Customer Decision-Making in the Interior Decorations Industry in Guangxi,China

The purpose of this study was to empirically examine the influence of marketing mix towards customer decision-making in the interior decoration industry of Guangxi,China.The interior decoration industry in Guangxi is growing at rapid pace.Therefore,this study is specially helpful for the interior decoration industry in Guangxi,China,and the interior decoration industry in Guangxi can use marketing mix for their business gained.In order to carry out this study,the population was taken to be those customers of the interior decoration.A sample(N=425)customers were taken using simple random sampling from the interior decoration consumers of Guangxi,China.It was hypothesized that marketing mix had positive influence on customer decision-making.It was hypothesized that marketing mix predicts customer decision-making.The results were analyzed with help SPSS software.Descriptive statistical analysis,Pearson correlation test,and regression analysis were used to test hypothesis.The results showed significant positive relationship and marketing mix was a predictor of customer decision-making.This research is significant and useful for all the interior decoration enterprises in Guangxi.

Optimization and coordination of South-to-North Water Diversion supply chain with strategic customer behavior

The South-to-North Water Diversion （SNWD） Project is a significant engineering project meant to solve water shortage problems in North China. Faced with market operations management of the water diversion system, this study defined the supply chain system for the SNWD Project, considering the actual project conditions, built a decentralized decision model and a centralized decision model with strategic customer behavior （SCB） using a floating pricing mechanism （FPM）, and constructed a coordination mechanism via a revenue-sharing contract. The results suggest the following： （1） owing to water shortage supplements and the excess water sale policy provided by the FPM, the optimal ordering quantity of water resources is less than that without the FPM, and the optimal profits of the whole supply chain, supplier, and external distributor are higher than they would be without the FPM; （2） wholesale pricing and supplementary wholesale pricing with SCB are higher than those without SCB, and the optimal profits of the whole supply chain, supplier, and external distributor are higher than they would be without SCB; and （3） considering SCB and introducing the FPM help increase the optimal profits of the whole supply chain, supplier, and external distributor, and improve the efficiency of water resources usage.

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.

The Impact of Fitness between Consumers and Product-harm Crisis on the Online Dysfunctional Customer Behavior

With the popularization of the Internet,this technology has changed the form of information dissemination. The information will spread quickly when food product harm crisis happens,and the phenomenon that information of product-harm spills over is very common. This article is based on " Frustration- Misbehaving" research paradigm,combined with the information dissemination motivation theory,discusses that the fitness between the consumers and the crisis will promote consumers' dysfunctional customer behavior. The study found that the fitness between the consumers and the crisis has significant effect on consumers' dysfunctional customer behavior,perceived social damage has a mediator effect between fitness and consumers' misbehavior,and the moderating effect of consumers' aggression tendency is significant,but not the emotional tendency. The theoretical contribution and valuable implications of findings for communication of crisis management are discussed at last.

The Effect of Service Quality on Behavioral Loyalty Through Customer Satisfaction and Attitudinal Loyalty in the 4-Star Hotel in East Java, Indonesia

This research was conducted to test and analyze the influence of service quality on behavioral loyalty through customer satisfaction and attitudinal loyalty to 4-Star Hotel in East Java.This research is a survey research.The sampling technique used is non probability sampling.Sample sizes are 200 respondents.The analysis tool used is Structural Equation Model(SEM)Analysis.The result of the research shows that hypothesis proposed in this research is accepted.Academic suggestion submitted is for research that will come dating need to be explored influence of other variables that influence behavioral loyalty.While practical suggestions submitted to business actors is for business actors to pay attention and keep the variables that effect behavioral loyalty.Especially studied in this study they are:service quality,customer satisfaction,and attitudinal loyalty.

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.

Customer Joining Behavior and Regulation Strategy in Visible Multi-Server Queues with Synchronous Vacations of Partial Servers

This paper studies customer joining behavior and system regulation strategy in nonexhaustive visible M/M/m queues with synchronous vacations of a part of the servers.Once this part of the servers are idle,they take multiple vacations simultaneously(vacation period).Until there are customers waiting in the queue,they are reactivated and all servers are busy or idle(busy period).The authors call this part of the servers as“partial servers”.In view of the fully visible queue and the almost visible queue,the authors obtain customers’equilibrium joining threshold strategies and their socially optimal joining threshold strategies,respectively,and observe that customer joining behavior in equilibrium generally makes the system overcrowded,which makes the equilibrium social welfare lower than the optimal social welfare.After regulation,interestingly,for optimizing social welfare,the system manager hopes not only customers arriving in vacation period pay attention to the number of partial servers,but also customers arriving in busy period should care about it rather than ignore.Moreover,arranging more servers for vacation does not necessarily lead to the decrease of social welfare on condition that the number of partial servers is close to m.As for the information advantage of the fully visible case,it is not obvious for increasing social welfare and even unfavorable to servers’profit unless the number of partial servers is big enough.Furthermore,given the different composition of social welfare,there exists the optimal number of partial servers and the optimal arrival rate of customers for maximizing social welfare.

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.

Empirical Study of Brand-Customer Relationship Based on Customer Lock-In in Chinese Banking

The central construct of customer lock-in （CL） is measured and its role along with that of consumer loyalty in influencing the brand-customer relationship is tested. Using data collected from focus groups, a measurement model for CL is developed, and a structural equation model consisted based on literature review and our own theory is established. Moreover, the moderating effects of CL on the relationship between perceived value （PV） and brand relationship quality （BRQ） , as well as that between BRQ and brand loyalty （BL） based on data collected through a survey have been tested. Results indicate that consumer satisfaction is a critical factor in establishing brand-customer relationship, and the attitudinal brand loyalty is the key to obtain brand behavioral loyalty. Furthermore, CL tactics, such as decreasing consumers＇ learning cost and transactional cost facilitate the relationship building between customer and brand, while involuntary lock-in may have an adverse effect in the relationship building process. In addition, involuntary lock-in and loyalty program help in obtaining consumers＇ behavioral brand loyalty but not their attitudinal loyalty.

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.

Customer Prioritization for Medical Supply Chain During COVID-19 Pandemic

During COVID-19,the escalated demand for various pharmaceutical products with the existing production capacity of pharmaceutical companies has stirred the need to prioritize its customers in order to fulfill their demand.This study considers a two-echelon pharmaceutical supply chain considering various pharma-distributors as its suppliers and hospitals,pharmacies,and retail stores as its customers.Previous studies have generally considered a balanced situation in terms of supply and demand whereas this study considers a special situation of COVID-19 pandemic where demand exceeds supply Various criteria have been identified from the literature that influences the selection of customers.A questionnaire has been developed to collect primary data from pharmaceutical suppliers pertaining to customerselection criteria.These criteria have been prioritized with respect to eigenvalues obtained from Principal Component Analysis and also validated with the experts’domain-related knowledge using Analytical Hierarchy Process.Profit potential appeared to be the most important criteria of customer selection followed by trust and service convenience brand loyalty,commitment,brand awareness,brand image,sustainable behavior,and risk.Subsequently,Multi Criteria Decision Analysis has been performed to prioritize the customerselection criteria and customers with respect to selection criteria.Three experts with seven and three and ten years of experience have participated in the study.Findings of the study suggest large hospitals,large pharmacies,and small retail stores are the highly preferred customers.Moreover,findings of prioritization of customer-selection criteria fromboth Principal Component Analysis and Analytical Hierarchy Process are consistent.Furthermore,this study considers the experience of three experts to calculate an aggregate score of priorities to reach an effective decision.Unlike traditional supply chain problems of supplier selection,this study considers a selection of customers and is useful for procurement and supply chain managers to prioritize customers while considering multiple selection criteria.

Customer Tiered Purchase Forecast by Mobile Edge Computing Based on Pareto/NBD and SVR

Mobile edge computing is trending nowadays for its computation efficiency and privacy.The rapid development of e-commerce show great interest in mobile edge computing due to numerous rise of small and middle-sized enterprises(SMEs)in the internet.This paper predicts the overall sales volume of the enterprise through the classic ARIMA model,and notes that the behavior and arrival differences between the new and old customer groups will affect the accuracy of our forecasts,so we then use Pareto/NBD to explore the repeated purchases of customers at the individual level of the old customer and the SVR model to predict the arrival of new customers,thus helping the enterprise to make layered and accurate marketing of new and old customers through machine learning.In general,machine learning relies on powerful computation and storage resources,while mobile edge computing typically provides limited computation resources locally.Therefore,it is essential to combine machine learning with mobile edge computing to further promote the proliferation of data analysis among SMEs.