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.

Non-Cooperative Behavior Management in Large-Scale Group Decision-Making Considering the Altruistic Behaviors of Experts and Its Application in Emergency Alternative Selection

Emergency decision-making problems usually involve many experts with different professional backgrounds and concerns,leading to non-cooperative behaviors during the consensus-reaching process.Many studies on noncooperative behavior management assumed that the maximumdegree of cooperation of experts is to totally accept the revisions suggested by the moderator,which restricted individuals with altruistic behaviors to make more contributions in the agreement-reaching process.In addition,when grouping a large group into subgroups by clustering methods,existing studies were based on the similarity of evaluation values or trust relationships among experts separately but did not consider them simultaneously.In this study,we introduce a clustering method considering the similarity of evaluation values and the trust relations of experts and then develop a consensusmodel taking into account the altruistic behaviors of experts.First,we cluster experts into subgroups by a constrained Kmeans clustering algorithm according to the opinion similarity and trust relationship of experts.Then,we calculate the weights of experts and clusters based on the centrality degrees of experts.Next,to enhance the quality of consensus reaching,we identify three kinds of non-cooperative behaviors and propose corresponding feedback mechanisms relying on the altruistic behaviors of experts.A numerical example is given to show the effectiveness and practicality of the proposed method in emergency decision-making.The study finds that integrating altruistic behavior analysis in group decision-making can safeguard the interests of experts and ensure the integrity of decision-making information.

Research on Maneuver Decision-Making of Multi-Agent Adversarial Game in a Random Interference Environment

The strategy evolution process of game players is highly uncertain due to random emergent situations and other external disturbances.This paper investigates the issue of strategy interaction and behavioral decision-making among game players in simulated confrontation scenarios within a random interference environment.It considers the possible risks that random disturbances may pose to the autonomous decision-making of game players,as well as the impact of participants’manipulative behaviors on the state changes of the players.A nonlinear mathematical model is established to describe the strategy decision-making process of the participants in this scenario.Subsequently,the strategy selection interaction relationship,strategy evolution stability,and dynamic decision-making process of the game players are investigated and verified by simulation experiments.The results show that maneuver-related parameters and random environmental interference factors have different effects on the selection and evolutionary speed of the agent’s strategies.Especially in a highly uncertain environment,even small information asymmetry or miscalculation may have a significant impact on decision-making.This also confirms the feasibility and effectiveness of the method proposed in the paper,which can better explain the behavioral decision-making process of the agent in the interaction process.This study provides feasibility analysis ideas and theoretical references for improving multi-agent interactive decision-making and the interpretability of the game system model.

Don’t forget emergency surgery! Lessons to learn from elective indocyanine green-guided gastrointestinal interventions

Fluorescence-based imaging has found application in several fields of elective surgery,but there is still a lack of evidence in the literature about its use in the emergency setting.Clinical trials have consistently shown that indocyanine green(ICG)-guided surgery can dramatically reduce the risk of postoperative complic-ations,length of in-hospital stay and total healthcare costs in the elective setting.It is well-known that emergency surgery has a higher complication rate than its elective counterpart,therefore an impelling need for research studies to explore,validate and develop this issue has been highlighted.The present editorial aims to provide a critical overview of currently available applications and pitfalls of ICG fluorescence in abdominal emergencies.Furthermore,we evidenced how the experience of ICG-fluorescence in elective surgery might be of great help in implementing its use in acute situations.In the first paragraph we analyzed the tips and tricks of ICG-guided cancer surgery that might be exploited in acute cases.We then deepened the two most described topics in ICG-guided emergency surgery:Acute cholecystitis and intestinal ischemia,focusing on both the advantages and limitations of green-fluorescence application in these two fields.In emergency situations,ICG fluorescence demonstrates a promising role in preventing undue intestinal resections or their entity,facilitating the detection of intestinal ischemic zones,identifying biliary tree anatomy,reducing post-operative complications,and mitigating high mortality rates.The need to improve its application still exists,therefore we strongly believe that the elective and routinary use of the dye is the best way to acquire the necessary skills for emer-gency procedures.

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.

Information Aggregation of Group Decision-Making in Emergency Events

Information is a key factor in emergency management, which helps decision makers to make effective decisions. In this paper, aiming at clarifying the information aggregation laws, and according to the characteristic of emergency information, information relative entropy is applied in the information aggregation to establish the information aggregation model of emergency group decision-making. The analysis shows that support and credibility of decision rule are the two factors in information aggregation. The results of four emergency decision-making groups in case study support the analysis in the paper.

Characteristics of Decision-Making by Family Members When Older Adults Are Hospitalized in Emergency Cases with Life-Threatening Conditions

Aim: This study aims to elucidate decision-making characteristics based on interviews with family members with experience in having to select treatments for older adult patients who have been hospitalized following emergency transport to the hospital. Design: Semi-structured interviews were conducted with 10 individuals with experience in surrogate decision-making for an older adult family member. Methods: The recorded interview data were transcribed verbatim, divided into minimum semantic units, and coded. Next, categories and subcategories were abstracted. A comparison was made with the conceptual constructs of a previous study that examined decision-making by families in a life-threatening crisis. Results: Four categories were extracted from 489 antecedents, 370 attributes, and 388 consequences. One new category was abstracted for each of: 1) antecedents: observing abnormalities and responding, while being worried about death;2) attributes: deliberating on the patient prognosis, the relationship with the patient, and what they believe the patient would want;and 3) consequences: continuing support during convalescence. It is desirable to provide support based on the characteristics of families of older adults, including considering the psychological burden on the families who make surrogate decisions, and also the burden of subsequent caregiving because it is not and in the present environment has not been common for patients to express their wishes beforehand.

A Method of Emergency Volunteer Team Internal Participation in Rescue Decision-Making Considering Psychological Behavior

A method for internal participation in rescue decision-making of emergency volunteer teams considering psychological behavior is proposed to address the time sequence of rescue tasks.Firstly,the problem of multi-tasking and multi-operation within the emergency volunteer team is described.Secondly,considering that task leaders are influenced by behavioral and psychological factors in the evaluation,the required time for the job is used as a reference point,and the expected time that volunteers can complete the job is used as an attribute value.The task leader's prospect satisfaction value for each volunteer is calculated based on prospect theory,and the perceived utility values of disappointment theory and regret theory are calculated to measure the task leader's satisfaction with each volunteer.Furthermore,a multilayer coded genetic algorithm is used to construct an optimization model for emergency volunteer decision-making with the objective of maximizing the satisfaction value.Finally,the feasibility and effectiveness of this method are illustrated by an example analysis.The result shows that the efficiency of rescue tasks can be improved through decision optimization within the volunteer team.

Construction of an Index System for Earthquake Emergency Loss Assessment and Decision-making

Loss assessment and decision-making are essential for earthquake emergency rescues,and for scientific prediction of seismic damage and determination of rescue objectives.In practice,however,there exist some problems,such as basic data not being precise and rich enough and decision making not having systematic and complete criteria.This paper tries to solve these problems using the method of data indexation by constructing an index system for earthquake emergency loss assessment and decision-making.

Key elements and checklist of shared decisionmaking conversation on life-sustaining treatment in emergency:a multispecialty study from China

BACKGROUND:Shared decision-making(SDM)has broad application in emergencies.Most published studies have focused on SDM for a certain disease or expert opinions on future research gaps without revealing the full picture or detailed guidance for clinical practice.This study is to investigate the optimal application of SDM to guide life-sustaining treatment(LST)in emergencies.METHODS:This study was a prospective two-round Delphi consensus-seeking survey among multiple stakeholders at the China Consortium of Elite Teaching Hospitals for Residency Education.Participants were identified based on their expertise in medicine,law,administration,medical education,or patient advocacy.All individual items and questions in the questionnaire were scored using a 5-point Likert scale,with responses ranging from"very unimportant"(a score of 1)to"extremely important"(a score of 5).The percentages of the responses that had scores of 4-5on the 5-point Likert scale were calculated.A Kendall’s W coefficient was calculated to evaluate the consensus of experts.RESULTS:A two-level framework consisting of 4 domains and 22 items as well as a ready-touse checklist for the informed consent process for LST was established.An acceptable Kendall’s W coefficient was achieved.CONCLUSION:A consensus-based framework supporting SDM during LST in an emergency department can inform the implementation of guidelines for clinical interventions,research studies,medical education,and policy initiatives.

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.

Association between Meeting 24-Hour Movement Guidelines and Psychological Features of Chinese Emerging Adults

Background:Emerging adulthood is a pivotal life stage,presenting significant psychological and social changes,such as decreased sociability,depression,and other mental health problems.Previous studies have associated these changes with an unhealthy lifestyle.The 24-h movement guidelines for healthy lifestyles have been developed to promote appropriate health behaviors and improve individual wellness.However,the relationship between adherence to the 24-h movement guidelines and different characteristics of Chinese emerging adults is yet to be explored.This cross-sectional study aimed to investigate the association between adherence to the 24-h movement guidelines and four characteristics(self-exploration,instability,possibilities,and responsibility)of Chinese emerging adults.Methods:Overall,1,510 Chinese emerging adults aged 18–29 years were included in this study.Each participant completed a self-administered questionnaire that included questions on adherence to the 24-h movement guidelines(physical activity,sedentary behavior,and sleep)and the inventory of dimensions of emerging adulthood.Multivariable logistic regression analysis was employed to investigate the associations between adherence to the 24-h movement guidelines and each of the four characteristics.Results:The proportion of participants who adhered to the 24-h movement guidelines was 31.72%.Multiple regression analysis revealed a significantly negative relationship between adhering to more guidelines and instability(β=−0.51,p<0.001).A statistically significant association was observed between instability and meeting only sedentary behavior(β=−1.27,95%confidence interval[CI]:[−2.32,−0.24],p=0.02),sedentary behavior+sleep(β=−1.30,95%CI:[−2.24,−0.35],p<0.01),and physical activity+sedentary behavior(β=−1.08,95%CI:[1.94,−0.21],p=0.02)guidelines.Further,positive and significant associations were observed between possibilities and meeting the guidelines for only physical activity(β=0.70,95%CI:[0.14,1.27),p=0.01),only sleep(β=0.61,95%CI:[0.01,1.21],p=0.04),physical activity+sedentary behavior(β=0.56,95%CI:[0.04,1.07),p=0.01),and physical activity+sleep(β=0.76,95%CI:[0.23,1.27],p=0.01).Conclusions:These findings suggest that adherence to the 24-h movement guidelines was associated with instability in Chinese emerging adults.Future studies are warranted to verify our findings to highlight the importance of maintaining a heath lifestyle to promote health in emerging adulthood.

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.

Intelligent decision-making method for vehicles in emergency conditions based on artificial potential fields and finite state machines

This study aims to propose a decision-making method based on artificial potential fields(APFs)and finite state machines(FSMs)in emergency conditions.This study presents a decision-making method based on APFs and FSMs for emergency conditions.By modeling the longitudinal and lateral potential energy fields of the vehicle,the driving state is identified,and the trigger conditions are provided for path planning during lane changing.In addition,this study also designed the state transition rules based on the longitudinal and lateral virtual forces.It established the vehicle decision-making model based on the finite state machine to ensure driving safety in emergency situations.To illustrate the performance of the decision-making model by considering APFs and finite state machines.The version of the model in the co-simulation platform of MATLAB and CarSim shows that the developed decision model in this study accurately generates driving behaviors of the vehicle at different time intervals.The contributions of this study are two-fold.A hierarchical vehicle state machine decision model is proposed to enhance driving safety in emergency scenarios.Mathematical models for determining the transition thresholds of lateral and longitudinal vehicle states are established based on the vehicle potential field model,leading to the formulation of transition rules between different states of autonomous vehicles(AVs).

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.

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.

Car-following model featured with factors reflecting emergency evacuation situation

A new emergency evacuation car-following model （EECM） is proposed. The model aims to capture the main characteristics of traffic flow and driver behavior under an emergency evacuation, and it is developed on the basis of minimum safety distances with parts of the drivers＇ abnormal behavior in a panic emergency situation. A thorough questionnaire survey is undertaken among drivers of different ages. Based on the results from the survey, a safety-distance car-following model is formulated by taking into account two new parameters： a differential distributing coefficient and a driver＇ s experiential decision coefficient, which are used to reflect variations of driving behaviors under an emergency evacuation situation when compared with regular conditions. The formulation and derivation of the new model, as well as its properties and applicability are discussed. A case study is presented to compare the car-following trajectories using observed data under regular peak-hour traffic conditions and theoretical EECM results. The results indicate the consistency of the analysis of assumptions on the EECM and observations.