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.

Emergency Decision-Making Based on q-Rung Orthopair Fuzzy Rough Aggregation Information

With the frequent occurrences of emergency events,emergency decision making(EDM)plays an increasingly significant role in coping with such situations and has become an important and challenging research area in recent times.It is essential for decision makers to make reliable and reasonable emergency decisions within a short span of time,since inappropriate decisions may result in enormous economic losses and social disorder.To handle emergency effectively and quickly,this paper proposes a new EDM method based on the novel concept of q-rung orthopair fuzzy rough(q-ROPR)set.A novel list of q-ROFR aggregation information,detailed description of the fundamental characteristics of the developed aggregation operators and the q-ROFR entropy measure that determine the unknown weight information of decision makers as well as the criteria weights are specified.Further an algorithm is given to tackle the uncertain scenario in emergency to give reliable and reasonable emergency decisions.By using proposed list of q-ROFR aggregation information all emergency alternatives are ranked to get the optimal one.Besides this,the q-ROFR entropy measure method is used to determine criteria and experts’weights objectively in the EDM process.Finally,through an illustrative example of COVID-19 analysis is compared with existing EDM methods.The results verify the effectiveness and practicability of the proposed methodology.

A Multi-Attribute Decision-Making Method Using Belief-Based Probabilistic Linguistic Term Sets and Its Application in Emergency Decision-Making

Probabilistic linguistic term sets(PLTSs)are an effective tool for expressing subjective human cognition that offer advantages in the field ofmulti-attribute decision-making(MADM).However,studies have found that PLTSs have lost their ability to accurately capture the views of decision-makers(DMs)in certain circumstances,such as when the DM hesitates between multiple linguistic terms or the decision information is incomplete,thus affecting their role in the decision-making process.Belief function theory is a leading streamof thought in uncertainty processing that is suitable for dealing with the limitations of PLTS.Therefore,the purpose of this study is to extend PLTS to incorporate belief function theory.First,we provide the basic concepts of the extended PLTS(i.e.,belief-based PLTS)through case analyses.Second,the aggregation operator of belief-based PLTS is defined with the ordered weighted average(OWA)-based soft likelihood function,which is improved by considering the reliability of the information source.Third,to measure the magnitude of different belief-based PLTSs,the belief interval of singleton is calculated,and the comparison method of belief-based PLTS is constructed based on probabilities.On the basis of the preceding discussion,we further develop an emergency decision framework that includes several novel techniques,such as attribute weight determination and decision information aggregation.Finally,the usefulness of the framework is demonstrated through a case study,and its effectiveness is illustrated through a series of comparisons.

A Dynamic Emergency Decision-Making Method Based on Group Decision Making with Uncertainty Information

In emergency decision making(EDM),it is necessary to generate an effective alternative quickly.Case-based reasoning(CBR)has been applied to EDM;however,choosing the most suitable case from a set of similar cases after case retrieval remains challenging.This study proposes a dynamic method based on case retrieval and group decision making(GDM),called dynamic casebased reasoning group decision making(CBRGDM),for emergency alternative generation.In the proposed method,first,similar historical cases are identified through case similarity measurement.Then,evaluation information provided by group decision makers for similar cases is aggregated based on regret theory,and comprehensive perceived utilities for the similar cases are obtained.Finally,the most suitable historical case is obtained from the case similarities and the comprehensive perceived utilities for similar historical cases.The method is then applied to an example of a gas explosion in a coal company in China.The results show that the proposed method is feasible and effective in EDM.The advantages of the proposed method are verified based on comparisons with existing methods.In particular,dynamic CBRGDM can adjust the emergency alternative according to changing emergencies.The results of application of dynamic CBRGDM to a gas explosion and comparison with existing methods verify its feasibility and practicability.

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.

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.

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.

Referrals to Emergency Departments—— The Processes and Factors That Influence Decision-Making among Community Nurses

The aim of the study was to describe the basis on which municipal care registered nurses (RN) make decisions and their experiences when referring older persons from nursing homes to emergency departments (EDs). RNs in the community are to ensure that older adults receive good care quality in nursing home. This study used a descriptive design with a qualitative content analysis. The analysis of the data from the 13 interviews revealed one theme “Shared responsibilities in the best interests of the older person reduce feelings of insufficiency”. The content was formulated, which revealed the RNs’ feelings, reasoning and factors influencing them and their actions in the decision-making situation, before the patients were referred to an emergency department. Complex illnesses, non-adapted organizations, considerations about what was good and right in order to meet the older person’s needs, taking account of her/his life-world, health, well-being and best interests were reported. Co-worker competencies and open dialogues in the “inner circle” were crucial for the nurses’ confidence in the decision. Hesitation to refer was associated with previous negative reactions from ED professionals. The RN sometimes express that they lacked medical knowledge and were uncertain how to judge the acute illness or changes. Access to the “outer circle”, i.e. physicians and hospital colleagues, was necessary to counteract feelings of insecurity about referrals. When difficult decisions have to be made, not only medical facts but also relationships are of importance. To strengthen the RNs’ and staff members’ competence by means of education seems to be important for avoiding unnecessary referrals. Guidelines and work routine need to be more transparent and referrals due to the lack of resources are not only wasteful but can worsen the older persons’ health.

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.

Low-temperature characteristicsof rubbers and performance testsof type 120 emergencyvalve diaphragms

Purpose–The type 120 emergency valve is an essential braking component of railway freight trains,butcorresponding diaphragms consisting of natural rubber(NR)and chloroprene rubber(CR)exhibit insufficientaging resistance and low-temperature resistance,respectively.In order to develop type 120 emergency valverubber diaphragms with long-life and high-performance,low-temperatureresistant CR and NR were processed.Design/methodology/approach–The physical properties of the low-temperature-resistant CR and NRwere tested by low-temperature stretching,dynamic mechanical analysis,differential scanning calorimetryand thermogravimetric analysis.Single-valve and single-vehicle tests of type 120 emergency valves werecarried out for emergency diaphragms consisting of NR and CR.Findings–The low-temperature-resistant CR and NR exhibited excellent physical properties.The elasticityand low-temperature resistance of NR were superior to those of CR,whereas the mechanical properties of thetwo rubbers were similar in the temperature range of 0℃–150℃.The NR and CR emergency diaphragms metthe requirements of the single-valve test.In the low-temperature single-vehicle test,only the low-temperaturesensitivity test of the NR emergency diaphragm met the requirements.Originality/value–The innovation of this study is that it provides valuable data and experience for futuredevelopment of type 120 valve rubber diaphragms.

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.

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.

Trends in oxycodone and oxycodone-containing analgesics administration for back pain in emergency departments in the USA(2007–2018)

BACKGROUND:To describe trends in oxycodone and oxycodone-containing analgesic prescribing for the treatment of back pain among adults in emergency departments(EDs) in the USA from 2007 to 2018.METHODS:Data were gathered from the National Hospital Ambulatory Medical Care Survey(NHAMCS) from 2007 to 2018.The study population included individuals of all ages presenting to USA EDs.The NHAMCS reasons for visit and oxycodone drug ID codes were used to isolate patients with back pain.The main outcome was the proportion of oxycodone and oxycodone-containing analgesics prescribed for back pain in the EDs over the specified time period.RESULTS:There was a relative decrease in the overall administration of oxycodone for back pain in the EDs by 62.3% from 2007(244,000 visits) to 2018(92,000 visits).The proportion of ED patients prescribed with oxycodone-containing analgesics for back pain increased among patients aged 45 years and older(from 43.8% to 57.6%),female patients(from 54.5% to 62.0%),black patients(from 22.5% to 30.4%),and Hispanic/Latino patients(from 9.4% to 19.6%).Oxycodone/acetaminophen was most prescribed and accounted for 90.2% of all oxycodone-containing analgesics in 2007,with a decrease to 68.5% in 2018.Pure oxycodone was the second most prescribed medication,accounting for 6.1% in 2007 and 31.5% in 2018.CONCLUSION:The overall number of oxycodone-containing analgesics decreased significantly from 2007 to 2018.However,that number trended upward in 45-year-old and older,female,black,or Hispanic/Latino patients from 2007 to 2018.The total amount of pure oxycodone increased significantly from 2007 to 2008.

Prediction of sepsis within 24 hours at the triage stage in emergency departments using machine learning

BACKGROUND:Sepsis is one of the main causes of mortality in intensive care units(ICUs).Early prediction is critical for reducing injury.As approximately 36%of sepsis occur within 24 h after emergency department(ED)admission in Medical Information Mart for Intensive Care(MIMIC-IV),a prediction system for the ED triage stage would be helpful.Previous methods such as the quick Sequential Organ Failure Assessment(qSOFA)are more suitable for screening than for prediction in the ED,and we aimed to fi nd a light-weight,convenient prediction method through machine learning.METHODS:We accessed the MIMIC-IV for sepsis patient data in the EDs.Our dataset comprised demographic information,vital signs,and synthetic features.Extreme Gradient Boosting(XGBoost)was used to predict the risk of developing sepsis within 24 h after ED admission.Additionally,SHapley Additive exPlanations(SHAP)was employed to provide a comprehensive interpretation of the model's results.Ten percent of the patients were randomly selected as the testing set,while the remaining patients were used for training with 10-fold cross-validation.RESULTS:For 10-fold cross-validation on 14,957 samples,we reached an accuracy of 84.1%±0.3%and an area under the receiver operating characteristic(ROC)curve of 0.92±0.02.The model achieved similar performance on the testing set of 1,662 patients.SHAP values showed that the fi ve most important features were acuity,arrival transportation,age,shock index,and respiratory rate.CONCLUSION:Machine learning models such as XGBoost may be used for sepsis prediction using only a small amount of data conveniently collected in the ED triage stage.This may help reduce workload in the ED and warn medical workers against the risk of sepsis in advance.

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.

Innovative pathways allow safe discharge of mild acute pancreatitis from the emergency room

Acute pancreatitis(AP)is a leading cause of gastrointestinal-related hospitalizations in the United States,resulting in 300000 admissions per year with an estimated cost of over$2.6 billion annually.The severity of AP is determined by the presence of pancreatic complications and end-organ damage.While moderate/severe pancreatitis can be associated with significant morbidity and mortality,the majority of patients have a mild presentation with an uncomplicated course and mortality rate of less than 2%.Despite favorable outcomes,the majority of mild AP patients are admitted,contributing to healthcare cost and burden.In this Editorial we review the performance of an emergency department(ED)pathway for patients with mild AP at a tertiary care center with the goal of reducing hospitalizations,resource utilization,and costs after several years of implementation of the pathway.We discuss the clinical course and outcomes of mild AP patients enrolled in the pathway who were successfully discharged from the ED compared to those who were admitted to the hospital,and identify predictors of successful ED discharge to select patients who can potentially be triaged to the pathway.We conclude that by implementing innovative clinical pathways which are established and reproducible,selected AP patients can be safely discharged from the ED,reducing hospitalizations and healthcare costs,without compromising clinical outcomes.We also identify a subset of patients most likely to succeed in this pathway.

Risk factors for postpartum posttraumatic stress disorder after emergency admission

BACKGROUND:Postpartum posttraumatic stress disorder(PTSD)can occur in women who give birth after emergency admission.The identification of risk factors for this condition is crucial for developing effective preventive measures.This retrospective study aimed to explore the incidence and risk factors for postpartum PTSD in women who give birth after emergency admission.METHODS:Medical records of women who gave birth after emergency admission were collected between March 2021 and April 2023.The patients’general conditions and perinatal clinical indicators were recorded.The puerperae were divided into PTSD group and control group based on symptom occurrence at six weeks postpartum.Multivariate logistic regression analysis was performed to identify risk factors.RESULTS:A total of 276 puerperae were included,with a PTSD incidence of 20.3% at six weeks postpartum.Multivariate logistic regression analysis identified emergency cesarean section(odds ratio[OR]=2.102;95%confidence interval[CI]:1.114-3.966,P=0.022),admission to the emergency department after midnight(12:00 AM)(OR=2.245;95%CI:1.170-4.305,P<0.001),and cervical dilation(OR=3.203;95%CI:1.670–6.141,P=0.039)as independent risk factors for postpartum PTSD.Analgesia pump use(OR=0.500;95%CI:0.259–0.966,P=0.015)was found to be a protective factor against postpartum PTSD.CONCLUSION:Emergency cesarean section,admission to the emergency department after midnight,and cervical dilation were identified as independent risk factors for postpartum PTSD,while analgesic pump use was a protective factor.These findings provide insights for developing more effective preventive measures for women who give birth after emergency admission.