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

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

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

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

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

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

Beyond dentistry:could prevention and screening for neurodegenerative diseases start in the dental office?

The differential diagnosis of neurodegenerative diseases is complex and relies on clinical assessment,biomarker levels in cerebrospinal fluid,neuroimaging and neuropsychological assessment.The efforts of the scientific community are focused on two aspects:a)the discovery of minimally invasive biomarkers;b)the discovery of early biomarkers that can predict the progression to clinical disease in the presymptomatic stage of a disease.Considering the impact of the number of patients affected by chronic neurodegenerative diseases on public health expenditures,early diagnosis seems to be a primary need of our society.

Cervical cancer prevention in China: where are we now, and what's next?

Cervical cancer(CC) epidemiology CC, the fourth most frequently diagnosed malignancy in women worldwide, is a major global health challenge, particularly in low-resource regions. Approximately 88.1% of the 604,000 CC new cases occurred in low-and middle-income countries in 2020, and more than 90% of the 342,000 CC deaths occurred in low-and middle-income countries in 2020~1.

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.

Community-based prevention and treatment of cardiovascular diseases

The Annual Report on Cardiovascular Health and Diseases in China(2022)intricate landscape of cardiovascular health in China.This is the third section of the report with a specific focus on community-based prevention and treatment of cardiovascular diseases(CVD).This section of the report underscores the importance of initiatives outlined in the"Healthy China 2030 Plan,"emphasizing the comprehensive prevention and control strategy for chronic diseases.A key aspect of this plan involves the establishment of national demonstration areas aimed at comprehensive prevention and control of chronic diseases.By 2020,488 such areas had been set up across China,surpassing the initial target and covering a significant proportion of counties and districts.The report highlights the successful implementation of these strategies in Lishan district,Anshan city,where demonstration areas for comprehensive prevention and control of chronic diseases were launched in 2013.Over the course of seven years,the number of healthy units increased substantially,leading to improvements in managing risk factors for CVD among residents.Significant reductions in prevalence rates of overweight,obesity,smoking,passive smoking,and drinking were observed,along with the development of healthier behaviors among residents.Similarly,Qiaokou district in Wuhan City,designated as a national demonstration area in 2014,implemented comprehensive public health promotion initiatives.Notably,special clinics for hypertension intervention were established,contributing to an increase in self-reported rates of hypertension,a slight decrease in prevalence,and a remarkable improvement in the control rate among treated patients.Overall,these efforts underscore the effectiveness of community-based approaches in driving positive health outcomes and advancing the comprehensive prevention and control of chronic diseases,particularly cardiovascular diseases,in China.

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.

Practice of Infection Prevention and Control Strategies in Risk Departments during the COVID-19 Epidemic

Objective: To explore the practice and application of infection prevention and control strategies in risk departments during the COVID-19 epidemic, and to formulate the infection prevention and control measures to provide advice and guidance in risk departments. Methods: According to the latest plan of diagnosis and treatment, prevention and control issued by the National Health Commission, expert advice and consensus, combined with the actual situation in our hospital, a series of infection prevention and control measures of COVID-19 in risk department was formulated. Results: During the epidemic period, the prevention and control measures of nine risk departments including emergency operation, anesthesiology, endoscopy center, blood purification center, otolaryngology, stomatology, medical imaging department, medical cosmetology department and pulmonary function room were established from six aspects, including pre-examination and screening, medical technology control, personnel management, personal protection, environmental disinfection, medical waste disposal, etc. Conclusion: During the epidemic period, the infection prevention and control strategy of risk departments is one of the key links to control the spread of the epidemic, and risk departments must pay attention to and strictly implement various infection prevention and control measures.

Assessment of Breast Cancer Prevention Practices among Women Attending Primary Health Care in Abha City, Aseer Region, Saudi Arabia

Cancer is a leading cause of death worldwide, with breast cancer being the most common (2.26 million new cases and 685,000 deaths). In Saudi Arabia, breast cancer ranked the first among females in 2014, accounting for 15.9% of all cancers reported among Saudi nationals and 28.7% of all cancers reported among females of all ages. Early detection of breast cancer could decrease the risks, have a better prognosis, and have better outcomes/more successful treatments. Prevalence of breast cancer reached more than 25% of all diagnosed cancer in the kingdom among women. Aim: This study aims to assess the knowledge and performance of women attending primary care centers about breast self-examination and mammogram screening for prevention and early detection of breast cancer in Abha city primary healthcare centers, Kingdom of Saudi Arabia. Research Method: cross sectional design was conducted by using questionnaire, which was distributed to primary care center nurses. The collected data was statistically analyzed using the Statistical Package for Social Sciences, version 25. Results: The study found that participants had poor awareness and knowledge about breast self-examination, risk factors for breast cancer, and trends and practices in early diagnosis of breast cancer. Conclusion and Recommendations: It recommends increasing awareness campaigns and providing educational programs to improve knowledge and practices.

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

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

Recent advances in promising drugs for primary prevention of gastroesophageal variceal bleeding with cirrhotic portal hypertension

Background:Gastroesophageal variceal bleeding is one of the most severe complications of patients with cirrhosis.Although primary prevention drugs,including non-selectiveβ-blockers,have effectively reduced the incidence of bleeding,their efficacy is limited due to side effects and related contraindications.With recent advances in precision medicine,precise drug treatment provides better treatment efficacy.Data sources:Literature search was conducted in PubMed,MEDLINE and Web of Science for relevant articles published up to May 2022.Information on clinical trials was obtained from https://clinicaltrials.gov/and http://www.chictr.org.cn/.Results:The in-depth understanding of the pathogenesis and advances of portal hypertension has enabled the discovery of multiple molecular targets for promising drugs.According to the site of action,these drugs could be classified into four classes:intrahepatic,extrahepatic,both intrahepatic and extrahepatic targets and others.All these classes of drugs offer advantages over traditional treatments in prevention of gastroesophageal variceal bleeding in patients with cirrhotic portal hypertension.Conclusions:This review classified and summarized the promising drugs,which prevent gastroesophageal variceal bleeding by targeting specific markers of pathogenesis of portal hypertension,demonstrating the significance of using the precision medicine strategy to discover and develop promising drugs for the primary prevention of gastroesophageal variceal bleeding in patients with cirrhotic portal hypertension.

Prevention and Control Strategies of Nosocomial Infection and Effectiveness Evaluation in a Tertiary Teaching Hospital during the Epidemic of COVID-19

Objective: To evaluate the role of prevention and control strategies for nosocomial infection in a tertiary teaching hospital during the sudden outbreak of Corona Virus Disease 2019 (COVID-19). Methods: The hospital initiated an emergency plan involving multi-departmental defense and control. It adopted a series of nosocomial infection prevention and control measures, including strengthening pre-examination and triage, optimizing the consultation process, improving the hospital’s architectural composition, implementing graded risk management, enhancing personal protection, and implementing staff training and supervision. Descriptive research was used to evaluate the short-term effects of these in-hospital prevention and control strategies. The analysis compared changes in related evaluation indicators between January 24, 2020 and February 12, 2020 (Chinese Lunar New Year’s Eve 2020 to lunar January 19) and the corresponding lunar period of the previous year. Results: Compared to the same period last year, the outpatient fever rate increased by 1.85-fold (P P Conclusion: The nosocomial infection prevention and control strategies implemented during this specific period improved the detection and control abilities for the COVID-19 source of infection and enhanced the compliance with measures. This likely contributed significantly to avoiding the occurrence of nosocomial infection.

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

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

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

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

Identification of Early Warning Signals of Infectious Diseases in Hospitals by Integrating Clinical Treatment and Disease Prevention

The global incidence of infectious diseases has increased in recent years,posing a significant threat to human health.Hospitals typically serve as frontline institutions for detecting infectious diseases.However,accurately identifying warning signals of infectious diseases in a timely manner,especially emerging infectious diseases,can be challenging.Consequently,there is a pressing need to integrate treatment and disease prevention data to conduct comprehensive analyses aimed at preventing and controlling infectious diseases within hospitals.This paper examines the role of medical data in the early identification of infectious diseases,explores early warning technologies for infectious disease recognition,and assesses monitoring and early warning mechanisms for infectious diseases.We propose that hospitals adopt novel multidimensional early warning technologies to mine and analyze medical data from various systems,in compliance with national strategies to integrate clinical treatment and disease prevention.Furthermore,hospitals should establish institution-specific,clinical-based early warning models for infectious diseases to actively monitor early signals and enhance preparedness for infectious disease prevention and control.

Analysis of risk factors for lymphedema of the lower limbs after endometrial cancer surgery and suggestions for prevention and treatment:Retraction

In the article titled“Analysis of risk factors for lymphedema of the lower limbs after endometrial cancer surgery and suggestions for prevention and treatment”by Zhang Q and Liu L(J Integr Nurs 2021;3(2):67-71.doi:10.4103/jin.jin_13_21),[1]the content and results data of this article was questioned by International database(Web of Science)institution.This article was then investigated by the publisher and Journal of Integrative Nursing(JIN).The investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:(1)Discrepancies in scope;(2)Discrepancies in the description of the research reported;(3)Discrepancies between the availability of data and the research described;(4)Inappropriate citations;(5)Incoherent,meaningless and/or irrelevant content included in the article;(6)The ethical approval by an Institutional Review Board(IRB)committee or equivalent not provided in this article.The editorial office of JIN sent emails many times but received no replies and reasonable explanations from the authors.Therefore,this article is retracted.

Overview on Mangrove Forest Disaster Prevention and Mitigation Functions

This paper provides a comprehensive overview on coastal protection and hazard mitigation by mangroves.Previous stud-ies have made great strides to understand the mechanisms and influencing factors of mangroves’protection function,including wave energy dissipation,storm surge damping,tsunami mitigation,adjustment to sea level rise and wind speed reduction,which are sys-tematically summarized in this study.Moreover,the study analyzes the extensive physical models,based on indoor flume experi-ments and numerical models,that consider the interaction between mangroves and hydrodynamics,to help our understanding of mangrove-hydrodynamic interactions.Additionally,quantitative approaches for valuing coastal protection services provided by man-groves,including index-based and process-resolving approaches,are introduced in detail.Finally,we point out the limitations of previous studies,indicating that efforts are still required for obtaining more long-term field observations during extreme weather events,to create more real mangrove models for physical experiments,and to develop numerical models that consider the flexible properties of mangroves to better predict wave propagation in mangroves having complex morphology and structures.

‘Prediabetes’ as a practical distinctive window for workable fruitful wonders: Prevention and progression alert as advanced professionalism

Diabetes is a devastating public health problem.Prediabetes is an intermediate stage in the disease processes leading to diabetes,including types 1 and 2 diabetes.In the article“Prediabetes in children and adolescents:An updated review,”the authors presented current evidence.We simplify and systematically clearly present the evidence and rationale for a conceptual framework we term the‘3ASs’:(1)Awareness Sensible;(2)Algorithm Simple;and(3)Appealing Strategies.Policy makers and the public need to be alerted.The prevalence of prediabetes should send alarm bells ringing for parents,individuals,clinicians,and policy makers.Prediabetes is defined by the following criteria:impaired fasting glucose(100-125 mg/dL);impaired glucose tolerance(2 h postprandial glucose 140-199 mg/dL);or hemoglobin A1c values of 5.7%–6.4%.Any of the above positive test alerts for intervention.Clinical guidelines do not recommend prioritizing one test over the others for evaluation.Decisions should be made on the strengths and shortfalls of each test.Patient preferences and test accessibility should be taken into consideration.An algorithm based on age,physiological stage,health status,and risk factors is provided.Primordial prevention targeting populations aims to eliminate risk factors through public education and encouraging practices through environmental modifications.Access to healthy foods is provided.Primary prevention is for individuals with a prediabetes diagnosis and involves a structured program to reduce body weight and increase physical activity along with a healthy diet.An overall methodical move to a healthy lifestyle for lifelong health is urgently needed.Early energetic prediabetes action is necessary.

Contemporary concepts of prevention and management of gastroesophageal variceal bleeding in liver cirrhosis patients

This editorial describes the contemporary concepts of prevention and management of gastroesophageal variceal bleeding in liver cirrhosis(LC)patients according to the current guidelines.Gastroesophageal variceal bleeding is the most dangerous complication of portal hypertension in LC patients.Risk stratification and determination of an individual approach to the choice of therapeutic measures aimed at their prevention and management has emerged as one of the top concerns in modern hepatology.According to the current guidelines,in the absence of clinically significant portal hypertension,etiological and nonetiological therapies of LC is advisable for the primary preventing gastroesophageal variceal bleeding,whereas its presence serves as an indication for the administration of non-selectiveβ-blockers,among which carvedilol is the drug of choice.Non-selectiveβ-blockers,as well as endoscopic variceal ligation and transjugular intrahepatic portosystemic shunt can be used to prevent recurrence of gastroesophageal variceal bleeding.Pharmacotherapy with vasoactive drugs(terlipressin,somatostatin,octreotide),endoscopic variceal ligation,endovascular techniques and transjugular intrahepatic portosystemic shunt are recommended for the treatment of acute gastroesophageal variceal bleeding.Objective and accurate risk stratification of gastroesophageal variceal bleeding will allow developing individual strategies for their prevention and management,avoiding the first and further decompensation in LC,which will improve the prognosis and survival of patients suffering from it.