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.

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.

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.

Analysis of the History and Current Situation of the Development of General Practice Teaching Clinics in China

A summary of the exploration of the teaching mode of the general practice teaching clinic, a summary of the deficiencies of the teaching clinic and a summary of the significance of the establishment of the general practice teaching clinic are presented with a view to promoting the development of general practice and cultivating more excellent successors in general practice.

Culturally competent care across borders: Implementing culturally responsive teaching for nurses in diverse workforces

The migration of healthcare professionals,including nurses,is a global phenomenon.It is driven by various factors,including the pursuit of better opportunities,living conditions,and professional development,as well as political instability or conflict in their home countries.The World Health Organization(WHO)has noted that high-income countries often rely on foreign-trained nurses to fill gaps in their healthcare systems[1].For instance,as of 2021,over 40%(52 million)of all nurses in the United States(US)were expatriates[2].In the United Kingdom(UK),the percentage of expatriate nurses was even higher,reaching approximately 18%in 2021[3].Owing to globalization and migration,healthcare providers must possess cultural competence to deliver improved care[4,5].Culturally responsive teaching(CRT)is rooted in the idea that culture plays a vital role in shaping people’s behaviors,beliefs,values,and communication styles[6].Furthermore,these cultural factors influence patients’perspectives on health,illness,healing,and their preferences for care and communication[7].By recognizing and embracing these cultural differences,nurses can provide more effective and compassionate care to their diverse patient population[8].

Exploration on Teaching Reform of Synthetic Biology Course Based on the New Engineering Concept

Synthetic biology is a new frontier of life science,which aims to design,transform and even synthesize organisms with engineering design concept.Doing a good job in the teaching of"synthetic biology"is of great significance to the cultivation and reserve of biotechnology professionals in China,and also has an important impact on students' employment competitiveness.Under the background of"new engineering",the course reform of"synthetic biology"was carried out in terms of the construction of teaching staff,teaching methods,students' participation and the innovation of course content,and specific reform suggestions were put forward,hoping to effectively promote the sustainable development of"synthetic biology"and effectively improve the quality of education.

Practical effect of different teaching modes in teaching gastrointestinal surgery nursing

BACKGROUND With the continuous development and progress of medical technology,the position of surgical nursing in the field of clinical medicine is becoming in-creasingly prominent.As an important branch of the surgical field,the nursing requirements and difficulty of gastrointestinal surgery are also increasing.In order to improve the teaching quality of nursing care in gastrointestinal surgery,many educators and researchers are actively exploring new teaching methods.Among them,the teaching method case-based learning(CBL),scene-simulated learning(SSL),task-based learning(TBL),combining self-evaluation and training mode is considered as an effective method.This method aims to help students to better master knowledge and skills and improve their comprehensive quality by cultivating their self-evaluation ability.AIM To explore the practical effect of CBL-SSL-TBL combined with training mode and student self-assessment in nursing teaching of gastrointestinal surgery.METHODS Seventy-one nursing interns in our hospital from December 2020 to December 2021 were selected.According to different teaching modes,they were divided into observation group CBL-SSL-TBL combined with training mode combined with student self-assessment and control group(conventional teaching mode),of which 36 were in observation group and 35 were in control group.The results of operational skills,theoretical knowledge,nursing students'satisfaction,learning effectiveness questionnaire and teaching effect were compared between the two groups.RESULTS Compared between the two groups,the operational skills and theoretical knowledge scores of the observation group were higher than those of the control group,and the difference was statistically significant(P<0.05).Compared between the two groups,the total satisfaction ratio of the observation group was higher than that of the control group,the difference was statistically significant(P<0.05).Compared between the two groups,the observation group was lower than the control group in the questionnaire results of learning efficacy,and the difference was statistically significant(P<0.05).Compared between the two groups,the proportion of thinking ability,subjective initiative and understanding of theoretical knowledge in the observation group was higher than that in the control group,the difference was statistically significant(P<0.05).CONCLUSION The use of CBL-SSL-TBL combined with training mode and student self-assessment in gastrointestinal surgery nursing teaching can improve the operational skills of nursing interns,theoretical knowledge and satisfaction scores of nursing students,improve the results of learning efficiency questionnaire and teaching effect,which can be popularized in clinical teaching.

Exploration of Teaching Reform of Food Machinery and Equipment Course Based on New Engineering Concept

The new engineering concept aims to train high-quality engineering talents to meet the needs of future science and technology and industrial development through the reform of education and teaching.Under the background of"new engineering",by introducing cutting-edge knowledge of the industry and interdisciplinary integration,adopting innovative teaching methods such as project-driven teaching and flipped classroom,strengthening experimental teaching and school-enterprise cooperation,and establishing comprehensive evaluation and feedback mechanism,Food Machinery and Equipment course is reformed to improve the teaching quality and train high-quality engineering talents to meet the needs of modern food processing industry.

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.

Research on Blended Teaching Mode:A Case Study of Floristry

In order to better carry out research on education and teaching,the author consulted relevant literature on blended teaching mode from 2011-2021 through CNKI,Web of Science and other websites,summarized and analyzed the research status of blended teaching mode,in order to lay a good foundation for studying blended teaching mode in course teaching.

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.

A Structured Method of Teaching Epidural Block reduces the incidence of accidental dural puncture in inexperienced trainees.

Background: The rate of accidental dural puncture is particularly high during the period of training, especially in novices. The structured epidural teaching model (SETM) includes three standardized video lessons, the construction of a 3D epidural module by trainees and practical training by using an epidural simulator with and without the CompuFlo™ Epidural instrument. In this study we report the retrospective analysis of the accidental dural puncture rate of inexperienced trainees during their 6 months clinical practice rotation in obstetrics before and after the introduction of the SETM in our Institution. Method: We evaluated the incidence of accidental dural puncture before the introduction of the SETM methodology and afterwards by analyzing our departmental database from February 2019 to January 2023. All epidural blocks were executed by trainees who had never previously performed an epidural block and were about to begin their obstetrics rotation. Results: We analyzed 7415 epidurals: 3703 were performed before the introduction of the SETM methodology (control group) and 3712 afterwards (study group). The incidence of accidental dural puncture was 0.37% for the control group and 0.13% for the study group (p<.05). The probability of making an accidental dural puncture was 64% (OR: 0.36) lower for trainees who had the training than for those who did not. Conclusions: After the introduction of the structured teaching method, we observed a significant reduction of accidental dural puncture during the training period. We hope that our observation will encourage a constructive discussion among experts about the need to use standardized and validated tools as a valuable aid in teaching epidural anesthesia.

Thinking on Improving Presentation Teaching-Based on the Empirical Analysis of Presentation in College English Classes of Junior Non-English Majors in Universities of Science and Engineering

Based on the empirical investigation of presentation in college English classes for junior non-English majors in science and engineering universities,the author explains her own thinking on how to improve presentation teaching from four aspects:setting teaching objectives,selecting teaching contents,using teaching methods,and evaluating and implementing methods.

Student-Centered Strategy in Academic English Teaching

Based on the current situation of academic English teaching, this paper discusses “student-centered” teaching strategy in the Academic English Reading and Writing, and explore how to integrate it in classroom activities.

Application of Perceived Teaching with Moderate Elimination of“Graphics”in Architectural Design Courses

Starting from the traditional form of“graphic”architectural education,this paper explores a teaching method of entity construction experience,instrument assistance,and digital virtual expression symbiosis symbiosis from three aspects:environmental perception,ontology perception,and extension perception.Using physical perception as a medium,and gradually rising from active perception to a comprehensive expression of visual audience perception through practical operation,it allows students to have a more comprehensive understanding of the meaning of architectural design on the basis of the“graphic”expression paradigm.

Opinions of Students on the Contribution of the DIVA 3D Virtual Dissection Table to the Teaching of Anatomy at the Faculty of Medicine and Odontostomatology of Bamako

Objective: Study the contribution of the DIVA 3D dissection table in the teaching of anatomy at the Faculty of Medicine and Odontology of Bamako. Material and Methods: This was a qualitative study carried out from November 1 to December 30, 2023 at the clinical and morphological anatomy laboratory of the Faculty of Medicine and Odontostomatology of Bamako. Included in this study were students who participated in practical and tutorial sessions. The variables studied during this study were: the previous performance of dissection on a cadaver by the students, the opinion of the students on dissection on a cadaver, the replacement of dissection on a cadaver by virtual dissection in the absence of a body, the level student satisfaction. Results: We surveyed 130 participants. The average age was 22 ± 0.2 years with extremes of 17 and 29 years. 95.3% of participants were students. According to 66.7% of participants, virtual dissection is a good palliative in the absence of a corpse. 95.3% of participants found using the virtual dissection table easy with an average of 7.88 ± 1.4. The overall assessment was well rated by 99.3% of participants. Conclusion: According to the results of this study, the virtual dissection table should be improved by integrating commented videos. The use of the DIVA 3D virtual dissection table during practical and tutorial sessions is well appreciated by the students. We believe that the teaching of anatomy using 3D digital technology should be included in the study programs of the Faculty of Medicine and Odontostomatology.

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.