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.

Spectral purification improves monitoring accuracy of the comprehensive growth evaluation index for film-mulched winter wheat

In order to further improve the utility of unmanned aerial vehicle(UAV)remote-sensing for quickly and accurately monitoring the growth of winter wheat under film mulching, this study examined the treatments of ridge mulching,ridge–furrow full mulching, and flat cropping full mulching in winter wheat.Based on the fuzzy comprehensive evaluation (FCE) method, four agronomic parameters (leaf area index, above-ground biomass, plant height, and leaf chlorophyll content) were used to calculate the comprehensive growth evaluation index (CGEI) of the winter wheat, and 14 visible and near-infrared spectral indices were calculated using spectral purification technology to process the remote-sensing image data of winter wheat obtained by multispectral UAV.Four machine learning algorithms, partial least squares, support vector machines, random forests, and artificial neural network networks(ANN), were used to build the winter wheat growth monitoring model under film mulching, and accuracy evaluation and mapping of the spatial and temporal distribution of winter wheat growth status were carried out.The results showed that the CGEI of winter wheat under film mulching constructed using the FCE method could objectively and comprehensively evaluate the crop growth status.The accuracy of remote-sensing inversion of the CGEI based on the ANN model was higher than for the individual agronomic parameters, with a coefficient of determination of 0.75,a root mean square error of 8.40, and a mean absolute value error of 6.53.Spectral purification could eliminate the interference of background effects caused by mulching and soil, effectively improving the accuracy of the remotesensing inversion of winter wheat under film mulching, with the best inversion effect achieved on the ridge–furrow full mulching area after spectral purification.The results of this study provide a theoretical reference for the use of UAV remote-sensing to monitor the growth status of winter wheat with film mulching.

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.

Socio-scientific quantification of the comprehensive benefits of debris flow mitigation measures for villages in western Sichuan, China

Debris flow hazards seriously threaten thesafety and sustainable development of mountainousareas. Numerous debris flow mitigation measures havebeen implemented worldwide;however, acomprehensive assessment of the specific disasterreduction effects of these measures and their economic,social and ecological benefits is yet to be performed.The western region of Sichuan Province frequentlysuffers from geohazards such as debris flow, and thegovernment has adopted many mitigation measures.This study assessed the benefits of debris flowmitigation measures and identified the key influencingfactors via a field-based study conducted in 81 villagesin western Sichuan province, China. A framework forthe evaluation of the benefits of rural debris flowmitigation measures was constructed andquantitatively evaluated using a survey. Snowballsampling was performed to recruit 81 village leadersand 468 farmers. The results showed that managementand engineering measures were the main methodsused to mitigate debris flow;ecological measures wereauxiliary. The average satisfaction scores of farmers forthese three types of measures were 4.07, 3.90, and 3.56,respectively (as measured on a five-point Likert scale).In contrast, in terms of the benefits of these mitigationmeasures, only a small proportion of villages (11.11%)obtained a high level of comprehensive benefits fromthe debris flow mitigation measures, while the majority(88.89%) received medium to low-level benefits. Toimprove this situation, we further studied and foundthat the main factors that restricted villages fromachieving high-level comprehensive benefits were theunpredictable nature of debris flows, labour forceoutflow and remoteness. Effective control measures, agood economic environment and strong governmentassistance were reported as crucial factors forimproving these comprehensive benefits. This studyprovides socio-scientific references for decisionmakingon rural debris flow mitigation measures while keeping villages at the centre of economic development.

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.

Comprehensive reutilization of herbal waste:Coproduction of magnolol,honokiol,and β-amyrin from Magnolia officinalis residue

Herbal extraction residues(HERs)cause serious environmental pollution and resource waste.In this study,a novel green route was designed for the comprehensive reutilization of all components in HERs,taking Magnolia officinalis residues(MOR)as an example.The reluctant structure of MOR was first destroyed by alkali pretreatment to release the functional ingredients(magnolol and honokiol)originally remaining in MOR and to make MOR more accessible for hydrolysis.A metal–organic frame material MIL-101(Cr)with a maximum absorption capacity of 255.64 mg g^(-1)was synthesized to absorb the released honokiol and magnolol from the pretreated MOR solutions,and 40 g L^(-1)reducing sugars were obtained with 81.8%enzymatic hydrolysis rate at 10%MOR solid loading.Finally,382 mg L-1β-amyrin was produced from MOR hydrolysates by an engineered yeast strain.In total,1 kg honokiol,8 kg magnolol,and 7.64 kg β-amyrin could produce from 1 ton MOR by this cleaner process with a total economic output of 170,700 RMB.

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.

Comprehensive analysis of gene mutations and mismatch repair in Chinese colorectal cancer patients

BACKGROUND RAS,BRAF,and mismatch repair(MMR)/microsatellite instability(MSI)are crucial biomarkers recommended by clinical practice guidelines for colorectal cancer(CRC).However,their characteristics and influencing factors in Chinese patients have not been thoroughly described.AIM To analyze the clinicopathological features of KRAS,NRAS,BRAF,and PIK3CA mutations and the DNA MMR status in CRC.METHODS We enrolled 2271 Chinese CRC patients at the China-Japan Friendship Hospital.MMR proteins were tested using immunohistochemical analysis,and the KRAS/NRAS/BRAF/PIK3CA mutations were determined using quantitative polymerase chain reaction.Microsatellite status was determined using an MSI detection kit.Statistical analyses were conducted using SPSS software and logistic regression.RESULTS The KRAS,NRAS,BRAF,and PIK3CA mutations were detected in 44.6%,3.4%,3.7%,and 3.9% of CRC patients,respectively.KRAS mutations were more likely to occur in patients with moderate-to-high differentiation.BRAF mutations were more likely to occur in patients with right-sided CRC,poorly differentiated,or no perineural invasion.Deficient MMR(dMMR)was detected in 7.9% of all patients and 16.8% of those with mucinous adenocarcinomas.KRAS,NRAS,BRAF,and PIK3CA mutations were detected in 29.6%,1.1%,8.1%,and 22.3% of patients with dMMR,respectively.The dMMR was more likely to occur in patients with a family history of CRC,aged<50 years,right-sided CRC,poorly differentiated histology,no perineural invasion,and with carcinoma in situ,stage I,or stage II tumors.CONCLUSION This study analyzed the molecular profiles of KRAS,NRAS,BRAF,PIK3CA,and MMR/MSI in CRC,identifying key influencing factors,with implications for clinical management of CRC.

Effect of comprehensive nursing on the quality of life and swallowing function in individuals diagnosed with ischemic stroke

BACKGROUND Ischemic stroke(IS)is a widely recognized disease characterized by high preva-lence,mortality,morbidity,disability,and recurrence rates.It ranks prominently in terms of mortality,constituting 60%-80%of stroke cases.AIM To explore the impact of comprehensive nursing care on the quality of life and swallowing function in individuals diagnosed with IS.METHODS This study comprised 172 patients with IS admitted to our hospital between February 2018 to March 2021.The participants were divided into two groups,namely the control group(n=80)receiving routine care and the research group(n=92)receiving comprehensive care.Various assessment scales,including the standard swallowing function assessment scale(SSA),National Institutes of Health Stroke scale(NIHSS),European stroke scale(ESS),self-rating anxiety scale(SAS),self-rating depression scale(SDS),Barthel index(BI),and the motor func-tion assessment scale(MAS),were employed to evaluate the improvement in swallowing function,neurological deficits,clinical outcomes,anxiety,depression,daily living activities,and motor function before and after care.Furthermore,the study compared the occurrence of adverse reactions during the nursing period,life quality before and after the intervention,rehabilitation compliance,and nursing satisfaction between the two groups.RESULTS After the nursing intervention,the research group exhibited significantly improved SSA and NIHSS scores compared to the control group(P<0.05).Moreover,both groups demonstrated significant reductions in SAS and SDS scores(P<0.05),with the research group showing more obvious advantages(P<0.05).Compared to the control group,the research group displayed significantly better ESS,BI,and MAS scores(P<0.05),coupled with a lower incidence of adverse reactions(P<0.05).Additionally,the research group demonstrated markedly higher levels of life quality,rehabilitation compliance,and nursing satisfaction compared to the control group(P<0.05).CONCLUSION Comprehensive nursing effectively improved swallowing function,quality of life,and patient satisfaction,high-lighting its clinical significance.

Comprehensive Prevention and Control Technology of Vegetable Diseases and Pests in Shandong Province

An in-depth research and practice has been conducted on vegetable diseases and pests in Shandong Province,and the principles of comprehensive and ecological control of diseases and pests are put forward,including agricultural control measures such as crop rotation,field cleaning,fertilizer and water management,physical control measures such as catching and killing,trapping,blocking,photoelectric energy treatment,biological control measures such as the use of natural enemies,pathogenic microorganisms,other beneficial organisms and metabolites,and scientific and rational chemical control measures.Comprehensive prevention and control not only controls vegetable diseases and pests effectively,but also protects the ecological environment.

Clinical comprehensive treatment protocol for managing diabetic foot ulcers:A retrospective cohort study

BACKGROUND Diabetic foot ulcers(DFUs)are a common complication of diabetes,often leading to severe infections,amputations,and reduced quality of life.The current standard treatment protocols for DFUs have limitations in promoting efficient wound healing and preventing complications.A comprehensive treatment approach targeting multiple aspects of wound care may offer improved outcomes for patients with DFUs.The hypothesis of this study is that a comprehensive treatment protocol for DFUs will result in faster wound healing,reduced amputation rates,and improved overall patient outcomes compared to standard treatment protocols.AIM To compare the efficacy and safety of a comprehensive treatment protocol for DFUs with those of the standard treatment protocol.METHODS This retrospective study included 62 patients with DFUs,enrolled between January 2022 and January 2024,randomly assigned to the experimental(n=32)or control(n=30)group.The experimental group received a comprehensive treatment comprising blood circulation improvement,debridement,vacuum sealing drainage,recombinant human epidermal growth factor and anti-inflammatory dressing,and skin grafting.The control group received standard treatment,which included wound cleaning and dressing,antibiotics administration,and surgical debridement or amputation,if necessary.Time taken to reduce the white blood cell count,number of dressing changes,wound healing rate and time,and amputation rate were assessed.RESULTS The experimental group exhibited significantly better outcomes than those of the control group in terms of the wound healing rate,wound healing time,and amputation rate.Additionally,the comprehensive treatment protocol was safe and well tolerated by the patients.CONCLUSION Comprehensive treatment for DFUs is more effective than standard treatment,promoting granulation tissue growth,shortening hospitalization time,reducing pain and amputation rate,improving wound healing,and enhancing quality of life.

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.

Comprehensive Evaluation of Distributed PV Grid-Connected Based on Combined Weighting Weights and TOPSIS-RSR Method

To effectively quantify the impact of distributed photovoltaic(PV)access on the distribution network,this paper proposes a comprehensive evaluation method of distributed PV grid connection combining subjective and objective combination of assignment and technique for order preference by similarity to an ideal solution(TOPSIS)—rank sum ratio(RSR)(TOPSIS-RSR)method.Based on the traditional distribution network evaluation system,a comprehensive evaluation system has been constructed.It fully considers the new development requirements of distributed PV access on the environmental friendliness and absorptive capacity of the distribution grid and comprehensively reflects the impact of distributed PV grid connection.The analytic hierarchy process(AHP)was used to determine the subjective weights of the primary indicators,and the Spearman consistency test was combined to determine the weights of the secondary indicators based on three objective assignment methods.The subjective and objective combination weights of each assessment indicator were calculated through the principle of minimum entropy.Calculate the distance between the indicators to be evaluated and the positive and negative ideal solutions,the relative closeness ranking,and qualitative binning by TOPSIS-RSR method to obtain the comprehensive evaluation results of different scenarios.By setting up different PV grid-connected scenarios and utilizing the IEEE33 node simulation algorithm,the correctness and effectiveness of the proposed subject-object combination assignment and integrated assessment method are verified.

The Application of Formative Assessment in Interactive Comprehensive English Classroom Teaching

Teaching evaluation is an important part of the teaching process of college English courses,and effective formative assessment can reflect the entire teaching process from multiple perspectives and ensure the smooth achievement of teaching objectives.Formative assessment is an assessment that runs through the entire learning process,based on humanism and constructivism,with various forms of evaluation.

A Study on Integrated Construction of Luzhou Comprehensive Transportation Hub

The target of integrated construction of comprehensive transportation hub is to integrate the traffic resources,achieve butt joint of pan-regional transportation mode,and finally realize the"seamless connection"of the goods and"zero transfer"of the passenger traffic.Relying on the particularity of the geographical location and the convenience of the Yangtze River channel,Luzhou puts forth effort to build a comprehensive transport hub in Southern Sichuan and has made great efforts in traffic infrastructure construction.However,there are still some problems.Combining practice of Luzhou,using the advanced experience of foreign and domestic cities for reference,the paper pointed out that for the sake of constructing a comprehensive transportation hub,we need to build the traffic integration on the basis of the efficiency.Besides,the paper proposed the strategies for construction.

Comprehensive Evaluation of Flower Border Application Value of New and Superior Plants in Hefei Area

An analytic hierarchy process(AHP)was employed to assess the applicability of 18 new and superior varieties of flowers in Hefei City flower border applications.A total of 12 indicators were selected from three distinct aspects of adaptability,ornamental characteristics and use traits,in order to establish a comprehensive evaluation model.The results demonstrate that grade I(J≥2.685)exhibits excellent application value,encompassing six species of plants,such asHydrangeamacrophylla‘Endless Summer’;grade II(2.684≤J≤2.420)is also of notable application value,encompassing five species of plants,such asCallistemonrigidus;grade III(2.419≤J≤2.615)is of average application value,including five species of plants,such asCrocosmiacrocosmiflora;grade IV(J≤2.16)is of relatively poor application value.The evaluation results may be utilized as a theoretical reference for the promotion of new and superior varieties in the flower border of Hefei.