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.

Regeneration of the heart:f rom molecular mechanisms to clinical therapeutics

Heart injury such as myocardial infarction leads to cardiomyocyte loss,fibrotic tissue deposition,and scar formation.These changes reduce cardiac contractility,resulting in heart failure,which causes a huge public health burden.Military personnel,compared with civilians,is exposed to more stress,a risk factor for heart diseases,making cardiovascular health management and treatment innovation an important topic for military medicine.So far,medical intervention can slow down cardiovascular disease progression,but not yet induce heart regeneration.In the past decades,studies have focused on mechanisms underlying the regenerative capability of the heart and applicable approaches to reverse heart injury.Insights have emerged from studies in animal models and early clinical trials.Clinical interventions show the potential to reduce scar formation and enhance cardiomyocyte proliferation that counteracts the pathogenesis of heart disease.In this review,we discuss the signaling events controlling the regeneration of heart tissue and summarize current therapeutic approaches to promote heart regeneration after injury.

Drug resistance mechanisms in cancers:Execution of prosurvival strategies

One of the quintessential challenges in cancer treatment is drug resistance.Several mechanisms of drug resistance have been described to date,and new modes of drug resistance continue to be discovered.The phenomenon of cancer drug resistance is now widespread,with approximately 90% of cancer-related deaths associated with drug resistance.Despite significant advances in the drug discovery process,the emergence of innate and acquired mechanisms of drug resistance has impeded the progress in cancer therapy.Therefore,understanding the mechanisms of drug resistance and the various pathways involved is integral to treatment modalities.In the present review,I discuss the different mechanisms of drug resistance in cancer cells,including DNA damage repair,epithelial to mesenchymal transition,inhibition of cell death,alteration of drug targets,inactivation of drugs,deregulation of cellular energetics,immune evasion,tumor-promoting inflammation,genome instability,and other contributing epigenetic factors.Furthermore,I highlight available treatment options and conclude with future directions.

Overview of the immunological mechanisms in hepatitis B virus reactivation:Implications for disease progression and management strategies

Hepatitis B virus(HBV)reactivation is a clinically significant challenge in disease management.This review explores the immunological mechanisms underlying HBV reactivation,emphasizing disease progression and management.It delves into host immune responses and reactivation’s delicate balance,spanning innate and adaptive immunity.Viral factors’disruption of this balance,as are interac-tions between viral antigens,immune cells,cytokine networks,and immune checkpoint pathways,are examined.Notably,the roles of T cells,natural killer cells,and antigen-presenting cells are discussed,highlighting their influence on disease progression.HBV reactivation’s impact on disease severity,hepatic flares,liver fibrosis progression,and hepatocellular carcinoma is detailed.Management strategies,including anti-viral and immunomodulatory approaches,are critically analyzed.The role of prophylactic anti-viral therapy during immunosuppressive treatments is explored alongside novel immunotherapeutic interventions to restore immune control and prevent reactivation.In conclusion,this compre-hensive review furnishes a holistic view of the immunological mechanisms that propel HBV reactivation.With a dedicated focus on understanding its implic-ations for disease progression and the prospects of efficient management stra-tegies,this article contributes significantly to the knowledge base.The more profound insights into the intricate interactions between viral elements and the immune system will inform evidence-based approaches,ultimately enhancing disease management and elevating patient outcomes.The dynamic landscape of management strategies is critically scrutinized,spanning anti-viral and immunomodulatory approaches.The role of prophylactic anti-viral therapy in preventing reactivation during immunosuppressive treatments and the potential of innovative immunotherapeutic interventions to restore immune control and proactively deter reactivation.

Role of methoxy and C_(α)-based substituents in electrochemical oxidation mechanisms and bond cleavage selectivity of β-O-4 lignin model compounds

In order to better understand the specific substituent effects on the electrochemical oxidation process of β-O-4 bond, a series of methoxyphenyl type β-O-4 dimer model compounds with different localized methoxyl groups, including 2-(2-methoxyphenoxy)-1-phenylethanone, 2-(2-methoxyphenoxy)-1-phenylethanol, 2-(2-methoxyphenoxy)-1-(4-methoxyphenyl)ethanone, 2-(2-methoxyphenoxy)-1-(4-methoxyphenyl)ethanol, 2-(2,6-dimethoxyphenoxy)-1-(4-methoxyphenyl)ethanone, 2-(2,6-dimethoxyphenoxy)-1-(4-methoxyphenyl)ethanol have been selected and their electrochemical properties have been studied experimentally by cyclic voltammetry, and FT-IR spectroelectrochemistry. Combining with electrolysis products distribution analysis and density functional theory calculations, oxidation mechanisms of all six model dimers have been explored. In particular, a total effect from substituents of both para-methoxy(on the aryl ring closing to Cα) and Cα-OH on the oxidation mechanisms has been clearly observed, showing a significant selectivity on the Cα-Cβbond cleavage induced by electrochemical oxidations.

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.

Deep tectonics and seismogenic mechanisms of the seismic source zone of the Jishishan M_(s)6.2 earthquake on December 18,2023,at the northeast margin of the Tibetan Plateau

On December 18,2023,an M_(s)6.2 earthquake occurred in Jishishan,Gansu Province,China.This earthquake happened in the eastern region of the Qilian Orogenic Belt,which is situated at the forefront of the NE margin of the Tibetan Plateau(i.e.,Qinghai-Tibet Plateau),encompassing a rhombic-shaped area that intersects the Qilian-Qaidam Basin,Alxa Block,Ordos Block,and South China Block.In this study,we analyzed the deep tectonic pattern of the Jishishan earthquake by incorporating data on the crustal thickness,velocity structure,global navigation satellite system(GNSS)strain field,and anisotropy.We discovered that the location of the earthquake was related to changes in the crustal structure.The results showed that the Jishishan M_(s)6.2 earthquake occurred in a unique position,with rapid changes in the crustal thickness,Vp/Vs,phase velocity,and S-wave velocity.The epicenter of the earthquake was situated at the transition zone between high and low velocities and was in proximity to a low-velocity region.Additionally,the source area is flanked by two high-velocity anomalies from the east and west.The principal compressive strain orientation near the Lajishan Fault is primarily in the NNE and NE directions,which align with the principal compressive stress direction in this region.In some areas of the Lajishan Fault,the principal compressive strain orientations show the NNW direction,consistent with the direction of the upper crustal fast-wave polarization from local earthquakes and the phase velocity azimuthal anisotropy.These features underscore the relationship between the occurrence of the Jishishan M_(s)6.2 earthquake and the deep inhomogeneous structure and deep tectonic characteristics.The NE margin of the Tibetan Plateau was thickened by crustal extension in the process of northeastward expansion,and the middle and lower crustal materials underwent structural deformation and may have been filled with salt-containing fluids during the extension process.The presence of this weak layer makes it easier for strong earthquakes to occur through the release of overlying rigid crustal stresses.However,it is unlikely that an earthquake of comparable or larger magnitude would occur in the short term(e.g.,in one year)at the Jishishan east margin fault.

Interplay between temperature-dependent strengthening mechanisms and mechanical stability in high-performance austenitic stainless steels

The effects of deformation temperature on the transformation-induced plasticity(TRIP)-aided 304L,twinning-induced plasti-city(TWIP)-assisted 316L,and highly alloyed stable 904L austenitic stainless steels were compared for the first time to tune the mechan-ical properties,strengthening mechanisms,and strength-ductility synergy.For this purpose,the scanning electron microscopy(SEM),electron backscattered diffraction(EBSD),X-ray diffraction(XRD),tensile testing,work-hardening analysis,and thermodynamics calcu-lations were used.The induced plasticity effects led to a high temperature-dependency of work-hardening behavior in the 304L and 316L stainless steels.As the deformation temperature increased,the metastable 304L stainless steel showed the sequence of TRIP,TWIP,and weakening of the induced plasticity mechanism;while the disappearance of the TWIP effect in the 316L stainless steel was also observed.However,the solid-solution strengthening in the 904L superaustenitic stainless steel maintained the tensile properties over a wide temper-ature range,surpassing the performance of 304L and 316L stainless steels.In this regard,the dependency of the total elongation on the de-formation temperature was less pronounced for the 904L alloy due to the absence of additional plasticity mechanisms.These results re-vealed the importance of solid-solution strengthening and the associated high friction stress for superior mechanical behavior over a wide temperature range.

A molecular dynamics study on mechanical performance and deformation mechanisms in nanotwinned NiCo-based alloys with nano-precipitates under high temperatures

Molecular dynamics simulations are performed to investigate the mechanical behavior of nanotwinned NiCo-based alloys containing coherent L12 nano-precipitates at different temperatures,as well as the interactions between the dislocations and nano-precipitates within the nanotwins.The simulation results demonstrate that both the yield stress and flow stress in the nanotwinned NiCo-based alloys with nano-precipitates decrease as the temperature rises,because the higher temperatures lead to the generation of more defects during yielding and lower dislocation density during plastic deformation.Moreover,the coherent L12 phase exhibits excellent thermal stability,which enables the hinderance of dislocation motion at elevated temperatures via the wrapping and cutting mechanisms of dislocations.The synergistic effect of nanotwins and nano-precipitates results in more significant strengthening behavior in the nanotwinned NiCo-based alloys under high temperatures.In addition,the high-temperature mechanical behavior of nanotwinned NiCo-based alloys with nano-precipitates is sensitive to the size and volume fraction of the microstructures.These findings could be helpful for the design of nanotwins and nano-precipitates to improve the high-temperature mechanical properties of NiCo-based alloys.

Determination of the best materials for development and designing product using a multi-criteria decision-making

Purpose–Material selection,driven by wide and often conflicting objectives,is an important,sometimes difficult problem in material engineering.In this context,multi-criteria decision-making(MCDM)methodologies are effective.An approach of MCDM is needed to cater to criteria of material assortment simultaneously.More firms are now concerned about increasing their productivity using mathematical tools.To occupy a gap in the previous literature this research recommends an integrated MCDM and mathematical Bi-objective model for the selection of material.In addition,by using the Technique for Order Preference by Similarity to Ideal Solution(TOPSIS),the inherent ambiguities of decision-makers in paired evaluations are considered in this research.It goes on to construct a mathematical bi-objective model for determining the best item to purchase.Design/methodology/approach–The entropy perspective is implemented in this paper to evaluate the weight parameters,while the TOPSIS technique is used to determine the best and worst intermediate pipe materials for automotive exhaust system.The intermediate pipes are used to join the components of the exhaust systems.The materials usually used to manufacture intermediate pipe are SUS 436LM,SUS 430,SUS 304,SUS 436L,SUH 409 L,SUS 441 L and SUS 439L.These seven materials are evaluated based on tensile strength(TS),hardness(H),elongation(E),yield strength(YS)and cost(C).A hybrid methodology combining entropy-based criteria weighting,with the TOPSIS for alternative ranking,is pursued to identify the optimal design material for an engineered application in this paper.This study aims to help while filling the information gap in selecting the most suitable material for use in the exhaust intermediate pipes.After that,the authors searched for and considered eight materials and evaluated them on the following five criteria:(1)TS,(2)YS,(3)H,(4)E and(5)C.The first two criteria have been chosen because they can have a lot of influence on the behavior of the exhaust intermediate pipes,on their performance and on the cost.In this structure,the weights of the criteria are calculated objectively through the entropy method in order to have an unbiased assessment.This essentially measures the quantity of information each criterion contribution,indicating the relative importance of these criteria better.Subsequently,the materials were ranked using the TOPSIS method in terms of their relative performance by measuring each material from an ideal solution to determine the best alternative.The results show that SUS 309,SUS 432L and SUS 436 LM are the first three materials that the exhaust intermediate pipe optimal design should consider.Findings–The material matrix of the decision presented in Table 3 was normalized through Equation 5,as shown in Table 5,and the matrix was multiplied with weighting criteriaß_j.The obtained weighted normalized matrix V_ij is presented in Table 6.However,the ideal,worst and best value was ascertained by employing Equation 7.This study is based on the selection of material for the development of intermediate pipe using MCDM,and it involves four basic stages,i.e.method of translation criteria,screening process,method of ranking and search for methods.The selection was done through the TOPSIS method,and the criteria weight was obtained by the entropy method.The result showed that the top three materials are SUS 309,SUS 432L and SUS 436 LM,respectively.For the future work,it is suggested to select more alternatives and criteria.The comparison can also be done by using different MCDM techniques like and Choice Expressing Reality(ELECTRE),Decision-Making Trial and Evaluation Laboratory(DEMATEL)and Preference Ranking Organization Method for Enrichment Evaluation(PROMETHEE).Originality/value–The results provide important conclusions for material selection in this targeted application,verifying the employment of mutual entropy-TOPSIS methodology for a series of difficult engineering decisions in material engineering concepts that combine superior capacity with better performance as well as cost-efficiency in various engineering design.

Activity-dependent mechanisms of neuroprotection:promising avenues against dementia

The study of the brain and its complex functions is highly fascinating and,at the same time,extremely important.Indeed,furthering our understanding of the biology of neurons and synapses is a prerequisite to uncover the mechanisms involved in memory formation and the coordination of movement as well as their alterations occurring in several neurological disorders.

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.

Wnt/β-catenin signaling components and mechanisms in bone formation,homeostasis,and disease

Wnts are secreted,lipid-modified proteins that bind to different receptors on the cell surface to activate canonical or non-canonical Wnt signaling pathways,which control various biological processes throughout embryonic development and adult life.Aberrant Wnt signaling pathway underlies a wide range of human disease pathogeneses.

Contribution of mechanical forces to structural synaptic plasticity:insights from 3D cellular motility mechanisms

Cells,tissues,and organs are constantly subjected to the action of mechanical forces from the extracellular environment-and the nervous system is no exception.Cell-intrinsic properties such as membrane lipid composition,abundance of mechanosensors,and cytoskeletal dynamics make cells more or less likely to sense these forces.Intrinsic and extrinsic cues are integrated by cells and this combined information determines the rate and dynamics of membrane protrusion growth or retraction(Yamada and Sixt,2019).Cell protrusions are extensions of the plasma membrane that play crucial roles in diverse contexts such as cell migration and neuronal synapse formation.In the nervous system,neurons are highly dynamic cells that can change the size and number of their pre-and postsynaptic elements(called synaptic boutons and dendritic spines,respectively),in response to changes in the levels of synaptic activity through a process called plasticity.Synaptic plasticity is a hallmark of the nervous system and is present throughout our lives,being required for functions like memory formation or the learning of new motor skills(Minegishi et al.,2023;Pillai and Franze,2024).

Obituary:Prof.Yun Zhang(1963-2023)-A scientist focused on toxins and their underlying mechanisms to decipher human diseases

Prof.Yun Zhang was born on 9 July 1963 in Kunming,Yunnan,China,during a tumultuous period which he often referenced.Throughout his life,he harbored a steadfast belief in using knowledge to unravel the mysteries of human diseases.His educational journey was marked by frequent changes in schools due to his parents’occupational relocations.However,despite these challenges,he consistently displayed diligence and was admitted to the East China University of Science and Technology,Shanghai,after completing high school in 1980.He remained an active and loyal member of the School of Biotechnology at the university.

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.