Engineering Strategies for Suppressing the Shuttle Effect in Lithium–Sulfur Batteries

Lithium–sulfur(Li–S)batteries are supposed to be one of the most potential next-generation batteries owing to their high theoretical capacity and low cost.Nevertheless,the shuttle effect of firm multi-step two-electron reaction between sulfur and lithium in liquid electrolyte makes the capacity much smaller than the theoretical value.Many methods were proposed for inhibiting the shuttle effect of polysulfide,improving corresponding redox kinetics and enhancing the integral performance of Li–S batteries.Here,we will comprehensively and systematically summarize the strategies for inhibiting the shuttle effect from all components of Li–S batteries.First,the electrochemical principles/mechanism and origin of the shuttle effect are described in detail.Moreover,the efficient strategies,including boosting the sulfur conversion rate of sulfur,confining sulfur or lithium polysulfides(LPS)within cathode host,confining LPS in the shield layer,and preventing LPS from contacting the anode,will be discussed to suppress the shuttle effect.Then,recent advances in inhibition of shuttle effect in cathode,electrolyte,separator,and anode with the aforementioned strategies have been summarized to direct the further design of efficient materials for Li–S batteries.Finally,we present prospects for inhibition of the LPS shuttle and potential development directions in Li–S batteries.

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.

Enhancing Autonomy Capability in Regional Power Grids:A Strategic Planning Approach with Multiple Autonomous Evaluation Indexes

After the integration of large-scale DistributedGeneration(DG)into the distribution network,the randomness and volatility of its output result in a reduction of spatiotemporal alignment between power generation and demand in the distribution network,exacerbating the phenomenon of wind and solar power wastage.As a novel power system model,the fundamental concept of Regional Autonomous Power Grids(RAPGs)is to achieve localized management and energy autonomy,thereby facilitating the effective consumption of DGs.Therefore,this paper proposes a distributed resource planning strategy that enhances the autonomy capabilities of regional power grids by considering multiple evaluation indexes for autonomy.First,a regional Energy Storage(ES)configuration strategy is proposed.This strategy can select a suitable reference value for the upper limit of ES configuration based on the regional load andDGoutput to maximize the elimination of source load deviations in the region as the upper limit constraint of ES capacity.Then,a control strategy for regional ES is proposed,the charging and discharging reference line of ES is set,and multiple autonomy and economic indexes are used as objective functions to select different proportions of ES to control the distributed resources of the regional power grid and establish evaluation indexes of the internal regional generation and load power ratio,the proportion of power supply matching hours,new energy consumption rate and tie line power imbalance outside the region to evaluate changes in the regional autonomy capabilities.The final simulation results showthat in the real regional grid example,the planning method in the planning year in the region of the overall power supply matching hour ratio and new energy consumption rate increased by 3.9%and 4.8%on average,and the power imbalance of the tie line decreased by 7.8%on average.The proposed planning approach enables the maximization of regional autonomy while effectively smoothing the fluctuation of power exchange between the regional grid and the higher-level grid.This presents a rational and effective planning solution for the regional grid,facilitating the coordinated development between the region and the distribution network.

Motion Planning for Autonomous Driving with Real Traffic Data Validation

Accurate trajectory prediction of surrounding road users is the fundamental input for motion planning,which enables safe autonomous driving on public roads.In this paper,a safe motion planning approach is proposed based on the deep learning-based trajectory prediction method.To begin with,a trajectory prediction model is established based on the graph neural network(GNN)that is trained utilizing the INTERACTION dataset.Then,the validated trajectory prediction model is used to predict the future trajectories of surrounding road users,including pedestrians and vehicles.In addition,a GNN prediction model-enabled motion planner is developed based on the model predictive control technique.Furthermore,two driving scenarios are extracted from the INTERACTION dataset to validate and evaluate the effectiveness of the proposed motion planning approach,i.e.,merging and roundabout scenarios.The results demonstrate that the proposed method can lower the risk and improve driving safety compared with the baseline method.

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.

How do the landslide and non-landslide sampling strategies impact landslide susceptibility assessment? d A catchment-scale case study from China

The aim of this study is to investigate the impacts of the sampling strategy of landslide and non-landslide on the performance of landslide susceptibility assessment(LSA).The study area is the Feiyun catchment in Wenzhou City,Southeast China.Two types of landslides samples,combined with seven non-landslide sampling strategies,resulted in a total of 14 scenarios.The corresponding landslide susceptibility map(LSM)for each scenario was generated using the random forest model.The receiver operating characteristic(ROC)curve and statistical indicators were calculated and used to assess the impact of the dataset sampling strategy.The results showed that higher accuracies were achieved when using the landslide core as positive samples,combined with non-landslide sampling from the very low zone or buffer zone.The results reveal the influence of landslide and non-landslide sampling strategies on the accuracy of LSA,which provides a reference for subsequent researchers aiming to obtain a more reasonable LSM.

Path Planning and Tracking Control for Parking via Soft Actor-Critic Under Non-Ideal Scenarios

Parking in a small parking lot within limited space poses a difficult task. It often leads to deviations between the final parking posture and the target posture. These deviations can lead to partial occupancy of adjacent parking lots, which poses a safety threat to vehicles parked in these parking lots. However, previous studies have not addressed this issue. In this paper, we aim to evaluate the impact of parking deviation of existing vehicles next to the target parking lot(PDEVNTPL) on the automatic ego vehicle(AEV) parking, in terms of safety, comfort, accuracy, and efficiency of parking. A segmented parking training framework(SPTF) based on soft actor-critic(SAC) is proposed to improve parking performance. In the proposed method, the SAC algorithm incorporates strategy entropy into the objective function, to enable the AEV to learn parking strategies based on a more comprehensive understanding of the environment. Additionally, the SPTF simplifies complex parking tasks to maintain the high performance of deep reinforcement learning(DRL). The experimental results reveal that the PDEVNTPL has a detrimental influence on the AEV parking in terms of safety, accuracy, and comfort, leading to reductions of more than 27%, 54%, and 26%respectively. However, the SAC-based SPTF effectively mitigates this impact, resulting in a considerable increase in the parking success rate from 71% to 93%. Furthermore, the heading angle deviation is significantly reduced from 2.25 degrees to 0.43degrees.

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.

Efficient Penetration Testing Path Planning Based on Reinforcement Learning with Episodic Memory

Intelligent penetration testing is of great significance for the improvement of the security of information systems,and the critical issue is the planning of penetration test paths.In view of the difficulty for attackers to obtain complete network information in realistic network scenarios,Reinforcement Learning(RL)is a promising solution to discover the optimal penetration path under incomplete information about the target network.Existing RL-based methods are challenged by the sizeable discrete action space,which leads to difficulties in the convergence.Moreover,most methods still rely on experts’knowledge.To address these issues,this paper proposes a penetration path planning method based on reinforcement learning with episodic memory.First,the penetration testing problem is formally described in terms of reinforcement learning.To speed up the training process without specific prior knowledge,the proposed algorithm introduces episodic memory to store experienced advantageous strategies for the first time.Furthermore,the method offers an exploration strategy based on episodic memory to guide the agents in learning.The design makes full use of historical experience to achieve the purpose of reducing blind exploration and improving planning efficiency.Ultimately,comparison experiments are carried out with the existing RL-based methods.The results reveal that the proposed method has better convergence performance.The running time is reduced by more than 20%.

Practical Strategies for Urban Rail Transit Loop Planning, Construction, and Operation

Urban rail transit loops are essential in urban rail transit systems and transportation networks.However,precise requirements and reference standards for rail transit loop design have yet to be established.There are certain areas for improvement in planning,construction,and operation.In the planning and design of urban rail transit loops,the scale of the city and the relationship between line operations should be considered to ensure that the line conforms to the city’s operating traffic conditions and can effectively cater to peak passenger flow requirements.This article presents strategies for planning,constructing,and operating urban rail transit loops,laying the foundation for the healthy operation of urban rail transit.

Selection of media tools for community planning:A study from the perspective of communicative planning

In community planning,due to the lack of evidence regarding the selection of media tools,this study examines how a common but differentiated ideal speech situation can be created as well as how more appropriate media tools can be defined and selected in the community planning process.First,this study describes the concept and theoretical basis of media used in community planning from the perspectives of the multiple effects of media evolution on communicative planning.Second,the classification criteria and typical characteristics of media tools used to support community planning are clarified from three dimensions:acceptability,cost effectiveness,and applicability.Third,strategies for applying media tools in the four phases of communicative planning-namely,state analysis,problem identification,contradictory solution and optimization-are described.Finally,trends in the development of media tools for community planning are explored in terms of multistakeholder engagement,supporting scientific decision-making and multiple-type media integration.The results provide a reference for developing more inclusive,effective,and appropriate media tools for enhancing decision-making capacity and modernizing governance in community planning and policy-making processes.

Optimal search path planning of UUV in battlefeld ambush scene

Aiming at the practical application of Unmanned Underwater Vehicle(UUV)in underwater combat,this paper proposes a battlefield ambush scene with UUV considering ocean current.Firstly,by establishing these mathematical models of ocean current environment,target movement,and sonar detection,the probability calculation methods of single UUV searching target and multiple UUV cooperatively searching target are given respectively.Then,based on the Hybrid Quantum-behaved Particle Swarm Optimization(HQPSO)algorithm,the path with the highest target search probability is found.Finally,through simulation calculations,the influence of different UUV parameters and target parameters on the target search probability is analyzed,and the minimum number of UUVs that need to be deployed to complete the ambush task is demonstrated,and the optimal search path scheme is obtained.The method proposed in this paper provides a theoretical basis for the practical application of UUV in the future combat.

Strategic Planning Practices of Hotels in Kenya

This study broadens perspectives of strategic planning procedures in hotels by analyzing hotel managers’perspectives on hotel strategy formulation and planning.The study was a descriptive quantitative study that targeted managers from various Kenyan hotels.180 questionnaires were returned from a sample of 280 hotel managers who had attended management development programs at a hospitality school.The primary goal of the study was to analyze two aspects of the strategic planning process:strategy formulation and strategic planning.The findings revealed that hotels in Kenya have developed methods and are keen on scanning their external environments.However,with the product offerings being so comparable,competitive analysis played an important role in the context.Creating a unique or specialty offering was not a popular option for most hotels.Cost-cutting was the most favored strategic inclination to achieve desired results.The balanced scorecard was underutilized in the sector.While managers were involved in the strategic planning process,other lower cadre employees were not,they were provided with the targets for the hotels’organizational goals to be achieved.The findings reveal that with similar products and services,the industry creates a hostile business climate with fierce rivalry and must invest in the most obvious alternative of cost reduction to survive.Alternative product innovation is not common in this industry for developing a differentiation approach that allows a property to control a market.

LDAS&ET-AD:Learnable Distillation Attack Strategies and Evolvable Teachers Adversarial Distillation

Adversarial distillation(AD)has emerged as a potential solution to tackle the challenging optimization problem of loss with hard labels in adversarial training.However,fixed sample-agnostic and student-egocentric attack strategies are unsuitable for distillation.Additionally,the reliability of guidance from static teachers diminishes as target models become more robust.This paper proposes an AD method called Learnable Distillation Attack Strategies and Evolvable Teachers Adversarial Distillation(LDAS&ET-AD).Firstly,a learnable distillation attack strategies generating mechanism is developed to automatically generate sample-dependent attack strategies tailored for distillation.A strategy model is introduced to produce attack strategies that enable adversarial examples(AEs)to be created in areas where the target model significantly diverges from the teachers by competing with the target model in minimizing or maximizing the AD loss.Secondly,a teacher evolution strategy is introduced to enhance the reliability and effectiveness of knowledge in improving the generalization performance of the target model.By calculating the experimentally updated target model’s validation performance on both clean samples and AEs,the impact of distillation from each training sample and AE on the target model’s generalization and robustness abilities is assessed to serve as feedback to fine-tune standard and robust teachers accordingly.Experiments evaluate the performance of LDAS&ET-AD against different adversarial attacks on the CIFAR-10 and CIFAR-100 datasets.The experimental results demonstrate that the proposed method achieves a robust precision of 45.39%and 42.63%against AutoAttack(AA)on the CIFAR-10 dataset for ResNet-18 and MobileNet-V2,respectively,marking an improvement of 2.31%and 3.49%over the baseline method.In comparison to state-of-the-art adversarial defense techniques,our method surpasses Introspective Adversarial Distillation,the top-performing method in terms of robustness under AA attack for the CIFAR-10 dataset,with enhancements of 1.40%and 1.43%for ResNet-18 and MobileNet-V2,respectively.These findings demonstrate the effectiveness of our proposed method in enhancing the robustness of deep learning networks(DNNs)against prevalent adversarial attacks when compared to other competing methods.In conclusion,LDAS&ET-AD provides reliable and informative soft labels to one of the most promising defense methods,AT,alleviating the limitations of untrusted teachers and unsuitable AEs in existing AD techniques.We hope this paper promotes the development of DNNs in real-world trust-sensitive fields and helps ensure a more secure and dependable future for artificial intelligence systems.

Eradication Treatment of Helicobacter pylori Infection: Evaluation of Therapeutic Strategies in N’Djamena

Background: Helicobacter pylori (Hp) infection is the most widespread bacterial infection in the world. The infection is generally acquired in childhood, but can persist into adulthood. Eradication therapy has undergone several modifications. The aim of this study was to evaluate the different therapeutic strategies used in the eradication of Helicobacter pylori infection in the Centre Hospitalier Universitaire La Reference Nationale of N’Djaména. Patients and Methods: This was a prospective, descriptive analytical study spread over one year, from September 2021 to September 2022. Patients at least 15 years of age presenting with dyspeptic symptoms, seen consecutively in a hepato-gastroenterology consultation and with a positive stool test for H. pylori infection, were included in the study. Equally, 1/3 of patients were treated with dual or triple therapy. The remaining third received quadritherapy. Results: A total of 268 patients were included in the study (mean age 38.40 ± 14.66 with extremes of 16 and 80 years). Males predominated in 58% of cases. Overall therapeutic efficacy was 88.9%. According to different therapeutic strategies, efficacy was 90.75% for dual therapy with PPI (Rabeprazole) and Amoxicillin. On the other hand, efficacy was 87% and 88.88% for PPI-based triple therapy and dual antibiotic therapy, and for PPI-based quadruple therapy and triple antibiotic therapy. Conclusion: H. pylori infection is a common disease in Chad. Dual therapy with rabeprazole combined with a high dose of amoxicillin over a period of at least two weeks showed similar if not better efficacy than triple or quadruple therapy.

Efficient Route Planning for Real-Time Demand-Responsive Transit

Demand Responsive Transit (DRT) responds to the dynamic users’ requests without any fixed routes and timetablesand determines the stop and the start according to the demands. This study explores the optimization of dynamicvehicle scheduling and real-time route planning in urban public transportation systems, with a focus on busservices. It addresses the limitations of current shared mobility routing algorithms, which are primarily designedfor simpler, single origin/destination scenarios, and do not meet the complex demands of bus transit systems. Theresearch introduces an route planning algorithm designed to dynamically accommodate passenger travel needsand enable real-time route modifications. Unlike traditional methods, this algorithm leverages a queue-based,multi-objective heuristic A∗ approach, offering a solution to the inflexibility and limited coverage of suburbanbus routes. Also, this study conducts a comparative analysis of the proposed algorithm with solutions based onGenetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO), focusing on calculation time, routelength, passenger waiting time, boarding time, and detour rate. The findings demonstrate that the proposedalgorithmsignificantly enhances route planning speed, achieving an 80–100-fold increase in efficiency over existingmodels, thereby supporting the real-time demands of Demand-Responsive Transportation (DRT) systems. Thestudy concludes that this algorithm not only optimizes route planning in bus transit but also presents a scalablesolution for improving urban mobility.

Live-streaming selling strategies for competitive firms

The booming live-streaming commerce has significantly changed the traditional e-commerce model,thus attracting much attention from both industry and academia.In recent years,an increasing number of scholars have applied analytical models to explore live-streaming strategies for firms in different scenarios.However,the previous literature mainly considers monopolists,while in the real world,competition is not rare.To fill this gap between the literature and practical observations,this paper applies a game theoretical model to study live-streaming adoption and pricing strategy for firms under competitive environments.The results show that,for competitive firms,the equilibrium strategy depends on the relation between the commission rate and the intensity of the market expansion effect.Additionally,compared to the case in which no firm adopts live-streaming,competitive firms do not always benefit from the adoption of live-streaming selling.The paper also shows that competition plays a negative role in inducing a firm to adopt live-streaming.

Spheroid construction strategies and application in 3D bioprinting

Tissue engineering has been striving toward designing and producing natural and functional human tissues.Cells are the fundamental building blocks of tissues.Compared with traditional two-dimensional cultured cells,cell spheres are threedimensional(3D)structures that can naturally form complex cell–cell and cell–matrix interactions.This structure is close to the natural environment of cells in living organisms.In addition to being used in disease modeling and drug screening,spheroids have significant potential in tissue regeneration.The 3D bioprinting is an advanced biofabrication technique.It accurately deposits bioinks into predesigned 3D shapes to create complex tissue structures.Although 3D bioprinting is efficient,the time required for cells to develop into complex tissue structures can be lengthy.The 3D bioprinting of spheroids significantly reduces the time required for their development into large tissues/organs during later cultivation stages by printing them with high cell density.Combining spheroid fabrication and bioprinting technology should provide a new solution to many problems in regenerative medicine.This paper systematically elaborates and analyzes the spheroid fabrication methods and 3D bioprinting strategies by introducing spheroids as building blocks.Finally,we present the primary challenges faced by spheroid fabrication and 3D bioprinting with future requirements and some recommendations.

Accurate models and nutritional strategies for specific oxidative stress factors: Does the dose matter in swine production?

Oxidative stress has been associated with a number of physiological problems in swine,including reduced production efficiency.Recently,although there has been increased research into regulatory mechanisms and antioxidant strategies in relation to oxidative stress-induced pig production,it remains so far largely unsuccessful to develop accurate models and nutritional strategies for specific oxidative stress factors.Here,we discuss the dose and dose intensity of the causes of oxidative stress involving physiological,environmental and dietary factors,recent research models and the antioxidant strategies to provide theoretical guidance for future oxidative stress research in swine.

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.