Impacts of bus holding strategy on the performance and pollutant emissions of a two-lane mixed traffic system

This paper investigates the impacts of a bus holding strategy on the mutual interference between buses and passenger cars in a non-dedicated bus route,as well as the impacts on the characteristics of pollutant emissions of passenger cars.The dynamic behaviors of these two types of vehicles are described using cellular automata(CA)models under open boundary conditions.Numerical simulations are carried out to obtain the phase diagrams of the bus system and the trajectories of buses and passenger cars before and after the implementation of the bus holding strategy under different probabilities of passenger cars entering a two-lane mixed traffic system.Then,we analyze the flow rate,satisfaction rate,and pollutant emission rates of passenger cars together with the performance of a mixed traffic system.The results show that the bus holding strategy can effectively alleviate bus bunching,whereas it has no significant impact on the flow rate and pollutant emission rates of passenger cars;the flow rate,satisfaction rate,and pollutant emission rates of passenger cars for either the traffic system or for each lane are influenced by the bus departure interval and the number of passengers arriving at bus stops.

Research on the Control Strategy of Micro Wind-Hydrogen Coupled System Based on Wind Power Prediction and Hydrogen Storage System Charging/Discharging Regulation

This paper addresses the micro wind-hydrogen coupled system,aiming to improve the power tracking capability of micro wind farms,the regulation capability of hydrogen storage systems,and to mitigate the volatility of wind power generation.A predictive control strategy for the micro wind-hydrogen coupled system is proposed based on the ultra-short-term wind power prediction,the hydrogen storage state division interval,and the daily scheduled output of wind power generation.The control strategy maximizes the power tracking capability,the regulation capability of the hydrogen storage system,and the fluctuation of the joint output of the wind-hydrogen coupled system as the objective functions,and adaptively optimizes the control coefficients of the hydrogen storage interval and the output parameters of the system by the combined sigmoid function and particle swarm algorithm(sigmoid-PSO).Compared with the real-time control strategy,the proposed predictive control strategy can significantly improve the output tracking capability of the wind-hydrogen coupling system,minimize the gap between the actual output and the predicted output,significantly enhance the regulation capability of the hydrogen storage system,and mitigate the power output fluctuation of the wind-hydrogen integrated system,which has a broad practical application prospect.

The Caching and Pricing Strategy for Information-Centric Networking with Advertisers’Participation

As users’access to the network has evolved into the acquisition of mass contents instead of IP addresses,the IP network architecture based on end-to-end communication cannot meet users’needs.Therefore,the Information-Centric Networking(ICN)came into being.From a technical point of view,ICN is a promising future network architecture.Researching and customizing a reasonable pricing mechanism plays a positive role in promoting the deployment of ICN.The current research on ICN pricing mechanism is focused on paid content.Therefore,we study an ICN pricing model for free content,which uses game theory based on Nash equilibrium to analysis.In this work,advertisers are considered,and an advertiser model is established to describe the economic interaction between advertisers and ICN entities.This solution can formulate the best pricing strategy for all ICN entities and maximize the benefits of each entity.Our extensive analysis and numerical results show that the proposed pricing framework is significantly better than existing solutions when it comes to free content.

Genome-wide SNP markers provided insights into the reproductive strategy and genetic diversity of the green tide causative species Ulva prolifera in China

Ulva prolifera is the causative species of the annually occurring large-scale green tides in China since 2007.Its specific biological features on reproductivity strategies,as well as intra-species genetic diversity,are still largely unknown,especially at the genome level,despite their importance in understanding the formation and outbreak of massive green tides.In the present study,the restriction site-associated DNA genotyping approach(2b-RAD)was adopted to identify the genome-wide single-nucleotide polymorphisms(SNPs)of 54 individual thalli including samples collected from Subei Shoal in 2019 and Qingdao coast from 2019 to 2021.SNPs genotype results revealed that most of the thalli in 2019 and 2020 were haploid gametophytes,while only half of the thalli were gametophytes in 2021,indicating flexibility in the reproductive strategies for the formation of the green tides among different years and the dominance of asexual and vegetative reproductive mode for the floating period.Besides,population analysis was conducted,and it revealed a very low genetic diversity among samples from Subei Shoal and the Qingdao coast in the same year and a higher divergence among samples in different years.The results showed the efficiency of 2b-RAD in the exploration of SNPs in U.prolifera and provided the first genome-wide scale evidence for the origin of the large-scale green tides on the Qingdao coast.This study improved our understanding of the reproductive strategy and genetic diversity of the green tide causative species and will help further reveal the biological causes of the green tide in China.

Ultimately Bounded Output Feedback Control for Networked Nonlinear Systems With Unreliable Communication Channel: A Buffer-Aided Strategy

This paper concerns ultimately bounded output-feedback control problems for networked systems with unknown nonlinear dynamics. Sensor-to-observer signal transmission is facilitated over networks that has communication constraints.These transmissions are carried out over an unreliable communication channel. In order to enhance the utilization rate of measurement data, a buffer-aided strategy is novelly employed to store historical measurements when communication networks are inaccessible. Using the neural network technique, a novel observer-based controller is introduced to address effects of signal transmission behaviors and unknown nonlinear dynamics.Through the application of stochastic analysis and Lyapunov stability, a joint framework is constructed for analyzing resultant system performance under the introduced controller. Subsequently, existence conditions for the desired output-feedback controller are delineated. The required parameters for the observerbased controller are then determined by resolving some specific matrix inequalities. Finally, a simulation example is showcased to confirm method efficacy.

Reshaping Li–Mg hybrid batteries:Epitaxial electrodeposition and spatial confinement on MgMOF substrates via the lattice‐matching strategy

The emergence of Li–Mg hybrid batteries has been receiving attention,owing to their enhanced electrochemical kinetics and reduced overpotential.Nevertheless,the persistent challenge of uneven Mg electrodeposition remains a significant impediment to their practical integration.Herein,we developed an ingenious approach that centered around epitaxial electrocrystallization and meticulously controlled growth of magnesium crystals on a specialized MgMOF substrate.The chosen MgMOF substrate demonstrated a robust affinity for magnesium and showed minimal lattice misfit with Mg,establishing the crucial prerequisites for successful heteroepitaxial electrocrystallization.Moreover,the incorporation of periodic electric fields and successive nanochannels within the MgMOF structure created a spatially confined environment that considerably promoted uniform magnesium nucleation at the molecular scale.Taking inspiration from the“blockchain”concept prevalent in the realm of big data,we seamlessly integrated a conductive polypyrrole framework,acting as a connecting“chain,”to interlink the“blocks”comprising the MgMOF cavities.This innovative design significantly amplified charge‐transfer efficiency,thereby increasing overall electrochemical kinetics.The resulting architecture(MgMOF@PPy@CC)served as an exceptional host for heteroepitaxial Mg electrodeposition,showcasing remarkable electrostripping/plating kinetics and excellent cycling performance.Surprisingly,a symmetrical cell incorporating the MgMOF@PPy@CC electrode demonstrated impressive stability even under ultrahigh current density conditions(10mAcm^(–2)),maintaining operation for an extended 1200 h,surpassing previously reported benchmarks.Significantly,on coupling the MgMOF@PPy@CC anode with a Mo_(6)S_(8) cathode,the assembled battery showed an extended lifespan of 10,000 cycles at 70 C,with an outstanding capacity retention of 96.23%.This study provides a fresh perspective on the rational design of epitaxial electrocrystallization driven by metal–organic framework(MOF)substrates,paving the way toward the advancement of cuttingedge batteries.

Reinforcement Learning-Based Energy Management for Hybrid Power Systems:State-of-the-Art Survey,Review,and Perspectives

The new energy vehicle plays a crucial role in green transportation,and the energy management strategy of hybrid power systems is essential for ensuring energy-efficient driving.This paper presents a state-of-the-art survey and review of reinforcement learning-based energy management strategies for hybrid power systems.Additionally,it envisions the outlook for autonomous intelligent hybrid electric vehicles,with reinforcement learning as the foundational technology.First of all,to provide a macro view of historical development,the brief history of deep learning,reinforcement learning,and deep reinforcement learning is presented in the form of a timeline.Then,the comprehensive survey and review are conducted by collecting papers from mainstream academic databases.Enumerating most of the contributions based on three main directions—algorithm innovation,powertrain innovation,and environment innovation—provides an objective review of the research status.Finally,to advance the application of reinforcement learning in autonomous intelligent hybrid electric vehicles,future research plans positioned as“Alpha HEV”are envisioned,integrating Autopilot and energy-saving control.

A Mean-Field Game for a Forward-Backward Stochastic System With Partial Observation and Common Noise

This paper considers a linear-quadratic(LQ) meanfield game governed by a forward-backward stochastic system with partial observation and common noise,where a coupling structure enters state equations,cost functionals and observation equations.Firstly,to reduce the complexity of solving the meanfield game,a limiting control problem is introduced.By virtue of the decomposition approach,an admissible control set is proposed.Applying a filter technique and dimensional-expansion technique,a decentralized control strategy and a consistency condition system are derived,and the related solvability is also addressed.Secondly,we discuss an approximate Nash equilibrium property of the decentralized control strategy.Finally,we work out a financial problem with some numerical simulations.

Machine learning solution for regional landslide susceptibility based on fault zone division strategy

Landslide susceptibility assessment is an essential tool for disaster prevention and management. In areas with multiple fault zones, the impact of fault zone on slope stability cannot be disregarded. This study performed qualitative analysis of fault zones and proposed a zoning method to assess the landslide susceptibility in Chengkou County, Chongqing Municipality, China. The region within a distance of 1 km from the faults was designated as sub-zone A, while the remaining area was labeled as sub-zone B. To accomplish the assessment, a dataset comprising 388 historical landslides and 388 non-landslide points was used to train the random forest model. 10-fold cross-validation was utilized to select the training and testing datasets for the model. The results of the models were analyzed and discussed, with a focus on model performance and prediction uncertainty. By implementing the proposed division strategy based on fault zone, the accuracy, precision, recall, F-score, and AUC of both two sub-zones surpassed those of the whole region. In comparison to the results obtained for the whole region, sub-zone B exhibited an increase in AUC by 6.15%, while sub-zone A demonstrated a corresponding increase of 1.66%. Moreover, the results of 100 random realizations indicated that the division strategy has little effect on the prediction uncertainty. This study introduces a novel approach to enhance the prediction accuracy of the landslide susceptibility mapping model in areas with multiple fault zones.

A guidance strategy for rendezvous and docking to the space station in the Earth-Moon NRHO orbit

With the development of space technology,it is possible to build a space station in Earth-Moon space as a transit for Earth-Moon round-trip and entering in the deep space.Rendezvous and docking is one of the key technologies for building an Earth-Moon space station.A guidance strategy for rendezvous and docking from the Earth orbit to the space station in the Earth-Moon NRHO orbit is proposed in this paper,which is suitable for engineering applications.Firstly,the rendezvous and docking process is divided into three sections,i.e.,the large-range orbit transfer section,far-range guidance section,and close-range approaching section.The suitable terminal of large-range orbit transfer is selected according to the eigenvalue of NRHO orbit state transition matrix.The two-impulse guidance method based on the relative motion equation in the three-body problem is adopted for the far-range guidance section.The impulse time and amplitude are solved with the optimization algorithm.The linear constant three-body relative motion equation is proposed for the close-range approaching section,and the rendezvous and docking is completed by a two-stage linear approximation.Finally,a simulation analysis is carried out,and the simulation results show that the adopted dynamics equations and the designed guidance law are effective,and the three flight phases are naturally connected to accomplish the rendezvous and docking mission from the Earth orbit to the space station on the Earth-Moon NRHO.

Interpretation of Chinese Expert Consensus on the Diagnosis and Management Strategy of Patients with Statin Intolerance: A guiding file for helping to lipid management for Chinese population

It is well-known that elevated low-density lipoprotein cholesterol(LDL-C)is a causal risk factor for atheroscler-otic cardiovascular disease(ASCVD),statins are cornerstone drugs for the cause-based treatment of ASCVD,which has created a new era for ASCVD therapy.However,statin intolerance is not clinically uncommon,which there are several issues with confu-sion and misunderstandings.Hence,a file named Chinese Expert Consensus on the Diagnosis and Management Strategy of Pa-tients With Statin Intolerance,like a navigator,has recently been published written by a team of experts from the Cardiovascular Metabolic Medicine Professional Committee,Expert Committee of the National Center for Cardiovascular Diseases aiming to en-hance the standardized clinical application of statins and improve the prevention and clinical outcome.In this article,author briefly summarized the key points of above consensus in order to helping to comprehending the content of the consensus sugges-tions.

The surface electron transfer strategy promotes the hole of PDI release and enhances emerging organic pollutant degradation

In semiconductor photocatalysts,the easy recombination of photogenerated carriers seriously affects the application of photocatalytic materials in water treatment.To solve the serious problem of electron−hole pair recombination in perylene diimide(PDI)organic semiconductors,we loaded ferric hydroxyl oxide(FeOOH)on PDI materials,successfully prepared novel FeOOH@PDI photocatalytic materials,and constructed a photo-Fenton system.The system was able to achieve highly efficient degradation of BPA under visible light,with a degradation rate of 0.112 min^(−1)that was 20 times higher than the PDI system,and it also showed universal degradation performances for a variety of emerging organic pollutants and anti-interference ability.The mechanism research revealed that the FeOOH has the electron trapping property,which can capture the photogenerated electrons on the surface of PDI,effectively reducing the compounding rate of photogenerated carriers of PDI and accelerating the iron cycling and H2O2 activation on the surface of FeOOH at the same time.This work provides new insights and methods for solving the problem of easy recombination of carriers in semiconductor photocatalysts and degrading emerging organic pollutants.

Clinical application of eight-zone laparoscopic dissection strategy for rectal cancer:Experience and discussion

BACKGROUND The incidence of rectal cancer is increasing worldwide,and surgery remains the primary treatment modality.With the advent of total mesorectal excision(TME)technique,the probability of tumor recurrence post-surgery has significantly decreased.Surgeons'focus has gradually shifted towards minimizing the impact of surgery on urinary and sexual functions.Among these concerns,the optimal dissection of the rectal lateral ligaments and preservation of the pelvic floor neuro-vascular bundle have become critical.To explore the optimal surgical technique for TME and establish a standardized surgical protocol to minimize the impact on urinary and sexual functions,we propose the eight-zone dissection strategy for pelvic floor anatomy.AIM To compare the differences in surgical specimen integrity and postoperative quality of life satisfaction between the traditional pelvic floor dissection strategy and the innovative eight-zone dissection strategy.METHODS We analyzed the perioperative data of patients who underwent laparoscopic radical resection of rectal cancer at Qilu Hospital of Shandong University between January 1,2021 and December 1,2023.This study included a total of 218 patients undergoing laparoscopic radical surgery for rectal cancer,among whom 109 patients underwent traditional pelvic floor dissection strategy,and 109 patients received the eight-zone dissection strategy.RESULTS There were no significant differences in general characteristics between the two groups.Patients in the eight-zone dissection group had higher postoperative specimen integrity(88.1%vs 78.0%,P=0.047).At the 3-month followup,patients in the eight-zone surgery group had better scores in urinary issues(6.8±3.3 vs 5.3±2.5,P=0.045)and male sexual desire(2.2±0.6 vs 2.5±0.5,P=0.047)compared to the traditional surgery strategy group.CONCLUSION This study demonstrates that the eight-zone dissection strategy for laparoscopic lateral ligament dissection of rectal cancer is safe and effective.Compared with the traditional pelvic floor dissection strategy,this approach can reduce the risk of nerve injury and minimize the impact on urinary and sexual functions.Therefore,we recommend the clinical application of this strategy to better serve patients with rectal cancer.

Resilience-driven cooperative reconfiguration strategy for unmanned weapon system-of-systems

As the unmanned weap system-of systems(UWSoS)becomes complex,the inevitable uncertain interference gradu-ally increases,which leads to a strong emphasis on the resilience of UWSoS.Hence,this paper presents a resilience-driven cooperative reconfiguration strategy to enhance the resilience of UWSoS.First,a unified resilience-driven coopera-tive reconfiguration strategy framework is designed to guide the UWSoS resilience enhancement.Subsequently,a cooperative reconfiguration strategy algorithm is proposed to identify the optimal cooperative reconfiguration sequence,combining the cooperative pair resilience contribution index(CPRCI)and coop-erative pair importance index(CPII).At last,the effectiveness and superiority of the proposed algorithm are demonstrated through various attack scenario simulations that include differ-ent attack modes and intensities.The analysis results can pro-vide a reference for decision-makers to manage UWSoS.

Nanoparticles and their crosstalk with stress mitigators:A novel approach towards abiotic stress tolerance in agricultural systems

Plants are exposed to adverse environmental conditions,including cold,drought,heat,salinity,and heavy metals,which negatively impact plant growth and productivity of edible crops worldwide.Although the previous literature summarized the nanoparticle's involvement in abiotic stress mitigation,the interaction of nanoparticles with other stress mitigators to overcome abiotic stress from plants remains unclear.Currently,nanotechnology is considered a growing new field in agriculture for understanding plants'adapted stress tolerance mechanisms.Recent research has shown that nanoparticles can effectively mitigate abiotic stress by interacting synergistically with plant growth regulators.To address this,we comprehensively demonstrated the combined positive potential of nanoparticles in combination with plant growth regulators(signaling molecules,phytohormones,nanoparticles-nanoparticles interaction,fungi,plant growth promoting rhizobacteria and other metal salts)to improve plant growth and mitigate abiotic stresses.Their co-applications augment the plant's growth,nutrient uptake,antioxidant defense system,water absorption,cell viability,water use efficiency,and photosynthetic and biochemical attributes by reducing oxidative stressors under various abiotic stresses in different plant species.This review provides a comprehensive overview of the combined applications of nanoparticles and plant growth regulators,a novel strategy to reduce the harmful effects of abiotic stress on plants.It identifies research gaps and recommends future studies to overcome their phytotoxicity worldwide.

Enhancing the Efficiency of Multi-Electrolyzer Clusters with Lye Mixer:Topology Design and Control Strategy

The rise in hydrogen production powered by renewable energy is driving the field toward the adoption of systems comprising multiple alkaline water electrolyzers.These setups present various operational modes:independent operation and multi-electrolyzer parallelization,each with distinct advantages and challenges.This study introduces an innovative configuration that incorporates a mutual lye mixer among electrolyzers,establishing a weakly coupled system that combines the advantages of two modes.This approach enables efficient heat utilization for faster hot-startup and maintains heat conservation post-lye interconnection,while preserving the option for independent operation after decoupling.A specialized thermal exchange model is developed for this topology,according to the dynamics of the lye mixer.The study further details startup procedures and proposes optimized control strategies tailored to this structural design.Waste heat from the caustic fully heats up the multiple electrolyzers connected to the lye mixing system,enabling a rapid hot start to enhance the system’s ability to track renewable energy.A control strategy is established to reduce heat loss and increase startup speed,and the optimal valve openings of the diverter valve and the manifold valve are determined.Simulation results indicate a considerable enhancement in operational efficiency,marked by an 18.28%improvement in startup speed and a 6.11%reduction in startup energy consumption inmulti-electrolyzer cluster systems,particularlywhen the systems are synchronized with photovoltaic energy sources.The findings represent a significant stride toward efficient and sustainable hydrogen production,offering a promising path for large-scale integration of renewable energy.

Resilience-Oriented Load Restoration Method and Repair Strategies for Regional Integrated Electricity-Natural Gas System

The rising frequency of extreme disaster events seriously threatens the safe and secure operation of the regional integrated electricity-natural gas system(RIENGS).With the growing level of coupling between electric and natural gas systems,it is critical to enhance the load restoration capability of both systems.This paper proposes a coordinated optimization strategy for resilience-enhanced RIENGS load restoration and repair scheduling and transforms it into a mixed integer second-order cone programming(MISOCP)model.The proposed model considers the distribution network reconfiguration and the coordinated repair strategy between the two systems,minimizing the total system load loss cost and repair time.In addition,a bi-directional gas flow model is used to describe the natural gas system,which can provide the RIENGS with more flexibility for load restoration during natural gas system failure.Finally,the effectiveness of the proposed approach is verified by conducting case studies on the test systems RIENGS E13-G7 and RIENGS E123-G20.

Research on Scheduling Strategy of Flexible Interconnection Distribution Network Considering Distributed Photovoltaic and Hydrogen Energy Storage

Distributed photovoltaic(PV)is one of the important power sources for building a new power system with new energy as the main body.The rapid development of distributed PV has brought new challenges to the operation of distribution networks.In order to improve the absorption ability of large-scale distributed PV access to the distribution network,the AC/DC hybrid distribution network is constructed based on flexible interconnection technology,and a coordinated scheduling strategy model of hydrogen energy storage(HS)and distributed PV is established.Firstly,the mathematical model of distributed PV and HS system is established,and a comprehensive energy storage system combining seasonal hydrogen energy storage(SHS)and battery(BT)is proposed.Then,a flexible interconnected distribution network scheduling optimization model is established to minimize the total active power loss,voltage deviation and system operating cost.Finally,simulation analysis is carried out on the improved IEEE33 node,the NSGA-II algorithm is used to solve specific examples,and the optimal scheduling results of the comprehensive economy and power quality of the distribution network are obtained.Compared with the method that does not consider HS and flexible interconnection technology,the network loss and voltage deviation of this method are lower,and the total system cost can be reduced by 3.55%,which verifies the effectiveness of the proposed method.

Enhanced Deep Reinforcement Learning Strategy for Energy Management in Plug-in Hybrid Electric Vehicles with Entropy Regularization and Prioritized Experience Replay

Plug-in Hybrid Electric Vehicles(PHEVs)represent an innovative breed of transportation,harnessing diverse power sources for enhanced performance.Energy management strategies(EMSs)that coordinate and control different energy sources is a critical component of PHEV control technology,directly impacting overall vehicle performance.This study proposes an improved deep reinforcement learning(DRL)-based EMSthat optimizes realtime energy allocation and coordinates the operation of multiple power sources.Conventional DRL algorithms struggle to effectively explore all possible state-action combinations within high-dimensional state and action spaces.They often fail to strike an optimal balance between exploration and exploitation,and their assumption of a static environment limits their ability to adapt to changing conditions.Moreover,these algorithms suffer from low sample efficiency.Collectively,these factors contribute to convergence difficulties,low learning efficiency,and instability.To address these challenges,the Deep Deterministic Policy Gradient(DDPG)algorithm is enhanced using entropy regularization and a summation tree-based Prioritized Experience Replay(PER)method,aiming to improve exploration performance and learning efficiency from experience samples.Additionally,the correspondingMarkovDecision Process(MDP)is established.Finally,an EMSbased on the improvedDRLmodel is presented.Comparative simulation experiments are conducted against rule-based,optimization-based,andDRL-based EMSs.The proposed strategy exhibitsminimal deviation fromthe optimal solution obtained by the dynamic programming(DP)strategy that requires global information.In the typical driving scenarios based onWorld Light Vehicle Test Cycle(WLTC)and New European Driving Cycle(NEDC),the proposed method achieved a fuel consumption of 2698.65 g and an Equivalent Fuel Consumption(EFC)of 2696.77 g.Compared to the DP strategy baseline,the proposed method improved the fuel efficiency variances(FEV)by 18.13%,15.1%,and 8.37%over the Deep QNetwork(DQN),Double DRL(DDRL),and original DDPG methods,respectively.The observational outcomes demonstrate that the proposed EMS based on improved DRL framework possesses good real-time performance,stability,and reliability,effectively optimizing vehicle economy and fuel consumption.

Treatment strategy and therapy based on immune response in patients with gastric cancers

In this editorial,we highlight the significance of a retrospective study“Analysis of the impact of immunotherapy efficacy and safety in patients with gastric cancer and liver metastasis”performed by Liu et al.The authors utilized data collected from gastric cancer(GC)patients and assessed immunotherapy effectiveness and survival status.They found significant differences in treatment response.Because immunotherapy seems to be a beneficial strategy for advanced GC patients,strati-fication of the data based on metastasis status may further improve treatment strategies.