Advances in Zika virus vaccines and therapeutics:A systematic review

Zika virus(ZIKV)is the causative agent of a viral infection that causes neurological complications in newborns and adults worldwide.Its wide transmission route and alarming spread rates are of great concern to the scientific community.Numerous trials have been conducted to develop treatment options for ZIKV infection.This review highlights the latest developments in the fields of vaccinology and pharmaceuticals developments for ZIKV infection.A systematic and comprehensive approach was used to gather relevant and up-to-date data so that inferences could be made about the gaps in therapeutic development.The results indicate that several therapeutic interventions are being tested against ZIKV infection,such as DNA vaccines,subunit vaccines,live-attenuated vaccines,virus-vector-based vaccines,inactivated vaccines,virus-like particles,and mRNA-based vaccines.In addition,approved anti-ZIKV drugs that can reduce the global burden are discussed.Although many vaccine candidates for ZIKV are at different stages of development,none of them have received Food and Drug Authority approval for use up to now.The issue of side effects associated with these drugs in vulnerable newborns and pregnant women is a major obstacle in the therapeutic pathway.

Systematic review of risk factors,prognosis,and management of colorectal signet-ring cell carcinoma

BACKGROUND Colorectal signet-ring cell carcinoma(CSRCC)is a rare clinical entity which accounts for approximately 1%of all colorectal cancers.Although multiple studies concerning this specific topic have been published in the past decades,the pathogenesis,associated risk factors,and potential implications on treatment are still poorly understood.Besides the low incidence,historically confusing histological criteria have resulted in confusing data.Nevertheless,the rising incidence of CSRCC along with relatively young age at presentation and associated dismal prognosis,highlight the actual interest to synthesize the known literature regarding CSRCC.AIM To provide an updated overview of risk factors,prognosis,and management of CSRCC.METHODS A literature search in the MEDLINE/PubMed database was conducted with the following search terms used:‘Signet ring cell carcinoma’and‘colorectal’.Studies in English language,published after January 1980,were included.Studies included in the qualitative synthesis were evaluated for content concerning epidemiology,risk factors,and clinical,diagnostic,histological,and molecular features,as well as metastatic pattern and therapeutic management.If possible,presented data was extracted in order to present a more detailed overview of the literature.RESULTS In total,67 articles were included for qualitative analysis,of which 54 were eligible for detailed data extraction.CSRCC has a reported incidence between 0.1%-2.4%and frequently presents with advanced disease stage at the time of diagnosis.CSRCC is associated with an impaired overall survival(5-year OS:0%-46%)and a worse stagecorrected outcome compared to mucinous and not otherwise specified adenocarcinoma.The systematic use of exploratory laparoscopy to determine the presence of peritoneal metastases has been advised.Surgery is the mainstay of treatment,although the rates of curative resection in CSRCC(21%-82%)are lower compared to those in other histological types.In case of peritoneal metastasis,cytoreductive surgery with hyperthermic intraperitoneal chemotherapy should only be proposed in selected patients.CONCLUSION CSRCC is a rare clinical entity most often characterized by young age and advanced disease at presentation.As such,diagnostic modalities and therapeutic approach should be tailored accordingly.

Strategies for improving crop comprehensive benefits via a decision-making system based on machine learning in the rice‒rape,rice‒wheat and rice‒garlic rotation systems in Southwest China

Rice‒rape,rice‒wheat and rice‒garlic rotations are common cropping systems in Southwest China,and they have played a significant role in ensuring ecological and economic benefits(EB)and addressing the challenges of China’s food security in the region.However,the crop yields in these rotation systems are 1.25‒14.73%lower in this region than the national averages.Intelligent decision-making with machine learning can analyze the key factors for obtaining better benefits,but it has rarely been used to enhance the probability of obtaining such benefits from rotations in Southwest China.Thus,we used a data-intensive approach to construct an intelligent decision‒making system with machine learning to provide strategies for improving the benefits of rice-rape,rice-wheat,and rice-garlic rotations in Southwest China.The results show that raising the yield and partial fertilizer productivity(PFP)by increasing seed input under high fertilizer application provided the optimal benefits with a 10%probability in the rice-garlic system.Obtaining high yields and greenhouse gas(GHG)emissions by increasing the N application and reducing the K application provided suboptimal benefits with an 8%probability in the rice-rape system.Reducing N and P to enhance PFP and yield provided optimal benefits with the lowest probability(8%)in the rice‒wheat system.Based on the predictive analysis of a random forest model,the optimal benefits were obtained with fertilization regimes by reducing N by 25%and increasing P and K by 8 and 74%,respectively,in the rice-garlic system,reducing N and K by 54 and by 36%,respectively,and increasing P by 38%in rice-rape system,and reducing N by 4%and increasing P and K by 65 and 23%in rice-wheat system.These strategies could be further optimized by 17‒34%for different benefits,and all of these measures can improve the effectiveness of the crop rotation systems to varying degrees.Overall,these findings provide insights into optimal agricultural inputs for higher benefits through an intelligent decision-making system with machine learning analysis in the rice-rape,rice‒wheat,and rice-garlic systems.

Ultra Reliable Load-Aware Connection Management(LACM)Algorithm in WIA-FA Systems

The increasing demand for industrial automation and intelligence has put forward higher requirements for the reliability of industrial wireless communication technology.As an international standard based on 802.11,Wireless networks for Industrial Automation-Factory Automation(WIA-FA)greatly improves the reliability in factory automation scenarios by Time Division Multiple Access(TDMA).However,in ultra-dense WIA-FA networks with mobile users,the basic connection management mechanism is inefficient.Most of the handover and resource management algorithms are all based on frequency division multiplexing,not suitable for the TDMA in the WIA-FA network.Therefore,we propose Load-aware Connection Management(LACM)algorithm to adjust the linkage and balance the load of access devices to avoid blocking and improve the reliability of the system.And then we simulate the algorithm to find the optimal settings of the parameters.After comparing with other existing algorithms,the result of the simulation proves that LACM is more efficient in reliability and maintains high reliability of more than 99.8%even in the ultra-dense moving scenario with 1500 field devices.Besides,this algorithm ensures that only a few signaling exchanges are required to ensure load bal-ancing,which is no more than 5 times,and less than half of the best state-of-the-art algorithm.

Optimization Control of Multi-Mode Coupling All-Wheel Drive System for Hybrid Vehicle

The all-wheel drive(AWD)hybrid system is a research focus on high-performance new energy vehicles that can meet the demands of dynamic performance and passing ability.Simultaneous optimization of the power and economy of hybrid vehicles becomes an issue.A unique multi-mode coupling(MMC)AWD hybrid system is presented to realize the distributed and centralized driving of the front and rear axles to achieve vectored distribution and full utilization of the system power between the axles of vehicles.Based on the parameters of the benchmarking model of a hybrid vehicle,the best model-predictive control-based energy management strategy is proposed.First,the drive system model was built after the analysis of the MMC-AWD’s drive modes.Next,three fundamental strategies were established to address power distribution adjustment and battery SOC maintenance when the SOC changed,which was followed by the design of a road driving force observer.Then,the energy consumption rate in the average time domain was processed before designing the minimum fuel consumption controller based on the equivalent fuel consumption coefficient.Finally,the advantage of the MMC-AWD was confirmed by comparison with the dynamic performance and economy of the BYD Song PLUS DMI-AWD.The findings indicate that,in comparison to the comparative hybrid system at road adhesion coefficients of 0.8 and 0.6,the MMC-AWD’s capacity to accelerate increases by 5.26%and 7.92%,respectively.When the road adhesion coefficient is 0.8,0.6,and 0.4,the maximum climbing ability increases by 14.22%,12.88%,and 4.55%,respectively.As a result,the dynamic performance is greatly enhanced,and the fuel savings rate per 100 km of mileage reaches 12.06%,which is also very economical.The proposed control strategies for the new hybrid AWD vehicle can optimize the power and economy simultaneously.

Enhancing Secure Development in Globally Distributed Software Product Lines: A Machine Learning-Powered Framework for Cyber-Resilient Ecosystems

Embracing software product lines(SPLs)is pivotal in the dynamic landscape of contemporary software devel-opment.However,the flexibility and global distribution inherent in modern systems pose significant challenges to managing SPL variability,underscoring the critical importance of robust cybersecurity measures.This paper advocates for leveraging machine learning(ML)to address variability management issues and fortify the security of SPL.In the context of the broader special issue theme on innovative cybersecurity approaches,our proposed ML-based framework offers an interdisciplinary perspective,blending insights from computing,social sciences,and business.Specifically,it employs ML for demand analysis,dynamic feature extraction,and enhanced feature selection in distributed settings,contributing to cyber-resilient ecosystems.Our experiments demonstrate the framework’s superiority,emphasizing its potential to boost productivity and security in SPLs.As digital threats evolve,this research catalyzes interdisciplinary collaborations,aligning with the special issue’s goal of breaking down academic barriers to strengthen digital ecosystems against sophisticated attacks while upholding ethics,privacy,and human values.

Etiology and management of urethral calculi:A systematic review of contemporary series

Objective:To conduct a systematic literature review on urethral calculi in a contemporary cohort describing etiology,investigation,and management patterns.Methods:A systematic search of MEDLINE and Cochrane Central Register of Controlled Trials(CENTRAL)databases was performed.Articles,including case reports and case series on urethral calculi published between January 2000 and December 2019,were included.Full-text manuscripts were reviewed for clinical parameters including symptomatology,etiology,medical history,investigations,treatment,and outcomes.Data were collated and analyzed with univariate methods.Results:Seventy-four publications met inclusion criteria,reporting on 95 cases.Voiding symptoms(41.1%),pain(40.0%),and acute urinary retention(32.6%)were common presenting features.Urethral calculi were most often initially investigated using plain X-ray(63.2%),with almost all radio-opaque(98.3%).Urethral calculi were frequently associated with coexistent bladder or upper urinary tract calculi(16.8%)and underlying urethral pathology(53.7%)including diverticulum(33.7%)or stricture(13.7%).Urethral calculi were most commonly managed with external urethrolithotomy(31.6%),retrograde manipulation(22.1%),and endoscopic in situ lithotripsy(17.9%).Conclusion:This unique systematic review of urethral calculi provided a summary of clinical features and treatment trends with a suggested treatment algorithm.Management in contemporary urological practice should be according to calculus size,shape,anatomical location,and presence of urethral pathology.

Adaptive battery thermal management systems in unsteady thermal application contexts

With the increasing attention paid to battery technology,the microscopic reaction mechanism and macroscopic heat transfer process of lithium-ion batteries have been further studied and understood from both academic and industrial perspectives.Temperature,as one of the key parameters in the physical fra mework of batteries,affects the performa nce of the multi-physical fields within the battery,a nd its effective control is crucial.Since the heat generation in the battery is determined by the real-time operating conditions,the battery temperature is essentially controlled by the real-time heat dissipation conditions provided by the battery thermal management system.Conventional battery thermal management systems have basic temperature control capabilities for most conventional application scenarios.However,with the current development of la rge-scale,integrated,and intelligent battery technology,the adva ncement of battery thermal management technology will pay more attention to the effective control of battery temperature under sophisticated situations,such as high power and widely varied operating conditions.In this context,this paper presents the latest advances and representative research related to battery thermal management system.Firstly,starting from battery thermal profile,the mechanism of battery heat generation is discussed in detail.Secondly,the static characteristics of the traditional battery thermal management system are summarized.Then,considering the dynamic requirements of battery heat dissipation under complex operating conditions,the concept of adaptive battery thermal management system is proposed based on specific research cases.Finally,the main challenges for battery thermal management system in practice are identified,and potential future developments to overcome these challenges are presented and discussed.

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.

Ecosystem service multifunctionality of mixed conifer-broad-leaved forests under climate change and forest management based on matrix growth modelling

Climate change and forest management are recognized as pivotal factors influencing forest ecosystem services and thus multifunctionality.However,the magnitude and the relative importance of climate change and forest management effects on the multifunctionality remain unclear,especially for natural mixed forests.In this study,our objective is to address this gap by utilizing simulations of climate-sensitive transition matrix growth models based on national forest inventory plot data.We evaluated the effects of seven management scenarios(combinations of various cutting methods and intensities)on the future provision of ecosystem services and multifunctionality in mixed conifer-broad-leaved forests in northeastern China,under four climate scenarios(SSP1-2.6,SSP2-4.5,SSP5-8.5,and constant climate).Provisioning,regulating,cultural,and supporting services were described by timber production,carbon storage,carbon sequestration,tree species diversity,deadwood volume,and the number of large living trees.Our findings indicated that timber production was significantly influenced by management scenarios,while tree species diversity,deadwood volume,and large living trees were impacted by both climate and management separately.Carbon storage and sequestration were notably influenced by both management and the interaction of climate and management.These findings emphasized the profound impact of forest management on ecosystem services,outweighing that of climate scenarios alone.We found no single management scenario maximized all six ecosystem service indicators.The upper story thinning by 5%intensity with 5-year interval(UST5)management strategy emerged with the highest multifunctionality,surpassing the lowest values by more than 20%across all climate scenarios.In conclusion,our results underlined the potential of climate-sensitive transition matrix growth models as a decision support tool and provided recommendations for long-term strategies for multifunctional forest management under future climate change context.Ecosystem services and multifunctionality of forests could be enhanced by implementing appropriate management measures amidst a changing climate.

Diabetic peripheral neuropathy and neuromodulation techniques:a systematic review of progress and prospects

Neuromodulation for diabetic peripheral neuropathy represents a significant area of interest in the management of chronic pain associated with this condition.Diabetic peripheral neuropathy,a common complication of diabetes,is characterized by nerve damage due to high blood sugar levels that lead to symptoms,such as pain,tingling,and numbness,primarily in the hands and feet.The aim of this systematic review was to evaluate the efficacy of neuromodulatory techniques as potential therapeutic interventions for patients with diabetic peripheral neuropathy,while also examining recent developments in this domain.The investigation encompassed an array of neuromodulation methods,including frequency rhythmic electrical modulated systems,dorsal root ganglion stimulation,and spinal cord stimulation.This systematic review suggests that neuromodulatory techniques may be useful in the treatment of diabetic peripheral neuropathy.Understanding the advantages of these treatments will enable physicians and other healthcare providers to offer additional options for patients with symptoms refractory to standard pharmacologic treatments.Through these efforts,we may improve quality of life and increase functional capacity in patients suffering from complications related to diabetic neuropathy.

Exploring the impacts of major events on the systemic risk of the international energy market

This study examines the systemic risk caused by major events in the international energy market(IEM)and proposes a management strategy to mitigate it. Using the tail-event driven network(TENET)method, this study constructed a tail-risk spillover network(TRSN) of IEM and simulated the dynamic spillover tail-risk process through the cascading failure mechanism. The study found that renewable energy markets contributed more to systemic risk during the Paris Agreement and the COVID-19pandemic, while fossil energy markets played a larger role during the Russia-Ukraine conflict. This study identifies systemically important markets(SM) and critical tail-risk spillover paths as potential sources of systemic risk. The research confirms that cutting off the IEM risk spillover path can greatly reduce systemic risk and the influence of SM. This study offers insights into the management of systemic risk in IEM and provides policy recommendations to reduce the impact of shock events.

A New Solution to Intrusion Detection Systems Based on Improved Federated-Learning Chain

In the context of enterprise systems,intrusion detection(ID)emerges as a critical element driving the digital transformation of enterprises.With systems spanning various sectors of enterprises geographically dispersed,the necessity for seamless information exchange has surged significantly.The existing cross-domain solutions are challenged by such issues as insufficient security,high communication overhead,and a lack of effective update mechanisms,rendering them less feasible for prolonged application on resource-limited devices.This study proposes a new cross-domain collaboration scheme based on federated chains to streamline the server-side workload.Within this framework,individual nodes solely engage in training local data and subsequently amalgamate the final model employing a federated learning algorithm to uphold enterprise systems with efficiency and security.To curtail the resource utilization of blockchains and deter malicious nodes,a node administration module predicated on the workload paradigm is introduced,enabling the release of surplus resources in response to variations in a node’s contribution metric.Upon encountering an intrusion,the system triggers an alert and logs the characteristics of the breach,facilitating a comprehensive global update across all nodes for collective defense.Experimental results across multiple scenarios have verified the security and effectiveness of the proposed solution,with no loss of its recognition accuracy.

Water and nitrogen footprint assessment of integrated agronomic practice management in a summer maize cropping system

The footprints of water and nitrogen(WF and NF)provide a comprehensive overview of the type and quantity of water consumption and reactive nitrogen(Nr)loss in crop production.In this study,a field experiment over two years(2019 and 2020)compared three integrated agronomic practice management(IAPM)systems:An improved management system(T2),a high-yield production system(T3),and an integrated soil-crop management system(ISCM)using a local smallholder farmer’s practice system(T1)as control,to investigate the responses of WF,Nr losses,water use efficiency(WUE),and nitrogen use efficiency(NUE)to IAPM.The results showed that IAPM optimized water distribution and promoted water use by summer maize.The evapotranspiration over the whole maize growth period of IAPM increased,but yield increased more,leading to a significant increase in WUE.The WUE of the T2,T3,and ISCM treatments was significantly greater than in the T1 treatment,in 2019 and 2020respectively,by 19.8-21.5,31.8-40.6,and 34.4-44.6%.The lowest WF was found in the ISCM treatment,which was 31.0%lower than that of the T1 treatment.In addition,the ISCM treatment optimized soil total nitrogen(TN)distribution and significantly increased TN in the cultivated layer.Excessive nitrogen fertilizer was applied in treatment T3,producing the highest maize yield,and resulting in the highest Nr losses.In contrast,the ISCM treatment used a reduced nitrogen fertilizer rate,sacrificing grain yield partly,which reduced Nr losses and eventually led to a significant increase in nitrogen use efficiency and nitrogen recovery.The Nr level in the ISCM treatment was34.8%lower than in the T1 treatment while NUE was significantly higher than in the T1 treatment by 56.8-63.1%in2019 and 2020,respectively.Considering yield,WUE,NUE,WF,and NF together,ISCM should be used as a more sustainable and clean system for sustainable production of summer maize.

Smart Energy Management System Using Machine Learning

Energy management is an inspiring domain in developing of renewable energy sources.However,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and demand.The major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental effects.Modern energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user comfort.To address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve it.An inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy costs.Machine Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)network.The IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource management.In this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management problem.The proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.

Analysis and Modeling of Mobile Phone Activity Data Using Interactive Cyber-Physical Social System

Mobile networks possess significant information and thus are considered a gold mine for the researcher’s community.The call detail records(CDR)of a mobile network are used to identify the network’s efficacy and the mobile user’s behavior.It is evident from the recent literature that cyber-physical systems(CPS)were used in the analytics and modeling of telecom data.In addition,CPS is used to provide valuable services in smart cities.In general,a typical telecom company hasmillions of subscribers and thus generatesmassive amounts of data.From this aspect,data storage,analysis,and processing are the key concerns.To solve these issues,herein we propose a multilevel cyber-physical social system(CPSS)for the analysis and modeling of large internet data.Our proposed multilevel system has three levels and each level has a specific functionality.Initially,raw Call Detail Data(CDR)was collected at the first level.Herein,the data preprocessing,cleaning,and error removal operations were performed.In the second level,data processing,cleaning,reduction,integration,processing,and storage were performed.Herein,suggested internet activity record measures were applied.Our proposed system initially constructs a graph and then performs network analysis.Thus proposed CPSS system accurately identifies different areas of internet peak usage in a city(Milan city).Our research is helpful for the network operators to plan effective network configuration,management,and optimization of resources.

Threshold-Based Software-Defined Networking(SDN)Solution for Healthcare Systems against Intrusion Attacks

The healthcare sector holds valuable and sensitive data.The amount of this data and the need to handle,exchange,and protect it,has been increasing at a fast pace.Due to their nature,software-defined networks(SDNs)are widely used in healthcare systems,as they ensure effective resource utilization,safety,great network management,and monitoring.In this sector,due to the value of thedata,SDNs faceamajor challengeposed byawide range of attacks,such as distributed denial of service(DDoS)and probe attacks.These attacks reduce network performance,causing the degradation of different key performance indicators(KPIs)or,in the worst cases,a network failure which can threaten human lives.This can be significant,especially with the current expansion of portable healthcare that supports mobile and wireless devices for what is called mobile health,or m-health.In this study,we examine the effectiveness of using SDNs for defense against DDoS,as well as their effects on different network KPIs under various scenarios.We propose a threshold-based DDoS classifier(TBDC)technique to classify DDoS attacks in healthcare SDNs,aiming to block traffic considered a hazard in the form of a DDoS attack.We then evaluate the accuracy and performance of the proposed TBDC approach.Our technique shows outstanding performance,increasing the mean throughput by 190.3%,reducing the mean delay by 95%,and reducing packet loss by 99.7%relative to normal,with DDoS attack traffic.

A Systematic Literature Review on Task Allocation and Performance Management Techniques in Cloud Data Center

As cloud computing usage grows,cloud data centers play an increasingly important role.To maximize resource utilization,ensure service quality,and enhance system performance,it is crucial to allocate tasks and manage performance effectively.The purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data centers.The aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies,categories,and gaps.A literature review was conducted,which included the analysis of 463 task allocations and 480 performance management papers.The review revealed three task allocation research topics and seven performance management methods.Task allocation research areas are resource allocation,load-Balancing,and scheduling.Performance management includes monitoring and control,power and energy management,resource utilization optimization,quality of service management,fault management,virtual machine management,and network management.The study proposes new techniques to enhance cloud computing work allocation and performance management.Short-comings in each approach can guide future research.The research’s findings on cloud data center task allocation and performance management can assist academics,practitioners,and cloud service providers in optimizing their systems for dependability,cost-effectiveness,and scalability.Innovative methodologies can steer future research to fill gaps in the literature.

Market Operation of Energy Storage System in Smart Grid:A Review

As a flexible resource,energy storage plays an increasingly significant role in stabilizing and supporting the power system,while providing auxiliary services.Still,the current high demand for energy storage contrasts with the fuzzy lack of market-oriented mechanisms for energy storage,the principle of market-oriented operation has not been embodied,and there is no unified and systematic analytical framework for the business model.However,the dispatch management model of energy storage in actual power system operation is not clear.Still,the specific scheduling process and energy storage strategy on the source-load-network side could be more specific,and there needs to be a greater understanding of the collaborative scheduling process of the multilevel scheduling center.On this basis,this paper reviews the energy storage operation model and market-based incentive mechanism,For different functional types and installation locations of energy storage within the power system,the operational models and existing policies for energy storage participation in the market that are adapted to multiple operating states are summarized.From the point of view of the actual scheduling and operation management of energy storage in China,an energy storage regulation and operation management model based on“national,provincial,and local”multilevel coordination is proposed,as well as key technologies in the interactive scenarios of source-load,network and storage.

Regenerative Braking Energy Recovery System of Metro Train Based on Dual-Mode Power Management

In order to fully utilize the regenerative braking energy of metro trains and stabilize the metro DC traction busbar voltage,a hybrid regenerative braking energy recovery system with a dual-mode power management strategy is proposed.Firstly,the construction of the hybrid regenerative braking energy recovery system is explained.Then,based on the power demand of low-voltage load in metro stations,a dual-mode power management strategy is proposed to allocate the reference power of each system according to the different working conditions,and the control methods of each system are set.Finally,the correctness and effectiveness of the dual-mode strategy are verified through simulation,and the proposed braking energy utilization scheme is compared with other singleform utilization schemes.The results illustrate that the hybrid system with the dual-mode strategy can effectively recycle the regenerative braking energy of metro train and inhibit the busbar voltage fluctuation;the proposed braking energy utilization scheme has certain advantages on energy recovery and DC bus voltage stabilization compared with other single-form schemes;the proposed power management strategy can correctly allocate the reference power of each system with a lower construction cost.