Bidirectional rotating direct-current triboelectric nanogenerator with self-adaptive mechanical switching for harvesting reciprocating motion

Amid the growing interest in triboelectric nanogenerators(TENGs)as novel energy-harvesting devices,several studies have focused on direct current(DC)TENGs to generate a stable DC output for operating electronic devices.However,owing to the working mechanisms of conventional DC TENGs,generating a stable DC output from reciprocating motion remains a challenge.Accordingly,we propose a bidirectional rotating DC TENG(BiR-TENG),which can generate DC outputs,regardless of the direction of rotation,from reciprocating motions.The distinct design of the BiR-TENG enables the mechanical rectification of the alternating current output into a rotational-direction-dependent DC output.Furthermore,it allows the conversion of the rotational-direction-dependent DC output into a unidirectional DC output by adapting the configurations depending on the rotational direction.Owing to these tailored design strategies and subsequent optimizations,the BiR-TENG could generate an effective unidirectional DC output.Applications of the BiR-TENG for the reciprocating motions of swinging doors and waves were demonstrated by harnessing this output.This study demonstrates the potential of the BiR-TENG design strategy as an effective and versatile solution for energy harvesting from reciprocating motions,highlighting the suitability of DC outputs as an energy source for electronic devices.

Causal genetic regulation of DNA replication on immune microenvironment in colorectal tumorigenesis: Evidenced by an integrated approach of trans-omics and GWAS

The interplay between DNA replication stress and immune microenvironment alterations is known to play a crucial role in colorectal tumorigenesis,but a comprehensive understanding of their association with and relevant biomarkers involved in colorectal tumorigenesis is lacking.To address this gap,we conducted a study aiming to investigate this association and identify relevant biomarkers.We analyzed transcriptomic and proteomic profiles of 904 colorectal tumor tissues and 342 normal tissues to examine pathway enrichment,biological activity,and the immune microenvironment.Additionally,we evaluated genetic effects of single variants and genes on colorectal cancer susceptibility using data from genome-wide association studies(GWASs)involving both East Asian(7062 cases and 195745 controls)and European(24476 cases and 23073 controls)populations.We employed mediation analysis to infer the causal pathway,and applied multiplex immunofluorescence to visualize colocalized biomarkers in colorectal tumors and immune cells.Our findings revealed that both DNA replication activity and the flap structure-specific endonuclease 1(FEN1)gene were significantly enriched in colorectal tumor tissues,compared with normal tissues.Moreover,a genetic variant rs4246215 G>T in FEN1 was associated with a decreased risk of colorectal cancer(odds ratio=0.94,95%confidence interval:0.90–0.97,P_(meta)=4.70×10^(-9)).Importantly,we identified basophils and eosinophils that both exhibited a significantly decreased infiltration in colorectal tumors,and were regulated by rs4246215 through causal pathways involving both FEN1 and DNA replication.In conclusion,this trans-omics incorporating GWAS data provides insights into a plausible pathway connecting DNA replication and immunity,expanding biological knowledge of colorectal tumorigenesis and therapeutic targets.

Dynamic Economic Scheduling with Self-Adaptive Uncertainty in Distribution Network Based on Deep Reinforcement Learning

Traditional optimal scheduling methods are limited to accurate physical models and parameter settings, which aredifficult to adapt to the uncertainty of source and load, and there are problems such as the inability to make dynamicdecisions continuously. This paper proposed a dynamic economic scheduling method for distribution networksbased on deep reinforcement learning. Firstly, the economic scheduling model of the new energy distributionnetwork is established considering the action characteristics of micro-gas turbines, and the dynamic schedulingmodel based on deep reinforcement learning is constructed for the new energy distribution network system with ahigh proportion of new energy, and the Markov decision process of the model is defined. Secondly, Second, for thechanging characteristics of source-load uncertainty, agents are trained interactively with the distributed networkin a data-driven manner. Then, through the proximal policy optimization algorithm, agents adaptively learn thescheduling strategy and realize the dynamic scheduling decision of the new energy distribution network system.Finally, the feasibility and superiority of the proposed method are verified by an improved IEEE 33-node simulationsystem.

Statistical Method for Block in Replication Design and Its SAS Program

Large-scale genetic population used for genetic breeding researches covers a large area in the field experiment,and the effect of local control would be gradually weakened.The block in replication(BIR)design is suitable for large population,which is applied to the field experiment of genetic population.The statistical methods of analysis of variance(ANOVA)and heritability estimation in single and multiple environments were derived and implemented using the statistical analysis system(SAS)program for the analysis of BIR.As a work example,a comparison of statistical analysis between BIR design and the completely random block(CRB)design were conducted for the protein content from a panel containing 455 soybean germplasms.The results indicated the different estimates of average heritability in multiple environments.The research results provided technical support for the application of BIR design in genetics and breeding studies.

Replication Strategy with Comprehensive Data Center Selection Method in Cloud Environments

As the amount of data continues to grow rapidly,the variety of data produced by applications is becoming more affluent than ever.Cloud computing is the best technology evolving today to provide multi-services for the mass and variety of data.The cloud computing features are capable of processing,managing,and storing all sorts of data.Although data is stored in many high-end nodes,either in the same data centers or across many data centers in cloud,performance issues are still inevitable.The cloud replication strategy is one of best solutions to address risk of performance degradation in the cloud environment.The real challenge here is developing the right data replication strategy with minimal data movement that guarantees efficient network usage,low fault tolerance,and minimal replication frequency.The key problem discussed in this research is inefficient network usage discovered during selecting a suitable data center to store replica copies induced by inadequate data center selection criteria.Hence,to mitigate the issue,we proposed Replication Strategy with a comprehensive Data Center Selection Method(RS-DCSM),which can determine the appropriate data center to place replicas by considering three key factors:Popularity,space availability,and centrality.The proposed RS-DCSM was simulated using CloudSim and the results proved that data movement between data centers is significantly reduced by 14%reduction in overall replication frequency and 20%decrement in network usage,which outperformed the current replication strategy,known as Dynamic Popularity aware Replication Strategy(DPRS)algorithm.

Formal Modeling of Self-Adaptive Resource Scheduling in Cloud

A self-adaptive resource provisioning on demand is a critical factor in cloud computing.The selection of accurate amount of resources at run time is not easy due to dynamic nature of requests.Therefore,a self-adaptive strategy of resources is required to deal with dynamic nature of requests based on run time change in workload.In this paper we proposed a Cloud-based Adaptive Resource Scheduling Strategy(CARSS)Framework that formally addresses these issues and is more expressive than traditional approaches.The decision making in CARSS is based on more than one factors.TheMAPE-K based framework determines the state of the resources based on their current utilization.Timed-Arc Petri Net(TAPN)is used to model system formally and behaviour is expressed in TCTL,while TAPAAL model checker verifies the underline properties of the system.

Comparison of the replication and neutralization of different SARS-CoV-2 Omicron subvariants in vitro

Background:New Omicron subvariants are emerging rapidly from BA.1 to BA.4 and BA.5.Their pathogenicity has changed from that of wild-type(WH-09)and Omicron variants have over time become globally dominant.The spike proteins of BA.4 and BA.5 that serve as the target for vaccine-induced neutralizing antibodies have also changed compared to the previous subvariants,which is likely to cause immune es-cape and the reduction of the protective effect of the vaccine.Our study addresses the above issues and provides a basis for formulating relevant prevention and control strategies.Methods:We collected cellular supernatant and cell lysates and measured the viral titers,viral RNA loads,and E subgenomic RNA(E sgRNA)loads in different Omicron subvariants grown in Vero E6 cells,using WH-09 and Delta variants as a reference.Additionally,we evaluated the in vitro neutralizing activity of different Omicron sub-variants and compared it to the WH-09 and Delta variants using macaque sera with different types of immunity.Results:As the SARS-CoV-2 evolved into Omicron BA.1,the replication ability in vitro began to decrease.Then with the emergence of new subvariants,the replication ability gradually recovered and became stable in the BA.4 and BA.5 subvariants.In WH-09-inactivated vaccine sera,geometric mean titers of neutralization antibodies against different Omicron subvariants declined by 3.7~15.4-fold compared to those against WH-09.In Delta-inactivated vaccine sera,geometric mean titers of neutrali-zation antibodies against Omicron subvariants declined by 3.1~7.4-fold compared to those against Delta.Conclusion:According to the findings of this research,the replication efficiency of all Omicron subvariants declined compared with WH-09 and Delta variants,and was lower in BA.1 than in other Omicron subvariants.After two doses of inactivated(WH-09 or Delta)vaccine,cross-neutralizing activities against various Omicron subvariants were seen despite a decline in neutralizing titers.

In-situ interfacial passivation and self-adaptability synergistically stabilizing all-solid-state lithium metal batteries

The function of solid electrolytes and the composition of solid electrolyte interphase(SEI)are highly significant for inhibiting the growth of Li dendrites.Herein,we report an in-situ interfacial passivation combined with self-adaptability strategy to reinforce Li_(0.33)La_(0.557)TiO_(3)(LLTO)-based solid-state batteries.Specifically,a functional SEI enriched with LiF/Li_(3)PO_(4) is formed by in-situ electrochemical conversion,which is greatly beneficial to improving interface compatibility and enhancing ion transport.While the polarized dielectric BaTiO_(3)-polyamic acid(BTO-PAA,BP)film greatly improves the Li-ion transport kinetics and homogenizes the Li deposition.As expected,the resulting electrolyte offers considerable ionic conductivity at room temperature(4.3 x 10~(-4)S cm^(-1))and appreciable electrochemical decomposition voltage(5.23 V)after electrochemical passivation.For Li-LiFePO_(4) batteries,it shows a high specific capacity of 153 mA h g^(-1)at 0.2C after 100 cycles and a long-term durability of 115 mA h g^(-1)at 1.0 C after 800 cycles.Additionally,a stable Li plating/stripping can be achieved for more than 900 h at 0.5 mA cm^(-2).The stabilization mechanisms are elucidated by ex-situ XRD,ex-situ XPS,and ex-situ FTIR techniques,and the corresponding results reveal that the interfacial passivation combined with polarization effect is an effective strategy for improving the electrochemical performance.The present study provides a deeper insight into the dynamic adjustment of electrode-electrolyte interfacial for solid-state lithium batteries.

Self-adaptive bulk/surface engineering of Bi_(x)O_(y)Br_(z) towards enhanced photocatalysis:Current status and future challenges

The bulk/surface states of semiconductor photocatalysts are imperative parameters to maneuver their performance by significantly affecting the key processes of photocatalysis including light absorption,separation of charge carrier,and surface site reaction.Recent years have witnessed the encouraging progress of self-adaptive bulk/surface engineered Bi_(x)O_(y)Br_(z) for photocatalytic applications spanning various fields.However,despite the maturity of current research,the interaction between the bulk/surface state and the performance of Bi_(x)O_(y)Br_(z) has not yet been fully understood and highlighted.In this regard,a timely tutorial overview is quite urgent to summarize the most recent key progress and outline developing obstacles in this exciting area.Herein,the structural characteristics and fundamental principles of Bi_(x)O_(y)Br_(z)for driving photocatalytic reaction as well as related key issues are firstly reviewed.Then,we for the first time summarized different self-adaptive engineering processes over Bi_(x)O_(y)Br_(z)followed by a classification of the generation approaches towards diverse Bi_(x)O_(y)Br_(z)materials.The features of different strategies,the up-to-date characterization techniques to detect bulk/surface states,and the effect of bulk/surface states on improving the photoactivity of Bi_(x)O_(y)Br_(z)in expanded applications are further discussed.Finally,the present research status,challenges,and future research opportunities of self-adaptive bulk/surface engineered Bi_(x)O_(y)Br_(z)are prospected.It is anticipated that this critical review can trigger deeper investigations and attract upcoming innovative ideas on the rational design of Bi_(x)O_(y)Br_(z)-based photocatalysts.

Transcriptional regulation of secondary metabolism and autophagy genes in response to DNA replication stress in Setosphaeria turcica

The fungal pathogen Setosphaeria turcica causes northern corn leaf blight(NCLB),which leads to considerable crop losses.Setosphaeria turcica elaborates a specialized infection structures called appressorium for maize infection.Previously,we demonstrated that the S.turcica triggers an S-phase checkpoint and ATR(Ataxia Telangiectasia and Rad3 related)-dependent self-protective response to DNA genotoxic insults during maize infection.However,how the regulatory mechanism works was still largely unknown.Here,we report a genome wide transcriptional profile analysis during appressorium formation in the present of DNA replication stress.We performed RNA-Seq analysis to identify S.tuicica genes responsive to DNA replication stress.In the current work,we found that appressorium-mediated maize infection by S.turcica is significantly blocked by S-phase checkpoint.A large serial of secondary metabolite and melanin biosynthesis genes were blocked in appressorium formation of S.turcica during the replication stress.The secondary metabolite biosynthesis genes including alcohol dehydrogenase GroES-like domain,multicopper oxidase,ABCtransporter families,cytochrome P450 and FAD-containing monooxygenase were related to plant pathogen infection.In addition,we demonstrated that autophagy in S.turcica is up-regulated by ATR as a defense response to stress.We identified StATG3,StATG4,StATG5,StATG7 and StATG16 genes for autophagy were induced by ATR-mediated S-phase checkpoint.We therefore propose that in response to genotoxic stress,S.turcica utilizes ATR-dependent pathway to turn off transcription of genes governing appressorium-mediated infection,and meanwhile inducing transcription of autophagy genes likely as a mechanism of self-protection,aside from the more conservative responses in eukaryotes.

A WSN Node Fault Diagnosis Model Based on BRB with Self-Adaptive Quality Factor

Wireless sensor networks (WSNs) operate in complex and harshenvironments;thus, node faults are inevitable. Therefore, fault diagnosis ofthe WSNs node is essential. Affected by the harsh working environment ofWSNs and wireless data transmission, the data collected by WSNs containnoisy data, leading to unreliable data among the data features extracted duringfault diagnosis. To reduce the influence of unreliable data features on faultdiagnosis accuracy, this paper proposes a belief rule base (BRB) with a selfadaptivequality factor (BRB-SAQF) fault diagnosis model. First, the datafeatures required for WSN node fault diagnosis are extracted. Second, thequality factors of input attributes are introduced and calculated. Third, themodel inference process with an attribute quality factor is designed. Fourth,the projection covariance matrix adaptation evolution strategy (P-CMA-ES)algorithm is used to optimize the model’s initial parameters. Finally, the effectivenessof the proposed model is verified by comparing the commonly usedfault diagnosis methods for WSN nodes with the BRB method consideringstatic attribute reliability (BRB-Sr). The experimental results show that BRBSAQFcan reduce the influence of unreliable data features. The self-adaptivequality factor calculation method is more reasonable and accurate than thestatic attribute reliability method.

A Broker-Based Task-Scheduling Mechanism Using Replication Approach for Cloud Systems

The reliability and availability of cloud systems have become major concerns of service providers,brokers,and end-users.Therefore,studying fault-tolerance mechanisms in cloud computing attracts intense attention in industry and academia.The task-scheduling mechanisms can improve the fault-tolerance level of cloud systems.A task-scheduling mechanism distributes tasks to a group of instances to be executed.Much work has been undertaken in this direction to improve the overall outcome of cloud computing,such as improving service qual-ity and reducing power consumption.However,little work on task scheduling has studied the problem of lost tasks from the broker’s perspective.Task loss can hap-pen due to virtual machine failures,server crashes,connection interruption,etc.The broker-based concept means that the backup task can be allocated by the bro-ker on the same cloud service provider(CSP)or a different CSP to reduce costs,for example.This paper proposes a novel fault-tolerant mechanism that employs the primary backup(PB)model of task scheduling to address this issue.The pro-posed mechanism minimizes the impact of failure events by reducing the number of lost tasks.The mechanism is further improved to shorten the makespan time of submitted tasks in cloud systems.The experiments demonstrated that the pro-posed mechanism decreased the number of lost tasks by about 13%–15%com-pared with other mechanisms in the literature.

Graph-Based Replication and Two Factor Authentication in Cloud Computing

Many cutting-edge methods are now possible in real-time commercial settings and are growing in popularity on cloud platforms.By incorporating new,cutting-edge technologies to a larger extent without using more infrastructures,the information technology platform is anticipating a completely new level of devel-opment.The following concepts are proposed in this research paper:1)A reliable authentication method Data replication that is optimised;graph-based data encryp-tion and packing colouring in Redundant Array of Independent Disks(RAID)sto-rage.At the data centre,data is encrypted using crypto keys called Key Streams.These keys are produced using the packing colouring method in the web graph’s jump graph.In order to achieve space efficiency,the replication is carried out on optimised many servers employing packing colours.It would be thought that more connections would provide better authentication.This study provides an innovative architecture with robust security,enhanced authentication,and low cost.

Efficient Heuristic Replication Techniques for High Data Availability in Cloud

Most social networks allow connections amongst many people based on shared interests.Social networks have to offer shared data like videos,photos with minimum latency to the group,which could be challenging as the storage cost has to be minimized and hence entire data replication is not a solution.The replication of data across a network of read-intensive can potentially lead to increased savings in cost and energy and reduce the end-user’s response time.Though simple and adaptive replication strategies exist,the solution is non-deter-ministic;the replicas of the data need to be optimized to the data usability,perfor-mance,and stability of the application systems.To resolve the non-deterministic issue of replication,metaheuristics are applied.In this work,Harmony Search and Tabu Search algorithms are used optimizing the replication process.A novel Har-mony-Tabu search is proposed for effective placement and replication of data.Experiments on large datasets show the effectiveness of the proposed technique.It is seen that the bandwidth saving for proposed harmony-Tabu replication per-forms better in the range of 3.57%to 18.18%for varying number of cloud data-centers when compared to simple replication,Tabu replication and Harmony replication algorithm.

Numerical simulation of LWD resistivity response of carbonate formation using self-adaptive hp-FEM

Most of the carbonate formation are highly heterogeneous with cavities of different sizes, which makes the prediction of cavity-filled reservoir in carbonate rocks difficult. Large cavities in carbonate formations pose serious threat to drilling operations. Logging-whiledrilling （LWD） is currently used to accurately identify and evaluate cavities in reservoirs during drilling. In this study, we use the self-adaptive hp-FEM algorithm simulate and calculate the LWD resistivity responses of fracture-cavity reservoir cavities. Compared with the traditional h-FEM method, the self-adaptive hp-FEM algorithm has the characteristics of the self-adaptive mesh refinement and the calculations exponentially converge to highly accurate solutions. Using numerical simulations, we investigated the effect of the cavity size, distance between cavity and borehole, and transmitted frequency on the LWD resistivity response. Based on the results, a method for recognizing cavities is proposed. This research can provide the theoretical basis for the accurate identification and quantitative evaluation of various carbonate reservoirs with cavities encountered in practice.

EVOLUTIONARY FUZZY GUIDANCE LAW WITH SELF-ADAPTIVE REGION

Effective guidance is one of the most important tasks to the performance of air-to-air missile. The fuzzy logic controller is able to perform effectively even in situations where the information about the plant is inaccurate and the operating conditions are uncertain. Based on the proportional navigation, the fuzzy logic and the genetic algorithm are combined to develop an evolutionary fuzzy navigation law with self-adapt region for the air-to-air missile guidance. The line of sight (LOS) rate and the closing speed between the missile and the target are inputs of the fuzzy controller. The output of the fuzzy controller is the commanded acceleration. Then a nonlinear function based on the conventional fuzzy logic control is imported to change the region. This nonlinear function can be changed with the input variables. So the dynamic change of the fuzzy variable region is achieved. The guidance law is optimized by the genetic algorithm. Simulation results of air-to-air missile attack using MATLAB show that the method needs less acceleration and shorter flying time, and its realization is simple.[KH*3/4D]

A Novel Replication-competent Adenovirus CNHK500 in the Treatment of Heptocellular Carcinoma In Vitro

Objective: To evaluate the therapeutic efficacy of replicative adenovirus CNHK500 in the treatment of hepatocellular carcinoma. Methods: Virus proliferation assay, cell viability assay and Western blot were performed to assess the selective replication and cytolysis of CNHK500 in telomerase positive liver cancer cells Hep3B, HepGII, SMMC7721 and in normal cells. Results: The replicative multiples of CNHK500 in HepGII, Hep3B and SMMC7221 after 96 h of virus proliferation were 52 000, 396 984.9 and 632 911.3 fold respectively, similar to those of wtAd5. However, CNHK500 demonstrated more significant attenuated replicative ability in normal cell lines than wtAd5. CNHK500 replicated only 3.1-100 fold at 96 h, while the wtAd5 still reached 3160-17 357 fold. CNHK500 could cause half of HepGII cells death within 7 days at MOI 2, in Hep3B cell lines the IC50 was as low as MOI 0.01, whereas the IC50 in BJ cell was as high as MOI 1000. CNHK500 E1A protein could only be detected in hepatocellular cancer cells but not in normal cells under normoxia. E1B protein could only be detected under hypoxia condition at a MOI of 1. Conclusion: CNHK500 can efficiently replicate in and kill liver cancer cells as well as wtAd5 do while it is severely attenuated in proliferation and cytolysis among normal cells. It would be a prominsing strategy for liver cancer tratment.

BIO-INSPIRED SELF-ADAPTIVE MANUFACTURING SYSTEM CONTROL ARCHITECTURE

Future manufacturing systems need to cope with frequent changes and disturbances, therefore their control architectures require constant adaptability, agility, stability, self-organization, intelligence, and robustness. Bio-inspired manufacturing system can well satisfy these requirements. For this purpose, by referencing the biological organization structure and the mechanism, a bio-inspired manufacturing cell is presented from a novel view, and then a bio-inspired self-adaptive manufacturing model is established based on the ultra-short feedback mechanism of the neuro-endocrine system. A hio-inspired self-adaptive manufacturing system coordinated model is also established based on the neuro-endocrine-immunity system （NEIS）. Finally, an example based on pheromone communication mechanism indicates that the robustness of the whole manufacturing system is improved by bio-inspired technologies.

SQL Server CE中RDA与Replication的研究

介绍移动数据库SQL Server CE及其特点,详细分析RDA(远程数据访问)和Replica-tion(合并复制)数据同步技术,探讨两种技术的各自特点和应用环境,描述两种数据同步技术的实现过程,比较这两种技术适合的环境。

一种基于Active Replication的TCP连接容错技术

本文提出一种基于ActiveReplication的TCP连接容错技术(ARTCP),采用两层的Cluster结构,并通过前端的分发器和聚合网关机维持多个同步的TCP连接副本,达到了相互容错的效果。测试结果表明,该技术不仅保证网络服务连接级的高可用性,而且具有恢复时间短、开销小的优势。