Accurate control of electronic energy is a necessary way to generate ideal plasma and achieve efficient conversion of physical and chemical states. Stable and controllable excitation energy can ...reduce the difficulty of electronic energy modulation and improve energy utilization efficiency. There is significant jitter during the charging and discharging state switching process of traditional transformation power supplies. A high-voltage pulse power supply based on BUCK module cascading is proposed. Utilizing the advantages of high stability and strong scalability of BUCK circuits. Low jitter voltage output achieved through independent control of a single-stage BUCK circuit. The zero delay magnetic coupling driving method is adopted, ensure fast synchronization of all levels of BUCK modules. The power supply can output 100 kV square waves with a power output over 20MW, and the rising edge time is less than 1us. While, the fluctuation range of the top voltage is below 3 \%. By optimizing parasitic parameters, the impact of stage output voltage on single-stage devices can be effectively reduced. The pulse power supply can be expends by adjust the amount of the module of for the output voltage from 10 kV to 300 kV, meeting application requirements such as highly controllable plasma discharge and electronic energy resonance conversion.
Blockchain technology has found widespread use in various fields, including finance, healthcare, the Internet of Things, and supply chain management, due to its ability to meet technical and ...non-technical requirements. Therefore, it is crucial for system designers to evaluate the performance and characteristics of a given blockchain platform before deploying it. One way to achieve this is through the use of blockchain simulators. However, existing simulators have limitations in areas such as model scaling, event scheduling, time control, object model evaluation, and simulation magnitude. To address these limitations, we propose a Blockchain Simulator based on Event-Layered Architecture (BSELA) in this paper. BSELA comprises multiple functional modules with effective model scalability, with the core being our proposed Event-Layered Architecture. This architecture introduces a new event-driven scheduling mechanism for discrete-event simulation, which improves the efficiency, stability, and maintainability of event scheduling during blockchain simulation. Furthermore, we propose a time advancement mechanism for subsequent events based on event rounds, which improves the accuracy of time advancement, simulation complexity, and real-time demand handling capability during the simulation. We begin by validating the efficiency of our simulator by comparing experiments with those of existing blockchain simulators. We then validate the accuracy of BSELA through simulation experiments on blockchain network connectivity, block propagation latency, and the number of INV messages propagated per hour. We validated the accuracy of the experimental results by conducting a comparison with Bitcoin data. Finally, in order to address the research gap in the performance of blockchain systems at the underlying peer-to-peer network level and in other fields, we designed and experimentally validated the node-trust network construction mechanism by adjusting the simulator’s blockchain object model. Our experiments show that this tuning mechanism improves data transfer efficiency by 30%–40%, and also improves security. The design and experiments of the BSELA tuning mechanism validate the scalability and flexibility of our simulator for blockchain research.
•A blockchain simulator with Event-Layered Architecture.•Blockchain simulation driven model and time advancement mechanism study.•Study of tuning optimization mechanism of simulation object model.
Scalability of Mobile Cloud Storage Hussien, Nur Syahela; Sulaiman, Sarina; Aborujilah, Abdulaziz ...
International journal of interactive mobile technologies,
11/2021, Letnik:
15, Številka:
21
Journal Article
Recenzirano
Today, there are high demands on Mobile Cloud Storage (MCS) services that need to manage the increasing number of works with stable performance. This situation brings a challenge for data management ...systems because when the number of works increased MCS needs to manage the data wisely to avoid latency occur. If latency occurs it will slow down the data performance and it should avoid that problem when using MCS. Moreover, MCS should provide users access to data faster and correctly. Hence, the research focuses on the scalability of mobile cloud data storage management, which is study the scalable on how deep the data folder itself that increase the number of works.
Modularity is a popular measure of community structure. However, maximizing the modularity can lead to many competing partitions, with almost the same modularity, that are poorly correlated with each ...other. It can also produce illusory ‘‘communities’’ in random graphs where none exist. We address this problem by using the modularity as a Hamiltonian at finite temperature and using an efficient belief propagation algorithm to obtain the consensus of many partitions with high modularity, rather than looking for a single partition that maximizes it. We show analytically and numerically that the proposed algorithm works all of the way down to the detectability transition in networks generated by the stochastic block model. It also performs well on real-world networks, revealing large communities in some networks where previous work has claimed no communities exist. Finally we show that by applying our algorithm recursively, subdividing communities until no statistically significant subcommunities can be found, we can detect hierarchical structure in real-world networks more efficiently than previous methods.
Significance Most work on community detection does not address the issue of statistical significance, and many algorithms are prone to overfitting. We address this using tools from statistical physics. Rather than trying to find the partition of a network that maximizes the modularity, our approach seeks the consensus of many high-modularity partitions. We do this with a scalable message-passing algorithm, derived by treating the modularity as a Hamiltonian and applying the cavity method. We show analytically that our algorithm succeeds all the way down to the detectability transition in the stochastic block model; it also performs well on real-world networks. It also provides a principled method for determining the number of groups or hierarchies of communities and subcommunities.
Blockchain (e.g., Bitcoin and Ethereum) has drawn much attention and has been widely-deployed in recent years. However, blockchain scalability is emerging as a challenging issue. This paper outlines ...the existing solutions to blockchain scalability, which can be classified into two categories: first layer and second layer solutions. First layer solutions propose modifications to the blockchain (i.e., changing the blockchain structure, such as block size) while second layer solutions propose mechanisms that are implemented outside of the blockchain. In particular, we focus on sharding as a promising first layer solution to the scalability issue; the basic idea behind sharding is to divide the blockchain network into multiple committees, each processing a separate set of transactions. More specifically, (a) we propose a taxonomy based on committee formation and intra-committee consensus; and (b) we compare the main existing sharding-based blockchain protocols. We also present a performance-based comparative analysis (i.e., throughput and latency), of the advantages, and disadvantages in existing scalability solutions.
In this paper, we present generic cloud performance models for evaluating Iaas, PaaS, SaaS, and mashup or hybrid clouds. We test clouds with real-life benchmark programs and propose some new ...performance metrics. Our benchmark experiments are conducted mainly on IaaS cloud platforms over scale-out and scale-up workloads. Cloud benchmarking results are analyzed with the efficiency, elasticity, QoS, productivity, and scalability of cloud performance. Five cloud benchmarks were tested on Amazon IaaS EC2 cloud: namely YCSB, CloudSuite, HiBench, BenchClouds, and TPC-W. To satisfy production services, the choice of scale-up or scale-out solutions should be made primarily by the workload patterns and resources utilization rates required. Scaling-out machine instances have much lower overhead than those experienced in scale-up experiments. However, scaling up is found more cost-effective in sustaining heavier workload. The cloud productivity is greatly attributed to system elasticity, efficiency, QoS and scalability. We find that auto-scaling is easy to implement but tends to over provision the resources. Lower resource utilization rate may result from auto-scaling, compared with using scale-out or scale-up strategies. We also demonstrate that the proposed cloud performance models are applicable to evaluate PaaS, SaaS and hybrid clouds as well.
•Our method is the first to apply graph neural networks in the prediciton of DDIs.•Our method is scalable and robust compared to conventional approaches.•The visualization of molecule graph attention ...distribution can provide insights conforming to domain knowledge.
One drug's pharmacological activity may be changed unexpectedly, owing to the concurrent administration of another drug. It is likely to cause unexpected drug-drug interactions (DDIs). Several machine learning approaches have been proposed to predict the occurrence of DDIs. However, existing approaches are almost dependent heavily on various drug-related features, which may incur noisy inductive bias. To alleviate this problem, we investigate the utilization of the end-to-end graph representation learning for the DDI prediction task. We establish a novel DDI prediction method named GCN-BMP (Graph Convolutional Network with Bond-aware Message Propagation) to conduct an accurate prediction for DDIs. Our experiments on two real-world datasets demonstrate that GCN-BMP can achieve higher performance compared to various baseline approaches. Moreover, in the light of the self-contained attention mechanism in our GCN-BMP, we could find the most vital local atoms that conform to domain knowledge with certain interpretability.
In this paper, we present the design, implementation, and evaluation of SEAR, a collaborative framework for Scaling Experiences in multi-user Augmented Reality (AR). Most AR systems benefit from ...computer vision (CV) algorithms to detect, classify, or recognize physical objects for augmentation. A widely used acceleration method for mobile AR is to offload the compute-intensive tasks ( e.g. , CV algorithms) to the network edge. However, we show that the end-to-end latency, an important metric of mobile AR, may dramatically increase when offloading AR tasks from a large number of concurrent users to the edge. SEAR tackles this scalability issue through the innovation of a lightweight collaborative local caching scheme. Our key observation is that nearby AR users may share some common interests, and may even have overlapped views to augment ( e.g. , when playing a multi-user AR game). Thus, SEAR opportunistically exchanges the results of offloaded AR tasks among users when feasible and leverages compute resources on mobile devices to relieve, if necessary, the edge workload by intelligently reusing these results. We build a prototype of SEAR to demonstrate its efficacy in scaling AR experiences. We conduct extensive evaluations through both real-world experiments and trace-driven simulations. We observe that SEAR not only reduces the end-to-end latency, by up to 130×, compared to the state-of-the-art adaptive edge offloading scheme, but also achieves high object-recognition accuracy for mobile AR.
Collaborative filtering (CF) has been extensively studied in recommendation, spawning various solutions. While graph convolution networks (GCNs) are effective at representation learning, their ...efficiency is lacking. Comparatively, item similarity model efficiently establishes direct relationships between items. In spite of this, the modeling problem grows quadratically as the number of items increases. This poses critical scalability issues. In this paper, through an investigation of the latest GCN model, we reveal the feasibility of optimizing the process of similarity modeling using the underlying group structure in the item set. Based on these findings, we propose a novel model which introduces graph partitioning to reduce the scale of similarity modeling problem, dubbed FPSR+. Specifically, we divide similarity modeling of items into sub-problems within each partition, and incorporate global and local prior knowledge to alleviate information loss. Following an analysis of the properties of different items in partitioning, we propose a new hub set selection strategy that improves the robustness of FPSR+ in the small partition case. Extensive experiments on four real-world datasets demonstrate the superior performance of FPSR+ compared with state-of-the-art GCN models and item similarity models, as well as several-fold speedups and reductions in parameter storage.
Efficient and smart business processes are heavily dependent on the Internet of Things (IoT) networks, where end-to-end optimization is critical to the success of the whole ecosystem. These systems, ...including industrial, healthcare, and others, are large scale complex networks of heterogeneous devices. This introduces many security and access control challenges. Blockchain has emerged as an effective solution for addressing several such challenges. However, the basic algorithms used in the business blockchain are not feasible for large scale IoT systems. To make them scalable for IoT, the complex consensus-based security has to be downgraded. In this article, we propose a novel lightweight proof of block and trade (PoBT) consensus algorithm for IoT blockchain and its integration framework. This solution allows the validation of trades as well as blocks with reduced computation time. Also, we present a ledger distribution mechanism to decrease the memory requirements of IoT nodes. The analysis and evaluation of security aspects, computation time, memory, and bandwidth requirements show significant improvement in the performance of the overall system.
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