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The Mobile Edge Computing (MEC) paradigm gives impetus to the vigorous advancement of the Internet of Things (IoT), through provisioning low-latency computing services at network edges. The emerging digital twin technique has been explosively growing in the IoT community, which bridges the gap between physical objects and their digital representations in an MEC network, enabling real-time monitoring and analysis, simulations on the dynamics of systems, accurate predictions on behaviours of objects, and optimization on network resource allocation. In this paper, we consider AoI-aware query services in an MEC network empowered by digital twin technology for diverse IoT applications. We aim to maximize the weighted sum of the accumulative freshness of query results measured by the Age of Information (AoI) and the total query service delay of admitted requests. To this end, we first formulate a novel minimization problem that explores nontrivial trade-offs between the two conflicting optimization objectives: the freshness of query results and service delays, and we show the NP-hardness of the problem. Then, we propose an approximation algorithm with a provable approximation ratio for the problem, at the expense of bounded computing capacity violations. We also develop a heuristic for the problem without any capacity violations. We finally evaluate the performance of the proposed algorithms via simulations. The simulation results demonstrate that the proposed algorithms are promising, and outperform the comparison benchmarks.

Federated continual learning (FCL) is emerging as a key technology for time-sensitive applications in highly adaptive environments including autonomous driving and industrial digital twin. Each FCL trains machine learning models using newly-generated datasets as soon as possible, to obtain a highly accurate machine learning model for new event predictions. The age of data, defined as the time difference between the generation time of a dataset and the current time, is widely adopted as a key criterion to evaluate both timeline and quality of training. In this paper, we study the problem of age-aware FCL in a mobile edge computing (MEC) network. We not only investigate optimization techniques that optimize the data selection and aggregator placement for FCL but also implement a real system as a prototype for age-aware FCL. Specifically, we first propose an approximation algorithm with a provable approximation ratio for the age-aware data selection and aggregator placement problem for FCL with a single request. In real application scenarios, there are usually multiple FCL requests that require to train models, and delays in the MEC network are usually uncertain. We then study the problem of age-aware data selection and aggregator placement problem for FCL with uncertain delays and multiple requests, by devising an online learning algorithm with a bounded regret based on contextual bandits. We finally implement a prototype for FCL in an MEC network, with various heterogeneous user equipments (UEs) and cloudlets with different computing capabilities in the network. Experiment results show that the performance of the proposed algorithms outperform existing studies, by achieving 47% lower age of data and 12% higher model accuracy.

In this paper, we study the deployment of $K$ heterogeneous UAVs to monitor Points of Interest (PoIs) in a disaster zone, where a PoI may represent a school building or an office building, in which people are trapped. A UAV can take images/videos of PoIs and send its collected information back to a nearby rescue station for decision-making. Unlike most existing studies that focused on only homogeneous UAVs, we here study the scheduling of $K$ heterogeneous UAVs, where different UAVs have different energy capacities and functionalities that lead to different monitoring qualities (monitoring rewards) of each PoI. For example, one type of UAVs can take only visual images while the other type of UAVs can take both visual and thermal infrared images. In this paper, we investigate a problem of scheduling $K$ heterogeneous UAVs to monitor PoIs so that the sum of monitoring rewards received by all UAVs is maximized, subject to energy capacity on each UAV. We propose the very first $\frac {1}{3}$ -approximation algorithm for this scheduling problem. We also evaluate the performance of the proposed algorithm, using real parameters of commercial UAVs. Experimental results show that the performance of the proposed algorithm is promising, which is improved by 25%, compared with existing algorithms.

The emerging digital twin technique enhances the network management efficiency and provides comprehensive insights on network performance, through mapping physical objects to their digital twins. The user satisfaction on digital twin-enabled service relies on the freshness of digital twin data, which is measured by the Age of Information (AoI). Due to long service delays, the use of the remote cloud for delay-sensitive service provisioning faces serious challenges. Mobile Edge Computing (MEC), as an ideal paradigm for delay-sensitive services, is able to realize real-time data communication between physical objects and their digital twins at the network edge. However, the mobility of physical objects and dynamics of user query arrivals make seamless service provisioning in MEC become challenging. In this paper, we investigate dynamic digital twin placements for improving user service satisfaction in MEC environments, by introducing a novel metric to measure user service satisfaction based on the AoI concept and formulating two user service satisfaction enhancement problems: the static and dynamic utility maximization problems under static and dynamic digital twin placement schemes. To this end, we first formulate an Integer Linear Programming (ILP) solution to the static utility maximization problem when the problem size is small; otherwise, we propose a performance-guaranteed approximation algorithm. We then propose an online algorithm with a provable competitive ratio for the dynamic utility maximization problem, by considering dynamic user query services. Finally, we evaluate the performance of the proposed algorithms via simulations. Simulation results demonstrate that the proposed algorithms outperform the comparison baseline algorithms, improving the algorithm performance by at least 10.7%, compared to the baseline algorithms.

In the coming era of Metaverse, Augmented Reality (AR) has become a key enabler of diverse applications including healthcare, education, smart cities, and entertainments. To provide users with interactive and immersive experience, most AR applications require extremely high responsiveness and ultra-low processing latency. Mobile edge computing (MEC) has demonstrated great potentials in meeting such stringent latency requirements and resource demands of AR applications, by implementing AR requests in edge servers within the proximity of users. In this paper, we investigate the reward maximization problem for AR applications with uncertain resource demands in an MEC network, such that the accumulative reward of services provided for AR applications is maximized, while ensuring that the responsiveness of AR applications is enhanced, subject to network resource capacity. To this end, we formulate an exact solution when the problem size is small, otherwise we devise an efficient approximation algorithm with a provable approximation ratio for the problem. We also develop an online learning algorithm with a bounded regret for the dynamic reward maximization problem without the knowledge of future arrivals of AR requests, by adopting the Multi-Armed Bandits (MAB) technique. Considering maximizing the reward may defer the implementations of some urgent yet low-award requests, we propose a fairness-aware online learning algorithm for the dynamic reward maximization problem, through a data rate prediction mechanism that adopts a multi-task and multi-timescale Long Short-Term Memory (MT2-LSTM) method. Finally, we evaluate the performance of the proposed algorithms for AR applications by building a real test bed. Experimental results show that the proposed algorithms outperform existing studies by improving the award by 13%.

Benefitting from the breakthrough of wireless power transfer technology, the lifetime of Wireless Sensor Networks (WSNs) can be significantly prolonged by scheduling a mobile charger (MC) to charge sensors. Compared with omnidirectional charging, the MC equipped with directional antenna can concentrate energy in the intended direction, making charging more efficient. However, all prior arts ignore the considerable energy leakage behind the directional antenna (i.e., back lobe), resulting in energy wasted in vain. To address this issue, we study a fundamental problem of how to utilize the neglected back lobe and schedule the directional MC efficiently. Towards this end, we first build and verify a directional charging model considering both main and back lobes. Then, we focus on jointly optimizing the number of dead sensors and energy usage effectiveness. We achieve these by introducing a scheduling scheme that utilizes both main and back lobes to charge multiple sensors simultaneously. Finally, extensive simulations and field experiments demonstrate that our scheme reduces the number of dead sensors by 49.5% and increases the energy usage effectiveness by 10.2% on average as compared with existing algorithms.

In this paper we study the deployment of an Unmanned Aerial Vehicle (UAV) network that consists of multiple UAVs to provide emergent communication service for people who are trapped in a disaster area, where each UAV is equipped with a base station that has limited computing capacity and power supply, and thus can only serve a limited number of people. Unlike most existing studies that focused on homogeneous UAVs, we consider the deployment of heterogeneous UAVs where different UAVs have different computing capacities. We study a problem of deploying $K$ heterogeneous UAVs in the air to form a temporarily connected UAV network such that the network throughput– the number of users served by the UAVs, is maximized, subject to the constraint that the number of people served by each UAV is no greater than its service capacity. We then propose a novel $O\left({\sqrt {\frac {s}{K}}}\right)$ -approximation algorithm for the problem, where $s$ is a given positive integer with $1 \le s\le K$ , e.g., $s=3$ . We also devise an improved heuristic, based on the approximation algorithm. We finally evaluate the performance of the proposed algorithms. Experimental results show that the numbers of users served by UAVs in the solutions delivered by the proposed algorithms are increased by 25% than state-of-the-arts.

Series compensation is an effective way to reduce the dc side passive filters of diode rectifier (DR), but the stability judgement of such series compensation system (SCS) by the impedance-ratio-based stability criteria is complicated, because this kind of system has many cascade forms and complex coupling interactions. This article proposes a stability analysis method for the SCS composed of DR and series voltage compensator, based on which an impedance optimization strategy is present to solve the instability problem. First, the simplified two-port small-signal model of SCS is established. The stability analysis is based on common characteristics of the system, which can fully reveal the dc bus oscillation mechanism combined with equivalent loop gain and the stable condition can be obtained for control system optimization. From the point of view of reshaping the impedance of series loop, a virtual-impedance-based control strategy with simple implementation is adopted to improve the system stability without affecting its dynamic performance, and the controller and parameters design are given considering the requirements of impedance optimization. An experimental prototype is established to validate the accuracy of stability analysis and the effectiveness of proposed control.

Mobile Edge Computing (MEC) has been identified as a desirable computing paradigm that provides efficient and effective services for various applications, while meeting stringent service delay requirements. Orthogonal to the MEC computing paradigm, Network Function Virtualization (NFV) technology is another enabling technology that provides the network resource management with great flexibility and scalability, where the instances of Virtual Network Functions (VNFs) are deployed in edge servers as Service Function Chains (SFCs) for SFC-enabled services. Although reliable service provisioning in MEC environments is fundamentally important, the deployed VNF instances usually are not reliable, which can be affected by their software implementation, their execution duration, the workload among edge servers, and so on. Empowered by digital twin techniques, the states of VNF instances can be maintained by their digital twins in a real-time manner and their reliability can be accurately predicted through their digital twins. In this paper, we study digital twin-assisted, SFC-enabled reliable service provisioning in MEC networks by exploiting the dynamics of VNF instance reliability. We concentrate on two novel optimization problems of reliable service provisioning: the service cost minimization problem, and the dynamic service admission maximization problem. We first show their NP-hardness. We then formulate an Integer Linear Program (ILP) solution, and devise an approximation algorithm with a constant approximation ratio for the service cost minimization problem. We third provide an ILP solution to the offline version of the dynamic service admission maximization problem. Built upon this offline ILP solution, we also develop an online algorithm with a provable competitive ratio for the problem, by adopting the primal-dual dynamic updating technique. We finally evaluate the performance of the proposed algorithms via simulations. Simulation results demonstrate that the proposed algorithms outperform their comparison benchmarks, and improve the performance of their comparison counterparts by no less than $10.2 \%$10.2%.

To unveil the hidden value in the datasets of user equipments (UEs) while preserving user privacy, federated learning (FL) is emerging as a promising technique to train a machine learning model using the datasets of UEs locally without uploading the datasets to a central location. Customers require to train machine learning models based on different datasets of UEs, through issuing FL requests that are implemented by FL services in a mobile edge computing (MEC) network. A key challenge of enabling FL in MEC networks is how to minimize the energy consumption of implementing FL requests while guaranteeing the accuracy of machine learning models, given that the availabilities of UEs usually are uncertain. In this paper, we investigate the problem of energy minimization for FL in an MEC network with uncertain availabilities of UEs. We first consider the energy minimization problem for a single FL request in an MEC network. We then propose a novel optimization framework for the problem with a single FL request, which consists of (1) an online learning algorithm with a bounded regret for the UE selection, by considering various contexts (side information) that influence energy consumption; and (2) an approximation algorithm with an approximation ratio for the aggregator placement for a single FL request. We third deal with the problem with multiple FL requests, for which we devise an online learning algorithm with a bounded regret. We finally evaluate the performance of the proposed algorithms by extensive experiments. Experimental results show that the proposed algorithms outperform their counterparts by reducing at least 13% of the total energy consumption while achieving the same accuracy.

Mobile Edge Computing (MEC) promises to provide mobile users with delay-sensitive services at the edge of network, and each user service request usually is associated with a Service Function Chain (SFC) requirement that consists of Virtualized Network Functions (VNFs) in order. The satisfaction of a user on his requested service is heavily impacted by the service reliability. In this article, we study user satisfaction on services provided by an MEC network through introducing a submodular function based metric to measure user satisfaction. We first formulate a novel user satisfaction problem with the aim to maximize the accumulative user satisfaction, assuming that all available computing resource in the MEC network can be used for service reliability enhancement. We show that the problem is NP-hard, and devise an approximation algorithm with a provable approximation ratio for it. We then consider the problem under a given computing resource budget constraint, for which we devise an approximation algorithm with a provable approximation ratio, at the expense of moderate budget violations. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms outperform the comparison baseline algorithms, improving the performance by more 16.1% in comparison with the baseline algorithms.

In this paper, we study the employment of a mobile charger to charge lifetime-critical sensors under the multi-node partial-charging model, in which the charger can simultaneously charge the sensors within its charging range and each sensor may be partially charged each time. We notice that existing studies only scheduled the charger to minimize the number of dead sensors, but did not consider the charging scheduling for the sensors that have already run out of their energy, and the dead sensors will be last charged by the mobile charger. Then, their dead durations may be very long. In this paper, we consider not only how to minimize the number of dead sensors but also reduce the dead durations of sensors. To this end, we first formulate a sensor lifetime maximization problem, which is to find a charging tour for a mobile charger to charge sensors, such that the sum of sensor lifetimes is maximized. We then propose a novel $\frac{1}{3}$13-approximation algorithm for the problem. We finally evaluate the performance of the proposed algorithm through experiments. Experimental results show that both the average and maximum sensor dead durations by the proposed algorithm are up to 70% shorter than those by existing algorithms.

Serverless computing is emerging as an enabling technology for elastic and low-cost AI applications in the edge of core networks. It allows AI developers to decompose a complex training and time-sensitive inference task into multiple functions with dependency, and upload the task to a Multi-access Edge Computing platform (MEC) for execution. Serverless computing adopts a popular design principle: the disaggregation of storage and computation, making the functions ‘stateless’. However, most AI applications are ‘stateful’ and rely on an external storage service to manage their states (ephemeral data). This will incur a prohibitively long delay for delay-sensitive AI applications if external services storing the states are far from the serverless functions. Motivated by this critical issue, in this paper we investigate a fundamental problem in serverless computing – the stateful serverless application placement problem, for which, we first propose an efficient heuristic algorithm, and then devise an approximation algorithm with a provable approximation ratio for one of its special cases. We also consider the online version of the problem, and develop an online learning-driven algorithm with a bounded regret. The crux of the online algorithm is the adoption of the multi-armed bandits technique for dynamic admissions of inference requests, under the uncertainty of both data volumes of requests and network delays. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results show that the proposed algorithms outperform their counterparts, reducing at least 32% in the total cost and 27% of the average delay.

This study is motivated by the maximum connected coverage problem (MCCP), which is to deploy a connected UAV network with given $K$ UAVs in the top of a disaster area such that the number of users served by the UAVs is maximized. The deployed UAV network must be connected, since the received data by a UAV from its served users need to be sent to the Internet through relays of other UAVs. Motivated by this application, in this paper we study a more generalized problem – the $h$ -hop independently submodular maximization problem, where the MCCP problem is one of its special cases with $h=4$ . We propose a $\frac {1-1/e}{2h+3}$ -approximation algorithm for the $h$ -hop independently submodular maximization problem, where $e$ is the base of the natural logarithm. Then, one direct result is a $\frac {1-1/e}{11}$ -approximate solution to the MCCP problem with $h=4$ , which significantly improves its currently best $\frac {1-1/e}{32}$ -approximate solution. We finally evaluate the performance of the proposed algorithm for the MCCP problem in the application of deploying UAV networks, and experimental results show that the number of users served by deployed UAVs delivered by the proposed algorithm is up to 12.5% larger than those by existing algorithms.

High step-down dc/dc converters with high efficiency and high power density are widely used in data centers, dc distribution grids and hybrid electric vehicles. In this article, a new resonant switched capacitor converter is proposed, which can not only realize high step-down conversion, but also operate in multiple-modes to achieve various voltage ratios. The duty cycle can be allocated to one-half and one-third, enabling the converter to operate in double-mode and triple-mode accordingly. In addition, the state of the basic resonant cell can be switched between series and parallel, which enriches the operating modes, and thus allowing the converter to achieve different voltage ratios. Furthermore, the principle and strategy of switching between operating modes with different voltage ratios and switching between operating modes with same voltage ratio are presented. With the mode switching strategy, this converter can operate in the optimal mode over the wide voltage range and the full load range, thus achieves high efficiency over the full operating range. Finally, five operating modes are selected to verify the feasibility and the correctness of the theoretical analysis

With the development of 5G technology, mobile edge computing is emerging as an enabling technique to reduce the response latency of network services by deploying cloudlets at 5G base stations to form mobile edge cloud (MEC) networks. Network service providers now shift their services from remote clouds to cloudlets of MEC networks in the proximity of users. However, the permanent placement of network services into an MEC network is not economic due to limited computing and bandwidth resources imposed on its cloudlets. A smart way is to cache frequently demanded services from remote clouds to cloudlets of the MEC network. In this paper, we study the problem of service caching in an MEC network under a service market with multiple network service providers competing for both computation and bandwidth resources in terms of Virtual Machines (VMs) in the MEC network. We first propose an Integer Linear Program (ILP) solution and a randomized rounding algorithm, for the problem without VM sharing among different network service providers. We then devise a distributed and stable game-theoretical mechanism for the problem with VM sharing among network service providers, with the aim to minimize the social cost of all network service providers, through introducing a novel cost sharing model and a coalition formation game. We also analyze the performance guarantee of the proposed mechanism, Strong Price of Anarchy (SPoA). We third consider the cost- and delay-sensitive service caching problem with temporal VM sharing, and propose a mechanism with provable SPoA. We finally evaluate the performance through extensive simulations and a real world test-bed implementation. Experimental results demonstrate that the proposed algorithms outperform existing approaches by achieving at least $40\%$40% lower social cost via service caching and resource sharing among different network service providers.

Multi-access edge computing (MEC) enables extreme low-latency AI services, such as Augmented Reality (AR) and Virtual Reality (VR), by deploying cloudlets in locations close to users. Meanwhile, a 5G hierarchical service market is emerging with both large-scale and small-scale network service providers competing for both computing and network bandwidth resources of an infrastructure provider. In this paper, we investigate the problem of caching services originally deployed in remote clouds to cloudlets in an MEC network in a hierarchical service market. For the service caching problem, we first propose a novel approximation-restricted framework that guarantees the stability of the 5G service market. Under the proposed framework, we first propose an approximation algorithm with a provable approximation ratio for the problem with non-selfish network service providers. We then design an efficient Stackelberg congestion game with selfish network service providers, and analyze the Price of Anarchy (PoA) of the proposed Stackelberg congestion game to measure the efficiency loss of the game due to selfishness of network service providers. Considering that the request rate of each service may not be given in advance, we study the service caching problem with the uncertainlity of request rates, and propose an approximation algorithm and a Stackelberg game via leveraging the randomized rounding technique. We finally evaluate the performance of the proposed algorithms and mechanisms by both simulations and implementations in a real test-bed. Results show that the performance of our proposed mechanisms achieve around 9.2% less cost than those of existing approaches.

In this paper, we study sensing data collection of IoT devices in a sparse IoT-sensor network, using an energy-constrained Unmanned Aerial Vehicle (UAV), where the sensory data is stored in IoT devices while the IoT devices may or may not be within the transmission range of each other. We formulate two novel data collection problems to fully or partially collect data stored from IoT devices using the UAV, by finding a closed tour for the UAV that consists of hovering locations and the sojourn duration at each of the hovering locations such that the accumulative volume of data collected within the tour is maximized, subject to the energy capacity on the UAV, where the UAV consumes energy on both hovering for data collection and flying from one hovering location to another hovering location. To this end, we first propose a novel data collection framework that enables the UAV to collect sensory data from multiple IoT devices simultaneously if these IoT devices are within the coverage range of the UAV, through adopting the orthogonal frequency division multiple access (OFDMA) technique. We then formulate two data collection maximization problems to deal with full or partial data collection from IoT devices at each hovering location, and show that both defined problems are NP-hard. We instead devise approximation and heuristic algorithms for the problems. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrated that the proposed algorithms are promising.

Pancreatic cancer has a poor prognosis. Lack of diagnostic markers prevents its early diagnosis and treatment. Pathogenic germline variation in BRCA1and BRCA2(BRCA) is genetic predisposition for cancer. The location of variants in different regions in BRCAis non-randomly enriched in different types of cancer as shown by the breast cancer cluster region (BCCR), ovarian cancer cluster region (OCCR) and prostate cancer cluster region (PrCCR). Although pathogenic BRCAvariation also contributes to pancreatic cancer, no pancreatic cancer cluster region (PcCCR) in BRCA1or BRCA2has been identified due to the relatively low incidence of pancreatic cancer and the lack of sufficient variation data from pancreatic cancer. Through comprehensive data mining, we identified 215 BRCApathogenic variants (PVs) (71 in BRCA1and 144 in BRCA2) from 27 118 pancreatic cancer cases. Through mapping the variants, we identified a region non-randomly enriched in pancreatic cancer between BRCA2c.3515 and c.6787. This region contained 59 BRCA2PVs and included 57% of pancreatic cancer cases (95% CI 43% to 70%). The PcCCR did not overlap with the BCCR and PrCCR but overlapped with the BRCA2OCCR, highlighting that this region may play similar aetiological roles in pancreatic cancer and ovarian cancer.

More and more 5G and AI applications demand flexible and low-cost processing of their traffic through diverse virtualized network functions (VNFs) to meet their security and privacy requirements. As such, the Network Function Virtualization (NFV) market has been emerged as a major service market that allows network service providers to trade their network services among customers. Since each service market usually involves complex interplays among players with different roles, efficient mechanisms that guarantee stable and efficient operations of the NFV market are urgently needed. One fundamental problem in the NFV market is how to maximize the social welfare of all players so that all players have incentives to participate in the activities of the market. In this paper, we first formulate a novel social welfare maximization problem in an NFV market of a multi-tier edge cloud network, with the aim to maximize the total revenue collected from all players, and we implement VNF services on Virtual Machines (VMs) leased by service providers to fulfill customers with service requests, where the edge cloud network consists of both cloudlets in edge networks and remote data centers in the core network. We then design an efficient incentive-compatible mechanism for the problem, and analyze the existence of a Nash equilibrium of the mechanism. Also, we consider an online social welfare maximization problem with uncertain values of customers and without the knowledge of future request arrivals, for which we devise an online learning algorithm by adopting the Multi-Armed Bandits (MAB) method with a bounded regret. We finally evaluate the performance of the proposed mechanisms through simulations and a testbed. Results show that the proposed mechanisms deliver up to 27% higher social welfare than those of existing studies

The combination of nontrivial topology, magnetism, and superconductivity could offer the potential to realize exotic excitations of quasiparticles. MnBi2Te4, as an intrinsic magnetic topological insulator, may be a good platform to create Majorana fermions if coupled to an s-wave superconductor. Here, we report the transport properties of a MnBi2Te4–NbN hybrid device. This device exhibits clear Coulomb blockade oscillations. We observe a large zero-bias conductance peak that exists over considerable changes in gate voltage, magnetic field, and temperature, which is interpreted as a not fully developed supercurrent. The zero-bias peak shows a nonmonotonic evolution with a magnetic field and an abrupt π phase shift with changing temperature. Zero-energy bound states and a topological phase transition may exist in this hybrid system. Our results provide the first experimental investigation into the properties of the intrinsic magnetic topological insulator/superconductor hybrid structures modulated by the Coulomb blockade effect.

The formation of the battery is a crucial step in its manufacturing process. The existing battery formation system suffers from low efficiency and high energy consumption costs due to long energy flow paths, high DC bus line losses, and additional balancing circuit applications. Therefore, a novel high-efficient battery series formation system (BSFS) that combines partial power processing architecture (PPPA) with the modular converter is proposed to address this problem in this article. The PPPA-BSFS includes two-stage isolated sub-modules consisting of Buck-Boost converter and LLC converter with input-serial and output-parallel connection. The isolated converter only processes the serial compensation power, about 1/3 of the full power. Consequently, the converter capacity can be shrunk in the PPPA-BSFS, which can reduce the system's cost and power loss. Furthermore, the paper establishes the steady-state mathematical model and energy efficiency model of PPPA-BSFS based on analyzing the power compensation principle, and the key factors affecting the operational efficiency of PPPA-BSFS are studied. Finally, we construct the simulation models with 10 cells and 120 cells to compare and analyze energy efficiency between PPPA-BSFS and existing battery formation topology under various operating conditions. Our simulation results show that the highest operating efficiency of the PPPA-BSFS proposed is 99.37 % when 120 cells are formed normally, where there is an increment of 5.95 %, 2.36 %, and 0.85 % compared with others, respectively. The high efficiency of the PPPA-BSFS is verified, and the PPPA-BSFS shows great potential for application in battery formation scenarios.

Wireless power transfer (WPT) technology has been a research and industrial hotspot with applications in many areas, such as wireless electric vehicle charging system that requires high power, high efficiency, and high power factor (PF). Usually, the power is drawn from a 50/60 Hz single-phase or three-phase ac power source. For a high power application, a three-phase ac source is commonly used. In this paper, a three-phase single-stage WPT resonant converter with PF correction (PFC) and bus voltage control is proposed to improve efficiency and power quality of three-phase input and reduce production cost and complexity for a high power WPT system. A T-type topology is applied as the common part to perform both the PFC and dc–dc WPT functionalities simultaneously. The proposed converter is much more advantageous than a conventional three-phase two-stage WPT converter with individual PF corrector. In addition, three-phase single-stage topologies have better power quality than single-phase single-stage topologies because zero-sequence components can be naturally eliminated.

An individual can obtain high profits by playing a bridge role among different communities in a social network, thus acquiring more potential resources from the communities or having control over the information transmitted within the network. Such an individual usually is referred to as a structural hole spanner in the network. Existing studies on the identification of important structural hole spanners focused on only whether a person bridges multiple communities without emphasizing the tie strength of that person connecting to his bridged communities. However, a recent study showed that such tie strength heavily affects the profit the person obtains by playing the bridge role. In this study, we aim to identify the most important hole spanners in a large-scale social network who have strong ties with their bridged communities. Accordingly, we first formulate the top- k structural hole spanner problem to identify k nodes in the network such that their removals will block the maximum numbers of information propagations within the network. Due to the NP-hardness of the problem, we then propose a novel (1 − ϵ) -approximation algorithm, where ϵ is a given constant with 0 < ϵ < 1. Although the approximate solution provides a guaranteed performance, it takes some time to deliver the solution, which may not be acceptable in practice. Therefore, we devise a fast, yet scalable, heuristic algorithm for the problem. Finally, we evaluate the performance of the proposed algorithms through extensive experiments, using real-world datasets. The experimental results show that the proposed algorithms are very promising. Especially, the found hole spanners block more than 24% of the information propagations compared to existing algorithms. [ABSTRACT FROM AUTHOR]

Abstract

ABSTRACTSilver/fluorocarbon (Ag/FC) nanocomposite films were successfully deposited on the polyester (PET) nonwoven using magnetron sputtering techniques with pure silver (Ag) and polytetrafluoroethylene (PTFE) targets. FT-IR, XPS, XRD and EDX were used to examine the compositions and structure of Ag and Ag/FC films. SEM and AFM were employed to observe the surface morphology. Hydrophobicity properties and electromagnetic shielding properties of the Ag- and Ag/FC-coated PET nonwoven were also investigated. The experimental results showed that the films deposited on PET nonwoven were even and dense. The Ag existed in the films was elemental silver which had high degree of crystallinity. In contrast, the FC film did not form crystalline structure containing five components, i.e. –CF3, –CF2–, –CF–, –C–CF– and –C–C–, C–F groups clearly observed by the FT-IR spectrum. Compared with the original PET nonwoven, hydrophobicity properties and electromagnetic shielding properties of the PET nonwoven deposited with Ag/FC nanocomposite films were improved significantly.

The influence maximization problem in a large-scale social network is to identify a few influential users such that their influence on the other users in the network is maximized, under a given influence propagation model. One common assumption adopted by two popular influence propagation models is that a user is more likely to be influenced if more his/her friends have already been influenced. This assumption recently however was challenged to be over simplified and inaccurate, as influence propagation process typically is much more complex than that, and the social decision of a user depends more subtly on the network structure, rather than how many his/her influenced friends. Instead, it has been shown that a user is very likely to be influenced by structural diversities of his/her friends. In this paper, we first formulate a novel influence maximization problem under this new structural diversity model. We then propose a constant approximation algorithm for the problem. We finally evaluate the effectiveness of the proposed algorithm by extensive experimental simulations, using different real datasets. Experimental results show that the users identified from a social network by the proposed algorithm have much larger influence than that found by existing algorithms. [ABSTRACT FROM AUTHOR]

HTPB/CL‐20 castable booster explosives were prepared successfully by a cast‐cured process. Scanning electron microscope (SEM) and the charge density test were employed to characterize the molding effect of HTPB/CL‐20 explosives. The propagation reliability, detonation velocity, mechanical sensitivity, thermal decomposition characteristics and thermal stability of the HTPB/CL‐20 explosives were also measured and analyzed. The results show that, when CL‐20 content is less than 91 wt.‐%, the charges with better molding effect were obtained easily. The critical diameter of HTPB/CL‐20 explosives is less than 1 mm, which exhibits good propagation reliability. When the density of HTPB/CL‐20 charge with 91 wt.‐% CL‐20 is 1.73 g cm−3, its detonation velocity can reach 8273 m s−1. Moreover, this kind of explosives has low mechanical sensitivity and good thermal stability.

To improve the safety of HMX without sacrificing energy properties, the composites of TNT and an energetic material (HP‐1) were used to coat HMX particles by a method of integrating solvent–nonsolvent with aqueous suspension‐melting. SEM (scanning electron microscopy) and XPS (X‐ray photoelectron spectrometry) were employed to characterize the samples. The effect of the processing parameters, such as mass ratio of HP‐1 to TNT (MRHT), stirring speed, and cooling rate, on the quality of coated samples were investigated and discussed. The mechanical sensitivity, thermal sensitivity, thermal decomposition characteristic, and heat of detonation of raw and coated HMX samples were also measured and contrasted. Results show that when MRHT, stirring speed in the second stage and cooling rate are 1 : 5, 1000 r⋅min−1and 5 °C⋅min−1respectively, the optimal coating effect is achieved. Compared with that of raw HMX, both impact and friction sensitivity of HMX coated with 2.5 wt.‐% TNT and 0.5 wt.‐% HP‐1 decrease obviously, whereas there is a slight change in their thermal sensitivity and thermal decomposition characteristics. Meanwhile, such surface coating does not result in the decrease of its energy properties.

In order to investigate the effect of crystal habit modifiers (CHM) on morphology, purity, thermal properties, and short duration shock pulses sensitivity of HNS, nanocrystalline HNS was recrystallized from ultra‐pure water by the prefilming twin‐fluid nozzle‐assisted precipitation (PTFN‐P) method with two different CHMs and without CHM. Sodium carboxymethyl cellulose (CMC‐Na) and white dextrine (WD) were selected as CHMs. The particles were characterized using SEM, BET, HPLC, DSC, and electrically exploded metal‐foil driven flyer plate. The morphology of HNS explosive without modifiers was demonstrated to be short plate‐like. However, in the presence of CMC‐Na and WD as modifiers, long plate‐like and ellipsoid morphologies were observed, respectively. The nanocrystalline HNS prepared with CMC‐Na was more receptive to high velocity flyer impact than samples produced under the other two conditions. Its sensitivity to short duration shock waves was elevated to twice the value of HNS obtained in the absence of modifiers. CMC‐Na was found to be a better modifier.

A structural hole spanner in a social network is a user who bridges multiple communities, and he can benefit from acting the bridging role, such as arbitrating information across different communities or getting earlier access to valuable and diverse information. Existing studies of finding hole spanners either identified redundant hole spanners (i.e., communities bridged by different hole spanners are redundant) or found nonredundant hole spanners only by network structure. Unlike the existing studies, we not only study a problem of finding top- k hole spanners that connect nonredundant communities in the social network, but also consider the tie strengths between different pairs of users and the different information sharing rates of different users, so that after removing the found users, the number of blocked information diffusion is maximized. In addition, we devise a novel (1 - 1 e) -approximation algorithm for the problem, where e is the base of the natural logarithm. We further propose a fast randomized algorithm with a smaller time complexity. Our experiment results demonstrate that, after removing the nodes found by the proposed two algorithms, the numbers of blocked information diffusion can be up to 80% larger than those by existing algorithms. [ABSTRACT FROM AUTHOR]