Distributed Matching Theory-Based Task Re-Allocating for Heterogeneous Multi-UAV Edge Computing

Many efforts have been devoted to efficient task scheduling in Multi-Unmanned Aerial Vehicle(UAV)edge computing.However,the heterogeneity of UAV computation resource,and the task re-allocating between UAVs have not been fully considered yet.Moreover,most existing works neglect the fact that a task can only be executed on the UAV equipped with its desired service function(SF).In this backdrop,this paper formulates the task scheduling problem as a multi-objective task scheduling problem,which aims at maximizing the task execution success ratio while minimizing the average weighted sum of all tasks’completion time and energy consumption.Optimizing three coupled goals in a realtime manner with the dynamic arrival of tasks hinders us from adopting existing methods,like machine learning-based solutions that require a long training time and tremendous pre-knowledge about the task arrival process,or heuristic-based ones that usually incur a long decision-making time.To tackle this problem in a distributed manner,we establish a matching theory framework,in which three conflicting goals are treated as the preferences of tasks,SFs and UAVs.Then,a Distributed Matching Theory-based Re-allocating(DiMaToRe)algorithm is put forward.We formally proved that a stable matching can be achieved by our proposal.Extensive simulation results show that Di Ma To Re algorithm outperforms benchmark algorithms under diverse parameter settings and has good robustness.

Physical Activity,Sedentary Behavior,and the Risk of Cardiovascular Disease in Type 2 Diabetes Mellitus Patients:The MIDiab Study

The aim of this study was to explore the associations of moderate-to-vigorous-intensity physical activity(MVPA)time and sedentary(SED)time with a history of cardiovascular disease(CVD)and multifactorial(i.e.,blood pressure(BP),body mass index(BMI),low-density lipoprotein cholesterol(LDL-C),and glycated hemoglobin A1c(HbA1c))control status among type 2 diabetes mellitus(T2DM)patients in China.A cross-sectional analysis of 9152 people with type 2 diabetes from the Multifactorial Intervention on Type 2 Diabetes(MIDiab)study was performed.Patients were grouped according to their self-reported MVPA time(low,<150 min·week−1;moderate,150 to<450 min·week−1;high,≥450 min·week−1)and SED time(low,<4 h·d–1;moderate,4 to<8 h·d–1;high,≥8 h·d–1).Participants who self-reported a history of CVD were identified as having a CVD risk.Odds ratios(ORs)and 95%confidence intervals(CIs)of CVD risk and multifactorial control status associated with MVPA time and SED time were estimated using mixed-effect logistic regression models,adjusting for China’s geographical region characteristics.The participants had a mean±standard deviation(SD)age of(60.87±8.44)years,44.5%were women,and 25.1%had CVD.After adjustment for potential confounding factors,an inverse association between high MVPA time and CVD risk that was independent of SED time was found,whereas this association was not observed in the moderate-MVPA group.A higher MVPA time was more likely to have a positive effect on the control of BMI.Compared with the reference group(i.e.,those with MVPA time≥450 min·week−1 and SED time<4 h·d–1),CVD risk was higher in the low-MVPA group:The OR associated with an SED time<4 h·d–1 was 1.270(95%CI,1.040–1.553)and that associated with an SED time≥8 h·d–1 was 1.499(95%CI,1.149–1.955).We found that a high MVPA time(i.e.,≥450 min·week−1)was associated with lower odds of CVD risk regardless of SED time among patients with T2DM.

Request pattern change-based cache pollution attack detection and defense in edge computing

Through caching popular contents at the network edge,wireless edge caching can greatly reduce both the content request latency at mobile devices and the traffic burden at the core network.However,popularity-based caching strategies are vulnerable to Cache Pollution Attacks(CPAs)due to the weak security protection at both edge nodes and mobile devices.In CPAs,through initiating a large number of requests for unpopular contents,malicious users can pollute the edge caching space and degrade the caching efficiency.This paper firstly integrates the dynamic nature of content request and mobile devices into the edge caching framework,and introduces an eavesdroppingbased CPA strategy.Then,an edge caching mechanism,which contains a Request Pattern Change-based Cache Pollution Detection(RPC2PD)algorithm and an Attack-aware Cache Defense(ACD)algorithm,is proposed to defend against CPAs.Simulation results show that the proposed mechanism could effectively suppress the effects of CPAs on the caching performance and improve the cache hit ratio.

Cascaded Multilevel Inverter Based Power and Signal Multiplex Transmission for Electric Vehicles

Power&signal multiplex transmission(P&SMT)is a technique that uses power electronic circuits for communication signal transmission.In this paper,a three-phase cascaded multilevel inverter-based P&S MT system is proposed.The proposed method can transmit communication signals without using a Controller Area Network bus,thereby reducing the wiring cost of the conventional electric vehicle(EV)communication system.The designed system can achieve motor speed regulation and battery balance discharging for EVs.With the combined pulse width modulation scheme and frequency shift keying method,both power and communication signals are transmitted successfully in a simulation model implemented in Matlab/Simulink.By evaluating the bit error rate of the transmitted signal,the maximum signal rate of the proposed system is determined as 600 bit/s.

Neural hand reconstruction using an RGB image

Background This study presents a neural hand reconstruction method for monocular 3D hand pose and shape estimation.Methods Alternate to directly representing hand with 3D data,a novel UV position map is used to represent a hand pose and shape with 2D data that maps 3D hand surface points to 2D image space.Furthermore,an encoder-decoder neural network is proposed to infer such UV position map from a single image.To train this network with inadequate ground truth training pairs,we propose a novel MANOReg module that employs MANO model as a prior shape to constrain high dimensional space of the UV position map.Results The quantitative and qualitative experiments demonstrate the effectiveness of our UV position map representation and MANOReg module.

A new parallel framework of distributed SWAT calibration

With the development of large-scale hydrologic modeling, computational efficiency is becoming more and more important. Rapid modeling and analysis are needed to deal with emergency environmental disasters. The Soil and Water Assessment Tool （SWAT） is a popular hydrologic model, which is less applied in large-scale watershed simulation because of its sequential characteristics. For improving the computational efficiency of the SWAT model, we present a new parallel processing solution for hydrologic cycle and calibration based on MPI （Message Passing Interface）. We partitioned sub-basins during the processes based on a load balancing method Then the calibration was parallelized using a master-slave scheme, in which different input parameters were allocated to different processes to run the hydrologic cycle and compute the function value. Because of the slow convergence and local optimization of the SCE-UA （Shuffled Complex Evolution-developed by University of Arizona） algorithm in SWAT calibration, a genetic algorithm （GA） is developed to optimize the calibration step. Then by dividing the default communicator into several sub-communicators, all the hydrologic cycles were par- allelized in their own sub-communicators to achieve further acceleration. In this paper the results show speedups for the hydrologic cycle calculations, as well as in the optimized calibration step. In the case study, we tested the parallel hydrologic cycle by four processes, and got a speedup of 3.06. In the calibration section, after applying the GA optimization, with 10 cores, we got a speed increase of 8.0 in our GA parallel framework compared with the GA sequential calibration, which is much better than the original SWAT calibration. After the sub-communicators added, this process was speeded up even further. The study demonstrated that the GA parallel framework with multi-sub-communicators is an effective and efficient solution for the hydrologists in large scale hydrology simulations.

A causal convolutional neural network for multi-subject motion modeling and generation

Inspired by the success of WaveNet in multi-subject speech synthesis,we propose a novel neural network based on causal convolutions for multi-subject motion modeling and generation.The network can capture the intrinsic characteristics of the motion of different subjects,such as the influence of skeleton scale variation on motion style.Moreover,after fine-tuning the network using a small motion dataset for a novel skeleton that is not included in the training dataset,it is able to synthesize high-quality motions with a personalized style for the novel skeleton.The experimental results demonstrate that our network can model the intrinsic characteristics of motions well and can be applied to various motion modeling and synthesis tasks.

AIGC challenges and opportunities related to public safety:A case study of ChatGPT

Artificial intelligence generated content(AIGC)is a production method based on artificial intelligence(AI)technology that finds rules through data and automatically generates content.In contrast to computational intelligence,generative AI,as exemplified by ChatGPT,exhibits characteristics that increasingly resemble human-level comprehension and creation processes.This paper provides a detailed technical framework and history of ChatGPT,followed by an examination of the challenges posed to political security,military security,economic security,cultural security,social security,ethical security,legal security,machine escape problems,and information leakage.Finally,this paper discusses the potential opportunities that AIGC presents in the realms of politics,military,cybersecurity,society,and public safety education.

Contact optimisation strategy for wafer-scale field-effect transistors based on two-dimensional semiconductors

Two-dimensional(2D)semiconductors can be utilized to continually miniaturize nanoscale electronic de-vices.However,achieving a practical solution for satisfying electrical contact with 2D semiconductors remains challenging.In this study,we developed a novel contact structure with transferred multilayer(t-ML)MoS 2 by integrating both edge and top contact.After in-situ plasma treatment for the edge of the MoS 2 channel and successive metal deposition,we achieved 16 times lower contact resistivity(22.8 kΩμm)than that of the top contacted devices.The thickness-dependent electrical measurement indicates that edge contact is highly effective with thick MoS 2 due to the alleviated current-crowding effect re-sulting from the small contact area.The temperature-dependent transport measurement further confirms the effective minimization of the influence from the Schottky barrier and tunnelling barrier.Finally,the simplified resistor network model and energy-band diagram were proposed to understand the carrier transport mechanism.Our work provides a practical strategy for achieving excellent electrical contact between bulk metals and 2D semiconductors,paving the way for future large-scale 2D electronic devices.

Jointly beam stealing attackers detection and localization without training:an image processing viewpoint

Recently revealed beam stealing attacks could greatly threaten the security and privacy of IEEE 802.11ad communications.The premise to restore normal network service is detecting and locating beam stealing attackers without their cooperation.Current consistency-based methods are only valid for one single attacker and are parametersensitive.From the viewpoint of image processing,this paper proposes an algorithm to jointly detect and locate multiple beam stealing attackers based on RSSI(Received Signal Strength Indicator)map without the training process involved in deep learning-based solutions.Firstly,an RSSI map is constructed based on interpolating the raw RSSI data for enabling high-resolution localization while reducing monitoring cost.Secondly,three image processing steps,including edge detection and segmentation,are conducted on the constructed RSSI map to detect and locate multiple attackers without any prior knowledge about the attackers.To evaluate our proposal’s performance,a series of experiments are conducted based on the collected data.Experimental results have shown that in typical parameter settings,our algorithm’s positioning error does not exceed 0.41 m with a detection rate no less than 91%.

评价西格列他钠在2型糖尿病患者中疗效与安全性的随机、双盲、安慰剂对照的Ⅲ期临床试验(CMAP)

西格列他钠(Chiglitazar,CGZ)是一种新型非噻唑烷二酮类结构的过氧化物酶体增殖物激活受体(PPAR)全激动剂,在Ⅱ期临床试验中显示出调控2型糖尿病(T2DM)患者的血糖和血脂作用.本项Ⅲ期临床试验比较CGZ与安慰剂(PBO)在经生活方式控制血糖不佳的T2DM患者中的疗效和安全性.受试患者随机至CGZ 32 mg(n=167)、CGZ48 mg(n=166)或PBO(n=202)组,接受每日一次口服药物治疗.试验主要疗效终点指标为24周糖化血红蛋白(HbA_(1c))较基线的变化值.结果显示,CGZ 32 mg组和CGZ 48 mg组24周时的HbA_(1c)显著降低,其较PBO组的差异分别为-0.87%(95%CI:-1.10 to-0.65;P<0.0001)和-1.05%(95%CI:-1.29 to-0.81;P<0.0001),CGZ两个剂量组在其他血糖、血脂、胰岛素敏感性等次要疗效终点指标上也较PBO组呈现显著改善.各组间不良事件(AE)总发生率和因AE退出试验率相似,CGZ两组的轻度水肿和体重增加略高于PBO组.试验结果表明,西格列他钠对生活方式控制血糖不佳的T2DM患者具有总体良好的疗效和安全性,为这一PPAR全激动剂治疗T2DM提供了确证性临床试验证据.

Modification of Flow Structure and Sound Source by Hybrid Porous-serrated Trailing Edge

The effects of hybrid porous-serrated trailing edge on flow structure and sound source of NACA65(12)-10 at moderate Reynolds number(Rec=5×1O^5)have been investigated by Delayed Detached Eddy Simulation(DDES).Compared with conventional serrated trailing edge,the pressure fluctuation in the vicinity of hybrid porous-serrated trailing edge is further decreased significantly.The typical necklace vortex structures stretching across adjacent serrations are suppressed by the porous additive.It is found that porous media changes the shear stress distribution along the serration edge and inside the serration gap,which consequently eliminates the generation of necklace vortex.Therefore,the deformation of vortex tube caused by velocity vector is weakened.The underlying mechanisms associated to the sound source modification are analyzed based on vortex sound theory.The magnitude of Lamb vector and the angle between vorticity and velocity vectors are synchronously reduced by the porous additive,which implies that the present hybrid porous-serrated trailing edge has important influence on the further attenuation of far-field aerodynamic noise.

Honeycomb-like g-C_(3)N_(4)/CeO_(2)-x nanosheets obtained via one step hydrothermal-roasting for efficient and stable Cr(Ⅵ) photo-reduction

Graphitic carbon nitride(g-C_(3)N_(4))-based materials are regarded as one of the most potential photocatalysts for utilizing solar energy.In this work,we reported a facile one step in-situ hydrothermal-roasting method for preparing honeycomb-like g-C_(3)N_(4)/CeO_(2) nanosheets with abundant oxygen vacancies(g-C_(3)N_(4)/CeO_(2)-x).The hydrothermal-roasting and incomplete-sealed state can(i)generate an in-situ reducing atmosphere(CO,N2,NH3) to tune the concentration of oxygen vacancies in CeO_(2);(ii) beneficial to prevent continuous growth of g-C_(3)N_(4) and results in honeycomb-like g-C_(3)N_(4)/CeO_(2)-x hybrid nanosheets.What is more,the g-C_(3)N_(4)/CeO_(2)-x photocatalyst exhibited extended photoresponse range,increased specific surface area and obviously enhanced separation efficiency of photogenerated electron-hole pairs.As a proof-of-concept application,the optimized g-C_(3)N_(4)/CeO_(2)-xnanosheets could achieve 98% removal efficiency for Cr(Ⅵ) under visible light irradiation(λ≥420 nm)within 2.5 h,which is significantly better than those of pure g-C_(3)N_(4) and CeO_(2).This work provides a new idea for more rationally designing and constructing g-C_(3)N_(4)-based catalysts for efficient extended photochemical application.

Thermal Management of Electrified Propulsion System for Low-Carbon Vehicles

An overview of current thermal challenges in transport electrification is introduced in order to underpin the research developments and trends of recent thermal management techniques.Currently,explorations of intelligent thermal management and control strategies prevail among car manufacturers in the context of climate change and global warming impacts.Therefore,major cutting-edge systematic approaches in electrified powertrain are summarized in the first place.In particular,the important role of heating,ventilation and air-condition system(HVAC)is emphasised.The trends in developing efficient HVAC system for future electrified powertrain are analysed.Then electric machine efficiency is under spotlight which could be improved by introducing new thermal management techniques and strengthening the efforts of driveline integrations.The demanded integration efforts are expected to provide better value per volume,or more power output/torque per unit with smaller form factor.Driven by demands,major thermal issues of high-power density machines are raised including the comprehensive understanding of thermal path,and multiphysics challenges are addressed whilst embedding power electronic semiconductors,non-isotropic electromagnetic materials and thermal insulation materials.Last but not least,the present review has listed several typical cooling techniques such as liquid cooling jacket,impingement/spray cooling and immersion cooling that could be applied to facilitate the development of integrated electric machine,and a mechanic-electric-thermal holistic approach is suggested at early design phase.Conclusively,a brief summary of the emerging new cooling techniques is presented and the keys to a successful integration are concluded.

A Study of the Truncated Square Pyramid Geometry for Enhancement of Super-hydrophobicity

Super-hydrophobic surfaces are quite common in nature,inspiring people to continually explore its water-repellence property and applications to our lives.It has been generally agreed that the property of super-hydrophobicity is mainly contributed by the microscale or nanoscale(or even smaller)architecture on the surface.Besides,there is an energy barrier between the Cassie-Baxter wetting state and the Wenzel wetting state.An optimized square post micro structure with truncated square pyramid geometry is introduced in this work to increase the energy barrier,enhancing the robustness of super-hydrophobicity.Theoretical analysis is conducted based on the wetting transition energy curves.Numerical simulation based on a phase-field lattice Boltzmann method is carried out to verify the theoretical analysis.The numerical simulation agrees well with the theoretical analysis,showing the positive significance of the proposed micro structure.Furthermore,another novel micro structure of rough surface is presented,which combines the advantages of truncated pyramid geometry and noncommunicating roughness elements.Theoretical analysis shows that the novel micro structure of rough surface can effectively hinder the Cassie-Baxter state to Wenzel state transition,furthefly enhancing the robustness of the surface hydrophobicity.

Impact of design and process variation on the fabrication of SiC diodes

We have studied the influence of design and process variations on the electrical performance of SiC Schottky diodes. On the design side, two design variations are used in the active cell of the diode（segment design and stripe design）. In addition, there are two more design variations employed for the edge termination layout of the diodes, namely, field limiting ring（FLR） and junction termination extension（JTE）. On the process side, some diodes have gone through an N;O annealing step. The segment design resulted in a lower forward voltage drop（VF） in the diodes and the FLR design turned out to be a better choice for blocking voltages, in the reverse bias.Also, N;O annealing has shown a detrimental effect on the diodes’ blocking performance, which have JTE as their termination design. It degrades the blocking capability of the diodes significantly.

Rational design of a bifunctional fluorescent probe for distinguishing Hcy/Cys from GSH with ideal properties

By pairing two fluoropho res according to their optical prope rties such as absorption spectral overlap and absorptivity,fluorescent quantum yield and emission spectral separation,a bifunctional fluorescent probe,TQBF-NBD,was rationally designed and synthesized to discriminatively sense Hcy/Cys and GSH with good selectivity and sensitivity.It is noted that this probe could work under a single-wave length excitation and displayed a mega-large Stokes shift.TQBF-NBD reacted with Hcy/Cys to give a mixed green-red fluorescence and displayed a red fluorescence upon the treatment with GSH.Distinguishable imaging of intracellular Hcy/Cys from GSH with the help of TQBF-NBD was realized in living cells and zebrafish.

Very Short-Term Forecasting of Distributed PV Power Using GSTANN

Photovoltaic(PV)power forecasting is essential for secure operation of a power system.Effective prediction of PV power can improve new energy consumption capacity,help power system planning,promote development of smart grids,and ultimately support construction of smart energy cities.However,different from centralized PV power forecasts,three critical challenges are encountered in distributed PV power forecasting:1)lack of on-site meteorological observation,2)leveraging extraneous data to enhance forecasting performance,3)spatial-temporal modelling methods of meteorological information around the distributed PV stations.To address these issues,we propose a Graph Spatial-Temporal Attention Neural Network(GSTANN)to predict the very short-term power of distributed PV.First,we use satellite remote sensing data covering a specific geographical area to supplement meteorological information for all PV stations.Then,we apply the graph convolution block to model the non-Euclidean local and global spatial dependence and design an attention mechanism to simultaneously derive temporal and spatial correlations.Subsequently,we propose a data fusion module to solve the time misalignment between satellite remote sensing data and surrounding measured on-site data and design a power approximation block to map the conversion from solar irradiance to PV power.Experiments conducted with real-world case study datasets demonstrate that the prediction performance of GSTANN outperforms five state-of-the-art baselines.