Magnetic and electronic properties of bulk and two-dimensional FeBi_(2)Te_(4):A first-principles study

Layered magnetic materials,such as MnBi_(2)Te_(4),have drawn much attention owing to their potential for realizing twodimensional(2D)magnetism and possible topological states.Recently,FeBi_(2)Te_(4),which is isostructural to MnBi_(2)Te_(4),has been synthesized in experiments,but its detailed magnetic ordering and band topology have not been clearly understood yet.Here,based on first-principles calculations,we investigate the magnetic and electronic properties of FeBi_(2)Te_(4)in bulk and 2D forms.We show that different from MnBi_(2)Te_(4),the magnetic ground states of bulk,single-layer,and bilayer FeBi_(2)Te_(4)all favor a 120°noncollinear antiferromagnetic ordering,and they are topologically trivial narrow-gap semiconductors.For the bilayer case,we find that a quantum anomalous Hall effect with a unit Chern number is realized in the ferromagnetic state,which may be achieved in experiment by an external magnetic field or by magnetic proximity coupling.Our work clarifies the physical properties of the new material system of FeBi_(2)Te_(4)and reveals it as a potential platform for studying magnetic frustration down to 2D limit as well as quantum anomalous Hall effect.

Tuning Lithiophilicity and Stability of 3D Conductive Scaffold via Covalent Ag-S Bond for High-Performance Lithium Metal Anode

Li metal anode holds great promise to realize high-energy battery systems.However,the safety issue and limited lifetime caused by the uncontrollable growth of Li dendrites hinder its commercial application.Herein,an interlayer-bridged 3D lithiophilic rGO-Ag-S-CNT composite is proposed to guide uniform and stable Li plating/stripping.The 3D lithiophilic rGO-Ag-S-CNT host is fabricated by incorporating Ag-modified reduced graphene oxide(rGO)with S-doped carbon nanotube(CNT),where the rGO and CNT are closely connected via robust Ag-S covalent bond.This strong Ag-S bond could enhance the structural stability and electrical connection between rGO and CNT,significantly improving the electrochemical kinetics and uniformity of current distribution.Moreover,density functional theory calculation indicates that the introduction of Ag-S bond could further boost the binding energy between Ag and Li,which promotes homogeneous Li nucleation and growth.Consequently,the rGO-Ag-S-CNT-based anode achieves a lower overpotential(7.3 mV at 0.5 mA cm^(−2)),higher Coulombic efficiency(98.1%at 0.5 mA cm^(−2)),and superior long cycling performance(over 500 cycles at 2 mA cm−2)as compared with the rGO-Ag-CNT-and rGO-CNT-based anodes.This work provides a universal avenue and guidance to build a robust Li metal host via constructing a strong covalent bond,effectively suppressing the Li dendrites growth to prompt the development of Li metal battery.

Achieving 500X Acceleration for Adversarial Robustness Verification of Tree-Based Smart Grid Dynamic Security Assessment

Dear Editor,This letter presents a novel and efficient adversarial robustness verification method for tree-based smart grid dynamic security assessment(DSA).Based on tree algorithms technique,the data-driven smart grid DSA has received significant research interests in recent years.

Generation of super-resolved optical needle and multifocal array using graphene oxide metalenses

Ultrathin flat metalenses have emerged as promising alternatives to conventional diffractive lenses,offering new possibilities for myriads of miniaturization and interfacial applications.Graphene-based materials can achieve both phase and amplitude modulations simultaneously at a single position due to the modification of the complex refractive index and thickness by laser conversion from graphene oxide into graphene like materials.In this work,we develop graphene oxide metalenses to precisely control phase and amplitude modulations and to achieve a holistic and systematic lens design based on a graphene-based material system.We experimentally validate our strategies via demonstrations of two graphene oxide metalenses:one with an ultra-long(~16λ)optical needle,and the other with axial multifocal spots,at the wavelength of 632.8 nm with a 200 nm thin film.Our proposed graphene oxide metalenses unfold unprecedented opportunities for accurately designing graphene-based ultrathin integratable devices for broad applications.

Editorial for Advances and applications of deep learning and soft computing in geotechnical underground engineering

We are privileged to be invited by the Honorary Editor-in-Chief,Professor Qihu Qian,Editor-in-Chief,Professor Xia-Ting Feng,and the editorial staff of the Journal of Rock Mechanics and Geotechnical Engineering(JRMGE),to serve as Guest Editors for this Special Issue(SI).The purpose of this SI is to review the latest development of machine learning(ML)techniques including the soft computing(SC)and deep learning(DL)methods as well as their key applications in geotechnical underground engineering problems.

CoRE:Constrained Robustness Evaluation of Machine Learning-Based Stability Assessment for Power Systems

Dear Editor,Machine learning(ML) approaches have been widely employed to enable real-time ML-based stability assessment(MLSA) of largescale automated electricity grids. However, the vulnerability of MLSA to malicious cyber-attacks may lead to wrong decisions in operating the physical grid if its resilience properties are not well understood before deployment. Unlike adversarial ML in prior domains such as image processing, specific constraints of power systems that the attacker must obey in constructing adversarial samples require new research on MLSA vulnerability analysis for power systems.

Matching while Learning: Wireless Scheduling for Age of Information Optimization at the Edge

In this paper,we investigate the minimization of age of information(AoI),a metric that measures the information freshness,at the network edge with unreliable wireless communications.Particularly,we consider a set of users transmitting status updates,which are collected by the user randomly over time,to an edge server through unreliable orthogonal channels.It begs a natural question:with random status update arrivals and obscure channel conditions,can we devise an intelligent scheduling policy that matches the users and channels to stabilize the queues of all users while minimizing the average AoI?To give an adequate answer,we define a bipartite graph and formulate a dynamic edge activation problem with stability constraints.Then,we propose an online matching while learning algorithm(MatL)and discuss its implementation for wireless scheduling.Finally,simulation results demonstrate that the MatL is reliable to learn the channel states and manage the users’buffers for fresher information at the edge.

Strong Coupled Magnetic and Electric Ordering in Monolayer of Metal Thio(seleno)phosphates

The coupling between electric ordering and magnetic ordering in two-dimensional(2D)materials is important for both fundamental research of 2D multiferroics and future development of magnetism-based information storage and operation.Here,we introduce a scheme for realizing a magnetic phase transition through the transition of electric ordering.We take CuMoP_(2)S_(6) monolayer as an example,which is a member of the large 2D transition-metal chalcogen-phosphates family.Based on first-principles calculations,we find that it is a multiferroic with unprecedented characters,namely,it exhibits two different phases:an antiferroelectric-antiferromagnetic phase and a ferroelectric-ferromagnetic phase,in which the electric and magnetic orderings are strongly coupled.Importantly,the electric polarization is out-of-plane,so the magnetism can be readily switched by using the gate electric field.Our finding reveals a series of 2D multiferroics with special magnetoelectric coupling,which hold great promise for experimental realization and practical applications.

gtate of the Art in Passive Bandpass Filter Solutions for 60 GHz Communications

This paper reviews the state-of-the-art filter designs for 60 GHz applications. The most promising filter solutions at this frequency include fiher-in-package where the filter itself is design in the packaging platform and filter-on-chip which is an on-chip filter co-design for miniaturized system size with low packaging cost. Design methodology, design technology, key performance parameters, similarities and dift）rences, advantages and drawbacks, and future trends are explored and studied. Filters in the printed circuit board （PCB）, low temperature co-fired ceramics （LTCC）, organie material, and bipolar complementary metal oxide semicomtuctor （BiCMOS） chips are summarized and compared in details. Future design trends and challenges are also given after the review.

Design of Intelligent Drunk Driving Detection System Based on Internet of Things

In recent years,with the rapid development of China’s economy and the continuous improvement of people’s living standards,the number of motor vehicles and the number of drivers in the country have grown rapidly.Due to the increase in the number of vehicles and the number of motorists,the traffic accident rate is increasing,causing serious economic losses to society.According to the traffic accident statistics of the Ministry of Communications of China in 2009,more than 300,000 car accidents occurred in the year,most of which were caused by drunk driving.Therefore,this paper proposes a design scheme based on the Internet of Things-based vehicle alcohol detection system.The system uses STM8S003F3 single-chip microcomputer as the main control chip of the system,combined with alcohol sensor MQ-3 circuit,LCD1602 liquid crystal display circuit,buzzer alarm circuit and button circuit to form a complete alcohol detection module hardware system.The main functions of the system are as follows:the alcohol sensor in the car detects the driver’s alcohol concentration value,and displays the value on the LCD screen.The buzzer alarm is exceeded and the information is sent to the traffic police department and the family’s mobile phone through the GPRS module.The system can effectively make up for the shortcomings of traffic police detection,which has certain research significance.

Additive‑Driven Interfacial Engineering of Aluminum Metal Anode for Ultralong Cycling Life

Rechargeable Al batteries(RAB)are promising candidates for safe and environmentally sustainable battery systems with low-cost investments.However,the currently used aluminum chloridebased electrolytes present a significant challenge to commercialization due to their corrosive nature.Here,we report for the first time,a novel electrolyte combination for RAB based on aluminum trifluoromethanesulfonate(Al(OTf)_(3))with tetrabutylammonium chloride(TBAC)additive in diglyme.The presence of a mere 0.1 M of TBAC in the Al(OTf)_(3) electrolyte generates the charge carrying electrochemical species,which forms the basis of reaction at the electrodes.TBAC reduces the charge transfer resistance and the surface activation energy at the anode surface and also augments the dissociation of Al(OTf)_(3) to generate the solid electrolyte interphase components.Our electrolyte’s superiority directly translates into reduced anodic overpotential for cells that ran for 1300 cycles in Al plating/stripping tests,the longest cycling life reported to date.This unique combination of salt and additive is non-corrosive,exhibits a high flash point and is cheaper than traditionally reported RAB electrolyte combinations,which makes it commercially promising.Through this report,we address a major roadblock in the commercialization of RAB and inspire equivalent electrolyte fabrication approaches for other metal anode batteries.

Preliminary Investigation of the Reversible 4D Printing of a Dual-Layer Component

The rapid development of additive manufacturing and advances in shape memory materials have fueled the progress of four-dimensional (4D) printing. With increasing improvements in design, reversible 4D printing or two-way 4D printing has been proven to be feasible. This technology will fully eliminate the need for human interference, as the programming is completely driven by external stimuli, which allows 4D-printed parts to be actuated in multiple cycles. This study proposes a new reversible 4D print- ing actuation method. The swelling of an elastomer and heat are used in the programming stage, and heat is used in the recovery stage. The main focus of this study is on the self-actuated programming step. To attain control over the bending, a simple predictive model has been developed to study the degree of cur- vature. The parameters, temperature, and elastomer thickness have also been studied in order to gain a better understanding of how well the model predicts the curvature. This understanding of the curvature will provide a great degree of control over the reversible 4D-printed structure.

Defect and Doping Co‑Engineered Non‑Metal Nanocarbon ORR Electrocatalyst

Exploring low-cost and earth-abundant oxygen reduction reaction(ORR)electrocatalyst is essential for fuel cells and metal–air batteries.Among them,non-metal nanocarbon with multiple advantages of low cost,abundance,high conductivity,good durability,and competitive activity has attracted intense interest in recent years.The enhanced ORR activities of the nanocarbons are normally thought to originate from heteroatom(e.g.,N,B,P,or S)doping or various induced defects.However,in practice,carbon-based materials usually contain both dopants and defects.In this regard,in terms of the co-engineering of heteroatom doping and defect inducing,we present an overview of recent advances in developing non-metal carbon-based electrocatalysts for the ORR.The characteristics,ORR performance,and the related mechanism of these functionalized nanocarbons by heteroatom doping,defect inducing,and in particular their synergistic promotion effect are emphatically analyzed and discussed.Finally,the current issues and perspectives in developing carbon-based electrocatalysts from both of heteroatom doping and defect engineering are proposed.This review will be beneficial for the rational design and manufacturing of highly efficient carbon-based materials for electrocatalysis.

Deep learning-based evaluation of factor of safety with confidence interval for tunnel deformation in spatially variable soil

The random finite difference method(RFDM) is a popular approach to quantitatively evaluate the influence of inherent spatial variability of soil on the deformation of embedded tunnels.However,the high computational cost is an ongoing challenge for its application in complex scenarios.To address this limitation,a deep learning-based method for efficient prediction of tunnel deformation in spatially variable soil is proposed.The proposed method uses one-dimensional convolutional neural network(CNN) to identify the pattern between random field input and factor of safety of tunnel deformation output.The mean squared error and correlation coefficient of the CNN model applied to the newly untrained dataset was less than 0.02 and larger than 0.96,respectively.It means that the trained CNN model can replace RFDM analysis for Monte Carlo simulations with a small but sufficient number of random field samples(about 40 samples for each case in this study).It is well known that the machine learning or deep learning model has a common limitation that the confidence of predicted result is unknown and only a deterministic outcome is given.This calls for an approach to gauge the model’s confidence interval.It is achieved by applying dropout to all layers of the original model to retrain the model and using the dropout technique when performing inference.The excellent agreement between the CNN model prediction and the RFDM calculated results demonstrated that the proposed deep learning-based method has potential for tunnel performance analysis in spatially variable soils.

Thoughts on Design for Intelligent Manufacturing

1.Introduction Given the emergence of intelligent manufacturing capabilities,we may wonder whether design for manufacturing(DFM)practices should be reconceptualized to take advantage of these capabilities.And,if so,what should design for intelligent manufacturing(DFIM)be?The integration of cloud computing,data analytics,artificial intelligence(AI),and the Internet of Things(IoT)with advanced manufacturing technologies has enabled the emergence of what has been called new-generation intelligent manufacturing[1].Such new-generation intelligent manufacturing capabilities will enable transformational new products and services with unprecedented levels of quality,responsiveness,and efficiency.

A spatial attentive and temporal dilated(SATD)GCN for skeleton-based action recognition

Current studies have shown that the spatial-temporal graph convolutional network(STGCN)is effective for skeleton-based action recognition.However,for the existing STGCN-based methods,their temporal kernel size is usually fixed over all layers,which makes them cannot fully exploit the temporal dependency between discontinuous frames and different sequence lengths.Besides,most of these methods use average pooling to obtain global graph feature from vertex features,resulting in losing much fine-grained information for action classification.To address these issues,in this work,the authors propose a novel spatial attentive and temporal dilated graph convolutional network(SATD-GCN).It contains two important components,that is,a spatial attention pooling module(SAP)and a temporal dilated graph convolution module(TDGC).Specifically,the SAP module can select the human body joints which are beneficial for action recognition by a self-attention mechanism and alleviates the influence of data redundancy and noise.The TDGC module can effectively extract the temporal features at different time scales,which is useful to improve the temporal perception field and enhance the robustness of the model to different motion speed and sequence length.Importantly,both the SAP module and the TDGC module can be easily integrated into the ST-GCN-based models,and significantly improve their performance.Extensive experiments on two large-scale benchmark datasets,that is,NTU-RGB+D and Kinetics-Skeleton,demonstrate that the authors’method achieves the state-of-the-art performance for skeleton-based action recognition.

Enabling intelligence in fog computing to achieve energy and latency reduction

Fog computing is an emerging architecture intended for alleviating the network burdens at the cloud and the core network by moving resource-intensive functionalities such as computation, communication, storage, and analytics closer to the End Users (EUs). In order to address the issues of energy efficiency and latency requirements for the time-critical Internet-of-Things (IoT) applications, fog computing systems could apply intelligence features in their operations to take advantage of the readily available data and computing resources. In this paper, we propose an approach that involves device-driven and human-driven intelligence as key enablers to reduce energy consumption and latency in fog computing via two case studies. The first one makes use of the machine learning to detect user behaviors and perform adaptive low-latency Medium Access Control (MAC)-layer scheduling among sensor devices. In the second case study on task offloading, we design an algorithm for an intelligent EU device to select its offloading decision in the presence of multiple fog nodes nearby, at the same time, minimize its own energy and latency objectives. Our results show a huge but untapped potential of intelligence in tackling the challenges of fog computing。

Boosting Sodium Storage of Fe1?xS/MoS2 Composite via Heterointerface Engineering

Improving the cycling stability of metal sulfide-based anode materials at high rate is of great significance for advanced sodium ion batteries.However,the sluggish reaction kinetics is a big obstacle for the development of high-performance sodium storage electrodes.Herein,we have rationally engineered the heterointerface by designing the Fe1?xS/MoS2 heterostructure with abundant“ion reservoir”to endow the electrode with excellent cycling stability and rate capability,which is proved by a series of in and ex situ electrochemical investigations.Density functional theory calculations further reveal that the heterointerface greatly decreases sodium ion diffusion barrier and facilitates charge-transfer kinetics.Our present findings not only provide a deep analysis on the correlation between the structure and performance,but also draw inspiration for rational heterointerface engineering toward the next-generation high-performance energy storage devices.

CONSENSUS FORMATION OF TWO-LEVEL OPINION DYNAMICS

Opinion dynamics have received significant attention in recent years. This paper proposes a bounded confidence opinion model for a group of agents with two different confidence levels. Each agent in the population is endowed with a confidence interval around her opinion wiih radius αd or （1 - α）d, where α∈ （0, 1/2] represents the differentiation of confidence levels. We analytically derived the critical confidence bound dc = 1/（4α） for the two-level opinion dynamics on Z. A single opinion cluster is formed with probability 1 above this critical value regardless of the ratio p of agents with high/low confidence. Extensive numerical simulations are performed to illustrate our theoretical results. Noticed is a clear impact of p on the collective behavior： more agents with high confidence lead to harder agreement. It is also experimentally revealed that the sharpness of the threshold dc increases with a but does not depend on p.

Thermal and thermoelectric response from Keldysh formalism with application to gapped Dirac fermions

Based on the Keldysh Green＇s functions theory, we present a general formula of the thermal and thermoelectric transport. In the clean limit, our formula recovers the previous results obtained from the semiclassical transport theory. In our approach, we propose an appropriate energy current operator and electric current operator, and the unphysical divergence from the direct application of the Kubo formula is eliminated. As an application, we study the thermal and the thermoelectric Hall conductivities of a gapped Dirac fermion model in the presence of impurity scattering.