Survey on digital twins for Internet of Vehicles:Fundamentals,challenges,and opportunities

As autonomous vehicles and the other supporting infrastructures(e.g.,smart cities and intelligent transportation systems)become more commonplace,the Internet of Vehicles(IoV)is getting increasingly prevalent.There have been attempts to utilize Digital Twins(DTs)to facilitate the design,evaluation,and deployment of IoV-based systems,for example by supporting high-fidelity modeling,real-time monitoring,and advanced predictive capabilities.However,the literature review undertaken in this paper suggests that integrating DTs into IoV-based system design and deployment remains an understudied topic.In addition,this paper explains how DTs can benefit IoV system designers and implementers,as well as describes several challenges and opportunities for future researchers.

HgaNets:Fusion of Visual Data and Skeletal Heatmap for Human Gesture Action Recognition

Recognition of human gesture actions is a challenging issue due to the complex patterns in both visual andskeletal features. Existing gesture action recognition (GAR) methods typically analyze visual and skeletal data,failing to meet the demands of various scenarios. Furthermore, multi-modal approaches lack the versatility toefficiently process both uniformand disparate input patterns.Thus, in this paper, an attention-enhanced pseudo-3Dresidual model is proposed to address the GAR problem, called HgaNets. This model comprises two independentcomponents designed formodeling visual RGB (red, green and blue) images and 3Dskeletal heatmaps, respectively.More specifically, each component consists of two main parts: 1) a multi-dimensional attention module forcapturing important spatial, temporal and feature information in human gestures;2) a spatiotemporal convolutionmodule that utilizes pseudo-3D residual convolution to characterize spatiotemporal features of gestures. Then,the output weights of the two components are fused to generate the recognition results. Finally, we conductedexperiments on four datasets to assess the efficiency of the proposed model. The results show that the accuracy onfour datasets reaches 85.40%, 91.91%, 94.70%, and 95.30%, respectively, as well as the inference time is 0.54 s andthe parameters is 2.74M. These findings highlight that the proposed model outperforms other existing approachesin terms of recognition accuracy.

E2E-MFERC:AMulti-Face Expression Recognition Model for Group Emotion Assessment

In smart classrooms, conducting multi-face expression recognition based on existing hardware devices to assessstudents’ group emotions can provide educators with more comprehensive and intuitive classroom effect analysis,thereby continuouslypromotingthe improvementof teaching quality.However,most existingmulti-face expressionrecognition methods adopt a multi-stage approach, with an overall complex process, poor real-time performance,and insufficient generalization ability. In addition, the existing facial expression datasets are mostly single faceimages, which are of low quality and lack specificity, also restricting the development of this research. This paperaims to propose an end-to-end high-performance multi-face expression recognition algorithm model suitable forsmart classrooms, construct a high-quality multi-face expression dataset to support algorithm research, and applythe model to group emotion assessment to expand its application value. To this end, we propose an end-to-endmulti-face expression recognition algorithm model for smart classrooms (E2E-MFERC). In order to provide highqualityand highly targeted data support for model research, we constructed a multi-face expression dataset inreal classrooms (MFED), containing 2,385 images and a total of 18,712 expression labels, collected from smartclassrooms. In constructing E2E-MFERC, by introducing Re-parameterization visual geometry group (RepVGG)block and symmetric positive definite convolution (SPD-Conv) modules to enhance representational capability;combined with the cross stage partial network fusion module optimized by attention mechanism (C2f_Attention),it strengthens the ability to extract key information;adopts asymptotic feature pyramid network (AFPN) featurefusion tailored to classroomscenes and optimizes the head prediction output size;achieves high-performance endto-end multi-face expression detection. Finally, we apply the model to smart classroom group emotion assessmentand provide design references for classroom effect analysis evaluation metrics. Experiments based on MFED showthat the mAP and F1-score of E2E-MFERC on classroom evaluation data reach 83.6% and 0.77, respectively,improving the mAP of same-scale You Only Look Once version 5 (YOLOv5) and You Only Look Once version8 (YOLOv8) by 6.8% and 2.5%, respectively, and the F1-score by 0.06 and 0.04, respectively. E2E-MFERC modelhas obvious advantages in both detection speed and accuracy, which can meet the practical needs of real-timemulti-face expression analysis in classrooms, and serve the application of teaching effect assessment very well.

MSEs Credit Risk Assessment Model Based on Federated Learning and Feature Selection

Federated learning has been used extensively in business inno-vation scenarios in various industries.This research adopts the federated learning approach for the first time to address the issue of bank-enterprise information asymmetry in the credit assessment scenario.First,this research designs a credit risk assessment model based on federated learning and feature selection for micro and small enterprises(MSEs)using multi-dimensional enterprise data and multi-perspective enterprise information.The proposed model includes four main processes:namely encrypted entity alignment,hybrid feature selection,secure multi-party computation,and global model updating.Secondly,a two-step feature selection algorithm based on wrapper and filter is designed to construct the optimal feature set in multi-source heterogeneous data,which can provide excellent accuracy and interpretability.In addition,a local update screening strategy is proposed to select trustworthy model parameters for aggregation each time to ensure the quality of the global model.The results of the study show that the model error rate is reduced by 6.22%and the recall rate is improved by 11.03%compared to the algorithms commonly used in credit risk research,significantly improving the ability to identify defaulters.Finally,the business operations of commercial banks are used to confirm the potential of the proposed model for real-world implementation.

Data Masking for Chinese Electronic Medical Records with Named Entity Recognition

With the rapid development of information technology,the electronifi-cation of medical records has gradually become a trend.In China,the population base is huge and the supporting medical institutions are numerous,so this reality drives the conversion of paper medical records to electronic medical records.Electronic medical records are the basis for establishing a smart hospital and an important guarantee for achieving medical intelligence,and the massive amount of electronic medical record data is also an important data set for conducting research in the medical field.However,electronic medical records contain a large amount of private patient information,which must be desensitized before they are used as open resources.Therefore,to solve the above problems,data masking for Chinese electronic medical records with named entity recognition is proposed in this paper.Firstly,the text is vectorized to satisfy the required format of the model input.Secondly,since the input sentences may have a long or short length and the relationship between sentences in context is not negligible.To this end,a neural network model for named entity recognition based on bidirectional long short-term memory(BiLSTM)with conditional random fields(CRF)is constructed.Finally,the data masking operation is performed based on the named entity recog-nition results,mainly using regular expression filtering encryption and principal component analysis(PCA)word vector compression and replacement.In addi-tion,comparison experiments with the hidden markov model(HMM)model,LSTM-CRF model,and BiLSTM model are conducted in this paper.The experi-mental results show that the method used in this paper achieves 92.72%Accuracy,92.30%Recall,and 92.51%F1_score,which has higher accuracy compared with other models.

Recent progress on fabrication and flat-band physics in 2D transition metal dichalcogenides moiré superlattices

Moiré superlattices are formed when overlaying two materials with a slight mismatch in twist angle or lattice constant. They provide a novel platform for the study of strong electronic correlations and non-trivial band topology, where emergent phenomena such as correlated insulating states, unconventional superconductivity, and quantum anomalous Hall effect are discovered. In this review, we focus on the semiconducting transition metal dichalcogenides(TMDs) based moiré systems that host intriguing flat-band physics. We first review the exfoliation methods of two-dimensional materials and the fabrication technique of their moiré structures. Secondly, we overview the progress of the optically excited moiré excitons, which render the main discovery in the early experiments on TMD moiré systems. We then introduce the formation mechanism of flat bands and their potential in the quantum simulation of the Hubbard model with tunable doping, degeneracies, and correlation strength. Finally, we briefly discuss the challenges and future perspectives of this field.

A Review of the Current Task Offloading Algorithms,Strategies and Approach in Edge Computing Systems

Task offloading is an important concept for edge computing and the Internet of Things(IoT)because computationintensive tasksmust beoffloaded tomore resource-powerful remote devices.Taskoffloading has several advantages,including increased battery life,lower latency,and better application performance.A task offloading method determines whether sections of the full application should be run locally or offloaded for execution remotely.The offloading choice problem is influenced by several factors,including application properties,network conditions,hardware features,and mobility,influencing the offloading system’s operational environment.This study provides a thorough examination of current task offloading and resource allocation in edge computing,covering offloading strategies,algorithms,and factors that influence offloading.Full offloading and partial offloading strategies are the two types of offloading strategies.The algorithms for task offloading and resource allocation are then categorized into two parts:machine learning algorithms and non-machine learning algorithms.We examine and elaborate on algorithms like Supervised Learning,Unsupervised Learning,and Reinforcement Learning(RL)under machine learning.Under the non-machine learning algorithm,we elaborate on algorithms like non(convex)optimization,Lyapunov optimization,Game theory,Heuristic Algorithm,Dynamic Voltage Scaling,Gibbs Sampling,and Generalized Benders Decomposition(GBD).Finally,we highlight and discuss some research challenges and issues in edge computing.

Edge Intelligence with Distributed Processing of DNNs:A Survey

Withthe rapiddevelopment of deep learning,the size of data sets anddeepneuralnetworks(DNNs)models are also booming.As a result,the intolerable long time for models’training or inference with conventional strategies can not meet the satisfaction of modern tasks gradually.Moreover,devices stay idle in the scenario of edge computing(EC),which presents a waste of resources since they can share the pressure of the busy devices but they do not.To address the problem,the strategy leveraging distributed processing has been applied to load computation tasks from a single processor to a group of devices,which results in the acceleration of training or inference of DNN models and promotes the high utilization of devices in edge computing.Compared with existing papers,this paper presents an enlightening and novel review of applying distributed processing with data and model parallelism to improve deep learning tasks in edge computing.Considering the practicalities,commonly used lightweight models in a distributed system are introduced as well.As the key technique,the parallel strategy will be described in detail.Then some typical applications of distributed processing will be analyzed.Finally,the challenges of distributed processing with edge computing will be described.

Corpus of Carbonate Platforms with Lexical Annotations for Named Entity Recognition

An obviously challenging problem in named entity recognition is the construction of the kind data set of entities.Although some research has been conducted on entity database construction,the majority of them are directed at Wikipedia or the minority at structured entities such as people,locations and organizational nouns in the news.This paper focuses on the identification of scientific entities in carbonate platforms in English literature,using the example of carbonate platforms in sedimentology.Firstly,based on the fact that the reasons for writing literature in key disciplines are likely to be provided by multidisciplinary experts,this paper designs a literature content extraction method that allows dealing with complex text structures.Secondly,based on the literature extraction content,we formalize the entity extraction task(lexicon and lexical-based entity extraction)for entity extraction.Furthermore,for testing the accuracy of entity extraction,three currently popular recognition methods are chosen to perform entity detection in this paper.Experiments show that the entity data set provided by the lexicon and lexical-based entity extraction method is of significant assistance for the named entity recognition task.This study presents a pilot study of entity extraction,which involves the use of a complex structure and specialized literature on carbonate platforms in English.

Person Re-Identification with Model-Contrastive Federated Learning in Edge-Cloud Environment

Person re-identification(ReID)aims to recognize the same person in multiple images from different camera views.Training person ReID models are time-consuming and resource-intensive;thus,cloud computing is an appropriate model training solution.However,the required massive personal data for training contain private information with a significant risk of data leakage in cloud environments,leading to significant communication overheads.This paper proposes a federated person ReID method with model-contrastive learning(MOON)in an edge-cloud environment,named FRM.Specifically,based on federated partial averaging,MOON warmup is added to correct the local training of individual edge servers and improve the model’s effectiveness by calculating and back-propagating a model-contrastive loss,which represents the similarity between local and global models.In addition,we propose a lightweight person ReID network,named multi-branch combined depth space network(MB-CDNet),to reduce the computing resource usage of the edge device when training and testing the person ReID model.MB-CDNet is a multi-branch version of combined depth space network(CDNet).We add a part branch and a global branch on the basis of CDNet and introduce an attention pyramid to improve the performance of the model.The experimental results on open-access person ReID datasets demonstrate that FRM achieves better performance than existing baseline.

Malicious code passive propagation model and vaccine distribution model of P2P networks

To fight against malicious codes of P2P networks, it is necessary to study the malicious code propagation model of P2P networks in depth. The epidemic of malicious code threatening P2P systems can be divided into the active and passive propagation models and a new passive propagation model of malicious code is proposed, which differentiates peers into 4 kinds of state and fits better for actual P2P networks. From the propagation model of malicious code, it is easy to find that quickly making peers get their patched and upgraded anti-virus system is the key way of immunization and damage control. To distribute patches and immune modules efficiently, a new exponential tree plus （ET＋） and vaccine distribution algorithm based on ET＋ are also proposed. The performance analysis and test results show that the vaccine distribution algorithm based on ET＋ is robust, efficient and much more suitable for P2P networks.

Research Progress in Improving the Cycling Stability of High-Voltage LiNi0.5Mn1.5O4 Cathode in Lithium-Ion Battery

High-voltage lithium-ion batteries(HVLIBs) are considered as promising devices of energy storage for electric vehicle, hybrid electric vehicle, and other high-power equipment. HVLIBs require their own platform voltages to be higher than 4.5 V on charge. Lithium nickel manganese spinel LiNi_(0.5)Mn_(1.5)O_4(LNMO) cathode is the most promising candidate among the 5 V cathode materials for HVLIBs due to its flat plateau at 4.7 V. However, the degradation of cyclic performance is very serious when LNMO cathode operates over 4.2 V. In this review, we summarize some methods for enhancing the cycling stability of LNMO cathodes in lithium-ion batteries, including doping, cathode surface coating,electrolyte modifying, and other methods. We also discuss the advantages and disadvantages of different methods.

The Surface Coating of Commercial LiFePO_4 by Utilizing ZIF-8 for High Electrochemical Performance Lithium Ion Battery

The requirement of energy-storage equipment needs to develop the lithium ion battery(LIB) with high electrochemical performance. The surface modification of commercial LiFePO_4(LFP) by utilizing zeolitic imidazolate frameworks-8(ZIF-8) offers new possibilities for commercial LFP with high electrochemical performances.In this work, the carbonized ZIF-8(C_(ZIF-8)) was coated on the surface of LFP particles by the in situ growth and carbonization of ZIF-8. Transmission electron microscopy indicates that there is an approximate 10 nm coating layer with metal zinc and graphite-like carbon on the surface of LFP/C_(ZIF-8) sample. The N_2 adsorption and desorptionisotherm suggests that the coating layer has uniform and simple connecting mesopores. As cathode material, LFP/C_(ZIF-8) cathode-active material delivers a discharge specific capacity of 159.3 m Ah g^(-1) at 0.1 C and a discharge specific energy of 141.7 m Wh g^(-1) after 200 cycles at 5.0 C(the retention rate is approximate 99%). These results are attributed to the synergy improvement of the conductivity,the lithium ion diffusion coefficient, and the degree of freedom for volume change of LFP/C_(ZIF-8) cathode. This work will contribute to the improvement of the cathode materials of commercial LIB.

Recent progresses in the suppression method based on the growth mechanism of lithium dendrite

Lithium secondary batteries（LSBs） with high energy densities need to be further developed for future applications in portable electronic devices, electric vehicles, hybrid electric vehicles and smart grids. Lithium metal is the most promising electrode for next-generation rechargeable batteries. However, the formation of lithium dendrite on the anode surface leads to serious safety concerns and low coulombic efficiency.Recently, researchers have made great efforts and significant progresses to solve these problems. Here we review the growth mechanism and suppression method of lithium dendrite for LSBs’ anode protection. We also establish the relationship between the growth mechanism and suppression method. The research direction for building better LSBs is given by comparing the advantages and disadvantages of these methods based on the growth mechanism.

Variance-based fingerprint distance adjustment algorithm for indoor localization

The multipath effect and movements of people in indoor environments lead to inaccurate localization. Through the test, calculation and analysis on the received signal strength indication （RSSI） and the variance of RSSI, we propose a novel variance-based fingerprint distance adjustment algorithm （VFDA）. Based on the rule that variance decreases with the increase of RSSI mean, VFDA calculates RSSI variance with the mean value of received RSSIs. Then, we can get the correction weight. VFDA adjusts the fingerprint distances with the correction weight based on the variance of RSSI, which is used to correct the fingerprint distance. Besides, a threshold value is applied to VFDA to improve its performance further. VFDA and VFDA with the threshold value are applied in two kinds of real typical indoor environments deployed with several Wi-Fi access points. One is a quadrate lab room, and the other is a long and narrow corridor of a building. Experimental results and performance analysis show that in indoor environments, both VFDA and VFDA with the threshold have better positioning accuracy and environmental adaptability than the current typical positioning methods based on the k-nearest neighbor algorithm and the weighted k-nearest neighbor algorithm with similar computational costs.

Dynamic defenses in cyber security:Techniques,methods and challenges Author links open overlay panel

Driven by the rapid development of the Internet of Things,cloud computing and other emerging technologies,the connotation of cyberspace is constantly expanding and becoming the fifth dimension of human activities.However,security problems in cyberspace are becoming serious,and traditional defense measures(e.g.,firewall,intrusion detection systems,and security audits)often fall into a passive situation of being prone to attacks and difficult to take effect when responding to new types of network attacks with a higher and higher degree of coordination and intelligence.By constructing and implementing the diverse strategy of dynamic transformation,the configuration characteristics of systems are constantly changing,and the probability of vulnerability exposure is increasing.Therefore,the difficulty and cost of attack are increasing,which provides new ideas for reversing the asymmetric situation of defense and attack in cyberspace.Nonetheless,few related works systematically introduce dynamic defense mechanisms for cyber security.The related concepts and development strategies of dynamic defense are rarely analyzed and summarized.To bridge this gap,we conduct a comprehensive and concrete survey of recent research efforts on dynamic defense in cyber security.Specifically,we firstly introduce basic concepts and define dynamic defense in cyber security.Next,we review the architectures,enabling techniques and methods for moving target defense and mimic defense.This is followed by taxonomically summarizing the implementation and evaluation of dynamic defense.Finally,we discuss some open challenges and opportunities for dynamic defense in cyber security.

Restoring the dampened expression of the core clock molecule BMAL1 protects against compression-induced intervertebral disc degeneration

The circadian clock participates in maintaining homeostasis in peripheral tissues,including intervertebral discs(IVDs).Abnormal mechanical loading is a known risk factor for intervertebral disc degeneration(IDD).Based on the rhythmic daily loading pattern of rest and activity,we hypothesized that abnormal mechanical loading could dampen the IVD clock,contributing to IDD.Here,we investigated the effects of abnormal loading on the IVD clock and aimed to inhibit compression-induced IDD by targeting the core clock molecule brain and muscle Arnt-like protein-1(BMAL1).In this study,we showed that BMAL1 KO mice exhibit radiographic features similar to those of human IDD and that BMAL1 expression was negatively correlated with IDD severity by systematic analysis based on 149 human IVD samples.The intrinsic circadian clock in the IVD was dampened by excessive loading,and BMAL1 overexpression by lentivirus attenuated compression-induced IDD.Inhibition of the RhoA/ROCK pathway by Y-27632 or melatonin attenuated the compression-induced decrease in BMAL1 expression.Finally,the two drugs partially restored BMAL1 expression and alleviated IDD in a diurnal compression model.Our results first show that excessive loading dampens the circadian clock of nucleus pulposus tissues via the RhoA/ROCK pathway,the inhibition of which potentially protects against compression-induced IDD by preserving BMAL1 expression.These findings underline the importance of the circadian clock for IVD homeostasis and provide a potentially effective therapeutic strategy for IDD.

Low-power task scheduling algorithm for large-scale cloud data centers

How to effectively reduce the energy consumption of large-scale data centers is a key issue in cloud computing. This paper presents a novel low-power task scheduling algorithm （L3SA） for large-scale cloud data centers. The winner tree is introduced to make the data nodes as the leaf nodes of the tree and the final winner on the purpose of reducing energy consumption is selected. The complexity of large-scale cloud data centers is fully consider, and the task comparson coefficient is defined to make task scheduling strategy more reasonable. Experiments and performance analysis show that the proposed algorithm can effectively improve the node utilization, and reduce the overall power consumption of the cloud data center.

Influence of the post-treatment of HZSM-5 zeolite on catalytic performance for alkylation of benzene with methanol

The porous material HZSM-5 zeolite with micro-mesopore hierarchical porosity was prepared by post-treatment （combined alkali treatment and acid leaching） of parent zeolite and its catalytic performance for benzene alkylation with methanol was investigated. The effect of post-treatment on the textural properties was characterized by various techniques （including ICP-AES, XRD, nitrogen sorption isotherms, SEM, NH3-TPD, Py-IR and TG）. The results indicated that the post-treatment could modify the structural and acidic properties of HZSM-5 zeolite. In this procedure, not only additional mesopores were created by selective extraction of silicon but also the acidity was tuned. Consequently, the modified HZSM-5 zeolite showed larger external surface area with less acid sites as compared to the parent zeolite. It was found out that the modified zeolite exhibited a higher benzene conversion and xylene selectivity for alkylation of benzene with methanol as well as excellent life span of the catalyst than conventional ones. This can be explained by the facts that the presence of additional mesopores improved the diffusion property in the reactions. Furthermore, the modified zeolite showed an appropriate Bronsted acidity for effective suppression of the side reaction of methanol to olefins, thus reduced the accumulation of coke on the HZSM-5 zeolite, which was favorable for the catalyst stability. In comparison with the parent HZSM-5 zeolite, the modified zeolite by alkali treatment and acid leaching showed better performance for the benzene alkylation with methanol.

Multi-authority proxy re-encryption based on CPABE for cloud storage systems

The dissociation between data management and data ownership makes it difficult to protect data security and privacy in cloud storage systems.Traditional encryption technologies are not suitable for data protection in cloud storage systems.A novel multi-authority proxy re-encryption mechanism based on ciphertext-policy attribute-based encryption（MPRE-CPABE） is proposed for cloud storage systems.MPRE-CPABE requires data owner to split each file into two blocks,one big block and one small block.The small block is used to encrypt the big one as the private key,and then the encrypted big block will be uploaded to the cloud storage system.Even if the uploaded big block of file is stolen,illegal users cannot get the complete information of the file easily.Ciphertext-policy attribute-based encryption（CPABE）is always criticized for its heavy overload and insecure issues when distributing keys or revoking user＇s access right.MPRE-CPABE applies CPABE to the multi-authority cloud storage system,and solves the above issues.The weighted access structure（WAS） is proposed to support a variety of fine-grained threshold access control policy in multi-authority environments,and reduce the computational cost of key distribution.Meanwhile,MPRE-CPABE uses proxy re-encryption to reduce the computational cost of access revocation.Experiments are implemented on platforms of Ubuntu and CloudSim.Experimental results show that MPRE-CPABE can greatly reduce the computational cost of the generation of key components and the revocation of user＇s access right.MPRE-CPABE is also proved secure under the security model of decisional bilinear Diffie-Hellman（DBDH）.