Chromatin loops serve as fundamental functional units of three-dimensional genome organization, playing pivotal roles in regulating gene expression and maintaining genomic spatial organization. Accurate identification of these fine-scale structures is crucial for advancing our understanding of cellular biological processes and the mechanisms underlying disease. However, due to the inherent complexity and dynamic of chromatin interactions, existing methods often fail to adequately characterize and capture multi-dimensional features. To address these limitations, we introduce GraphLooper, a novel framework using hierarchical multi-view graph pooling to enhance training and inference on large-scale data. GraphLooper transforms Hi-C data into a graph-structured representation, integrating multi-dimensional epigenomic features to construct a robust chromatin interaction model. Employing a hierarchical multi-view graph pooling mechanism, it effectively aggregates multi-scale features, enhancing representation learning. Evaluations across diverse cell lines demonstrate that GraphLooper outperforms state-of-the-art methods in prediction accuracy and generalization, particularly in capturing long-range chromatin interactions critical for precise spatial gene regulation.

The evolution of Artificial Intelligence (AI) has reached a critical point, where agentic AI systems demonstrate strong capabilities in goal formulation and planning but remain difficult to deploy in real-world settings due to their limited grounding in physical environments. These limitations arise from the challenges of partial observability, actuation uncertainty, and strict resource constraints that characterize the physical world. This survey argues that the Artificial Intelligence of Things (AIoT) provides the necessary foundation to embed agentic intelligence into such environments by enabling continuous interaction between sensing, reasoning, and action. We analyze the synergy between goal-driven agentic AI and distributed AIoT infrastructures and present a unified taxonomy of AIoT-enabled agentic architectures, highlighting trade-offs across centralized, edge-native, and hybrid deployment models. The survey further examines key enabling technologies, including edge intelligence, semantic communication, digital twins, and trust mechanisms, and discusses how they integrate into cognitive control loops. Through representative applications in smart cities, industrial automation, healthcare, and energy systems, we show how this convergence moves automation beyond rule-based behavior toward context-aware autonomy. Finally, we identify open challenges related to long-horizon safety, resource-aware intelligence, and ethical governance, and outline research directions toward robust, trustworthy, and socially embedded autonomous systems.

Implementing internal tree hollow detection using IoT technology is a crucial method for forestry conservation. Current methodologies primarily rely on stress wave sensors for such inspections. However, the inherently low signal acquisition density of these sensors leads to significant discrepancies between reconstructed wave velocity tomography and conventional optical imaging principles. This sparse signal distribution severely limits the ability of existing image processing algorithms to resolve internal hollow features, creating a bottleneck of insufficient precision in hollow localization and dimensional estimation for detection systems. To address the aforementioned problems, the research team independently developed a sensor for detecting internal tree defects and proposed a transformer-based model (RCE-DETR) for internal tree hollow detection. The sensor comprises three core modules: stress wave signal detection probes, a signal processor, and a display module. The proposed model in this study is deployed in the signal processor. By replacing the original convolution in the transformer framework with receptive-field attention convolution, the model retains important feature information, effectively fuses signal features, and further improves the accuracy of defect detection. Additionally, the model incorporates Cascaded Group Attention (CGA) and Efficient Multi-scale Attention (EMA) to address the difficulty that existing methods have in precisely determining the size of internal tree defects. Experimental results demonstrate that compared with existing models, the RCE-DETR model increases the mean average precision (mAP) by 10.5%, 10.9%, 5.2%, and 6.3% respectively, when compared to the commonly used YOLOv11, YOLOv8, EfficientDet, and CenterNet++ models.

Detection of lung nodules is key in the treatment of early-stage lung cancer. Computed tomography (CT) scanning technology is an essential contactless tool. However, stray radiation caused by a patient's slight movements and equipment operation can impair CT images, hindering accurate lung nodule detection. To address these issues, this study proposes an artificial intelligence-based anti-interference lung nodule detection method, which is primarily structured with Yolov8 and combines the modules of adaptive gating sparse attention (AGSA) and haar wavelet downsampling (HWD), referred to as Yolov8-AH. This model aimed to improve the accuracy of lung nodule detection in lung CT images under interference conditions. AGSA focuses on key areas of the image, promoting detection stability even when CT images are disturbed. Furthermore, HWD prioritizes the frequency components corresponding to the size and shape of the nodules, enhancing their visibility for easier detection and analysis. HWD effectively reduces image noise without significantly blurring the lung nodule edges, emphasizing them prominently within the lung tissue. Furthermore, when combined with the Yolov8 deep learning model driven by artificial intelligence, the model could accurately detect lung nodules, significantly aiding in early diagnosis and treatment. The effectiveness of the Yolov8-AH detection model was verified through ablation experiments, experiments under varying noise intensities, and experiments under different noise application ratios. The experimental results demonstrate that, compared to existing lung nodule detection models, the Yolov8-AH model achieves a 24% improvement in mAP50 and an 8.2% improvement in precision.

The meteorological communication networks provide critical data support for agriculture and environmental monitoring. However, covert gradient-based attacks persistently inject subtle perturbations, threatening data integrity and increasing the operational overhead for network operators. To achieve proactive service assurance and security-aware network management, this paper proposes a data integrity monitoring mechanism as a managed network function, named EK-IGNN. Unlike traditional passive detection, EK-IGNN functions as an active security service. It first employs the Empirical Mode Decomposition Kalman Filter (EMD-KF) to extract high-fidelity attack fingerprints, which are then analyzed by an Intrinsic Graph Neural Network (IGNN). The IGNN model captures complex dependencies and adaptively amplifies weak attack features, enabling closed-loop network security management. Experimental results demonstrate that the proposed algorithm achieving an average improvement of 16.07% in accuracy and 15.27% in F1-score over state-of-the-art benchmarks.

Federated learning is a collaborative training paradigm designed to protect private data and is widely used in the cooperative training of Internet of Things (IoT) devices. However, despite its focus on privacy protection, federated learning remains susceptible to poisoning attacks from malicious clients. These attacks can degrade system performance and potentially lead to data privacy breaches. Moreover, real-world IoT datasets are often heterogeneous, further increasing the difficulty of detecting malicious clients. Existing defense mechanisms often struggle to effectively identify malicious clients while maintaining high model performance. To address this issue, we propose a defense mechanism called Malicious client exclusion aggregation (MaEA). This method utilizes KL divergence to preliminarily filter out anomalous clients, aggregates the remaining (preliminarily filtered) clients to obtain a pre-center model, and then identifies and excludes malicious clients by measuring their deviations from this pre-center model. We executed a series of extensive experiments on the CIFAR-10 dataset to demonstrate the effectiveness of MaEA. The results demonstrate that our approach can efficiently detect and identify malicious clients while correcting model performance.

Frequent fluctuations of client nodes in highly dynamic mobile clusters can lead to significant changes in feature space distribution and data drift, posing substantial challenges to the robustness of existing federated learning (FL) strategies. To address these issues, we proposed a mobile cluster federated learning framework (MoCFL). MoCFL enhances feature aggregation by introducing an affinity matrix that quantifies the similarity between local feature extractors from different clients, addressing dynamic data distribution changes caused by frequent client churn and topology changes. Additionally, MoCFL integrates historical and current feature information when training the global classifier, effectively mitigating the catastrophic forgetting problem frequently encountered in mobile scenarios. This synergistic combination ensures that MoCFL maintains high performance and stability in dynamically changing mobile environments. Experimental results on the UNSW-NB15 dataset show that MoCFL excels in dynamic environments, demonstrating superior robustness and accuracy while maintaining reasonable training costs.

The dynamic environment of UAV swarms in forest management is characterized by communication instability, heterogeneous nodes, and frequent topology changes due to challenging terrain. These systems are vulnerable to network attacks, requiring advanced intrusion detection technologies. Traditional methods struggle with rapid changes due to data privacy concerns and centralized computational limits, while existing federated learning (FL) algorithms lack robustness against client heterogeneity and dynamic data distribution, especially in complex forest environments. To address these challenges, we propose federated intrusion detection for securing UAV swarms (FIDSUS). FIDSUS improves intrusion detection systems by leveraging collaborative sensing among UAVs, enabling better monitoring and response to security threats in forestry. By quantifying the similarity between UAVs’ local feature extractors through an affinity matrix, FIDSUS guides the aggregation of feature extractors, improving detection capabilities. It also uses AI-driven aerial and distributed computing to enhance data processing efficiency and decision-making speed. The framework addresses data heterogeneity by cross-round feature fusion, improving detection in dynamic environments. Experimental results on the NSL-KDD and UNSW-NB15 datasets show that FIDSUS outperforms existing FL methods with a 4%–34% accuracy improvement. FIDSUS shows robustness and accuracy in dynamic environments, providing an effective solution for securing UAV swarms in forestry.

With the rapid development of distributed edge intelligence (DEI) within Internet of Vehicle (IoV) network, it is required to support heterogeneous rapid, reliable and lightweight authentication which prevents eavesdropping, tampering and replay attacks. Radio frequency fingerprinting (RFF), which leverages unique and tamper-proof hardware characteristics, is an emerging deep learning-based physical layer technology poised to achieve excellent authentication within DEI enhanced heterogeneous IoV. However, centralized collection of critical datasets will bring severe privacy concerns as well as huge communication overheads toward resources-constrained IoV nodes. In this article, we propose a deep federated fractional scattering fingerprinting network (FFSFNet) which amalgamates fractional wavelet scattering and federated learning to achieve excellent identification. Particularly, we first exploit fractional wavelet scattering to extract RFF characteristics from nonstationary waveform, eliminate redundancies and enhance interpretability. To improve the training efficiency and privacy protection capability, we design a novel federated framework, which not only completes distributed training, reduces overhead but also protects privacy. Furthermore, we conducted a comprehensive comparative analysis of different model quantization schemes and validated the proposed scheme with field programmable gate array (FPGA) accelerators. Experimental results demonstrate that the proposed FFSFNet can maintain excellent identification performance with only 5.08% of original samples. The model size and inference latency can be effectively improved by quantization with limited degradation. Moreover, the identification testing accuracy of FFSFNet can eventually converge to 99.4% with 0.64 ms inference latency per sample.

Traditional traffic anomaly-based attack detection methods in Zero-trust Networks (ZTN) suffer from inherent security vulnerabilities, as they neglect considerations regarding their security defenses. Compromising the attack detection model itself can result in the breakdown of normal attack detection capabilities. Ensuring the security of the attack detection model during runtime presents a novel challenge. To address these shortcomings, we propose a novel attack detection model, termed Security within Security: Attack Detection Model with Defenses Against Attacks Capability for Zero-Trust Networks (SWS), aimed at enhancing the security of ZTN. SWS focuses on achieving attack detection in non-secure detection environments, to maintain its detection capability even when under attack. By employing a soft thresholding method, SWS adapts to the dynamic changes in network traffic, thus reducing the interference of attack signals. The incorporation of an attention mechanism enables SWS to concentrate on analyzing the most indicative traffic features of attack behavior. Additionally, we integrate Residual Networks (ResNet) and Bidirectional Long Short-Term Memory (BiLSTM) to enhance the robustness of identifying complex network attack behaviors. The effectiveness of the SWS is validated through ablation studies, model comparisons, experiments conducted over different training epochs, and experiments conducted on various components of the dataset. Experimental results demonstrate that compared to existing attack detection models, SWS achieves improvements in detection accuracy and recall rate by 13.4% and 10.6%, respectively, while reducing the False Positive Rate (FPR) by 16.9%.

Gait recognition aims to identify users by their walking patterns. Compared with appearance-based methods, skeleton-based methods exhibit well robustness to cluttered backgrounds, carried items, and clothing variations. However, skeleton extraction faces the wrong human tracking and keypoints missing problems, especially under multiperson scenarios. To address above issues, this article proposes a novel gait recognition method using deep neural network specifically designed for multiperson scenarios. The method consists of individual gait separate module (IGSM) and fuzzy skeleton completion network (FU-SCN). To achieve effective human tracking, IGSM employs root–skeleton keypoints predictions and object keypoint similarity (OKS)-based skeleton calculation to separate individual gait sets when multiple persons exist. In addition, keypoints missing renders human poses estimation fuzzy. We propose FU-SCN, a deep neuro-fuzzy network, to enhances the interpretability of the fuzzy pose estimation via generating fine-grained gait representation. FU-SCN utilizes fuzzy bottleneck structure to extract features on low-dimension keypoints, and multiscale fusion to extract dissimilar relations of human body during walking on each scale. Extensive experiments are conducted on the CASIA-B dataset and our multigait dataset. The results show that our method is one of the SOTA methods and shows outperformance under complex scenarios. Compared with PTSN, PoseMapGait, JointsGait, GaitGraph2, and CycleGait, our method achieves an average accuracy improvement of 53.77%, 42.07%, 25.3%, 13.47%, and 9.5%, respectively, and it keeps low time cost with average 180 ms using edge devices.

Embodied AI has notably advanced the autonomy of physical agents such as robots, vehicles, and AAVs, expanding their application scope. However, existing systems are predominantly data-driven, relying on static programming and pre-trained models. This limits their adaptability to dynamic and unforeseen scenarios. Additionally, the high computational cost of training large-scale models locally hinders their practical deployment. One promising solution lies in integrating Large Language Models (LLMs) into Embodied AI frameworks. Although LLMs excel in reasoning, coding, and perception, most existing frameworks adopt a single-LLM architecture, which restricts their effectiveness in addressing complex, multimodal tasks. The diverse strengths of individual LLMs, ranging from natural language understanding, visual processing to code generation, are seldom utilized in a collaborative and structured manner. To address these challenges, we propose a knowledge-driven framework, called EUF, that incorporates multi-LLMs into the Embodied AI architecture for AAV-as-a-Service in Intelligent Transportation Systems. Each LLM is dedicated to a specific stage of the AAV task, including user intent interpretation, adaptive path planning with code generation, and error-feedback mechanisms. Our research explores both One-shot and Segmented Code Generation approaches using various LLM-driven models to identify the optimal strategy. We conduct an in-depth analysis of different code errors to evaluate the strengths and limitations of each approach, including the feedback capabilities of the LLM-driven models. Experiments conducted in the AirSim environment demonstrate the framework’s robustness and accuracy in complex AAV path planning tasks, highlighting its practical potential for real-world ITS deployments.

Session-based recommendation (SBR) aims to predict the next item a user might click within an ongoing session, without relying on user profiles or historical data. Modern approaches typically use graph networks to learn item embeddings in Euclidean space via graph convolution operations. However, they often struggle to capture the diversity of user interactions within short, hierarchically structured sessions, which is essential for accurate predictions in SBR. To tackle these challenges, we propose a multi-interest hyperbolic representation network (MIHRN) to enhance the performance of SBR by adeptly modeling both intricate high-order spatial structures and sequence relationships among items in hyperbolic geometry space. Specifically, we use a hyperbolic hypergraph neural network to exploit the high-order spatial relationships and local clustering structures inherent within sessions. Subsequently, a multiaspect interest representation module is designed to articulate the diversity of user interests. Extensive experiments on three real-world datasets demonstrate that the proposed method achieves performance improvements of 23.81%, 14.81%, and 36.84%, respectively, under the P@10 metric.

Predicting vehicle trajectories is a crucial component of intelligent transportation systems, bearing significant research significance. Leveraging the latest advancements in deep learning and data processing technologies enables us to model intricate interactions among multiple vehicles in complex traffic scenarios. Traditional trajectory prediction methods typically rely on sensor data and vehicle behavior models, which may struggle to capture the intricate relationships between vehicles in dynamic, high-traffic situations, as well as the topological complexities of road networks. To tackle these challenges, we introduce a novel approach: the Vehicle-Driven Dynamic Graph Neural Network (V-DGNN) model. This model starts by constructing an interaction graph among vehicles, enabling the simultaneous capture of both temporal and spatial dependencies between them. Moreover, it incorporates a spatiotemporal attention network for extracting vehicle motion patterns. We also propose a unique mechanism to address the challenges posed by high-speed spatiotemporal changes. This mechanism involves the sensitive sampling of nearby timestamps to effectively learn the dynamic distribution of vehicles. Additionally, the model incorporates vehicle behavior features and road network topology information as supplementary inputs while minimizing prediction variances. This equips our model with the ability to make robust predictions even in the face of distribution changes. Experimental results on two real-world datasets convincingly demonstrate that our approach outperforms current state-of-the-art models, delivering superior long-term predictive performance.

Given the severity of air pollution, air quality monitoring has become a crucial aspect of Artificial Intelligence of Things (AIoT) applications, providing essential information for forecasting air pollution. However, the training process for air quality monitoring models heavily relies on the high-performance computing resources, leading to significant energy consumption and associated carbon emissions. This contradicts the objectives of low-carbon and sustainable computing. This article proposes a new hybrid PM2.5 prediction model (NHPPM) for air quality monitoring to address the above challenges. NHPPM prioritizes energy efficiency while maintaining high prediction accuracy by integrating several power-efficient strategies. First, Wiener filtering is used to denoise the multisource air quality data enhancing the efficiency of the multisource data fusion. Second, variational mode decomposition (VMD) decomposes different components of the multisource air quality data, helping to identify and separate the most important factors affecting pollutants. This reduces the data needed for model training and leads to lower resource consumption. Kernel principal component analysis (KPCA) transforms the high-dimensional data into a lower-dimensional representation while retaining the critical information, further minimizing computational demands. Additionally, this article utilizes the informer deep learning model to analyse the trends in air quality data. The model’s effectiveness is validated through the ablation studies, performance evaluation experiments, and short- and long-term prediction experiments. The experimental results show that our model reduces the mean absolute error (MAE) and root mean-square error (RMSE) by 16.2% and 14.9%, respectively, compared to the existing PM2.5 prediction models. Furthermore, it reduces the energy consumption of the model training by 33.8%.

In the context of the booming Internet of Vehicles, predicting vehicle trajectories is crucial for intelligent transportation systems. Existing methods, reliant on sensor data and behavior models, struggle with intricate relationships between vehicles and dynamic road networks. To overcome these challenges, we propose the Vehicle Interaction-based Dynamic Graph Neural Network (VI-DGNN) model. This model constructs a vehicle interaction graph to capture temporal and spatial dependencies among vehicles. A spatiotemporal attention network is employed to discern patterns in vehicle movements, addressing high-speed changes. Our model introduces a vehicle interaction mechanism for dynamic movement, leveraging proximity timestamp graph structures. By incorporating vehicle behavioral features and road network topology, our model minimizes distribution prediction variance, enhancing stability. Experimental results on real datasets demonstrate superior long-term prediction performance compared to state-of-the-art baselines.

The off-target activities within the CRISPR-Cas9 system remains a formidable barrier to its broader application and development. Recent advancements have highlighted the potential of deep learning models in predicting these off-target effects, yet they encounter significant hurdles including imbalances within datasets and the intricacies associated with encoding schemes and model architectures. To surmount these challenges, our study innovatively introduces an Efficiency and Specificity-Based (ESB) class rebalancing strategy, specifically devised for datasets featuring mismatches-only off-target instances, marking a pioneering approach in this realm. Furthermore, through a meticulous evaluation of various One-hot encoding schemes alongside numerous hybrid neural network models, we discern that encoding and models of moderate complexity ideally balance performance and efficiency. On this foundation, we advance a novel hybrid model, the CRISPR-MCA, which capitalizes on multi-feature extraction to enhance predictive accuracy. The empirical results affirm that the ESB class rebalancing strategy surpasses five conventional methods in addressing extreme dataset imbalances, demonstrating superior efficacy and broader applicability across diverse models. Notably, the CRISPR-MCA model excels in off-target effect prediction across four distinct mismatches-only datasets and significantly outperforms contemporary state-of-the-art models in datasets comprising both mismatches and indels. In summation, the CRISPR-MCA model, coupled with the ESB rebalancing strategy, offers profound insights and a robust framework for future explorations in this field.

Biomedical evidence has demonstrated the relevance of microRNA (miRNA) dysregulation in complex human diseases, and determining the relationship between miRNAs and diseases can aid in the early detection and prevention of diseases. Traditional biological experimental methods have the disadvantages of high cost and low efficiency, which are well compensated by computational methods. However, many computational methods have the challenge of excessively focusing on the neighbor relationship, ignoring the structural information of the graph, and belittling the redundant information of the graph structure. This study proposed a computational model based on a graph-masking autoencoder named MGAEMDA. MGAEMDA is an asymmetric framework in which the encoder maps partially observed graphs into latent representations. The decoder reconstructs the masked structural information based on the edge and node levels and combines it with linear matrices to obtain the result. The empirical results on the two datasets reveal that the MGAEMDA model performs better than its counterparts. We also demonstrated the predictive performance of MGAEMDA using a case study of four diseases, and all the top 30 predicted miRNAs were validated in the database, providing further evidence of the excellent performance of the model.

Existing review-based recommendation methods learn a latent representation of user and item from user-generated reviews by a static strategy, which are unable to capture the dynamic evolution of users' interests and the dynamic attraction of items. Here, we propose a dynamic and static representation learning network (DSRLN) to improve the rating prediction accuracy by exploring fine-grained representations of users and items. Specifically, we built DSRLN with a dynamic representation extractor to model the dynamic evolution of users' interests by exploring the inner relations of an interaction sequence, and with a static representation extractor to model the users' intrinsic preferences by learning the semantic coherence and feature strength information from reviews. To identify the different influences of dynamic and static features for different users, a personalized adaptive fusion module was designed using a weighted attention mechanism. Extensive experiments on five real-world datasets from Amazon demonstrated the superiority of the proposed model, and the additional ablation studies verified the effectiveness of the components designed in the DSRLN model.

Numerous experimental studies have indicated that alteration and dysregulation in mircroRNAs (miRNAs) are associated with serious diseases. Identifying disease-related miRNAs is therefore an essential and challenging task in bioinformatics research. Computational methods are an efficient and economical alternative to conventional biomedical studies and can reveal underlying miRNA-disease associations for subsequent experimental confirmation with reasonable confidence. Despite the success of existing computational approaches, most of them only rely on the known miRNA-disease associations to predict associations without adding other data to increase the prediction accuracy, and they are affected by issues of data sparsity. In this paper, we present MRRN, a model that combines matrix reconstruction with node reliability to predict probable miRNA-disease associations. In MRRN, the most reliable neighbors of miRNA and disease are used to update the original miRNA-disease association matrix, which significantly reduces data sparsity. Unknown miRNA-disease associations are reconstructed by aggregating the most reliable first-order neighbors to increase prediction accuracy by representing the local and global structure of the heterogeneous network. Five-fold cross-validation of MRRN produced an area under the curve (AUC) of 0.9355 and area under the precision-recall curve (AUPR) of 0.2646, values that were greater than those produced by comparable models. Two different types of case studies using three diseases were conducted to demonstrate the accuracy of MRRN, and all top 30 predicted miRNAs were verified.

Applying artificial intelligence to Chinese language translation in computational linguistics is of practical significance for economic boosts and cultural exchanges. In the present work, the bi-directional long short-term memory (BiLSTM) network is employed to extract Chinese text features regarding the overlapping semantic roles in Chinese language translation and hard-to-converge training of high-dimensional text word vectors in text classification during translation. In addition, AlexNet is optimized to extract the local features of the text and meanwhile update and learn network parameters in the deep network. Then, the attention mechanism is introduced to build a forecasting algorithm of Chinese language translation based on BiLSTM and improved AlexNet. Last, the forecasting algorithm is simulated to validate its performance. Some state-of-the-art algorithms are selected for a comparative experiment, including long short-term memory, regions with convolutional neural network features, AlexNet, and support vector machine. Results demonstrate that the forecasting algorithm proposed here can achieve a feature identification accuracy of 90.55%, at least an improvement of 4.24% over other algorithms. In addition, it provides an area under the curve of above 90%, a training duration of about 54.21 seconds, and a test duration of about 19.07 seconds. Regarding the performance of Chinese language translation, the algorithm proposed here provides a bilingual evaluation understudy (BLEU) value of 28.21 on the training set, with a performance gain ratio reaching 111.55%; on the test set, its BLEU reaches 40.45, with a performance gain ratio of 129.80%. Hence, this forecasting algorithm is notably superior to other algorithms, which can enhance the machine translation performance. Through experiments, the Chinese language translation algorithm constructed here improves translation performance while ensuring a high correct identification rate, providing experimental references for the later intelligent development of Chinese language translation in computational linguistics.

The purpose of this exploration of blockchain in vehicle management based on Digital Twins in Vehicular Adhoc Networks (VANETs) is to further improve intelligent transportation in smart cities. In view of the complexity of pedestrians in the real road network, the Digital Twins (DTs) technology is used to map the traffic situation in the real road network to the virtual space. Furthermore, the concrete interaction of vehicle data information is stored and transmitted by using blockchain technology. Finally, the DTs model of vehicle-mounted Ad Hoc network based on blockchain is constructed, and its performance is analyzed by simulation. The results suggest that the model algorithm adopted in this work shows a lower average delay time, its data message delivery rate is basically stable at 80%, the data message leakage rate is basically stable at approximately 10%, and the communication overhead does not exceed 700 bytes. Therefore, the in-vehicle self-organizing network model constructed in this work shows high network security performance while ensuring low latency performance, enabling information to interact more efficiently. Therefore, it can provide an experimental basis for the intelligent development and safety performance improvement of the transportation field of smart cities.

The purposes are to explore the effect of Digital Twins (DTs) in Unmanned Aerial Vehicles (UAVs) on providing medical resources quickly and accurately during COVID-19 prevention and control. The feasibility of UAV DTs during COVID-19 prevention and control is analyzed. Deep Learning (DL) algorithms are introduced. A UAV DTs information forecasting model is constructed based on improved AlexNet, whose performance is analyzed through simulation experiments. As end-users and task proportion increase, the proposed model can provide smaller transmission delays, lesser energy consumption in throughput demand, shorter task completion time, and higher resource utilization rate under reduced transmission power than other state-of-art models. Regarding forecasting accuracy, the proposed model can provide smaller errors and better accuracy in Signal-to-Noise Ratio (SNR), bit quantizer, number of pilots, pilot pollution coefficient, and number of different antennas. Specifically, its forecasting accuracy reaches 95.58% and forecasting velocity stabilizes at about 35 Frames-Per-Second (FPS). Hence, the proposed model has stronger robustness, making more accurate forecasts while minimizing the data transmission errors. The research results can reference the precise input of medical resources for COVID-19 prevention and control.

The purpose is to solve the security problems of the Cooperative Intelligent Transportation System (CITS) Digital Twins (DTs) in the Deep Learning (DL) environment. The DL algorithm is improved; the Convolutional Neural Network (CNN) is combined with Support Vector Regression (SVR); the DTs technology is introduced. Eventually, a CITS DTs model is constructed based on CNN-SVR, whose security performance and effect are analyzed through simulation experiments. Compared with other algorithms, the security prediction accuracy of the proposed algorithm reaches 90.43%. Besides, the proposed algorithm outperforms other algorithms regarding Precision, Recall, and F1. The data transmission performances of the proposed algorithm and other algorithms are compared. The proposed algorithm can ensure that emergency messages can be responded to in time, with a delay of less than 1.8s. Meanwhile, it can better adapt to the road environment, maintain high data transmission speed, and provide reasonable path planning for vehicles so that vehicles can reach their destinations faster. The impacts of different factors on the transportation network are analyzed further. Results suggest that under path guidance, as the Market Penetration Rate (MPR), Following Rate (FR), and Congestion Level (CL) increase, the guidance strategy’s effects become more apparent. When MPR ranges between 40% ~ 80% and the congestion is level III, the ATT decreases the fastest, and the improvement effect of the guidance strategy is more apparent. The proposed DL algorithm model can lower the data transmission delay of the system, increase the prediction accuracy, and reasonably changes the paths to suppress the sprawl of traffic congestions, providing an experimental reference for developing and improving urban transportation.

The particularity of the marine underwater environment has brought many challenges to the development of underwater sensor networks (UWSNs) . This research realized the effective monitoring of targets by UWSNs and achieved higher quality of service in various applications such as communication, monitoring, and data transmission in the marine environment. After analysis of the architecture, the marine integrated communication network system (MICN system) is constructed based on the maritime wireless Mesh network (MWMN) by combining with the UWSNs. A distributed hybrid fish swarm optimization algorithm (FSOA) based on mobility of underwater environment and artificial fish swarm (AFS) theory is proposed in response to the actual needs of UWSNs. The proposed FSOA algorithm makes full use of the perceptual communication of sensor nodes and lets the sensor nodes share the information covered by each other as much as possible, enhancing the global search ability. In addition, a reliable transmission protocol NC-HARQ is put forward based on the combination of network coding (NC) and hybrid automatic repeat request (HARQ) . In this work, three sets of experiments are performed in an area of 200 × 200 × 200 m. The simulation results show that the FSOA algorithm can fully cover the events, effectively avoid the blind movement of nodes, and ensure consistent distribution density of nodes and events. The NC-HARQ protocol proposed uses relay nodes for retransmission, and the probability of successful retransmission is much higher than that of the source node. At a distance of more than 2,000 m, the successful delivery rate of data packets is as high as 99.6%. Based on the MICN system, the intelligent ship constructed with the digital twins framework can provide effective ship operating state prediction information. In summary, this study is of great value for improving the overall performance of UWSNs and advancing the monitoring of marine data information.

Cancerlectins, lectins linked to tumor progression, have become the focus of cancer therapy research for their carbohydrate-binding specificity. However, the specific characterization for cancerlectins involved in tumor progression is still unclear. By taking advantage of the g-gap tripeptide and tetrapeptide composition feature descriptors, we increased the accuracy of the classification model of cancerlectin and lectin to 98.54% and 95.38%, respectively. About 36 cancerlectin and 135 lectin features were selected for functional characterization by P/N feature ranking method, which particularly selects the features in positive samples. The specific protein domains of cancerlectins are found to be p-GalNAc-T, crystal and annexin by comparing with lectins through the exclusion method. Moreover, the combined GO analysis showed that the conserved cation binding sites of cancerlectin specific domains are covered by selected feature peptides, suggesting that the capability of cation binding, critical for enzyme activity and stability, could be the key characteristic of cancerlectins in tumor progression. These results will help to identify potential cancerlectin and provide clues for mechanism study of cancerlectin in tumor progression.

Human motion prediction, which aims at predicting future human skeletons given the past ones, is a typical sequence-to-sequence problem. Therefore, extensive efforts have been devoted to exploring different RNN-based encoder-decoder architectures. However, by generating target poses conditioned on the previously generated ones, these models are prone to bringing issues such as error accumulation problem. In this paper, we argue that such issue is mainly caused by adopting autoregressive manner. Hence, a novel Non-AuToregressive model (NAT) is proposed with a complete non-autoregressive decoding scheme, as well as a context encoder and a positional encoding module. More specifically, the context encoder embeds the given poses from temporal and spatial perspectives. The frame decoder is responsible for predicting each future pose independently. The positional encoding module injects positional signal into the model to indicate the temporal order. Besides, a multitask training paradigm is presented for both low-level human skeleton prediction and high-level human action recognition, resulting in the considerable improvement for the prediction task. Our approach is evaluated on Human3.6M and CMU-Mocap benchmarks and outperforms state-of-the-art autoregressive methods.

Target tracking in underwater wireless sensor networks (UWSNs) has two fundamental issues that tracking accuracy and energy consumption. Although the application of particle filter in target tracking is considered in recent years, degeneracy phenomenon and particle impoverishment always restrict its capacity and application. This paper proposes an improved square root cubature particle filter (ISRCPF) to improve tracking accuracy. The authors employ self‐adaptive artificial fish swarm algorithm (AFSA) to optimise the particles, which makes the particles move towards to high likelihood region and maintains the diversity of the particles. Moreover, a sensor selection scheme is provided, which reduces energy consumption of the network by exactly waking up four sensor nodes at each time, while preserving tracking accuracy. Additionally, the authors propose a novel fusion method called similarity fusion method (SFM) to fuse local estimates together and then obtain a better result for distributed fusion architecture (DFA). The simulation results demonstrate that the proposed methods have superior performance.

Redundant data transmission, which is very common in computer networks, degrades bandwidth efficiency and wastes energy. To reduce such redundancy, many redundancy elimination (RE) techniques have been proposed. Most of them require strict history synchronization between the sender and receiver, and therefore assume the existence of bidirectional links to maintain the synchronization. This paper presents MinMax, a new RE mechanism that is specially designed for unidirectional lossy links. Using an existing fingerprinting algorithm, MinMax eliminates a region of payload in an outgoing packet only when the region is common in a certain number of previous packets. Thus over a lossy link, MinMax significantly reduces decompression failures at the receiver without resorting to any forms of feedback and retransmission. Experiments on synthetic data and real-world traffic show that MinMax is able to maintain fair bandwidth savings while inducing negligible decompression failures even when the packet loss rate of the link is as high as 20%.

This study proposes a distributed outlier detection algorithm based on credibility feedback in wireless sensor networks. The algorithm consists of three stages, which are evaluating the initial credibility of sensor nodes, evaluating the final credibility based on credibility feedback and Bayesian theorem, and adjusting for the outlier set. Simulation results verify that the algorithm can achieve high detection accuracy and low false alarm rate, even in the situation that the network with a large number of outliers.

Most methods of system reliability analysis assume that the precise probability distributions of the component lifetime to failure are available, and the system components are independent. However this assumption may be unreasonable in a wide scope of cases. Therefore, imprecise reliability modes of a bridge system structure are put forward in this paper, for cases when the above assumptions are violated. The analysis is based on only some partial information about the component lifetime distributions including points of unknown distributions, and probabilities on nested intervals. The effect of the component independence condition on the reliability of systems is studied. The formulas of the unreliability of the bridge system structure are obtained in the explicit form under different conditions.