Concurrency and Computation-Practice & Experience最新文献

The development of virtual platform frameworks has made it possible to perform early soft error analysis of more realistic multicore systems, that is, real software stacks and state-of-the-art ISAs. Because of the underlying frameworks' strong observability and simulation performance, more error/failure related data may be generated and collected in a reasonable amount of time even with complicated software stack setups. Parameters (i.e., features) that do not directly connect to the system soft error analysis must be filtered away when working with sizable failure-related data sets that come from several fault campaigns. In this regard, the paper proposes an assessment of multicore processor soft error reliability using BBRO-DNN and SSF-FIS models. At first, source code is converted into the executable code using LLVM compiler and applied over the Gem 5 virtual platform. Then, faults are injected into the fault injection module of the virtual platform. Profiling module analysis the faults and the reaction of the system and submits the report. The fault report is given into the proposed BBRO-DNN model for classifying the fault type. Finally, the system's reliability is evaluated using classified fault type. Experimental results are done by comparing the proposed and existing models to show the superiority of the developed model.

Cross-chain interoperability is a focal point in blockchain research. However, existing efforts predominantly concentrate on functional realization, such as asset transfer and message relaying, while often overlooking the critical dimension of performance. The effective implementation of these functions faces significant performance challenges, including data verification overhead, traceability delays, and inflexible contract execution. Precisely addressing this gap, this paper proposes a performance-optimized, relay-based architecture. Three dedicated core modules are introduced to address these performance bottlenecks: A Shared Data Life-cycle Management Module for efficient data governance, a Real-time Cross-chain Traceability Module for low-latency tracking, and a Dynamic Smart Contract Management Module for agile cross-chain logic execution. Implemented on BitXHub, our system demonstrates superior performance, successfully processing 937 out of 1000 transactions and achieving a latency of 6.7 ms under 800 concurrent requests. The framework's practical effectiveness is further validated through deployments in a cross-border seafood supply chain and a multi-party Deoxyribonucleic Acid (DNA) data sharing network, proving its value as a high-performance solution for complex real-world applications.

Electricity load data is usually significantly trending, cyclical, and stochastic in nature; it is also influenced by external factors such as weather, holidays, and socioeconomic activities. In addition, electricity loads usually have long-time dependencies, such as daily, weekly, and yearly periodicity. To address these challenges, we propose an electricity load forecasting method that combines PCA-PSO-Kmeans++ clustering with improved depthwise separable convolution. This article consists of two parts: data processing and electricity load forecasting. The data processing part of this includes seasonal decomposition of raw data, the Pearson correlation coefficient to select suitable exogenous variables, manual feature processing of raw electricity load data, and cluster analysis of feature-processed data. The electricity load forecasting part of the model is trained using an improved depthwise separable convolution that incorporates an attention mechanism and residual connection. Electricity load forecasting on datasets from the US and Nordic region. Experimental results show that clustering combined with improved depthwise separable convolution is more accurate and reliable in electricity load forecasting. Based on experimental results, we quantify the performance gain contributed by clustering relative to the strongest non-clustered baseline and demonstrate that clustering combined with the improved DSC further enhances accuracy.

Spider silk has exceptional mechanical properties, notably its high toughness, which is a measure of a material's ability to absorb energy before failure. Predicting toughness using physical, biochemical, and genetic features is a challenging task due to the nonlinear and multivariate interactions involved. This study presents a comprehensive machine learning framework to predict the toughness and the tensile strength of spider silk fibers using interpretable and high-performing models. A curated dataset with varied physicochemical and structural features was used to train, tune, and evaluate multiple machine learning models, including Decision Tree, support vector machines, Random Forest, Gradient Boosting, and XGBoost. Feature engineering steps introduced domain-specific constructs such as a toughness proxy and modulus transformations. Hyperparameter tuning was conducted via Bayesian optimization to enhance model performance. Among all tested models, the tuned XGBoost regressor achieved the highest predictive accuracy ($��{�R�}^{�2�}�=�0�.�855�$ and 0.765), outperforming all other models. Feature importance analysis highlighted several key predictors among Young's modulus, aligning with known biological mechanisms for both toughness and tensile strength. This work demonstrates how machine learning can be used not only for accurate prediction but also to uncover the underlying determinants of silk toughness. The proposed framework sets the stage for data-driven design of bioinspired synthetic fibers and represents a significant step toward the computational modeling of high-performance biomaterials.

Document level biomedical relation extraction aims to identify complex relationships between entity pairs in biomedical literature, which is crucial for the automation of medical knowledge applications. Existing methods face limitations when handling non-local and multi-layered semantic dependencies, making it difficult to effectively integrate global semantics with local interactions. The goal of this study is to propose a novel model to address this issue and enhance the ability to model complex dependencies. This paper proposes a new model that combines global dependency graphs with multi-level semantic information graphs (DMK). By utilizing a dual-graph collaborative mechanism, it integrates document-level contextual information to accurately model complex dependencies between entities. We introduce the KanChebConv convolutional layer based on the Kolmogorov–Arnold Network (KAN), replacing traditional linear weight matrices with learnable spline functions, thereby enhancing the model's ability to capture non-linear dependencies. We evaluated our model on the chemical–disease relation (CDR) dataset and the gene–disease relation (GDA) dataset. The results demonstrate that our model achieved the highest F1 score among the selected baselines on both datasets, thereby validating its robustness and competitiveness. Through the collaborative mechanism of global and local information and the innovative KAN convolutional layer, our model effectively improves the accuracy and robustness of document-level biomedical relation extraction, showcasing strong potential for practical applications.

Disaster management, disaster preparedness, disaster risk reduction, and post-disaster recovery processes are of strategic importance today in terms of increasing their effectiveness. Technological innovations and tools play an important role in the effective implementation of these processes. The rapid development of artificial intelligence and UAV technologies has enabled their effective use in disaster situations, and the integration of artificial intelligence has increased the competence of these tools. In this study, the detection of open roads and the optimization of the shortest route to reach the target were carried out in the context of disaster management. For this purpose, computer vision techniques were used to detect the condition of roads using image data obtained from UAVs, and the shortest route to reach the target point was determined. A unique dataset representing the disaster situation was created for model training. The image segmentation process was performed using current YOLO models. In the shortest route optimization phase, Dijkstra, A*, BFS, and DFS algorithms were applied. As a result of comparing the models developed in the route finding process, it was determined that YOLOv9e-seg provided the fastest and most accurate results with an average processing speed of 624 ms and a mAP value of 84.4%. Among the shortest path algorithms, Dijkstra and A* were found to provide the fastest access to the target point with average times of 385 and 387 ms, respectively. These results demonstrate that the developed model is successful in accurately determining the shortest path using a UAV in earthquake-affected areas.

Container-based cloud technology has been widely used in the digital transformation of enterprises, and the combination of cloud computing and container technology has achieved efficient resource management. However, container technology also has security weaknesses and vulnerabilities and is vulnerable to cyberattacks. Most of the existing security studies focus on specific vulnerabilities and subsystems and fail to provide reliable verification of the overall security of container environments. To solve this problem, the concept of container-oriented PUF (CPUF) is proposed, which draws upon the characteristics of PUF. By integrating the PCR value from TPM with container attributes and encapsulating them through TEE, a unique and hardware-secure container identity is generated, which enables container integrity verification. Simultaneously, a lightweight remote attestation for container (PLRAC) is proposed in this paper based on TEE and CPUF as the foundation of trusted root. By integrating PUF and TEE technology, this scheme achieves a low-cost, high-efficiency remote verification mechanism for container, effectively detecting whether containers have been tampered with or compromised. We formally verified the security of this scheme using the AVISPA tool and, combined with theoretical analysis, demonstrated its resistance to typical attacks such as replay and forgery. Performance evaluations indicate that compared to other authentication schemes, PLRAC reduces communication overhead by up to approximately 21.5% while providing additional security properties, such as anonymity and uniqueness.

Detecting potholes in complex environments poses challenges such as varying illumination, shadows, and occlusions. Traditional methods often suffer from insufficient detection accuracy and poor real-time performance. To enhance detection robustness without sacrificing inference speed, this paper adopts the RT-DETR (Real-Time Detection Transformer) framework—which requires no NMS (Non-Maximum Suppression) post-processing and features an efficient hybrid encoder—as its foundation. We propose the lightweight and efficient ARCH-RTDETR detection model. The model introduces targeted enhancements to the backbone, feature-fusion module, and multi-scale architecture. Specifically, an AFGCA (Adaptive Fusion Global Context Attention) mechanism strengthens sensitivity to subtle cues; RepBN (Reparameterized Batch Normalization) is deeply integrated into the AIFI (Adaptive Instance Feature Integration) module to optimize feature distributions and increase multi-scale representational capacity; and the proposed CA-HSFPN (Coordinate Attention-guided Hierarchical Scale Feature Pyramid Network) improves the effectiveness of cross-scale feature fusion. Experiments on diverse datasets show that ARCH-RTDETR achieves an average detection accuracy of 85%, outperforming the RT-DETR baseline by 2.9%, while also improving detection precision and inference efficiency. These results indicate strong potential for deployment in intelligent transportation systems. This research provides a technical reference for small object detection, addressing the low efficiency of traditional manual inspections and the high detection latency of existing equipment in intelligent transportation systems, thereby offering a reliable technical solution for road safety assurance.