Concurrency and Computation-Practice & Experience最新文献

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.

The prevalence of pulpal and periapical diseases exceeds 50% in the general population. Root canal treatment is currently recognized as the gold standard treatment, in which precise measurement of working length (WL) is critical for treatment success. In this study, a total of 100 eligible extracted teeth were collected and scanned using cone-beam computed tomography (CBCT) to obtain high-resolution three-dimensional images. For WL calculation, we employed a V-net-based segmentation network for simulated paths of the root canal file, incorporating an encoder–decoder structure and a multi-task learning strategy, and achieved a sensitivity of 94.7%. Ablation studies revealed that integrating the decoder's mask branch, key point branch, and boundary branch significantly improved the segmentation accuracy. The WL calculation comprised three stages: skeleton extraction and noise suppression, branch extraction and generation of the simulated paths of the root canal file, and length calculation based on three-dimensional spline curves. The model achieved an average prediction error of 0.28 mm and an accuracy of 86.67% in WL prediction. These findings indicate that this V-net-based multi-branch framework for precise WL estimation from CBCT holds substantial clinical application potential. Future work will focus on enhancing generalization and addressing challenges posed by calcified or anatomically complex root canals.

A competency-based approach supported by personalized learning paths and prompt feedback accelerates skill development by continuously adapting to learners' needs and maintaining high levels of engagement. Capturing and understanding learner competency development through interaction data offers the potential for early intervention and optimized educational design, yet introduces challenges related to scalability and complexity. We present TracePath, a novel graph-based framework that models learner trajectories as directed graphs, where nodes correspond to competencies or learner states and edges denote transitions such as validation or rejection events. This approach uncovers common learning pathways, identifies bottlenecks, and supports predictive analytics. At the core, a generic metamodel formalizes Competency Transition Graphs (CTGs), enabling comprehensive graph-based analytics implemented over a hybrid polystore architecture that integrates both relational and NoSQL databases. Our design decouples data extraction from graph exploration, allowing efficient querying, clustering, and pattern matching to deliver timely and explainable learning insights. Empirical validation using real-world data from the écri+ e-certification project demonstrates TracePath's effectiveness in providing scalable, dynamic, and low-latency learning analytics to support personalized education.

The LSM-tree based on extendible hashing has been adopted in key-value storage systems due to its high write throughput, good scalability, and balanced read-write performance. However, it faces several challenges when accessing SSTables, including low cache efficiency, uneven data distribution across hash buckets, and frequent directory expansions. To address these issues, this paper proposes a cache-friendly LSM-tree based on extendible hashing. To solve the problem that similar keys in SSTables are not stored adjacently in SSTables, a key-to-value mapping strategy based on Locality-Sensitive Hashing (LSH) is employed. Second, to address the uneven data distribution across hash buckets in extendible hashing, a logically uniform extendible hashing scheme is designed, along with a novel HTable structure to replace the traditional SSTables in LSM-tree. In addition, an HTable indexing strategy based on the LSH-Cuckoo filter is proposed to accurately locate the target HTable. Based on the Intel Optane DC Persistent Memory driver, a prototype of a cache-friendly key-value storage system named DLMS was implemented on non-volatile memory (NVM), and evaluated using the YCSB benchmark. Experimental results show that, compared to the LSM-tree-based storage system RocksDB, DLMS achieves an average improvement of 9.8% in read throughput and 11.9% in write throughput, while reducing insertion latency by 7.8%.

The existence of Bushfire is a significant problem. So not only are environmental threats to life and society a risk, but also the threatening of the safety of all. Thus it is extremely critical to detect these fires in time. This identification can assist in the successful application of fire management. Existing approaches might not be effective under conditions such as bad weather. A decrease in visible light also can affect accuracy. These techniques focus on the identification of fires during early stages. We introduce an innovative method in this study. The early detection of bushfires is achieved through UAVs. Each drone in the network has a thermal image camera onboard to provide real-time surveillance of the assigned area. The drones are also equipped with sensors that detect smoke and other indications of potential fire. A control network receives up-to-the-minute information from drones. Machine learning algorithms are used to interpret this data. The aim is to detect potential fires. The above detailed evaluations were captured using the eBee X and DJI Matrice 300 RTK. This study was carried out in a dedicated area provided by a National Park and involved the use of fixed and multirotor UAVs. State of the art sensor system could be successfully deployed on board the UAVs. The integrated technology was comprised of a Sequoia+ multispectral camera, H20T Quad Sensor and PGS 813 Gas Sensor. These were test burns, including controlled prescribed burning and fire simulations. They highlighted the system's impressive ability to detect bushfires in their early stages. The system has provided a quantum leap in preventive fire management. And it has heightened cost-mindedness in real-world settings. This enhancement is due to its high-quality monitoring and precise detection of fire signs. The proposed method might be a useful and powerful tool for proactive fire fighting and early warning, such as fighting wildfire and related scenarios.

Accurate discrimination of edible mushrooms is critical for food safety, yet it remains challenging due to the high visual similarity between edible and toxic species, leading to frequent misidentification by conventional vision-based systems. In this paper, we propose a novel Belief Measure-Weighted Fusion enhanced model that integrates Dempster–Shafer evidence theory into mushroom recognition for the first time. Our enhanced model consists of two core components: a Probabilistic Classification Module with Parallel Color Representation that extracts multidimensional features through complementary color spaces and produces diversified soft predictions; and a multisource classification decision fusion (MCDF) Module that effectively reconciles and integrates conflicting evidence from these predictions. A key innovation within MCDF is the belief cosine similarity coefficient (BCSC), which quantitatively assesses inter-evidence conflict, enabling an adaptive evidence fusion method that enhances robustness and decision reliability. Extensive experiments on a hybrid dataset containing challenging species show consistent performance gains across six mainstream deep networks, demonstrating strong generalization. This work not only bridges evidence theory with practical mushroom identification but also offers a transferable framework for recognizing visually similar species in wild food contexts.