Search Results (216)

MRI (magnetic resonance imaging) technology aims to map the internal structure image of organisms. It is an important application scenario of Non-Uniform Fast Fourier Transform (NUFFT), which can help doctors quickly locate the lesion site of patients. However, in practical application, it has disadvantages such as large computation and difficulty in parallel. Under the architecture of multi-core shared memory, using block pretreatment, color block scheduling NUFFT convolution interpolation offers a parallel solution, and then using a static linked list solves the problem of large memory requirements after the parallel solution on the basis of multithreading to cycle through more source code versions. Then, manual vectorization, such as processing, using short vector components, further accelerates the process. Through a series of optimizations, the final Random, Radial, and Spiral dataset obtained an acceleration effect of 273.8×, 291.8× and 251.7×, respectively. Full article

Bugs, especially those in concurrent systems, are often hard to reproduce because they manifest only under rare conditions. Testers frequently encounter failures that occur only under specific inputs, often at low probability. We propose an approach to systematically amplify the occurrence of such elusive bugs. We treat the system under test as a black-box system and use repeated trial executions to train a predictive model that estimates the probability of a given input configuration triggering a bug. We evaluate this approach on a dataset of 17 representative concurrency bugs spanning diverse categories. Several model-based search techniques are compared against a brute-force random sampling baseline. Our results show that an ensemble stacking classifier can significantly increase bug occurrence rates across nearly all scenarios, often achieving an order-of-magnitude improvement over random sampling. The contributions of this work include the following: (i) a novel formulation of bug amplification as a rare-event classification problem; (ii) an empirical evaluation of multiple techniques for amplifying bug occurrence, demonstrating the effectiveness of model-guided search; and (iii) a practical, non-invasive testing framework that helps practitioners to expose hidden concurrency faults without altering the internal system architecture. Full article

Hash maps are a widely used and efficient data structure for storing and accessing data organized as key-value pairs. Multithreading with hash maps refers to the ability to concurrently execute multiple lookup, insert, and delete operations, such that each operation runs independently while sharing the underlying data structure. One of the main challenges in hash map implementation is the management of collisions. Arguably, separate chaining is among the most well-known strategies for collision resolution. In this paper, we present a comprehensive study comparing two common approaches to implementing separate chaining—linked lists and dynamic arrays—in a multithreaded environment using a lock-based concurrent hash map design. Our study includes a performance evaluation covering parameters such as cache behavior, energy consumption, contention under concurrent access, and resizing overhead. Experimental results show that dynamic arrays maintain more predictable memory access and lower energy consumption in multithreaded environments. Full article

The paper advances the theory of queuing networks by presenting generalized product-form solutions that explicitly take into account the service intensity depending on the number of customers in the network nodes, including the presence of multiple service channels and multi-threaded nodes. This represents a significant extension of the classical results on the Jackson network by integrating graph-theoretic methods, including basic subgraphs with service rates depending on the number of requests. The originality of the article is in the combination of stationary and non-stationary approaches to modeling service networks within a single approach. In particular, acyclic networks with deterministic service time and non-stationary Poisson input flow are considered. Such systems present a significant difficulty, which is noted in well-known works. A stationary model of an open queuing network with service intensity depending on the number of customers in the network nodes is constructed. The stationary network model is related to the problem of marine linear navigation along a strictly defined route and schedule. A generalization of the product theorem with a new form of stationary distribution is developed for it. It is shown that even a small increase in the service intensity with a large number of requests in a queuing network node can significantly reduce its average value. A non-stationary model of an acyclic queuing network with deterministic service time in network nodes and a non-stationary Poisson input flow is constructed. The non-stationary model is associated with irregular (tramp) sea transportation. The intensities of non-stationary Poisson flows in acyclic networks are represented by product formulas using paths between the initial node and other network nodes. The parameters of Poisson distributions of the number of customers in network nodes are calculated. The simplest formulas for calculating such queuing networks are obtained for networks in the form of trees. Full article

Numerical simulation has played a crucial role in modeling the behavior of natural gas hydrate (NGH). However, the existing numerical simulators worldwide have exhibited limitations in functionality, convergence, and computational efficiency. In this study, we present a novel numerical simulator, GPSFlow/Hydrate, for modeling the behavior of hydrate-bearing geologic systems and for addressing the limitations in the existing simulators. It is capable of simulating multiphase and multicomponent flow in hydrate-bearing subsurface reservoirs under ambient conditions. The simulator incorporates multiple mass components, various phases, as well as heat transfer, and sand is treated as an independent non-Newtonian flow and modeled as a Bingham fluid. The CH₄ or binary/ternary gas hydrate dissociation or formation, phase changes, and corresponding thermal effects are fully accounted for, as well as various hydrate formation and dissociation mechanisms, such as depressurization, thermal stimulation, and sand flow behavior. In terms of computation, the simulator utilizes a domain decomposition technology to achieve hybrid parallel computing through the use of distributed memory and shared memory. The verification of the GPSFlow/Hydrate simulator are evaluated through two 1D simulation cases, a sand flow simulation case, and five 3D gas production cases. A comparison of the 1D cases with various numerical simulators demonstrated the reliability of GPSFlow/Hydrate, while its application in modeling the sand flow further highlighted its capability to address the challenges of gas hydrate exploitation and its potential for broader practical use. Several successful 3D gas hydrate reservoir simulation cases, based on parameters from the Shenhu region of the South China Sea, revealed the correlation of initial hydrate saturation and reservoir condition with hydrate decomposition and gas production performance. Furthermore, multithread parallel computing achieved a 2–4-fold increase in efficiency over single-thread approaches, ensuring accurate solutions for complex physical processes and large-scale grids. Overall, the development of GPSFlow/Hydrate constitutes a significant scientific contribution to understanding gas hydrate formation and decomposition mechanisms, as well as to advancing multicomponent flow migration modeling and gas hydrate resource development. Full article

Three-dimensional environment reconstruction refers to the creation of mathematical models of three-dimensional objects suitable for computer representation and processing. This paper proposes a novel 3D environment reconstruction approach that addresses the field-of-view limitations commonly faced by LiDAR-based systems. A rotary-driven LiDAR mechanism is designed to enable uniform and seamless full-field-of-view scanning, thereby overcoming blind spots in traditional setups. To complement the hardware, a multi-sensor fusion framework—LV-SLAM (LiDAR-Visual Simultaneous Localization and Mapping)—is introduced. The framework consists of two key modules: multi-threaded feature registration and a two-phase loop closure detection mechanism, both designed to enhance the system’s accuracy and robustness. Extensive experiments on the KITTI benchmark demonstrate that LV-SLAM outperforms state-of-the-art methods including LOAM, LeGO-LOAM, and FAST-LIO2. Our method reduces the average absolute trajectory error (ATE) from 6.90 m (LOAM) to 2.48 m, and achieves lower relative pose error (RPE), indicating improved global consistency and reduced drift. We further validate the system in real-world indoor and outdoor environments. Compared with fixed-angle scans, the rotary LiDAR mechanism produces more complete reconstructions with fewer occlusions. Geometric accuracy evaluation shows that the root mean square error between reconstructed and actual building dimensions remains below 5 cm. The proposed system offers a robust and accurate solution for high-fidelity 3D reconstruction, particularly suitable for GNSS-denied and structurally complex environments. Full article

The response time of Artificial Neural Network (ANN) inference is critical in embedded systems processing sensor data close to the source. This is particularly important in applications such as predictive maintenance, which rely on timely state change predictions. This study enables estimation of model response times based on the underlying platform, highlighting the importance of benchmarking generic ANN applications on edge devices. We analyze the impact of network parameters, activation functions, and single- versus multi-threading on response times. Additionally, potential hardware-related influences, such as clock rate variances, are discussed. The results underline the complexity of task partitioning and scheduling strategies, stressing the need for precise parameter coordination to optimise performance across platforms. This study shows that cutting-edge frameworks do not necessarily perform the required operations automatically for all configurations, which may negatively impact performance. This paper further investigates the influence of network structure on model calibration, quantified using the Expected Calibration Error (ECE), and the limits of potential optimisation opportunities. It also examines the effects of model conversion to Tensorflow Lite (TFLite), highlighting the necessity of considering both performance and calibration when deploying models on embedded systems. Full article

Biotin (vitamin B7) is a common, naturally occurring water-soluble vitamin. It belongs to the broad group of B vitamins. It is a common ingredient in dietary supplements, cosmetics, medicines, and parapharmaceutical preparations administered orally or applied topically (to the skin, hair, nails). The problem of the relationship between vitamin B supplementation and sensitivity seems to be multi-threaded. There is little literature data that would confirm that oral vitamin B supplementation or local exposure to biotin is a significant sensitizing factor. Moreover, it seems that allergy to vitamin B7 is very rare. It is possible, however, that the relationship between biotin and hypersensitivity is not limited to its direct action, but results from its essential metabolic function. Vitamin B7, as a cofactor of five carboxylases, affects the main pathways of cellular metabolism. Both deficiency and excess of biotin can result in metabolic disorders, which can have a significant impact on the homeostasis of the entire organism, including the efficient functioning of the immune system. Dysregulation of immune systems leads to its dysfunctional functioning, which can also lead to sensitization to various environmental antigens (allergens). Biotin is also used as an element of some methodological models in immunochemical tests (in vitro diagnostics), including methods used to measure the concentration of immunoglobulin E (IgE), both total (tIgE) and allergen-specific (sIgE). For this reason, vitamin B7 supplementation can be a significant interfering factor in some immunochemical tests, which can lead to false laboratory test results, both false positive and false negative, depending on the test format. This situation can have a direct impact on the quality and effectiveness of diagnostics in various clinical situations, including allergy diagnostics. This review focuses on the role of biotin in allergic reactions, both as a causative factor (allergen/hapten), a factor predisposing to the development of sensitization to various allergens, and an interfering factor in immunochemical methods used in laboratory diagnosis of hypersensitivity reactions and how it can be prevented. Full article

Riverbed dynamics are natural processes that are strongly driven by human and climatic factors. In the last two centuries, the anthropogenic influence and impact of climate change on European rivers has resulted in significant degradation of riverbeds. This research paper aims to determine the historical evolution (1856–2021) and future trends of the Ialomița riverbed (Romania) under the influence of anthropogenic impact and climate change. The case study is a reach of 66 km between the confluences with the Ialomicioara and Pâscov rivers. The localisation in a contact zone between the Curvature Subcarpathians and the Târgoviște Plain, the active recent tectonic uplift of the area, and the intense anthropogenic intervention gives to this river reach favourable conditions for pronounced riverbed dynamics over time. To achieve the aim of the study, we developed a complex methodology which involves the use of Geographical Information System (GIS) techniques, hierarchical cluster analysis (HCA), the Mann–Kendall test (MK), and R programming. The results indicate that the evolution of the Ialomița River aligns with the general trends observed across Europe and within Romania, characterised by a reduction in riverbed geomorphological complexity and a general transition from a braided, multi-thread into a sinuous, single-thread fluvial style. The main processes consist of channel narrowing and incision alternating with intense meandering. However, specific temporal and spatial evolution patterns were identified, mainly influenced by the increasingly anthropogenic local influences and confirmed climate changes in the study area since the second half of the 20th century. Future evolutionary trends suggest that, in the absence of river restoration interventions, the Ialomița riverbed is expected to continue degrading on a short-term horizon, following both climatic and anthropogenic signals. The findings of this study may contribute to a better understanding of recent river behaviours and serve as a valuable tool for the management of the Ialomița River. Full article

This paper introduces an efficient 3D point cloud downsampling algorithm (DFPS) based on adaptive multi-level grid partitioning. By leveraging an adaptive hierarchical grid partitioning mechanism, the algorithm dynamically adjusts computational intensity in accordance with terrain complexity. This approach effectively balances the global feature retention of point cloud data with computational efficiency, making it highly adaptable to the growing trend of large-scale 3D point cloud datasets. DFPS is designed with a multithreaded parallel acceleration architecture, which significantly enhances processing speed. Experimental results demonstrate that, for a point cloud dataset containing millions of points, DFPS reduces processing time from approximately 161,665 s using the original FPS method to approximately 71.64 s at a 12.5% sampling rate, achieving an efficiency improvement of over 2200 times. As the sampling rate decreases, the performance advantage becomes more pronounced: at a 3.125% sampling rate, the efficiency improves by nearly 10,000 times. By employing visual observation and quantitative analysis (with the chamfer distance as the measurement index), it is evident that DFPS can effectively preserve global feature information. Notably, DFPS does not depend on GPU-based heterogeneous computing, enabling seamless deployment in resource-constrained environments such as airborne and mobile devices, which makes DFPS an effective and lightweighting tool for providing high-quality input data for subsequent algorithms, including point cloud registration and semantic segmentation. Full article

With the hierarchical evolution of distribution network control architectures, distributed state estimation has become a focal point of research. To address communication delays arising from inter-level data exchanges, this paper proposes a multi-level, asynchronous, robust state estimation algorithm that accounts for such delays. First, a multi-level state estimation model is formulated based on the concept of a maximum normal measurement rate, and a hierarchical decoupling modeling approach is developed. Then, an event-driven broadcast transmission strategy is designed to unify boundary information exchanged between levels during iteration. A multi-threaded parallel framework is constructed to decouple receiving, computation, and transmission tasks, thereby enhancing asynchronous scheduling capabilities across threads. Additionally, a round-based synchronization mechanism is proposed to enforce fully synchronized iterations in the initial stages, thereby improving the overall process of asynchronous state estimation. Case study results demonstrate that the proposed algorithm achieves high estimation accuracy and strong robustness, while reducing the average number of iterations by nearly 40% and shortening the runtime by approximately 35% compared to conventional asynchronous methods, exhibiting superior estimation performance and computational efficiency under communication delay conditions. Full article

This paper presents the design and implementation of an autonomous robotic facility for textile sorting and recycling, leveraging advanced computer vision and machine learning technologies. The system enables real-time textile classification, localization, and sorting on a dynamically moving conveyor belt. A custom-designed pneumatic gripper is developed for versatile textile handling, optimizing autonomous picking and placing operations. Additionally, digital simulation techniques are utilized to refine robotic motion and enhance overall system reliability before real-world deployment. The multi-threaded architecture facilitates the concurrent and efficient execution of textile classification, robotic manipulation, and conveyor belt operations. Key contributions include (a) dynamic and real-time textile detection and localization, (b) the development and integration of a specialized robotic gripper, (c) real-time autonomous robotic picking from a moving conveyor, and (d) scalability in sorting operations for recycling automation across various industry scales. The system progressively incorporates enhancements, such as queuing management for continuous operation and multi-thread optimization. Advanced material detection techniques are also integrated to ensure compliance with the stringent performance requirements of industrial recycling applications. Full article

Autonomous Underwater Vehicles (AUVs) operating in dynamic, constrained underwater environments demand sophisticated navigation and detection fusion capabilities that traditional methods often fail to provide. This paper introduces a novel hybrid framework that synergistically fuses a Multithreaded Energy-Optimized Batch Informed Trees (MEO-BIT*) algorithm with Deep Q-Networks (DQN) to achieve robust AUV autonomy. The MEO-BIT* component delivers efficient global path planning through (1) a multithreaded batch sampling mechanism for rapid state-space exploration, (2) heuristic-driven search accelerated by KD-tree spatial indexing for optimized path discovery, and (3) an energy-aware cost function balancing path length and steering effort for enhanced endurance. Critically, the DQN component facilitates dynamic obstacle detection and adaptive local navigation, enabling the AUV to adjust its trajectory intelligently in real time. This integrated approach leverages the strengths of both algorithms. The global path intelligence of MEO-BIT* is dynamically informed and refined by the DQN’s learned perception. This allows the DQN to make effective decisions to avoid moving obstacles. Experimental validation in a simulated Achao waterway (Chile) demonstrates the MEO-BIT* + DQN system’s superiority, achieving a 46% reduction in collision rates (directly reflecting improved detection and avoidance fusion), a 15.7% improvement in path smoothness, and a 78.9% faster execution time compared to conventional RRT* and BIT* methods. This work presents a robust solution that effectively fuses two key components: the computational efficiency of MEO-BIT* and the adaptive capabilities of DQN. This fusion significantly advances the integration of navigation with dynamic obstacle detection. Ultimately, it enhances AUV operational performance and autonomy in complex maritime scenarios. Full article

Whole-genome sequencing (WGS) technology has made significant progress in obtaining the genomic information of organisms and is now the primary way to uncover genetic variation. However, due to the complexity of the genome and technical limitations, large genome segments remain ungenotyped. Imputation is a useful strategy for predicting missing genotypes. The accuracy and computing speed of imputation software are important criteria that should inform future developments in genomic research. In this study, the K-Means algorithm and multithreading were used to cluster reference individuals to reduce the number and improve the length of haplotypes in the subpopulation. We named this strategy “KBeagle”. In the comparison test, we determined that the KBeagle-imputed dataset (KID) can identify more single-nucleotide polymorphism (SNP) loci associated with the specified traits compared to the Beagle-imputed dataset (BID), while also achieving much lower false discovery rates (FDRs) and Type I error rates under the same power of detection of association signals. We envision that the main application of KBeagle will focus on livestock sequencing studies under a strong genetic structure. In summary, we have generated an accurate and efficient imputation method, improving the imputation matching rate and calculation time. Full article

Network exposure surface analysis aims to identify network assets that are exposed to the Internet and is critical for enterprise security. However, existing tools face two key challenges: combinatorial explosion in traditional packet testing, and high false positive rates in firewall-based static analysis. To address these issues, this paper proposes a network model-based approach to accurately characterize the forwarding behaviors of devices in enterprise networks, and performs network-level static analysis on the established graph model. Specifically, we construct a device-level forwarding graph using detailed element models for switches and firewalls, capturing the semantics of the forwarding information base, virtual routing and forwarding, virtual systems, and security zones. We further introduce a parallelized multi-threaded breadth-first search (MTBFS) algorithm to efficiently identify reachable assets from Internet-facing ingress interfaces. Experimental results demonstrate a 20× speedup over traditional methods in a large-scale enterprise network consisting of 7970 switches and 16 Internet-facing interfaces. Full article