Search Results (229)

The growing density of space objects in low Earth orbit (LEO), driven by the deployment of large satellite constellations, has elevated the risk of orbital collisions and the need for high-precision conjunction analysis. Traditional methods based on Two-Line Element (TLE) data suffer from limited accuracy and insufficient uncertainty modeling. This study proposes a hybrid collision assessment framework that combines Monte Carlo simulation, spline-based refinement of the time of closest approach (TCA), and a multi-stage deterministic refinement process. The methodology begins with probabilistic sampling of TLE uncertainties, followed by a coarse search for TCA using the SGP4 propagator. A cubic spline interpolation then enhances temporal resolution, and a hierarchical multi-stage refinement computes the final TCA and minimum distance with sub-second and sub-kilometer accuracy. The framework was validated using real-world TLE data from over 2600 debris objects and active satellites. Results demonstrated a reduction in average TCA error to 0.081 s and distance estimation error to 0.688 km. The approach is computationally efficient, with average processing times below one minute per conjunction event using standard hardware. Its compatibility with operational space situational awareness (SSA) systems and scalability for high-volume screening make it suitable for integration into real-time space traffic management workflows. Full article

The automotive suspension must perform competently to support comfort and safety when driving. Traditionally, car suspension control tuning is performed through trial and error or with classical techniques that cannot guarantee optimal performance under varying road conditions. The study aims at designing a Linear Quadratic Regulator-based Bacterial Memetic Algorithm (LQR-BMA) for suspension systems of automobiles. BMA combines the bacterial foraging optimization algorithm (BFOA) and the memetic algorithm (MA) to enhance the effectiveness of its search process. An LQR control system adjusts the suspension’s behavior by determining the optimal feedback gains using BMA. The control objective is to significantly reduce the random vibration and oscillation of both the vehicle and the suspension system while driving, thereby making the ride smoother and enhancing road handling. The BMA adopts control parameters that support biological attraction, reproduction, and elimination-dispersal processes to accelerate the search and enhance the program’s stability. By using an algorithm, it explores several parts of space and improves its value to determine the optimal setting for the control gains. MATLAB 2024b software is used to run simulations with a randomly generated road profile that has a power spectral density (PSD) value obtained using the Fast Fourier Transform (FFT) method. The results of the LQR-BMA are compared with those of the optimized LQR based on the genetic algorithm (LQR-GA) and the Virus Evolutionary Genetic Algorithm (LQR-VEGA) to substantiate the potency of the proposed model. The outcomes reveal that the LQR-BMA effectuates efficient and highly stable control system performance compared to the LQR-GA and LQR-VEGA methods. From the results, the BMA-optimized model achieves reductions of 77.78%, 60.96%, 70.37%, and 73.81% in the sprung mass displacement, unsprung mass displacement, sprung mass velocity, and unsprung mass velocity responses, respectively, compared to the GA-optimized model. Moreover, the BMA-optimized model achieved a −59.57%, 38.76%, 94.67%, and 95.49% reduction in the sprung mass displacement, unsprung mass displacement, sprung mass velocity, and unsprung mass velocity responses, respectively, compared to the VEGA-optimized model. Full article

Medical image registration is an indispensable preprocessing step to align medical images to a common coordinate system before in-depth analysis. The registration precision is critical to the following analysis. In addition to representative image features, the initial pose settings and multiple poses in images will significantly affect the registration precision, which is largely neglected in state-of-the-art works. To address this, the paper proposes a dual-pose medical image registration algorithm based on improved differential evolution. More specifically, the proposed algorithm defines a composite similarity measurement based on contour points and utilizes this measurement to calculate the similarity between frontal–lateral positional DRR (Digitally Reconstructed Radiograph) images and X-ray images. In order to ensure the accuracy of the registration algorithm in particular dimensions, the algorithm implements a dual-pose registration strategy. A PDE (Phased Differential Evolution) algorithm is proposed for iterative optimization, enhancing the optimization algorithm’s ability to globally search in low-dimensional space, aiding in the discovery of global optimal solutions. Extensive experimental results demonstrate that the proposed algorithm provides more accurate similarity metrics compared to conventional registration algorithms; the dual-pose registration strategy largely reduces errors in specific dimensions, resulting in reductions of 67.04% and 71.84%, respectively, in rotation and translation errors. Additionally, the algorithm is more suitable for clinical applications due to its lower complexity. Full article

Many challenges in solving large graph coloring through parallel strategies remain unresolved. Previous algorithms based on Pregel-like frameworks, such as Apache Giraph, encounter parallelism bottlenecks due to sequential execution and the need for a full graph traversal in certain stages. Additionally, GPU-based algorithms face the dilemma of costly and time-consuming processing when moving complex graph applications to GPU architectures. In this study, we propose Spardex, a novel parallel and distributed graph coloring optimization algorithm designed to overcome and avoid these challenges. We design a symmetry-driven optimization approach wherein the EdgePartition1D strategy in GraphX induces partitioning asymmetry, leading to overlapping locally symmetric regions. This structure is leveraged through asymmetric partitioning and symmetric reassembly to reduce the search space. A two-stage pipeline consisting of partitioned repaint and core conflict detection is developed, enabling the precise correction of conflicts without traversing the entire graph as in previous algorithms. We also integrate symmetry principles from combinatorial optimization into a distributed computing framework, demonstrating that leveraging locally symmetric subproblems can significantly enhance the efficiency of large-scale graph coloring. Combined with Spark-specific optimizations such as AQE skew join optimization, all these techniques contribute to an efficient parallel graph coloring optimization in Spardex. We conducted experiments using the Aliyun Cloud platform. The results demonstrate that Spardex achieves a reduction of 8–72% in the number of colors and a speedup of 1.13–10.27 times over concurrent algorithms. Full article

Background and Objectives: Postoperative pancreatic fistula and post-hepatectomy liver failure remain significant complications after HPB surgery; however, superficial surgical site infection (SSI) is the most frequent wound-related complication. Closed-incision negative-pressure wound therapy (ciNPWT) has been proposed to reduce superficial contamination, yet no liver-focused quantitative synthesis exists. We aimed to evaluate the effectiveness and safety of prophylactic ciNPWT after hepatopancreatobiliary (HPB) surgery. Methods: MEDLINE, Embase, and PubMed were searched from inception to 30 April 2025. Randomized and comparative observational studies that compared ciNPWT with conventional dressings after elective liver transplantation, hepatectomy, pancreatoduodenectomy, and liver resections were eligible. Two reviewers independently screened, extracted data, and assessed risk of bias (RoB-2/ROBINS-I). A random-effects Mantel–Haenszel model generated pooled risk ratios (RRs) for superficial SSI; secondary outcomes were reported descriptively. Results: Twelve studies (seven RCTs, five cohorts) encompassing 15,212 patients (3561 ciNPWT; 11,651 control) met the inclusion criteria. Device application lasted three to seven days in all trials. The pooled analysis demonstrated a 29% relative reduction in superficial SSI with ciNPWT (RR 0.71, 95% CI 0.63–0.79; p < 0.001) with negligible heterogeneity (I² 0%). Absolute risk reduction ranged from 0% to 13%, correlating positively with the baseline control-group SSI rate. Deep/organ-space SSI (RR 0.93, 95% CI 0.79–1.09) and 90-day mortality (RR 0.94, 95% CI 0.69–1.28) were unaffected. Seven studies documented a 1- to 3-day shorter median length of stay; only two reached statistical significance. Device-related adverse events were rare (one seroma, no skin necrosis). Conclusions: Prophylactic ciNPWT safely reduces superficial SSI after high-risk HPB surgery, with the greatest absolute benefit when baseline SSI risk exceeds ≈10%. Its influence on deep infection and mortality is negligible. 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

Background: The Vehicle Routing Problem with Time Windows (VRPTW) has been extensively researched due to its applicability across various real-world domains, including logistics, healthcare, and distribution systems. With the global elderly population projected to continue increasing, the demand for home healthcare (HHC) services is also on the rise. This work focuses on a specific application within an HHC system, aiming to minimize the total completion time for a fleet of vehicles delivering healthcare services to patients at home. Methods: We propose a mathematical model for the VRPTW, targeting a reduction in both customer and server waiting times on each route and seeking to decrease the total completion time. Our proposed algorithm employs a tabu search to narrow the search space, leveraging a greedy algorithm to establish the tabu list. Results: Our experimental results, based on Solomon’s benchmark datasets, demonstrate that the proposed algorithms achieve optimal solutions, particularly in minimizing total completion time compared to traditional methods, in a case study involving 400 customers where vehicles’ hours are restricted to align with caregivers’ average daily working hours. Conclusions: Our algorithm resulted in a 59% reduction in the number of vehicles required compared to the most recent algorithms, which combine k-mean clustering and local search. Full article

Hybrid electric vertical take-off and landing (HEVTOL) flying vehicles serve as effective platforms for efficient transportation, forming a cornerstone of the emerging low-altitude economy. However, the current lack of co-optimization methods for powertrain component sizing and energy controller design often leads to suboptimal HEVTOL performance. To address this, this paper proposes a hierarchical manifold-enhanced Bayesian evolutionary optimization (HM-BEO) approach for HEVTOL systems. This framework employs lightweight manifold dimensionality reduction to compress the decision space, enabling Bayesian optimization (BO) on low-dimensional manifolds for a global coarse search. Subsequently, the approximate Pareto solutions generated by BO are utilized as initial populations for a non-dominated sorting genetic algorithm III (NSGA-III), which performs fine-grained refinement in the original high-dimensional design space. The co-optimization aims to minimize fuel consumption, battery state-of-health (SOH) degradation, and manufacturing costs while satisfying dynamic and energy management constraints. Evaluated using representative HEVTOL duty cycles, the HM-BEO demonstrates significant improvements in optimization efficiency and solution quality compared to conventional methods. Specifically, it achieves a 5.3% improvement in fuel economy, a 7.4% mitigation in battery SOH degradation, and a 1.7% reduction in system manufacturing cost compared to standard NSGA-III-based optimization. Full article

The room for reducing carbon emissions and improving low-carbon practices in the building industry is significant. In this study, a bibliometric analysis shows that technology is the primary mechanism adopted for driving carbon reduction in the existing practices building industry, which is conducted by using the CiteSpace 6.2.R4 tool. It is considered that there is a limitation in promoting further low-carbon practices by technological dominance without a proper management paradigm. This paper, therefore, aims to search for a new management paradigm in order to help further reduce emissions in the building industry. This study adopts an innovative synergy theory to explain the mechanism by which the efforts of all management dimensions can be synergized to promote low-carbon practices in the building sector. Consequently, the outcome of this paper is the introduction of a total low-carbon management (TLCM) paradigm. Synergy theory supports our assertion that a joint force can be formed within the building industry system to drive TLCM practice, as all building-related elements (government departments, building organizations, building personnel, building activities, and building processes) in the system will have to follow the government’s actions towards low-carbon practices. The TLCM paradigm is integrated by five components: whole regulation, whole industry, whole enterprise, whole staff, and whole process. The new paradigm should be promoted to replace the existing technology-dominated paradigm in order to achieve low-carbon practices in the building industry. The TLCM paradigm will guide low-carbon management decisions and practices across all phases of the building project’s lifecycle, together with integrating quality and risk management. Full article

With the increasing severity of global climate change and the growing international attention being paid to carbon emission reduction, forestry carbon sinks have emerged as a key strategy for mitigating climate change and achieving carbon neutrality due to their natural and economic benefits. To identify research hotspots and development trends in forestry carbon sinks, the literature search identified a total of 958 papers from Web of Science (N = 627) and China National Knowledge Infrastructure (N = 331). CiteSpace was used to conduct a visual co-occurrence and comparative analysis of the Chinese and international literature. The results indicated a significant increase in publications on forestry carbon sinks after 2020. English-language research was more deeply embedded in environmental science and ecology, with a focus on leveraging technological innovations for precise carbon sink measurement. In contrast, Chinese-language research placed greater emphasis on policy formulation and optimization related to carbon sinks. Based on the findings, several potential future research directions were proposed to support the sustainable development of forestry carbon sinks. Full article

To comprehensively address the challenges of aperture design, element spacing optimization, and sidelobe suppression in sparse radar array antennas, this paper proposes a hybrid particle swarm optimization (PSO) algorithm that integrates quantum-behavior mechanisms with genetic mutation. The algorithm enhances global search capability through the introduction of a quantum potential well model, while incorporating adaptive mutation operations to prevent premature convergence, thereby improving optimization accuracy during later iterations. The simulation results demonstrate that for sparse linear arrays, planar rectangular arrays, and multi-ring concentric circular arrays, the proposed algorithm achieves a sidelobe level (SLL) reduction exceeding 0.24 dB compared to conventional approaches, including the grey wolf optimizer (GWO), the whale optimization algorithm (WOA), and classical PSO. Furthermore, it exhibits superior global iterative search performance and demonstrates broader applicability across various array configurations. Full article

Sparse recovery space–time adaptive processing (STAP) has an off-grid feature and high computational complexity. To address these shortcomings, this study proposes a self-calibrating STAP algorithm based on sparse Bayesian learning (SBL). The proposed algorithm constructs a dimensionality reduction dictionary by selecting the steering vectors corresponding to atoms with high power values. Then, a small-scale auxiliary dictionary is constructed with a stepwise search approach to calibrate the uniformly discretized dictionary. In this way, the atoms of the auxiliary dictionary can converge to the clutter ridge adaptively when off-grid occurs. The clutter plus noise covariance matrix is estimated via SBL combined with the updated dictionary. The experimental results demonstrate that the proposed algorithm can effectively suppress the clutter ridge expansion caused by the off-grid problem while reducing the computation burden significantly compared with the existing methods. Full article

Background/Objectives: The homeless population (HP) is a heterogeneous group characterized by the absence of stable and conventional housing, often relying on public spaces and deteriorated environments for shelter and survival, either temporarily or permanently. This group is exposed to multiple health vulnerabilities, with substance use disorder (SUD) identified as a significant risk factor for suicidal behavior. The aim of this study was to conduct a scoping review of the relationship between PAS use and suicide among homeless individuals. Methods: A comprehensive literature search was performed using five databases: PubMed, Scopus, SciELO, LILACS, and Google Scholar. Studies were selected based on their relevance to the topic, and data were extracted regarding substance use, suicide-related outcomes, and associated sociodemographic and clinical factors. Results: The findings indicated a strong association between PAS use and increased suicidal ideation and behavior among homeless individuals, particularly among youth, men, and women. Opioids and alcohol were the most frequently reported substances in this context. Additional factors such as unemployment, exposure to violence, social inequalities, and mental health disorders further exacerbated the risk of suicide in this population Conclusions: The reviewed literature underscores the urgent need for integrated, context-sensitive interventions addressing both substance use and mental health among the homeless. Tailored public health strategies focused on prevention, harm reduction, and psychosocial support are essential to reducing suicide risk and promoting overall well-being in this highly vulnerable group. Full article

Switched Reluctance Machines (SRMs) are gaining increasing traction within the industrial sector, primarily due to their inherently simple and robust structure. Nevertheless, SRMs are characterized by two major drawbacks—high torque ripple and strong radial forces—both of which render them less suitable for applications requiring smooth operation, such as Electric Vehicles (EVs). To address these limitations, researchers and designers focus on optimizing these critical performance metrics during the design phase. In recent years, the concept of System-Level Design Optimization (SLDOM) has been introduced and applied to SRM drive systems, where both the machine and the controller are simultaneously considered within the optimization framework. This integrated approach has shown significant improvements in mitigating the aforementioned issues. This paper aims to review the existing literature concerning the SLDOM applied to SRMs, highlighting the key methodologies and findings from studies conducted in recent years. Despite its promising outcomes, the adoption of SLDOM remains limited due to its high computational cost and complexity. In response to these challenges, the paper discusses complementary techniques used to enhance the optimization process, such as search space and computational time reduction strategies, along with the associated challenges and potential solutions. Finally, two critical directions for future research are identified, which are expected to influence the development of the SLDOM and its application to SRMs in the coming years. Full article

In this article, an improved real-valued dimension-reduction MUSIC (IRDR-MUSIC) algorithm is proposed for a crossed-dipole array. Initially, conjugate symmetry of the spatial component in the manifold vector is derived such that two real-valued matrices for the sum and difference covariance are constructed, which consist of the real and imaginary parts of the complex covariance matrix respectively. However, sum and difference covariance matrices with information loss would deteriorate the performance. Thus, given that the sum and difference covariance matrices have an identical null space, a joint sum–difference covariance matrix combining both the sum and difference covariance matrices is constructed, which includes the complete information of a complex covariance matrix. Accordingly, a computationally efficient IRDR-MUSIC algorithm with an enhanced performance is proposed. Compared with the existing dimension-reduction MUSIC algorithm, the proposed IRDR-MUSIC algorithm greatly reduce the complexity reduction almost without any performance loss since singular-value decomposition of the joint sum–difference covariance matrix operates in the real-valued domain, and only half of the range of the spatial spectrum search is required. Furthermore, the proposed IRDR-MUSIC algorithm outperforms the state-of-art complex-valued, symmetry-compressed, dimension-reduction MUSIC algorithm in both its multi-target resolution and computational efficiency. Numerical simulations and analyses verify the superiority of the proposed IRDR-MUSIC algorithm. Full article