Search Results (68)

The objective of this paper is to develop an adaptable and affordable technical tool for managing small urban areas. It demonstrates a low-cost, reliable, and fast method for integrating BIMs, IFC data, and GIS to support fit-for-purpose, crowdsourcing, and participatory applications through an online dashboard. Open data and existing geoportals are used to create the necessary geospatial infrastructure. Geometric information such as building area size and volume is combined with other data from multiple sources such as market values and CO₂ emissions, which can be updated dynamically through real-time interactions. A case study is presented for a small urban neighborhood in Athens. Full article

The three-dimensional borehole-to-surface transient electromagnetic (BSTEM) method plays a critical role in resolving subsurface conductivity structures under complex geological conditions. However, its application is often constrained by the high computational costs associated with large-scale simulations and fine temporal resolution. In this study, a time-parallel forward modeling strategy is employed by integrating the finite volume method (FVM) with the Multigrid Reduction-in-Time (MGRIT) algorithm. Maxwell’s equations are discretized in space using unstructured octree meshes, while the MGRIT algorithm enables parallelism along the time axis through coarse–fine temporal grid hierarchy and multilevel iterative correction. Numerical experiments on synthetic and field-scale models demonstrate that the MGRIT-based solver significantly reduces computational time compared to conventional direct solvers, particularly when a large number of processors are utilized. In a field-scale hematite mine model, the MGRIT-based solver reduces the total runtime by more than 40% while maintaining numerical accuracy. The method exhibits parallel scalability and is especially advantageous in problems involving a large number of time channels, where simultaneous time-step updates offer substantial performance gains. These results confirm the effectiveness and robustness of the proposed approach for large-scale 3D TEM simulations under complex conditions and provide a practical foundation for future applications in high-resolution electromagnetic modeling and imaging. Full article

Deep reinforcement learning (DRL) has demonstrated considerable potential for autonomous collision avoidance in unmanned surface vessels (USVs). However, its application in complex multi-agent maritime environments is often limited by challenges such as convergence issues and high computational costs. To address these issues, this paper proposes an expert-guided DRL algorithm that integrates a Dual-Priority Experience Replay (DPER) mechanism with a Hybrid Reciprocal Velocity Obstacles (HRVO) expert module. Specifically, the DPER mechanism prioritizes high-value experiences by considering both temporal-difference (TD) error and collision avoidance quality. The TD error prioritization selects experiences with large TD errors, which typically correspond to critical state transitions with significant prediction discrepancies, thus accelerating value function updates and enhancing learning efficiency. At the same time, the collision avoidance quality prioritization reinforces successful evasive actions, preventing them from being overshadowed by a large volume of ordinary experiences. To further improve algorithm performance, this study integrates a COLREGs-compliant HRVO expert module, which guides early-stage policy exploration while ensuring compliance with regulatory constraints. The expert mechanism is incorporated into the Soft Actor-Critic (SAC) algorithm and validated in multi-vessel collision avoidance scenarios using maritime simulations. The experimental results demonstrate that, compared to traditional DRL baselines, the proposed algorithm reduces training time by 60.37% and, in comparison to rule-based algorithms, achieves shorter navigation times and lower rudder frequencies. Full article

Hierarchical Federated Learning (HFL) alleviates the trade-off between communication overhead and privacy protection in mobile scenarios via multi-level aggregation and mobility consideration. However, its idealized convex loss assumption and full-dimension parameter upload deviate from real-world non-convex tasks and edge channel constraints, causing excessive energy consumption, high communication cost, and compromised convergence that hinder practical deployment. To address these issues in mobile/UAV networks, this paper proposes an energy-efficient optimization scheme for HFL under non-convex loss, integrating a dynamically adjustable partial-dimension model upload mechanism. By screening key update dimensions, the scheme reduces uploaded data volume. We construct a total energy minimization model that incorporates communication/computation energy formulas related to upload dimensions and introduces an attendance rate constraint to guarantee learning performance. Using Lyapunov optimization, the long-term optimization problem is transformed into single-round solvable subproblems, with a step-by-step strategy balancing minimal energy consumption and model accuracy. Simulation results show that compared with the original HFL algorithm, our proposed scheme achieves significant energy reduction while maintaining high test accuracy, verifying the positive impact of mobility on system performance. Full article

Automatic fault detection based on machine vision technology is crucial for the operational safety of trains. However, when imaging moving trains, system errors may induce localized geometric distortions in the captured images, altering the shapes of critical train components. This, in turn, undermines the precision of subsequent diagnostic algorithms. Therefore, image registration prior to anomaly detection is essential. To address this need, we redefine the horizontal registration of line-scan images as a disparity estimation problem on rectified stereo pairs, which is solved using a proposed dense matching network. The disparity is iteratively refined through a GRU-based update module that constructs a multi-scale cost volume with positional encoding and self-attention. To overcome the absence of real-world disparity ground truth, we generate a physics-based simulation dataset by analytically modeling the nonlinear relationship between train velocity variations and line-scan image distortions. Extensive experiments on diverse real-world train image datasets under varied operational conditions demonstrate that our method consistently outperforms alternatives, achieving 5.8% higher registration accuracy and a fourfold increase in processing speed over state-of-the-art approaches. This advantage is particularly evident in challenging scenarios involving repetitive patterns or texture-less regions. Full article

Currently, supermarket-run shopping centers, known as “Big Marts,” monitor sales information for every single item in order to predict potential customer demand and update inventory management. Anomalies and general trends are commonly discovered through data warehouse mining using a range of machine learning techniques, and businesses such as Big Marts can use the obtained data to forecast future sales volumes. Compared to other research publications, this one forecasted sales with higher accuracy using machine learning models including KNN (K Nearest Neighbors), Naïve Bayes, and Random Forest. To adapt the proposed business model to anticipated outcomes, the sales forecast is based on Big Mart sales for various stores. Using different machine learning methods, the data that is produced may then be used to predict potential sales volumes for retailers such as Big Marts. The projected cost of the suggested system includes the following identifiers: price, outlet, and outlet location. In order to facilitate data-driven decision-making in retail operations and help Big Marts optimize their business models and effectively satisfy anticipated demand, this study emphasizes the importance of incorporating cutting-edge machine learning approaches. Full article

Silicon carbide (SiC) and silicon nanoparticle-decorated carbon (Si/C) materials are electrodes that can potentially be used in various rechargeable batteries, owing to their inimitable merits, including non-flammability, stability, eco-friendly nature, low cost, outstanding theoretical capacity, and earth abundance. However, SiC has inferior electrical conductivity, volume expansion, a low Li⁺ diffusion rate during charge–discharge, and inevitable repeated formation of a solid–electrolyte interface layer, which hinders its commercial utilization. To address these issues, extensive research has focused on optimizing preparation methods, engineering morphology, doping, and creating composites with other additives (such as carbon materials, metal oxides, nitrides, chalcogenides, polymers, and alloys). Owing to the upsurge in this research arena, providing timely updates on the use of SiC and Si/C for batteries is of great importance. This review summarizes the controlled design of SiC-based and Si/C composites using various methods for rechargeable metal-ion batteries like lithium-ion (LIBs), sodium-ion (SIBs), zinc-air (ZnBs), and potassium-ion batteries (PIBs). The experimental and predicted theoretical performance of SiC composites that incorporate various carbon materials, nanocrystals, and non-metal dopants are summarized. In addition, a brief synopsis of the current challenges and prospects is provided to highlight potential research directions for SiC composites in batteries. Full article

The Bi-directional Evolutionary Structural Optimization (BESO) method, owing to its algorithmic simplicity and strong scalability, has emerged as one of the most prevalent topology optimization methodologies in current research and industrial applications. To overcome the limitations of existing commercial finite element software (e.g., ABAQUS), particularly regarding the closed architecture of topology optimization modules and low efficiency in 3D complex structure optimization, this study proposes an ABAQUS–MATLAB cooperative framework. This innovative approach implements direct read/write operations via Python scripts on CAE/ODB model databases, coupled with MATLAB-based master control programs for sensitivity analysis, mesh filtering, and design variable updating. Compared with conventional integration methods employing INP/FIL file interactions, the proposed framework reduces computational time through MATLAB’s advanced matrix operations while maintaining solution accuracy. Validation cases including 2D cantilever beams and 3D wheel hubs demonstrate the method’s precision and computational efficiency. Practical applications in lightweight design of a hydraulic transmission test bench adapter support achieved 31% volume reduction while satisfying strength and stiffness requirements, significantly lowering material costs. The developed cooperative framework provides an extensible solution for high-efficiency topology optimization of complex engineering structures, balancing algorithmic transparency with practical applicability. Full article

The less-than-truckload (LTL) freight problem is a general pain point in logistics applications. Its challenge resides in the fact that these loads cannot be shipped in a timely manner due to their relatively small volumes. Traditional LTL matching methods are challenged by delays in updating logistic information and higher distribution costs. In order to solve LTL challenges, we developed a novel SubChain Salp Swarm Algorithm (SSSA) by improving the traditional Salp Swarm Algorithm with the utilization of a SubChain operation. Our method aims to find the optimal strategy for maintaining a balance between lower operating costs and customer satisfaction. Our SSSA method combines multiple disconnected SubChain points to separate individual chains to find local optima and obtain better convergence results in the final decision. We have compared our method with mainstream metaheuristic algorithms using logistics datasets from a road freight company in Hangzhou, and the results demonstrate that our method converges faster than other methods and has a lower variance. Our method solves the limitation of local optima observed in other optimization methods and improves customer service in relation to the transportation issue. Full article

This paper addresses the critical challenges of renewable energy integration and regional power balance in smart grids, which have become increasingly complex with the rapid growth of distributed energy resources. It proposes a novel three-layer scheduling framework with a dynamic peer-to-peer (P2P) trading mechanism to address these challenges. The framework incorporates a preliminary local supply–demand balance considering renewable energy, followed by an inter-regional P2P trading layer and, ultimately, flexible resource deployment for final balance adjustment. The proposed dynamic continuous P2P trading mechanism enables regions to autonomously switch roles between buyer and seller based on their internal energy status and preferences, facilitating efficient trading while protecting regional privacy. The model features an innovative price update mechanism that initially leverages historical trading data and dynamically adjusts prices to maximize trading success rates. To address the heterogeneity of regional resources and varying energy demands, the framework implements a flexible trading strategy that allows for differentiated transaction volumes and prices. The effectiveness of the proposed framework is validated through simulation experiments using k-means clustered typical daily data from four regions in Northeast China. The results demonstrate that the proposed approach successfully promotes renewable energy utilization, reduces the operational costs of flexible resources, and achieves an efficient inter-regional energy balance while maintaining regional autonomy and information privacy. Full article

Stereo matching plays a vital role in underwater environments, where accurate depth estimation is crucial for applications such as robotics and marine exploration. However, underwater imaging presents significant challenges, including noise, blurriness, and optical distortions that hinder effective stereo matching. This study develops two specialized stereo matching networks: UWNet and its lightweight counterpart, Fast-UWNet. UWNet utilizes self- and cross-attention mechanisms alongside an adaptive 1D-2D cross-search to enhance cost volume representation and refine disparity estimation through a cascaded update module, effectively addressing underwater imaging challenges. Due to the need for timely responses in underwater operations by robots and other devices, real-time processing speed is critical for task completion. Fast-UWNet addresses this challenge by prioritizing efficiency, eliminating the reliance on the time-consuming recurrent updates commonly used in traditional methods. Instead, it directly converts the cost volume into a set of disparity candidates and their associated confidence scores. Adaptive interpolation, guided by content and confidence information, refines the cost volume to produce the final accurate disparity. This streamlined approach achieves an impressive inference speed of 0.02 s per image. Comprehensive tests conducted in diverse underwater settings demonstrate the effectiveness of both networks, showcasing their ability to achieve reliable depth perception. Full article

This article proposes the application of the AHP method in an office furniture remanufacturing company, with the aim of optimizing knowledge retention and management. In particular, it seeks to establish the optimal retrieval route for returned products. To this end, a bibliographic analysis was first carried out, which revealed the scarcity of previous studies on the subject, thus validating the relevance of this work. Subsequently, a practical application of the AHP method was carried out to define the weighting matrix of the evaluation criteria, applied to three specific pieces of furniture, which confirmed the effectiveness of the tool. In a complementary manner, Nonaka and Takeuchi’s SECI model of knowledge management was used, guaranteeing the continuous updating of the matrices and the adequate retention of knowledge in the company. This methodology will increase the volume of remanufactured products and improve operating margins. By reaping both the economic and environmental benefits of this practice, the company will be able to reduce costs, generate additional revenue, improve its corporate image, and build customer loyalty. At the same time, this study promotes the sustainability and sustainable development of this practice within the company and, by extension, in the broader office furniture manufacturing industry. It can serve as a reference for other companies in this sector across different countries. Full article

For binocular stereo matching techniques, the most advanced method currently is using an iterative structure based on GRUs. Methods in this class have shown high performance on both high-resolution images and standard benchmarks. However, simply replacing cost aggregation with a GRU iterative method leads to the original cost volume for disparity calculation lacking non-local geometric and contextual information. Based on this, this paper proposes a new GRU iteration-based adaptive kernel convolution deep recurrent network architecture for stereo matching. This paper proposes a kernel convolution-based adaptive multi-scale pyramid pooling (KAP) module that fully considers the spatial correlation between pixels and adds new matching attention (MAR) to refine the matching cost volume before inputting it into the iterative network for iterative updates, enhancing the pixel-level representation ability of the image and improving the overall generalization ability of the network. At present, the AKC-Stereo network proposed in this paper has a higher improvement than the basic network. On the Sceneflow dataset, the EPE of AKC-Stereo reaches 0.45, which is 0.02 higher than the basic network. On the KITTI 2015 dataset, the AKC-Stereo network outperforms the base network by 5.6% on the D1-all metric. Full article

With the paradigm shift in minimally invasive surgery from the video-assisted thoracoscopic platform to the robotic platform, thoracic surgeons are applying the new technology through various commonly practiced thoracic surgeries, striving to improve patient outcomes and reduce morbidity and mortality. This review will discuss the updates in lung resections, lung transplantation, mediastinal surgeries with a focus on thymic resection, rib resection, tracheal resection, tracheobronchoplasty, diaphragm plication, esophagectomy, and paraesophageal hernia repair. The transition from open surgery to video-assisted thoracoscopic surgery (VATS) to now robotic video-assisted thoracic surgery (RVATS) allows complex surgeries to be completed through smaller and smaller incisions with better visualization through high-definition images and finer mobilization, accomplishing what might be unresectable before, permitting shorter hospital stay, minimizing healing time, and encompassing broader surgical candidacy. Moreover, better patient outcomes are not only achieved through what the lead surgeon could carry out during surgeries but also through the training of the next generation via accessible live video feedback and recordings. Though larger volume randomized controlled studies are pending to compare the outcomes of VATS to RVATS surgeries, published studies show non-inferiority data from RVATS performances. With progressive enhancement, such as overcoming the lack of haptic feedback, and future incorporation of artificial intelligence (AI), the robotic platform will likely be a cost-effective route once surgeons overcome the initial learning curve. Full article

Perovskite-type structures have unique crystal architecture and chemical composition, which make them highly attractive for the design of solar cells. For instance, perovskite-based solar cells have been shown to perform better than silicon cells, capable of adsorbing a wide range of light wavelengths, and they can be relatively easily manufactured at a low cost. Importantly, the perovskite-based structures can also adsorb a significant amount of hydrogen atoms into their own structure; therefore, perovskite holds promise in the solid-state storage of hydrogen. It is widely expected by the scientific community that the controlled adsorption/desorption of the hydrogen atoms into/from perovskite-based structures can help to overcome the main hydrogen storage issues such as a low volumetric density and the safety concerns (i.e., the hydrogen embrittlement affects strongly the mechanical properties of metals and, as such, the storage or transport of the gaseous hydrogen in the vessels is, especially for large vessel volumes, challenging). The purpose of this review is to provide an updated overview of the recent results and studies focusing on the perovskite materials used for both solar cells and hydrogen storage applications. Particular attention is given to (i) the preparation and the achievable efficiency and stability of the perovskite solar cells and (ii) the structural, thermodynamic, and storage properties of perovskite hydrides and oxides. We show that the perovskite materials can not only reach the efficiency above current Si-based solar cells but also, due to good stability and reasonable price, can be preferable in the solid-state storage of hydrogen. Then, the future trends and directions in the research and application of perovskite in both solar cells and hydrogen storage are also highlighted. Full article