Search Results (7,731)

With the widespread deployment of 5G technology, high-precision positioning in global navigation satellite system (GNSS)-denied environments is a critical yet challenging task for emerging 5G applications, enabling enhanced spatial resolution, real-time data acquisition, and more accurate geolocation services. Traditional methods relying on single-source measurements like received signal strength information (RSSI) or time of arrival (TOA) often fail in complex multipath conditions. To address this, the positional encoding multi-scale residual network (PE-MSRN) is proposed, a novel deep learning framework that enhances positioning accuracy by deeply mining spatial information from 5G channel state information (CSI). By designing spatial sampling with multigranular data and utilizing multi-source information in 5G CSI, a dataset covering a variety of positioning scenarios is proposed. The core of PE-MSRN is a multi-scale residual network (MSRN) augmented by a positional encoding (PE) mechanism. The positional encoding transforms raw angle of arrival (AOA) data into rich spatial features, which are then mapped into a 2D image, allowing the MSRN to effectively capture both fine-grained local patterns and large-scale spatial dependencies. Subsequently, the PE-MSRN algorithm that integrates ResNet residual networks and multi-scale feature extraction mechanisms is designed and compared with the baseline convolutional neural network (CNN) and other comparison methods. Extensive evaluations across various simulated scenarios, including indoor autonomous driving and smart factory tool tracking, demonstrate the superiority of our approach. Notably, PE-MSRN achieves a positioning accuracy of up to 20 cm, significantly outperforming baseline CNNs and other neural network algorithms in both accuracy and convergence speed, particularly under real measurement conditions with higher SNR and fine-grained grid division. Our work provides a robust and effective solution for developing high-fidelity 5G positioning systems. Full article

The failure of large gravity dams during an earthquake could lead to calamitous flooding, severe infrastructural damage, and massive environmental destruction. This paper aims to demonstrate reliable methods for evaluating dam performance after a seismic event. The work included a seismic hazard analysis and nonlinear finite element modelling of concrete cracking for two large dams (D1 and D2, of 35 and 90 m in height, respectively) in Eastern Canada. Dam D1 is located in Montreal, and Dam D2 is located in La Malbaie, Quebec. The modelling approach was validated using the Koyna Dam, which was subjected to the 1967 Mw 6.5 earthquake. This paper reports tensile cracks of D1 and D2 under combined hydrostatic and seismic loading. The latter was generated from ground motion records from 11 sites during the 1988 Mw 5.9 Saguenay earthquake. These records were each scaled to two times the design level. It is shown that D1 remained stable, with minor localised cracking, whereas D2 experienced widespread tensile damage, particularly at the crest and base under high-energy and transverse inputs. These findings highlight the influence of dam geometry and frequency characteristics on seismic performance. The analysis and modelling procedures reported can be adopted for seismic risk classification and safety compliance verification of other dams and for recommendations such as monitoring and upgrading. Full article

Data of operational conditions of structural components, acquired, e.g., in structural health monitoring (SHM), is of great interest to optimise products from one generation to the next, for example, by adapting them to occurring operational loads. To acquire data for this purpose in the desired quality, an optimal sensor placement for so-called shape and load sensing is required. In the case of large-scale structural components, wireless sensor networks (WSN) could be used to process and transmit the acquired data for real-time monitoring, which furthermore requires an optimisation of sensor node positions. Since most publications focus only on the optimal sensor placement or the optimisation of sensor node positions, a methodology for both is implemented in a Python tool, and an optimised WSN is realised on a demonstration part, loaded at a test bench. For this purpose, the modal method is applied for shape sensing as well as a physics-informed neural network for solving inverse problems in shape sensing (iPINN). The WSN is realised with strain gauges, HX711 analogue-digital (A/D) converters, and Arduino Nano 33 IoT microprocessors for data submission to a server, which allows real-time visualisation and data processing on a Python Flask server. The results demonstrate the applicability of the presented methodology and its implementation in the Python tool for achieving high-accuracy shape sensing with WSNs. Full article

Synthetic data has become an increasingly important tool for semantic segmentation, where collecting large-scale annotated datasets is often costly and impractical. Prior work has leveraged computer graphics and game engines to generate training data, but many pipelines remain limited to single modalities and constrained environments or require substantial manual setup. To address these limitations, we present a fully automated pipeline built within Unreal Engine 5 (UE5) that procedurally generates diverse, labeled environments and collects multimodal visual data for semantic segmentation tasks. Our system integrates UE5’s biome-based procedural generation framework with a spline-following drone actor capable of capturing both RGB and depth imagery, alongside pixel-perfect semantic segmentation labels. As a proof of concept, we generated a dataset consisting of 1169 samples across two visual modalities and seven semantic classes. The pipeline supports scalable expansion and rapid environment variation, enabling high-throughput synthetic data generation with minimal human intervention. To validate our approach, we trained benchmark computer vision segmentation models on the synthetic dataset and demonstrated their ability to learn meaningful semantic representations. This work highlights the potential of game-engine-based data generation to accelerate research in multimodal perception and provide reproducible, scalable benchmarks for future segmentation models. Full article

In large-scale, multi-national research projects on agricultural cropping systems such as SoilCare (Horizon 2020), ensuring consistency, comparability, and timely reporting of the (meta)data of the agricultural experiments across diverse partners has been a persistent challenge. To address these concerns, the SoilCare project developed a comprehensive data management system centered around a standardized template for the collection, organization, and sharing of experimental data and metadata from cropping systems. This template, designed to support harmonized sharing, analysis, and documentation through a common structure and terminology, meets the interdisciplinary requirements of modern agricultural research. Experimental data and metadata were structured into five core pools: 1. basic information, 2. field information, 3. experimental setup, 4. agricultural management data and 5. measured data/results. The Excel-based template was carefully structured to support integration into a relational database, enabling robust monitoring, analysis, and traceability of experimental processes and outcomes. The database schema and template, together with the (meta)data collected using this system during the SoilCare projects, were made openly available via Zenodo. The standardized approach ultimately enabled unified analyses and harmonized reporting across all experimental sites, demonstrating the system’s effectiveness in facilitating collaborative agricultural research at scale. Full article

The increasing integration of automation and intelligent sensing technologies in daily-use ceramic manufacturing poses new challenges for efficient scheduling under hybrid flow-shop and shared-kiln constraints. To address these challenges, this study proposes a Mixed-Integer Linear Programming (MILP) model and an Improved Discrete Hippopotamus Optimization (IDHO) algorithm designed for smart, network-aware production environments. The MILP formulation captures key practical features such as batch processing, no-idle kiln constraints, and machine re-entry dynamics. The IDHO algorithm enhances global search performance via segment-based encoding, nonlinear population reduction, and operation-specific mutation strategies, while a parallel evaluation framework accelerates computational efficiency, making the solution viable for industrial-scale, time-sensitive scenarios. The experimental results from 12 benchmark cases demonstrate that IDHO achieves superior performance over six representative metaheuristics (e.g., PSO, GWO, Jaya, DBO), with an average ARPD of 1.04%, statistically significant improvements (p < 0.05), and large effect sizes (Cohen’s d > 0.8). Compared to the commercial solver CPLEX, IDHO provides near-optimal results with substantially lower runtime. The proposed approach contributes to the development of intelligent networked scheduling systems for cyber-physical manufacturing environments, enabling responsive, scalable, and data-driven optimization in smart sensing-enabled production settings. Full article

Background: Whole-slide images (WSIs) are crucial in pathology for digitizing tissue slides, enabling pathologists and AI models to analyze cancer patterns at gigapixel scale. However, their large size incorporates artifacts and non-tissue regions that slow AI processing, consume resources, and introduce errors like false positives. Tissue detection serves as the essential first step in WSI pipelines to focus on relevant areas, but deep learning detection methods require extensive manual annotations. Methods: This study benchmarks four thumbnail-level tissue detection methods—Otsu’s thresholding, K-Means clustering, our novel annotation-free Double-Pass hybrid, and GrandQC’s UNet++ on 3322 TCGA WSIs from nine cancer cohorts, evaluating accuracy, speed, and efficiency. Results: Double-Pass achieved an mIoU of 0.826—very close to the deep learning GrandQC model’s 0.871—while processing slides on a CPU in just 0.203s per slide, markedly faster than GrandQC’s 2.431s per slide on the same hardware. As an annotation-free, CPU-optimized method, it therefore enables efficient, scalable thumbnail-level tissue detection on standard workstations. Conclusions: The scalable, annotation-free Double-Pass pipeline reduces computational bottlenecks and facilitates high-throughput WSI preprocessing, enabling faster and more cost-effective integration of AI into clinical pathology and research workflows. Comparing Double-Pass against established methods, this benchmark demonstrates its novelty as a fast, robust and annotation-free alternative to supervised methods. Full article

Evaluation of socio-ecological vulnerability is crucial for sustainable management in mining cities. This study selected Xintai City, China, as a case and constructed a comprehensive vulnerability assessment framework based on 2010–2020 multi-source data. By integrating the Geodetector, spatial autocorrelation analysis, and ordered weighted averaging (OWA), we systematically explored the spatio-temporal patterns and driving mechanisms of socio-ecological vulnerability. The Theil index at the village level revealed finer spatial heterogeneity than large-scale analyses. The results show the following: (1) Socio-ecological vulnerability in Xintai City is generally moderate, with high-vulnerability areas concentrated in the urban center and former coal mining zones. Over the past decade, high—vulnerability levels in these areas have improved, whereas the urban-rural fringe has experienced a significant increase in vulnerability, primarily driven by industrial transfer and uneven resource allocation. (2) Geodetector analysis indicated a shift in dominant drivers from natural to socio-economic factors, with population density and construction land proportion surpassing natural conditions such as average annual rainfall by 2020. Additionally, mining land proportion, land use change, and the spatial distribution of social services played key roles in shaping vulnerability patterns, while ecological and public service factors showed weaker explanatory power. (3) Scenario simulation based on OWA demonstrated that an economic-priority pathway leads to the outward expansion of vulnerable areas into mountainous regions, while an ecological-priority approach promotes spatial contraction and optimization of vulnerability zones. These findings provide scientific guidance for identifying key vulnerable areas and formulating differentiated management strategies, offering reference value for the sustainable development of resource-exhausted mining cities. Full article

Resonant acoustic technology utilizes low-frequency vertical harmonic vibrations to induce full-field mixing effects in processed materials, and it is regarded as a “disruptive technology in the field of energetic materials”. Although numerous scholars have investigated the mechanisms of resonant acoustic mixing, there remains a lack of parameter selection methods for improving product quality and production efficiency in engineering practice. To address this issue, this study employs phase-field modeling and fluid–structure coupling methods to numerically simulate the transport process of glycerol during resonant acoustic mixing. The research reveals the mass transfer mechanism within the flow field, establishes a liquid-phase distribution index for quantitatively characterizing mixing effectiveness, and clarifies the enhancement effect of fluid transport on solid particle mixing through particle tracking methods. Furthermore, parameter studies on vibration frequency and amplitude were conducted, yielding a critical curve for guiding parameter selection in engineering applications. The results demonstrate that Faraday instability first occurs at the fluid surface, generating Faraday waves that drive large-scale vortices for global mass transfer, followed by localized mixing through small-scale vortices. The transport process of glycerol during resonant acoustic mixing comprises three distinct stages: stable Faraday wave oscillation, rapid mass transfer during flow field destabilization, and localized mixing upon stabilization. Additionally, increasing either vibration frequency or amplitude effectively enhances both the rate and effectiveness of mass transfer. These findings offer theoretical guidance for optimizing process parameters in resonant acoustic mixing applications. Full article

The use of consumer-grade cameras for stereo vision provides a cost-effective, non-contact method for measuring three-dimensional displacement in civil engineering experiments. However, obtaining accurate 3D coordinates requires accurate temporal alignment of several unsynchronized cameras, which is often lacking in consumer-grade devices. Current synchronization software methods usually only achieve precision at the frame level. As a result, they fall short for high-frequency shake table experiments, where even minor timing differences can cause significant triangulation errors. To address this issue, we propose a novel image-based synchronization method and a graphical user interface (GUI)-based software for acquiring stereo videos during shake table testing. The proposed method estimates the time lag between unsynchronized videos by minimizing reprojection errors. Then, the estimate is refined to sub-frame accuracy using polynomial interpolation. This method was validated using a high-precision motion capture system (Mocap) as a benchmark through large- and small-scale experiments. The proposed method reduces the RMSE of triangulation by up to 78.79% and achieves maximum displacement errors of less than 1 mm for small-scale experiments. The proposed approach reduces the RMSE of displacement measurements by 94.21% and 62.86% for small- and large-scale experiments, respectively. The results demonstrate the effectiveness of the proposed method for precise 3D displacement measurement with low-cost equipment. This method offers a practical alternative to expensive vision-based measurement systems commonly used in structural dynamics research. Full article

This comprehensive review examines the multifaceted interactions between influenza viruses and the ocular system, integrating viral pathogenesis, clinical manifestations, and vaccine-related considerations. Influenza A subtypes (H7, H1N1, H5N1) and influenza B viruses induce a spectrum of ocular complications, from mild conjunctivitis—predominantly associated with H7 avian strains—to sight-threatening disorders like uveal effusion syndrome, acute macular neuroretinopathy, and optic neuritis. Experimental evidence confirms viral replication in human corneal and retinal cells, with H7N7 demonstrating unique tropism for ocular tissues via NF-κB-mediated inflammatory pathways. Clinical cases highlight direct viral invasion and immune-mediated mechanisms, such as Vogt–Koyanagi–Harada disease exacerbation and retinal vasculitis. Rarely, influenza vaccination has been linked to oculorespiratory syndrome, uveitis, and demyelinating events, though large-scale epidemiological studies (e.g., WHO safety reports) confirm vaccines’ favorable risk–benefit profile, distinguishing true adverse events from temporal associations. This synthesis emphasizes the need for ophthalmologists to prioritize surveillance during influenza seasons, integrating diagnostic tools like conjunctival RT-PCR and optical coherence tomography. Future research should focus on defining viral receptor-binding mechanisms in ocular tissues and developing targeted therapies for severe retinopathies, while reinforcing vaccination as a cornerstone of public health despite rare ocular risks. Full article

This paper investigates a swarming strength allocation Bayesian game approach under incomplete information to address the high-value targets protection problem of swarming unmanned systems. The swarming strength allocation Bayesian game model is established by analyzing the non-zero sum incomplete information game mechanism during the protection process, considering high-tech and low-tech interception players. The model incorporates a game benefit quantification method based on an improved Lanchester equation. The method regards massive swarm individuals as a collective unit for overall cost calculation, thus avoiding the curse of dimensionality from increasing numbers of individuals. Based on it, a Bayesian Nash equilibrium solving approach is presented to determine the optimal swarming strength allocation for the protection player. Finally, compared with random allocation, greedy heuristic, rule-based assignment, and Colonel Blotto game, the simulations demonstrate the proposed method’s robustness in large-scale strength allocation. Full article

To overcome the limitations of conventional dampers and enhance seismic resilience in multi-span continuous bridges, this study synthesized a magnetorheological shear-stiffening gel (MRSSG) that integrates shear-stiffening (SS) materials with magnetorheological (MR) components, enabling passive rate-sensitive adaptation and magnetic-field-driven directionality. Leveraging this material, we developed a multifunctional MR damper combining high-frequency load-sharing locking and low-frequency magnetically controlled damping mechanisms. Numerical simulations under diverse seismic waves (El Centro, Koyna, and Wenchuan) demonstrated the damper’s effectiveness: it redistributed internal forces from fixed to movable piers, reducing fixed-pier shear forces by up to 62.3% (e.g., from 258,714 kN to 97,419 kN under Wenchuan waves), and under semi-active control via a semi-step on–off strategy, it suppressed displacement responses by >95% at movable-pier deck measurement points. This work establishes a robust solution for improving seismic performance in large-scale civil infrastructure. Full article

Background: Quantum machine learning (QML) holds significant promise for advancing medical image classification. However, its practical application to large-scale, high-resolution datasets is constrained by the limited number of qubits and the inherent noise in current quantum hardware. Methods: In this study, we propose the Fused Quantum Dual-Backbone Network (FQDN), a novel hybrid architecture that integrates classical convolutional neural networks (CNNs) with quantum circuits. This design is optimized for the noisy intermediate-scale quantum (NISQ), enabling efficient computation despite hardware limitations. We evaluate FQDN on the task of gastrointestinal (GI) disease classification using wireless capsule endoscopy (WCE) images. Results: The proposed model achieves a substantial reduction in parameter complexity, with a 29.04% decrease in total parameters and a 94.44% reduction in trainable parameters, while outperforming its classical counterpart. FQDN achieves an accuracy of 95.80% on the validation set and 95.42% on the test set. Conclusions: These results demonstrate the potential of QML to enhance diagnostic accuracy in medical imaging. Full article

Graphene has promise as a channel connecting separate units of large-scale spintronic circuits owing to its outstanding theoretical spin transport properties. However, spin transport properties of experimental devices consistently fall short of theoretical estimates due to impacts from the substrate, electrodes, or defects in the graphene itself. In this study, we fabricate both traditional non-local spin valves (NLSVs) and novel hybrid drift-diffusion spin valves (HDDSVs) to explore the impact of charge current and AC spin injection efficiency on spin transport. HDDSVs feature channel branches that allow investigation of charge-based spin drift enhancement compared to diffusion-only configurations. We investigate the modulation of spin transport through hybrid drift-diffusion, observing a decrease in spin signal by 11% for channels with a 45° branch angle, and a 21% increase in spin signal for 135° branch angle channels. We then fabricate symmetrical 90° channel branch angle devices, which do not produce consistent spin transport modulation in drift diffusion mode. These findings highlight the role of carrier drift in enhancing or suppressing spin transport, depending on channel geometry and injection configuration. Overall, our work demonstrates a promising approach to optimizing spin transport in graphene devices by leveraging hybrid drift-diffusion effects without requiring additional DC current sources. Full article