Search Results (877)

Wind turbine blade damage detection is crucial for advancing wind energy as a sustainable alternative to fossil fuels. Existing methods based on image processing technologies face challenges such as limited adaptability to complex environments, trade-offs between model accuracy and computational efficiency, and inadequate real-time inference capabilities. In response to these limitations, we put forward MSWindD-YOLO, a lightweight real-time detection model for wind turbine blade damage. Building upon YOLOv5s, our work introduces three key improvements: (1) the replacement of the Focus module with the Stem module to enhance computational efficiency and multi-scale feature fusion, integrating EfficientNetV2 structures for improved feature extraction and lightweight design, while retaining the SPPF module for multi-scale context awareness; (2) the substitution of the C3 module with the GBC3-FEA module to reduce computational redundancy, coupled with the incorporation of the CBAM attention mechanism at the neck network’s terminus to amplify critical features; and (3) the adoption of Shape-IoU loss function instead of CIoU loss function to facilitate faster model convergence and enhance localization accuracy. Evaluated on the Wind Turbine Blade Damage Visual Analysis Dataset (WTBDVA), MSWindD-YOLO achieves a precision of 95.9%, a recall of 96.3%, an mAP@0.5 of 93.7%, and an mAP@0.5:0.95 of 87.5%. With a compact size of 3.12 MB and 22.4 GFLOPs inference cost, it maintains high efficiency. After TensorRT acceleration on Jetson Orin NX, the model attains 43 FPS under FP16 quantization for real-time damage detection. Consequently, the proposed MSWindD-YOLO model not only elevates detection accuracy and inference efficiency but also achieves significant model compression. Its deployment-compatible performance in edge environments fulfills stringent industrial demands, ultimately advancing sustainable wind energy operations through lightweight lifecycle maintenance solutions for wind farms. Full article

People with disabilities often face challenges like moving around independently and depending on personal caregivers for daily life activities. Traditional assistive devices are universally accepted by these communities, but they are designed with one-size-fits-all approaches that cannot adjust to individual human sizes, are not easily customized, and are made from rigid materials that do not adapt as a person’s condition changes over time. This systematic review examines the integration of smart materials, sensors, actuators, and 3D/4D printing technologies in advancing assistive devices, with a particular emphasis on mobility aids. In this work, the authors conducted a comparative analysis of traditional devices with commercially available innovative prototypes and research stage assistive devices by focusing on smart adaptable materials and sustainable additive manufacturing techniques. The results demonstrate how artificial intelligence drives smart assistive devices in hospital decentralized additive manufacturing, and policy frameworks agree with the Sustainable Development Goals, representing the future direction for adaptive assistive technology. Also, by combining 3D/4D printing and AI, it is possible to produce adaptive, affordable, and patient centered rehabilitation with feedback and can also provide predictive and preventive healthcare strategies. The successful commercialization of adaptive assistive devices relies on cost effective manufacturing techniques clinically aligned development supported by cross disciplinary collaboration to ensure scalable, sustainable, and universally accessible smart solutions. Ultimately, it paves the way for smart, sustainable, and clinically viable assistive devices that outperform conventional solutions and promote equitable access for all users. Full article

Using renewable D-gluconic acid δ-lactone as the starting material, two novel glucosamide-based organosilicon quaternary ammonium surfactants (2/3SiDDGPBH) were synthesized through an environmentally friendly three-step process involving amidation, hydrophobic modification, and quaternization. Comprehensive characterization demonstrated their exceptional performance: surface tension reduction to 33.4 mN/m (2SiDDGPBH) and 33.64 mN/m (3SiDDGPBH), uniform spherical micelles (1–10 nm and 30–100 nm) were formed, and outstanding foam properties with 3SiDDGPBH developed, showing superior foamability and stability. Material modification tests on polymethyl methacrylate (PMMA) plates, mature acacia leaves, oilpaper, vegetable-tanned top-grain leather, and melamine-formaldehyde resin (MFR) faced with plywood revealed excellent spreading performance and durability, particularly for 3SiDDGPBH-treated MFR plywood, which maintained excellent spreading performance even after 80 washing cycles. Scanning electron microscopy (SEM) analysis confirmed that the Si wt% of MFR plywood treated with 2/3SiDDGPBH and scrubbed MFR plywood both exhibited a significant increase, and the 3SiDDGPBH-treated MFR plywood demonstrated superior bonding properties. These surfactants combine low surface tension, excellent foaming properties, and outstanding spreading performance, demonstrating broad application prospects in fields such as pesticide adjuvants, industrial and household cleaning agents, cosmetics, oilfield extraction, textile printing and dyeing, and functional coatings. Full article

In recent years, controlling the integrity of shallow-buried natural gas pipelines within surface subsidence zones caused by high-intensity underground longwall mining in the Daniudi Gas Field of China’s Ordos Basin has emerged as a critical challenge impacting both mine planning and the safe, efficient co-exploitation of coal and deep natural gas resources. This study included field measurements and an analysis of surface subsidence data from high-intensity longwall mining operations at the Xiaobaodang No. 2 Coal Mine, revealing characteristic ground movement patterns under intensive extraction conditions. The subsidence basin was systematically divided into pipeline hazard zones using three key deformation indicators: horizontal strain, tilt, and curvature. Through ABAQUS-based 3D numerical modeling of coupled pipeline–coal seam mining systems, this research elucidated the spatiotemporal evolution of pipeline Von Mises stress under varying mining parameters, including working face advance rates, mining thicknesses, and pipeline orientation angles relative to the advance direction. The simulations further uncovered non-synchronous deformation behavior between the pipeline and its surrounding sand and soil, identifying two distinct evolutionary phases and three characteristic response patterns. Based on these findings, targeted pipeline integrity preservation measures were developed, with numerical validation demonstrating that maintaining advance rates below 10 m/d, restricting mining heights to under 2.5 m within the 260 m pre-mining influence zone, and where geotechnically feasible, the maximum stress of the pipeline laid perpendicular to the propulsion direction (90°) can be controlled below 480 MPa, and the separation amount between the pipe and the sand and soil can be controlled below 8.69 mm, which can effectively reduce the interference caused by mining. These results provide significant engineering guidance for optimizing longwall mining parameters while ensuring the structural integrity of shallow-buried pipelines in high-intensity extraction environments. Full article

As digitalization and environmental sustainability advance globally, small and medium-sized enterprises (SMEs) are facing transformative pressures as well as emerging opportunities. Rapid digital innovation promotes intelligent production, cost reduction, efficiency gains, and improved management practices, while green development mandates emphasize energy conservation, emissions reduction, and sustainable supply chains. Amid concurrent digital and green transformations, SMEs are leveraging digital technologies to bolster green learning and enhance sustainable product development. This study investigates the digital–green learning orientation (DGLO) and its influence on ambidextrous research and development (R&D) capabilities, which in turn shape sustainable product development performance (SPDP). Drawing on survey data from 306 SMEs in eastern and southern China, multiple regression analysis was employed to assess the relationships between DGLO, ambidextrous R&D capabilities, and SPDP. The findings reveal that DGLO significantly enhances SPDP. Moreover, DGLO promotes SPDP through both exploitative and exploratory R&D capabilities, with each playing a complementary role. Full article

Cyprus faces mounting pressure on freshwater resources from climate change, recurrent drought, and rising demand. This study evaluates the feasibility of a rain enhancement program through cloud seeding, integrating long-term rain gauge records (1991–2024), lightning climatology (2021–2025), and local X-band weather radar data (30 October 2024–4 January 2025) to quantify the frequency and characteristics of seedable clouds. Rain gauge analysis shows mean monthly rainfall exceeding 20 mm during October to April, with up to 16 rainfall events per month, indicating ample seeding opportunities. Lightning records show between 40–60 annual average thunderstorm occurrences, peaking in December (~10 days) along the Troodos Mountains in the central region and Limassol-Akrotiri in the south. Radar data analysis confirms the presence of both glaciogenic (≥25 dBZ at 5 km MSL) and hygroscopic (≥10 dBZ with ≥4 km depth) seedable cloud structures, with hotspots over the Troodos orography, southern plains, and maritime inflow zone. The combined results support the viability of an initial 7-month (October–April) cloud seeding program demonstration, integrated within a scientific framework, as a complementary and cost-effective freshwater augmentation tool for Cyprus. Full article

Brazil is a leading producer of multi-purpose crops—such as corn, soybean, and sugarcane—used for human consumption, animal feed, and biofuel production. This study generated agricultural inventories for these three crops based on state-level information. For sugarcane, we used primary data submitted by ethanol producers to RenovaBio. For soybean and corn, we retrieved and updated data from a previous study, which gathered information through panel consultations with farmers and sector experts. We also calculated the greenhouse gas (GHG) emissions associated with the crops using the Life Cycle Assessment (LCA) method. Our analysis revealed significant variability in emissions across states, especially for corn and sugarcane. Without considering direct land use change (dLUC), the states with the highest and lowest emissions for each crop were as follows: (i) sugarcane: Paraíba at 54 and Goiás at 37, with a national average of 42 kg CO₂e/t cane; (ii) soybean: Maranhão at 344 and Minas Gerais at 300, average of 323 kg CO₂e/t soy; (iii) first-crop corn: Maranhão at 416 and Mato Grosso at 264, average of 300 kg CO₂e/t corn; (iv) second-crop corn: Paraná at 306 and Minas Gerais at 153, average of 255 kg CO₂e/t corn. Emissions were inversely related to crop yields, with the exception of second-crop corn. In general, lower yields were observed in states of the Northeast region (e.g., Maranhão and Paraíba), which face challenges due to irregular climate patterns and water deficits. For sugarcane cultivated in the same region, emissions from straw burning had a significant impact, with the practice being applied to more than 60% of the crop area. If dLUC emissions were included, variability would increase dramatically—particularly for corn and soybean in some states—due to patterns of cropland expansion into native vegetation areas over the 2000–2019 period. In particular, total soybean emissions would range from 471 in Paraná to 2173 in Maranhão, with a national average of 1022 kg CO₂e/t soy. These findings can be valuable as references for life cycle databases, for the development of state-specific emission factors for biofuels produced from the investigated crops, and as supporting information for decarbonization programs. Full article

The Central Yunnan Water Diversion Project (CYWDP) is currently under construction and represents China’s most extensive and geologically challenging water transfer infrastructure, facing significant geohazard risks induced by intensive engineering activities, posing severe threats to its entire lifecycle safety. Therefore, monitoring and spatiotemporal evolution analysis of surface deformation along the CYWDP is critically important. This study presents the first integrated analysis of geometric distortions and multi-dimensional spatiotemporal deformation characteristics along the CYWDP, utilizing both ascending and descending orbit data from Sentinel-1. First, by integrating the Layover-Shadow Mask (LSM) model and R-Index method, we identified geometric distortion types in SAR imagery and evaluated their suitability for deformation monitoring. Subsequently, SBAS-InSAR technology was employed to derive line-of-sight (LOS) deformation information from 124 images (ascending) and 90 images (descending) acquisitions (2022–2024), enabling the identification of significant deformation zones and analyzing their spatial distribution characteristics. Finally, two-dimensional (2D) deformation fields were obtained through the joint inversion of ascending and descending orbit data in typical deformation zones. The results reveal that geometric distortions in Sentinel-1 imagery along the CYWDP are dominated by foreshortening effects, accounting for 35.3% of the study area in the ascending-orbit data and 37.9% in the descending-orbit data. A total of 10 significant deformation-prone areas were detected, and the most pronounced subsidence, amounting to −164 mm/y, was observed in the northern Jinning District (Luoci-Qujiang section), showing expansion trends toward water conveyance infrastructure. This study reveals surface deformation’s multi-dimensional spatiotemporal evolution patterns along the CYWDP. The findings support geohazard mitigation and provide a methodological reference for safety monitoring of major water conservancy projects in complex geological environments. Full article

Hydraulic turbine governor servomotors in wind solar hydro complementary energy systems face significant fatigue failure challenges due to high-frequency regulation. This study develops an intelligent fatigue monitoring and prediction system based on strain sensors, specifically designed for the frequent regulation requirements of complementary systems. A multi-point monitoring network was constructed using resistive strain sensors, integrated with temperature and vibration sensors for multimodal data fusion. Field validation was conducted at an 18.56 MW hydroelectric unit, covering guide vane opening ranges from 13% to 63%, with system response time <1 ms and a signal-to-noise ratio of 65 dB. A simulation model combining sensor measurements with finite element simulation was established through fine-mesh modeling to identify critical fatigue locations. The finite element analysis results show excellent agreement with experimental measurements (error < 8%), validating the simulation model approach. The fork head was identified as the critical component with a stress concentration factor of 3.4, maximum stress of 51.7 MPa, and predicted fatigue life of 1.2 × 10⁶ cycles (12–16 years). The cylindrical pin shows a maximum shear stress of 36.1 MPa, with fatigue life of 3.8 × 10⁶ cycles (16–20 years). Monte Carlo reliability analysis indicates a system reliability of 51.2% over 20 years. This work provides an effective technical solution for the predictive maintenance and digital operation of wind solar hydro complementary systems. Full article

Coral reefs are essential to the cultural, ecological, and economic well-being of Hawai‘i’s communities, yet they face increasing threats from environmental changes and localized stressors, including coral disease. Detecting coral disease often relies on the visible appearance of lesions; however, in the case of black-band disease (BBD), this visual cue appears too late, as disease progression can cause an average rate of tissue loss of up to 5.7 cm² per day over two months, followed by partial or full colony mortality. Reflectance spectroscopy offers a promising tool for detecting subtle spectral changes associated with coral health before visible symptoms emerge, yet few studies have applied this method to coral disease. In situ spectroscopy was used to measure the spectral reflectance of health conditions in Montiporid corals at ‘Anini Reef, Kaua‘i, USA. Discriminant analysis revealed that visually identical tissue types—live tissue on colonies with BBD (live_D) and live tissue on colonies without BBD (live_L)—were spectrally distinct. In contrast, BBD lesions (disease) and adjacent tissue that appeared healthy (transition) exhibited similar spectral signatures. Analyses identified three spectrally distinct tissue health conditions with a misclassification rate of 12.8%. These findings highlight the potential of reflectance spectroscopy for early coral disease detection, which could improve response times and support more effective coral reef conservation efforts. Full article

When mechanically excavating deep-buried large-section metro stations, stringent deformation control requirements for the surrounding rock must be adhered to. Calculations indicate that horizontal convergence in certain areas of the station exceeds acceptable limits, necessitating adjustments to construction parameters to comply with these requirements. This study, based on a project for the Chongqing Metro Line 18, establishes a three-dimensional numerical analysis model for an underground excavation station by utilizing the characteristics of the stratum-structure model. A comprehensive 3D numerical simulation was conducted to evaluate the deformation characteristics of the stratum and surrounding rock resulting from excavation, and to determine optimal excavation parameters based on deformation control. The key findings are as follows: (1) Under the original excavation design parameters, the horizontal convergence displacement at the arch foot met specification requirements and was smaller than that at the sidewall. However, the horizontal convergence displacement at the sidewall exceeded the 20 mm limit specified by the relevant codes, failing to satisfy deformation control standards. (2) The deformation of the surrounding rock increased with factors such as the distance between the excavation face and the initial support, as well as the length of the excavation step. While the spacing between adjacent pilot tunnels had a relatively minor impact on overall station deformation, the number of pilot tunnels, in conjunction with other parameters, proved beneficial for controlling surrounding rock deformation. (3) Among the parameters examined, the distance between the excavation face and the initial support, along with the excavation step length, exerted the greatest influence on deformation. Based on deformation control criteria, the optimal excavation parameters were determined as follows: the distance between the excavation face and the initial support should not exceed 6 m; the excavation step length is set to 1.5 m; the number of pilot tunnels is established at 11; and the spacing between adjacent pilot tunnels is set at 10.5 m. (4) Field monitoring data closely corresponded with the effects observed from implementing the optimized parameters, thus validating the reliability of the optimization scheme. The results of this study provide a valuable reference for the excavation of metro stations under similar conditions in the future. Full article

Feature map pooling in convolutional neural networks (CNNs) serves the dual purpose of reducing spatial dimensions and enhancing feature invariance. Current pooling approaches face a fundamental trade-off: deterministic methods (e.g., MaxPool and AvgPool) lack regularization benefits, while stochastic approaches introduce beneficial randomness but can suffer from sampling biases and may require careful hyperparameter tuning (e.g., S3Pool). To address these limitations, this paper introduces LDS3Pool, a novel pooling method that leverages low-discrepancy sequences (LDSs) for quasi-random spatial sampling. LDS3Pool first linearizes 2D feature maps to 1D sequences using Hilbert space-filling curves to preserve spatial locality, then applies LDS-based sampling to achieve quasi-random downsampling with mathematical guarantees of uniform coverage. This framework provides the regularization benefits of randomness while maintaining comprehensive feature representation, without requiring sensitive hyperparameter tuning. Extensive experiments demonstrate that LDS3Pool consistently outperforms baseline methods across multiple datasets and a range of architectures, from classic models like VGG11 to modern networks like ResNet18, achieving significant accuracy gains with moderate computational overhead. The method’s empirical success is supported by a rigorous theoretical analysis, including a quantitative evaluation of the Hilbert curve’s superior, size-independent locality preservation. In summary, LDS3Pool represents a theoretically sound and empirically effective pooling method that enhances CNN generalization through a principled, quasi-random sampling framework. Full article

Artificial intelligence adoption in financial services presents uncertain implications for competitive dynamics, particularly for smaller institutions. The literature on AI in finance is growing, but there remains a notable absence regarding the impacts on small- and medium-sized financial services firms. We conduct a meta-analysis combining a systematic literature review, sentiment bibliometrics, and network analysis to examine how AI is transforming competition across different firm sizes in the financial sector. Our analysis of 160 publications reveals predominantly positive academic sentiment toward AI in finance (mean positive sentiment 0.725 versus negative 0.586, Cohen’s d = 0.790, p < 0.0001), with anticipatory sentiment increasing significantly over time ($\beta =2.10\times {10}^{-2},p=0.007$). However, network analysis reveals substantial conceptual fragmentation in the research discourse, with a low connectivity coefficient ($\varphi =0.125$) indicating that the field lacks unified terminology. These findings expose a critical knowledge gap: while scholars increasingly view AI as competitively advantageous, research has not coalesced around coherent models for understanding differential impacts across firm sizes. The absence of size-specific research leaves practitioners and policymakers without clear guidance on how AI adoption affects competitive positioning, particularly for smaller institutions that may face resource constraints or technological barriers. The research fragmentation identified here has direct implications for strategic planning, regulatory approaches, and employment dynamics in financial services. Full article

Background: Electrocardiogram (ECG) signals are crucial for cardiovascular diagnosis, but their analysis face challenges from noise contamination, compression difficulties due to their non-stationary nature, and the inherent complexity of its morphological components, particularly for low-amplitude P- and T-waves obscured by noise. Methodology: This study proposes a novel, multi-stage framework for ECG signal denoising, compressing, and component decomposition. The proposed framework leverages the sparsity of ECG signal to denoise and compress these signals using autoencoder-driven dictionary (AE-DD) with matching pursuit. In this work, a data-driven dictionary was developed using a regularized autoencoder. Appropriate trained weights along with matching pursuit were used to compress the denoised ECG segments. This study explored different weight regularization techniques: L1- and L2-regularization. Results: The proposed framework achieves remarkable performance in simultaneous ECG denoising, compression, and morphological decomposition. The L1-DAE model delivers superior noise suppression (SNR improvement up to 18.6 dB at $-3$ dB input SNR) and near-lossless reconstruction ($\mathrm{MSE}<{10}^{-5}$). The L1-AE dictionary enables high-fidelity compression (CR = 28:1 ratio, $\mathrm{MSE}\approx 0.58\times {10}^{-5}$, PRD = 2.1%), outperforming non-regularized models and traditional dictionaries (DCT/wavelets), while its trained weights naturally decompose into interpretable sub-dictionaries for P-wave, QRS complex, and T-wave enabling precise, label-free analysis of ECG components. Moreover, the learned sub-dictionaries naturally decompose into interpretable P-wave, QRS complex, and T-wave components with high accuracy, yielding strong correlation with the original ECG ($r=0.98$, $r=0.99$, and $r=0.95$, respectively) and very low MSE ($1.93\times {10}^{-5}$, $9.26\times {10}^{-4}$, and $3.38\times {10}^{-4}$, respectively). Conclusions: This study introduces a novel autoencoder-driven framework that simultaneously performs ECG denoising, compression, and morphological decomposition. By leveraging L1-regularized autoencoders with matching pursuit, the method effectively enhances signal quality while enabling direct decomposition of ECG signals into clinically relevant components without additional processing. This unified approach offers significant potential for improving automated ECG analysis and facilitating efficient long-term cardiac monitoring. Full article

The rapid growth of high-dimensional biometric data requires fast and accurate similarity search methods for real-time applications. This study proposes, for the first time, two efficient parallel implementations of the exact Sorting-based Nearest Neighbor (SNN) algorithm using OpenMP for CPUs and CUDA for GPUs. Comparative evaluation against conventional exact search methods—k-d tree and ball tree—on LFW embeddings, including FaceNet512 and VGG-Face, demonstrates an up to 58× speedup on GPUs while maintaining full accuracy. Analysis of the full recognition pipeline shows that parallelization reduces search times to about 27% of total processing, highlighting the method’s stability and efficiency for modern embeddings. These results confirm the applicability of the proposed approaches for real-time biometric identification, with potential extensions to streaming data, hybrid computing environments, and other high-dimensional representations. Full article