Search Results (59)

Ice loads continue to pose challenges to the structural safety of offshore wind turbines (OWTs), while the rapid development of offshore wind power in cold regions is enabling the deployment of OWTs in deeper waters. To accurately simulate the dynamic response of an OWT under combined ice–wind loading, this paper proposes a Discrete Element Method–Wind Turbine Integrated Analysis (DEM-WTIA) framework. The framework can synchronously simulate discontinuous ice-crushing processes and aeroelastic–structural dynamic responses through a holistic turbine model that incorporates rotor dynamics and control systems. To address the issue of insufficient prediction accuracy for dynamic responses, we introduced a multivariate time series forecasting method that integrates a secondary decomposition strategy with a hybrid prediction model. First, we developed a parallel signal processing mechanism, termed Adaptive Complete Ensemble Empirical Mode Decomposition with Improved Singular Spectrum Analysis (CEEMDAN-ISSA), which achieves adaptive denoising via permutation entropy-driven dynamic window optimization and multi-feature fusion-based anomaly detection, yielding a noise suppression rate of 76.4%. Furthermore, we propose the F-Transformer prediction model, which incorporates a Frequency-Enhanced Channel Attention Mechanism (FECAM). By integrating the Discrete Cosine Transform (DCT) into the Transformer architecture, the F-Transformer mines hidden features in the frequency domain, capturing potential periodicities in discontinuous data. Experimental results demonstrate that signals processed by ISSA exhibit increased signal-to-noise ratios and enhanced fidelity. The F-Transformer achieves a maximum reduction of 31.86% in mean squared error compared to the standard Transformer and maintains a coefficient of determination (R²) above 0.91 under multi-condition coupled testing. By combining adaptive decomposition and frequency-domain enhancement techniques, this framework provides a precise and highly adaptable ultra-short-term response forecasting tool for the safe operation and maintenance of offshore wind power in cold regions. Full article

Driven by dual carbon targets, the scale of wind power integration has surged dramatically. However, its strong volatility causes insufficient short-term prediction accuracy, severely constraining grid security and economic dispatch. To address three key challenges in extracting temporal characteristics of strong volatility, adaptive fusion of multi-source features, and enhancing model interpretability, this paper proposes a Time-Domain Dual-Channel Adaptive Learning Model (TDDCALM). The model employs dual-channel feature decoupling: one Transformer encoder layer captures global dependencies while the raw state layer preserves local temporal features. After TCN-based feature compression, an adaptive weighted early fusion mechanism dynamically optimizes channel weights. The ACON adaptive activation function autonomously learns optimal activation patterns, with fused features visualized through visualization techniques. Validation on two wind farm datasets (A/B) demonstrates that the proposed method reduces RMSE by at least 8.89% compared to the best deep learning baseline, exhibits low sensitivity to time window sizes, and establishes a novel paradigm for forecasting highly volatile renewable energy power. Full article

With the advent of Industry 4.0, sensor-based human activity recognition has become increasingly vital for improving worker safety, enhancing operational efficiency, and optimizing workflows in Internet-of-Things (IoT) environments. This study introduces a novel deep learning-based framework for construction worker activity recognition, employing a deep residual convolutional neural network (ResNet) architecture integrated with multi-sensor fusion techniques. The proposed system processes data from multiple inertial measurement unit sensors strategically positioned on workers’ bodies to identify and classify construction-related activities accurately. A comprehensive pre-processing pipeline is implemented, incorporating Butterworth filtering for noise suppression, data normalization, and an adaptive sliding window mechanism for temporal segmentation. Experimental validation is conducted using the publicly available VTT-ConIoT dataset, which includes recordings of 16 construction activities performed by 13 participants in a controlled laboratory setting. The results demonstrate that the ResNet-based sensor fusion approach outperforms traditional single-sensor models and other deep learning methods. The system achieves classification accuracies of 97.32% for binary discrimination between recommended and non-recommended activities, 97.14% for categorizing six core task types, and 98.68% for detailed classification across sixteen individual activities. Optimal performance is consistently obtained with a 4-second window size, balancing recognition accuracy with computational efficiency. Although the hand-mounted sensor proved to be the most effective as a standalone unit, multi-sensor configurations delivered significantly higher accuracy, particularly in complex classification tasks. The proposed approach demonstrates strong potential for real-world applications, offering robust performance across diverse working conditions while maintaining computational feasibility for IoT deployment. This work advances the field of innovative construction by presenting a practical solution for real-time worker activity monitoring, which can be seamlessly integrated into existing IoT infrastructures to promote workplace safety, streamline construction processes, and support data-driven management decisions. Full article

Collision welding represents a promising solid-state joining technique for combining both similar and dissimilar metals without the thermal degradation of mechanical properties typically associated with fusion-based methods. This makes it particularly attractive for lightweight structural applications. In the context of collision welding, it is typically assumed that ideally smooth and defect-free surface conditions exist prior to welding. However, this does not consistently reflect industrial realities, where surface imperfections such as scratches are often unavoidable. Despite this, the influence of such surface irregularities on weld integrity and quality has not been comprehensively investigated to date. In this study, collision welding is applied to the material combination of AA6110A-T6 and AA6060-T6. Initially, the process window for this material combination is determined by systematically varying the collision velocity and collision angle—the two primary process parameters—using a special model test rig. Subsequently, the effect of surface imperfections in the form of defined scratch geometries on the resulting weld quality is investigated. In addition to evaluating the welding ratio and tensile shear strength, weld quality is assessed through scanning electron microscopy (SEM) of the bonding interface and high-speed imaging of jet formation during the collision process. Full article

Background/Objectives: Spinal muscular atrophy (SMA) is a neuromuscular disorder frequently associated with progressive scoliosis requiring posterior spinal fusion (PSF). While Nusinersen offers significant clinical benefit, its intrathecal administration is challenging in patients with extensive spinal instrumentation and solid fusion. This study aimed to evaluate the safety, feasibility, and patient acceptance of lumbar laminectomy as a method to restore intrathecal access for repeated Nusinersen delivery in this population. Methods: A retrospective review was conducted in eleven patients with SMA who underwent lumbar laminectomy following prior PSF and confirmed radiographic fusion. Surgical data, injection outcomes, and patient-reported experiences were collected. A structured questionnaire assessed technical success, imaging requirements, sedation, functional response, and satisfaction. Results: Nine out of eleven patients (81.8%) successfully initiated intrathecal Nusinersen therapy through the laminectomy window, receiving a mean of 11.7 injections (range: 10–14). Imaging guidance was used in five cases; three required sedation or anesthesia. Intraoperative dural tears occurred in three patients and were managed without complications. Eight out of nine treated patients reported subjective motor improvement and expressed willingness to undergo the procedure again. No hardware revisions or major adverse events were observed during a mean follow-up of 48.8 months. Conclusions: Lumbar laminectomy is a viable and well-tolerated technique to establish intrathecal access in SMA patients with prior PSF. This approach enables sustained drug delivery and may remain clinically relevant as new intrathecal therapies emerge. Full article

Accurate indoor positioning services have become increasingly important in modern applications. Various new indoor positioning methods have been developed. Among them, visual–inertial odometry (VIO)-based techniques are notably limited by lighting conditions, while ultrawideband (UWB)-based algorithms are highly susceptible to environmental interference. To address these limitations, this study proposes a hybrid indoor positioning algorithm that combines UWB and VIO. The method first utilizes a tightly coupled UWB/inertial measurement unit (IMU) fusion algorithm based on a sliding-window factor graph to obtain initial position estimates. These estimates are then combined with VIO outputs to formulate the system’s motion and observation models. Finally, an extended Kalman filter (EKF) is applied for data fusion to achieve optimal state estimation. The proposed hybrid positioning algorithm is validated on a self-developed mobile platform in an indoor environment. Experimental results show that, in indoor environments, the proposed method reduces the root mean square error (RMSE) by 67.6% and the maximum error by approximately 67.9% compared with the standalone UWB method. Compared with the stereo VIO model, the RMSE and maximum error are reduced by 55.4% and 60.4%, respectively. Furthermore, compared with the UWB/IMU fusion model, the proposed method achieves a 50.0% reduction in RMSE and a 59.1% reduction in maximum error. Full article

Synthetic aperture radar (SAR) is an all-weather and all-day imaging technique for Earth observation. Achieving efficient observation, high resolution, and wide swath coverage have remained critical development goals in SAR technology, which inherently require extended synthetic aperture time. However, various non-ideal factors, including atmospheric disturbances, orbital perturbations, and antenna vibrations. degrade imaging performance, causing defocusing and ghost targets. Furthermore, the long synthetic time and large imaging swath further enlarge the temporal and spatial variability of these factors and seriously degrade the imaging effect. These inherent challenges make autofocusing indispensable for SAR imaging with a long synthetic time and large swath. In this paper, a novel autofocus method specifically designed to address these non-ideal factors is proposed for SAR imaging with a long synthetic time and large swath. The innovation of the method mainly consists of two parts. The first is the autofocus for multiple non-ideal factors, which is accomplished by an improved phase gradient autofocus (PGA) equipped with amplitude error estimation and discrete windowing. PGA with amplitude error estimation can solve the problem of defocus, and discrete windowing can focus the energy of paired echoes. The second is an error fusion and interpolation method for a long synthetic time and large swath. This method fuses errors among sub-apertures in the long synthetic time and can fulfill autofocus for blocks where strong scatterers are not sufficient in the large swath. The proposed method can effectively achieve SAR focusing with a long synthetic time and large swath, considering spatial and temporal variant non-ideal factors. Point target simulations and distributed target simulations based on real scenarios are conducted to validate the proposed method. Full article

The accurate detection of surface bolt defects in railway steel truss bridges plays a vital role in maintaining structural integrity. Conventional manual inspection techniques require extensive labor and introduce subjective assessments, frequently yielding variable results across inspections. While UAV-based approaches have recently been developed, they still encounter significant technical obstacles, including small target recognition, background complexity, and computational limitations. To overcome these challenges, CSEANet is introduced—an improved YOLOv8-based framework tailored for bolt defect detection. Our approach introduces three innovations: (1) a sliding-window SAF preprocessing method that improves small target representation and reduces background noise, achieving a 0.404 mAP improvement compared with not using it; (2) a refined network architecture with BSBlock and MBConvBlock for efficient feature extraction with reduced redundancy; and (3) a novel BoltFusionFPN module to enhance multi-scale feature fusion. Experiments show that CSEANet achieves an mAP@50:95 of 0.952, confirming its suitability for UAV-based inspections in resource-constrained environments. This framework enables reliable, real-time bolt defect detection, supporting safer railway operations and infrastructure maintenance. Full article

In the context of the clean and low-carbon transformation of power systems, addressing the challenge of day-ahead electricity market price prediction issues triggered by the strong stochastic volatility of power supply output due to high-penetration renewable energy integration, as well as problems such as limited dataset scales and short market cycles in test sets associated with existing electricity price prediction methods, this paper introduced an innovative prediction approach based on a multi-modal feature fusion and BiGRUSA-ResSE-KAN deep learning model. In the data preprocessing stage, maximum–minimum normalization techniques are employed to process raw electricity price data and exogenous variable data; the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) methods are utilized for multi-modal decomposition of electricity price data to construct a multi-scale electricity price component matrix; and a sliding window mechanism is applied to segment time-series data, forming a three-dimensional input structure for the model. In the feature extraction and prediction stage, the BiGRUSA-ResSE-KAN multi-branch integrated network leverages the synergistic effects of gated recurrent units combined with residual structures and attention mechanisms to achieve deep feature fusion of multi-source heterogeneous data and model complex nonlinear relationships, while further exploring complex coupling patterns in electricity price fluctuations through the knowledge-adaptive network (KAN) module, ultimately outputting 24 h day-ahead electricity price predictions. Finally, verification experiments conducted using test sets spanning two years from five major electricity markets demonstrate that the introduced method effectively enhances the accuracy of day-ahead electricity price prediction, exhibits good applicability across different national electricity markets, and provides robust support for electricity market decision making. Full article

Spoken Language Identification (SLID)-based applications have become increasingly important in everyday life, driven by advancements in artificial intelligence and machine learning. Multilingual countries utilize the SLID method to facilitate speech detection. This is accomplished by determining the language of the spoken parts using language recognizers. On the other hand, when working with multilingual datasets, the presence of multiple languages that have a shared origin presents a significant challenge for accurately classifying languages using automatic techniques. Further, one more challenge is the significant variance in speech signals caused by factors such as different speakers, content, acoustic settings, language differences, changes in voice modulation based on age and gender, and variations in speech patterns. In this study, we introduce the DBODL-MSLIS approach, which integrates biomimetic optimization techniques inspired by natural intelligence to enhance language classification. The proposed method employs Dung Beetle Optimization (DBO) with Deep Learning, simulating the beetle’s foraging behavior to optimize feature selection and classification performance. The proposed technique integrates speech preprocessing, which encompasses pre-emphasis, windowing, and frame blocking, followed by feature extraction utilizing pitch, energy, Discrete Wavelet Transform (DWT), and Zero crossing rate (ZCR). Further, the selection of features is performed by DBO algorithm, which removes redundant features and helps to improve efficiency and accuracy. Spoken languages are classified using Bayesian optimization (BO) in conjunction with a long short-term memory (LSTM) network. The DBODL-MSLIS technique has been experimentally validated using the IIIT Spoken Language dataset. The results indicate an average accuracy of 95.54% and an F-score of 84.31%. This technique surpasses various other state-of-the-art models, such as SVM, MLP, LDA, DLA-ASLISS, HMHFS-IISLFAS, GA base fusion, and VGG-16. We have evaluated the accuracy of our proposed technique against state-of-the-art biomimetic computing models such as GA, PSO, GWO, DE, and ACO. While ACO achieved up to 89.45% accuracy, our Bayesian Optimization with LSTM outperformed all others, reaching a peak accuracy of 95.55%, demonstrating its effectiveness in enhancing spoken language identification. The suggested technique demonstrates promising potential for practical applications in the field of multi-lingual voice processing. Full article

Three-Dimensional High-Efficiency Video Coding (3D-HEVC) efficiently compresses 3D video by incorporating depth map encoding techniques. However, the quadtree partitioning of depth map coding units (CUs) greatly increases computational complexity, contributing to over 90% of the total encoding time. To overcome the limitations of existing methods in complex edge modeling and partitioning efficiency, this paper presents Swin-Hier Net, a hierarchical CU partitioning prediction model based on the Swin Transformer. First, a multi-branch feature fusion architecture is designed: the Swin Transformer’s shifted window attention mechanism extracts global contextual features, lightweight CNNs capture local texture details, and traditional edge/variance features enhance multi-scale representation. Second, a recursive hierarchical decision mechanism dynamically activates sub-CU prediction branches based on the partitioning probability of parent nodes, ensuring strict compliance with the HEVC standard quadtree syntax. Additionally, a hybrid pooling strategy and dilated convolutions improve edge feature retention. Experiments on 3D-HEVC standard test sequences show that, compared to exhaustive traversal methods, the proposed algorithm reduces encoding time by 72.7% on average, lowers the BD-Rate by 1.16%, improves CU partitioning accuracy to 94.5%, and maintains a synthesized view PSNR of 38.68 dB (baseline: 38.72 dB). The model seamlessly integrates into the HTM encoder, offering an efficient solution for real-time 3D video applications. Full article

In the context of Additive Manufacturing (AM), particularly the Laser Powder Bed Fusion (L-PBF) technique, optimizing process parameters is essential for achieving dense, defect-free materials. This study investigates the optimization of L-PBF process parameters for a Ni-Cr-Mo-Nb-based superalloy using an integrated three-stage methodology. Stage A applies Grey Relational Analysis to identify the most favourable parameter sets. Stage B uses Response Surface Methodology to develop regression models that correlate process parameters with material characteristics, introducing the specific energy of layer fusion as a key factor. Stage C employs the Gradient Ascent Method to determine the global optimum using a desirability function. The proposed approach reduces the number of required experiments while ensuring optimal mechanical properties: yield strength of 774.73 ± 4.94 MPa, tensile strength of 1022.83 ± 5.19 MPa, and elongation at break of 23.1 ± 0.70%, with minimal LoF area (0.003 mm²) and gas pore diameter (0.02 mm). The results demonstrate that integrating Grey Relational Analysis, Response Surface Methodology, and the Gradient Ascent Method effectively identifies the printability window, accelerating material characterization. Full article

This paper presents a novel 3D Gaussian Splatting (3DGS)-based Simultaneous Localization and Mapping (SLAM) system that integrates Light Detection and Ranging (LiDAR) and vision data to enhance dynamic scene tracking and reconstruction. Existing 3DGS systems face challenges in sensor fusion and handling dynamic objects. To address these, we introduce a hybrid uncertainty-based 3D segmentation method that leverages uncertainty estimation and 3D object detection, effectively removing dynamic points and improving static map reconstruction. Our system also employs a sliding window-based keyframe fusion strategy that reduces computational load while maintaining accuracy. By incorporating a novel dynamic rendering loss function and pruning techniques, we suppress artifacts such as ghosting and ensure real-time operation in complex environments. Extensive experiments show that our system outperforms existing methods in dynamic object removal and overall reconstruction quality. The key innovations of our work lie in its integration of hybrid uncertainty-based segmentation, dynamic rendering loss functions, and an optimized sliding window strategy, which collectively enhance robustness and efficiency in dynamic scene reconstruction. This approach offers a promising solution for real-time robotic applications, including autonomous navigation and augmented reality. Full article

ETV6::RUNX1-positive pediatric acute lymphoblastic leukemia frequently has a prenatal origin and follows a two-hit model: a first somatic alteration leads to the formation of the oncogenic fusion gene ETV6::RUNX1 and the generation of a preleukemic clone in utero. Secondary hits after birth are necessary to convert the preleukemic clone into clinically overt leukemia. However, prenatal factors triggering the first hit have not yet been determined. Here, we explore the influence of maternal factors during pregnancy on the prevalence of the ETV6::RUNX1 fusion. To this end, we employed a nested interventional cohort study (IMPACT-BCN trial), including 1221 pregnancies (randomized into usual care, a Mediterranean diet, or mindfulness-based stress reduction) and determined the prevalence of the fusion gene in the DNA of cord blood samples at delivery (n = 741) using the state-of-the-art GIPFEL (genomic inverse PCR for exploration of ligated breakpoints) technique. A total of 6.5% (n = 48 of 741) of healthy newborns tested positive for ETV6::RUNX1. Our multiple regression analyses showed a trend toward lower ETV6::RUNX1 prevalence in offspring of the high-adherence intervention groups. Strikingly, corticosteroid use for lung maturation during pregnancy was significantly associated with ETV6::RUNX1 (adjusted OR 3.9, 95% CI 1.6–9.8) in 39 neonates, particularly if applied before 26 weeks of gestation (OR 7.7, 95% CI 1.08–50) or if betamethasone (OR 4.0, 95% CI 1.4–11.3) was used. Prenatal exposure to corticosteroids within a critical time window may therefore increase the risk of developing ETV6::RUNX1+ preleukemic clones and potentially leukemia after birth. Taken together, this study indicates that ETV6::RUNX1 preleukemia prevalence may be modulated and potentially prevented. Full article

Friction stir welding (FSW) is a pivotal technology with ongoing relevance across industries. Renowned for its ability to join materials with dissimilar melting points while mitigating thermal distortions, FSW offers relevant advantages over traditional fusion welding. However, the adoption of FSW for high-strength alloys poses notable challenges, including: (i) accelerated tool wear, (ii) the need for special tool features tailored to these alloys, and (iii) a narrow process window. This review provides a comprehensive overview of FSW as an advanced technique for joining metal alloys for several industrial fields. Emphasis is on materials such as Mg-, Cu-, Ti-, and Ni-based alloys, automotive steels, stainless steels, and maraging steels. The research highlights the critical influence of tool design—main dimensions, features, and materials—and process parameters—rotational and welding speeds, tilt angle, and plunge depth or vertical load—also considering their influences on defect formation. Detailed insights are provided into material flow and the formation of the different weld regions, including SZ, TMAZ, and HAZ. Full article