Search Results (1,527)

The aim of the research was to reduce the irregularity of mineral fertilizer granule flow by developing a tedder-vaulting breaker that prevents the formation of vaults over the sowing windows of the seeder hopper. Existing dosing devices for mineral fertilizers do not provide stable application of the required doses of mineral fertilizers due to vaulting as well as accumulation and sticking of fertilizers in hoppers. In order to achieve a stable and precise metering of high fertilizer doses, a crank tedder is suggested to be mounted inside the hopper. Its function is to break the constantly appearing dynamic vaults above the sowing windows and to crush the fertilizer clods, i.e., to provide the fertilizer sowing units with a continuous flow of material. Theoretical studies were carried out using methods of classical and applied mechanics, special sections of higher mathematics. The following optimal parameters were established: the tedder blade width 0.05–0.09 m, the radius of the elbow 0.028–0.034 m, the blade installation angle 23–27°, and the kinematic mode of the tedder k = 1.5–1.9. Experimental studies have shown that the use of a crank tedder provides a stable flow of mineral fertilizer granules through sowing windows and reduces the sowing unevenness between seeding units by 12–15% and sowing instability by 7–10%. At the same time, the degree of damage to granules of 1–5 mm size is insignificant and is within 2.8–3.5%. Full article

S355NL structural steel is extensively employed in bridges, ships, and power station equipment owing to its excellent tensile strength, weldability, and low-temperature toughness. However, pronounced fluctuations in its Charpy impact energy at low temperatures significantly compromise the reliability and service life of critical components. In this study, vacuum-induction-melted ingots of S355NL steel containing 0–0.086 wt.% rare earth cerium were prepared. The effects of Ce on microstructures, inclusions, and impact toughness were systematically investigated using optical microscopy (OM), scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), and Charpy V-notch testing. The results indicate that appropriate Ce additions (0.0011–0.0049 wt.%) refine the average grain size from 5.27 μm to 4.88 μm, reduce the pearlite interlamellar spacing from 204 nm to 169 nm, and promote the transformation of large-size Al₂O₃-MnS composite inclusions into fine, spherical, Ce-rich oxysulfides. Charpy V-notch tests at –50 °C reveal that 0.0011 wt.% Ce enhances both longitudinal (269.7 J) and transverse (257.4 J) absorbed energies while minimizing anisotropy (E_t/E_l  =  1.01). Conversely, excessive Ce addition (0.086 wt.%) leads to coarse inclusions and deteriorates impact performance. These findings establish an optimal Ce window (0.0011–0.0049 wt.%) for microstructural and inclusion engineering to enhance the low-temperature impact toughness of S355NL steel. Full article

Objectives: We compared the effects of two active re-warm-up protocols applied during halftime’s last three minutes, after a warm-up, testing, and a simulated first-half match. Methods: Twenty-two professional players from a first Portuguese division club were randomized into two re-warm-up protocols during a simulated match interval: (i) a strength, plyometrics, and balance protocol (SPBP); and (ii) a soccer-specific protocol (SSP). Players were assessed for a 20-m linear sprint and countermovement jump (CMJ) after the warm-up and the re-warm-up. Descriptive statistics and mixed ANOVA were performed, with effect size assessed using partial eta-squared. The Acute Readiness Monitoring Scale (ARMS) questionnaire was administered after the simulated match and re-warm-up and was analyzed using a multifactorial ANOVA. Results: No significant interaction effects were observed (p > 0.05). Comparing pre-match to post-re-warm-up, there was a slight decrease in sprint (significant) and jump performance (non-significant). Additionally, there were no between-protocol differences in perceived readiness (ARMS). Conclusions: After the three-minute re-warm-up protocols, similar results were observed in the 20-m sprint performance, CMJ, and perceived readiness when comparing SPBP and SSP. These re-warm-up protocols (SPBP and SSP) are practical to implement within a 3-min time window, and, given their apparent lack of differences, players’ preferences could be considered. However, the SSP is currently subject to restrictions that limit teams’ access to the field during this period. Future research should compare active re-warm-up protocols with passive controls to more clearly assess their effectiveness. Full article

Over the last ten years, transcatheter tricuspid valve interventions (TTVIs) have emerged as effective options for symptomatic patients with moderate-to-severe tricuspid regurgitation (TR) who are at prohibitive surgical risk. Successful application of these therapies depends on a patient-tailored, multimodal imaging workflow. Transthoracic and transesophageal echocardiography remain the first-line diagnostic tools, rapidly stratifying TR severity, mechanism, and right ventricular function, and identifying cases requiring further evaluation. Cardiac computed tomography (CT) then provides anatomical detail—quantifying tricuspid annular dimension, leaflet tethering, coronary artery course, and venous access anatomy—to refine candidacy and simulate optimal device sizing and implantation angles. In patients with suboptimal echocardiographic windows or equivocal functional data, cardiovascular magnetic resonance (CMR) offers gold-standard quantification of RV volumes, ejection fraction, regurgitant volume, and tissue characterization to detect fibrosis. Integration of echo-derived parameters, CT anatomical notes, and CMR functional assessment enables the heart team to better select patients, plan procedures, and determine the optimal timing, thereby maximizing procedural success and minimizing complications. This review describes the current strengths, limitations, and future directions of multimodality imaging in comprehensive evaluations of TTVI candidates. Full article

Style transfer technology has seen substantial attention in image synthesis, notably in applications like oil painting, digital printing, and Chinese landscape painting. However, it is often difficult to generate migrated images that retain the essence of paper-cutting art and have strong visual appeal when trying to apply the unique style of Chinese paper-cutting art to style transfer. Therefore, this paper proposes a new method for Chinese paper-cutting style transformation based on the Transformer, aiming at realizing the efficient transformation of Chinese paper-cutting art styles. Specifically, the network consists of a frequency-domain mixture block and a multi-level feature contrastive learning module. The frequency-domain mixture block explores spatial and frequency-domain interaction information, integrates multiple attention windows along with frequency-domain features, preserves critical details, and enhances the effectiveness of style conversion. To further embody the symmetrical structures and hollowed hierarchical patterns intrinsic to Chinese paper-cutting, the multi-level feature contrastive learning module is designed based on a contrastive learning strategy. This module maximizes mutual information between multi-level transferred features and content features, improves the consistency of representations across different layers, and thus accentuates the unique symmetrical aesthetics and artistic expression of paper-cutting. Extensive experimental results demonstrate that the proposed method outperforms existing state-of-the-art approaches in both qualitative and quantitative evaluations. Additionally, we created a Chinese paper-cutting dataset that, although modest in size, represents an important initial step towards enriching existing resources. This dataset provides valuable training data and a reference benchmark for future research in this field. 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

The crossover of the product gases hydrogen and oxygen in alkaline electrolyzer operation is a critical factor, severely limiting the operational window in terms of current density and pressure. In prior experiments, it was found that a large degree of oversaturation of the reaction products in the liquid electrolyte phase leads to high amounts of crossover. We are proposing to reduce this amount of oversaturation by introducing micro-cracks in the Zirfon diaphragm. These cracks are meant to induce the formation of hydrogen and oxygen bubbles on the respective sides, and thereby reduce the oversaturation and amount of crossover. In theory, the size of the bubble corresponds to the size of the cracks, and from our computational fluid dynamics simulations, we conclude that the bubbles should be as large as possible to minimize the ohmic resistance in the electrolyte phase. The results suggest that an increase in bubble diameter from 50 microns to 150 microns results in a 10% higher current density at a cell voltage of 2.1 V. Full article

Accurate detection of cotton pests and diseases is essential for agricultural productivity yet remains challenging due to complex field environments, the small size of pests and diseases, and significant occlusions. To address the challenges presented by these factors, a novel cotton disease and pest detection method is proposed. This method builds upon the YOLOv8 baseline model and incorporates a Multi-Scale Sliding Window Attention Module (MSFE) within the backbone architecture to enhance feature extraction capabilities specifically for small targets. Furthermore, a Depth-Separable Dilated Convolution Module (C2f-DWR) is designed to replace the existing C2f module in the neck of the network. By employing varying dilation rates, this modification effectively expands the receptive field and alleviates the loss of detailed information associated with the downsampling processes. In addition, a Multi-Head Attention Detection Head (MultiSEAMDetect) is introduced to supplant the original detection head. This new head utilizes diverse patch sizes alongside adaptive average pooling mechanisms, thereby enabling the model to adjust its responses in accordance with varying contextual scenarios, which significantly enhances its ability to manage occlusion during detection. For the purpose of experimental validation, a dedicated dataset for cotton disease and pest detection was developed. In this dataset, the improved model’s mAP50 and mAP50:95 increased from 73.4% and 46.2% to 77.2% and 48.6%, respectively, compared to the original YOLOv8 algorithm. Validation on two Kaggle datasets showed that mAP50 rose from 92.1% and 97.6% to 93.2% and 97.9%, respectively. Meanwhile, mAP50:95 improved from 86% and 92.5% to 87.1% and 93.5%. These findings provide compelling evidence of the superiority of the proposed algorithm. Compared to other advanced mainstream algorithms, it exhibits higher accuracy and recall, indicating that the improved algorithm performs better in the task of cotton pest and disease detection. Full article

Lung cancer is one of the most prevalent and deadly forms of cancer, accounting for a significant portion of cancer-related deaths worldwide. It typically originates in the lung tissues, particularly in the cells lining the airways, and early detection is crucial for improving patient survival rates. Computed tomography (CT) imaging has become a standard tool for lung cancer screening, providing detailed insights into lung structures and facilitating the early identification of cancerous nodules. In this study, an improved Faster R-CNN model is employed to detect early-stage lung cancer. To enhance the performance of Faster R-CNN, a novel dual-attention optimized multi-scale CNN (DA OMS-CNN) architecture is used to extract representative features of nodules at different sizes. Additionally, dual-attention RoIPooling (DA-RoIpooling) is applied in the classification stage to increase the model’s sensitivity. In the false-positive reduction stage, a combination of multiple 3D shift window transformers (3D SwinT) is designed to reduce false-positive nodules. The proposed model was evaluated on the LUNA16 and PN9 datasets. The results demonstrate that integrating DA OMS-CNN, DA-RoIPooling, and 3D SwinT into the improved Faster R-CNN framework achieves a sensitivity of 96.93% and a CPM score of 0.911. Comprehensive experiments demonstrate that the proposed approach not only increases the sensitivity of lung cancer detection but also significantly reduces the number of false-positive nodules. Therefore, the proposed method can serve as a valuable reference for clinical applications. Full article

Reliable short-term forecasts of hepatitis B incidence are indispensable for sizing national vaccine and antiviral procurement. However, predictive modelling is complicated when surveillance streams experience reporting delays and episodic under-reporting, as has occurred in Ukraine since 2022. We address this challenge by training a deliberately compact two-layer long short-term memory (LSTM) network on 72 monthly observations (January 2018–December 2023) drawn from the Public Health Center electronic registry and evaluating performance on a strictly held-out 12-month horizon (January–December 2024). Grid-search optimisation selected a 12-month sliding input window, 64 hidden units per layer, 0.20 dropout, the Adam optimiser, and early stopping. Walk-forward validation showed that the network attained mean squared errors of 411 for acute infection and 76 for chronic infection on the monthly series. When forecasts were aggregated to the cumulative scale, the mean absolute percentage error remained below 1%. This study presents the first peer-reviewed hepatitis B forecasts calibrated on Ukraine’s registry during a period of pronounced reporting instability, demonstrating that robust accuracy is attainable without missing-value imputation. Full article

The exponential growth of Distributed Denial-of-Service (DDoS) attacks in high-speed networks presents significant real-time detection and mitigation challenges. The existing detection frameworks are categorized into flow-based and packet-based detection approaches. Flow-based approaches usually suffer from high latency and controller overhead in high-volume traffic. In contrast, packet-based approaches are prone to high false-positive rates and limited attack classification, resulting in delayed mitigation responses. To address these limitations, we propose a real-time DDoS detection architecture that combines hardware-accelerated statistical preprocessing with GPU-accelerated deep learning models. The raw packet header information is transformed into multivariate time series data to enable classification of complex traffic patterns using Temporal Convolutional Networks (TCN), Long Short-Term Memory (LSTM) networks, and Transformer architectures. We evaluated the proposed system using experiments conducted under low to high-volume background traffic to validate each model’s robustness and adaptability in a real-time network environment. The experiments are conducted across different time window lengths to determine the trade-offs between detection accuracy and latency. The results show that larger observation windows improve detection accuracy using TCN and LSTM models and consistently outperform the Transformer in high-volume scenarios. Regarding model latency, TCN and Transformer exhibit constant latency across all window sizes. We also used SHAP (Shapley Additive exPlanations) analysis to identify the most discriminative traffic features, enhancing model interpretability and supporting feature selection for computational efficiency. Among the experimented models, TCN achieves the most balance between detection performance and latency, making it an applicable model for the proposed architecture. These findings validate the feasibility of the proposed architecture and support its potential as a real-time DDoS detection application in a realistic high-speed network. 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

As remote and hybrid work models continue to grow, the design of workspaces and their surrounding environments has gained even more importance. This study explores the impact of window design on the restorative potential of Prefabricated Movable Buildings (PMBs) of smart/co-working located in small villages. Using Immersive Virtual Reality (IVR), seven window configurations, varying in size, frame ratio, and number of glass panes, were evaluated. Participants’ sense of presence, defined as the subjective feeling of ‘being there’ in the virtual environment, and perceived restoration, referring mainly to the psychological (attention and emotions) and physiological (stress) resources recovery, were assessed using, respectively, Igroup Presence Questionnaire (IPQ) and the Perceived Restorativeness Scale (PRS). The overall IPQ results suggest that the virtual environment in this study provides a “High” sense of presence, highlighting the validity of IVR to evaluate architectural designs. The PRS results found that larger, uninterrupted windows with a higher Window-to-Wall Ratio and lower Frame Ratio significantly enhance participants’ perceived restoration. Restoration effects were also higher when offices were located in small villages rather than in business districts. These results highlight the importance of incorporating large windows in smart/co-working spaces within culturally rich small villages to promote worker well-being and office sustainability. Full article

The flight capability of drones expands the surveillance area and allows drones to be mobile platforms. Therefore, it is important to estimate the kinematic state of drones. In this paper, the kinematic state of a mini drone in flight is estimated based on the video captured by its camera. A novel frame-to-frame template-matching technique is proposed. The instantaneous velocity of the drone is measured through image-to-position conversion and frame-to-frame template matching using optimal windows. Multiple templates are defined by their corresponding windows in a frame. The size and location of the windows are obtained by minimizing the sum of the least square errors between the piecewise linear regression model and the nonlinear image-to-position conversion function. The displacement between two consecutive frames is obtained via frame-to-frame template matching that minimizes the sum of normalized squared differences. The kinematic state of the drone is estimated by a Kalman filter based on the velocity computed from the displacement. The Kalman filter is augmented to simultaneously estimate the state and velocity bias of the drone. For faster processing, a zero-order hold scheme is adopted to reuse the measurement. In the experiments, two 150 m long roadways were tested; one road is in an urban environment and the other in a suburban environment. A mini drone starts from a hovering state, reaches top speed, and then continues to fly at a nearly constant speed. The drone captures video 10 times on each road from a height of 40 m at a 60-degree camera tilt angle. It will be shown that the proposed method achieves average distance errors at low meter levels after the flight. Full article

This work introduces a novel approach to extract beam bridge mode shapes using the residual response between consecutive contact points of vehicles passing through a bridge. A comprehensive investigation is conducted on several critical parameters, including window size, vehicle velocity, road roughness, and beam damping property, as well as the influence of traffic flow. To enhance the mode shape extraction performance using the approximate expression of the contact points’ displacements under noisy disturbance, two new signal denoising methods, CEEMDAN-NSPCA and CEEMDAN-IWT, are proposed based on complete ensemble empirical mode decomposition (CEEMDAN). CEEMDAN-NSPCA integrates CEEMDAN with principal component analysis and a coefficient-based filtering strategy. While CEEMDAN-IWT utilizes an improved wavelet thresholding technique with adaptive threshold selection. The numerical simulations demonstrate that both methods could effectively attenuate high-frequency noise with small amplitudes and retain low-frequency components. Among them, CEEMDAN-IWT exhibits superior denoising performance and greater stability, making it particularly suitable for robust modal identification in noisy environments. Full article