Search Results (3,667)

Concrete-filled steel tube (CFST) columns are composite structural elements preferred in various engineering structures due to their superior properties compared to those of traditional structural elements. However, fire resistance analyses are complex due to CFST columns consisting of two components with different thermal and mechanical properties. Significant challenges arise because current design codes and guidelines do not provide clear guidance for determining the time-dependent fire performance of these composite elements. This study aimed to address the existing design gap by investigating the fire behavior of circular long CFST columns under axial compressive load and developing robust, accurate, and reliable design models to predict their fire performance. To this end, an up-to-date database consisting of 62 data-points obtained from experimental studies involving variable material properties, dimensions, and load ratios was created. Analytical design models were meticulously developed using two advanced soft computing techniques: artificial neural networks (ANNs) and genetic expression programming (GEP). The model inputs were determined as six main independent parameters: steel tube diameter (D), wall thickness (t_s), concrete compressive strength (f_c), steel yield strength (f_sy), the slenderness ratio (L/D), and the load ratio (μ). The performance of the developed models was comprehensively compared with experimental data and existing design models. While existing design formulas could not predict time-based fire performance, the developed models demonstrated superior prediction accuracy. The GEP-based model performed well with an R-squared value of 0.937, while the ANN-based model achieved the highest prediction performance with an R-squared value of 0.972. Furthermore, the ANN model demonstrated its excellent prediction capability with a minimal mean absolute percentage error (MAPE = 4.41). Based on the nRMSE classification, the GEP-based model proved to be in the good performance category with an nRMSE value of 0.15, whereas the ANN model was in the excellent performance category with a value of 0.10. Fitness function (f) and performance index (PI) values were used to assess the models’ accuracy; the ANN (f = 1.13; PI = 0.05) and GEP (f = 1.19; PI = 0.08) models demonstrated statistical reliability by offering values appropriate for the expected targets (f ≈ 1; PI ≈ 0). Consequently, it was concluded that these statistically convincing and reliable design models can be used to consistently and accurately predict the time-dependent fire resistance of axially loaded, circular, long CFST columns when adequate design formulas are not available in existing codes. Full article

A probabilistic–deterministic design of experiments (PDDoE) approach was employed to optimize laser-induced breakdown spectroscopy (LIBS) parameters for the quantitative determination of minor components in lead-based alloys. The PDDoE optimization identified 18 J laser pump lamp energy, 1 µs delay, and 1 µs exposure as optimal conditions, minimizing spectral dispersion (5–8%) and ensuring stable plasma formation. The acquired spectra were subsequently processed in an R-based automated workflow, where Linear, Lasso, and Ridge regression models were used to establish quantitative relationships between normalized line intensities and atomic absorption spectroscopy (AAS) reference data. The resulting models demonstrated high accuracy (R² = 0.97 for Sn, 0.985 for Sb, 0.982 for Bi, 0.919 for As, and 0.905 for Ag), with prediction errors (RMSE) below 10% and limits of quantification (LOQ) under 0.05 wt.%. Principal component analysis (PCA) applied to 43 historical (19th–20th century) and technogenic samples (19th–20th century) allowed us to isolate clusters of Pb–Sb alloys corresponding to secondary accumulator materials, alongside a diffuse group of nearly pure Pb specimens containing variable minor impurities. The combined PDDoE–LIBS–R analytical framework provides a reproducible, non-destructive, and chemometrically validated methodology for the quantitative characterization and classification of archeological and industrial lead alloys. Full article

Accurate and reliable short-term wind speed forecasting plays a crucial role in efficient operation and integration of wind energy generation. This research study introduces an innovative deep hybrid model that combines Convolutional Neural Networks (CNN) with Double Bidirectional Long Short-Term Memory (DBiLSTM) networks to enhance wind speed forecasting accuracy in Australia. Thirteen years of hourly wind speed data were collected from two wind-rich potential sites in Tasmania, Australia. The CNN component effectively captures local temporal patterns, while the DBiLSTM layers model long-range dependencies in both forward and backward directions. The proposed CNNDBiLSTM model was compared against three traditional benchmark models: Multiple Linear Regression (MLR), Support Vector Regression (SVR), and Categorical Boosting (CatBoost). The proposed framework can effectively support wind farm planning, operational reliability, and grid integration strategies within the renewable energy sector. A comprehensive evaluation framework across both Australian study sites (Flinders Island Airport, Scottsdale) showed that the CNNDBiLSTM consistently outperformed the baseline models. It achieved the highest correlation coefficients (r = 0.987–0.988), the lowest error rates (RMSE = 0.392–0.402, MAE = 0.294–0.310), and superior scores across multiple efficiency metrics (ENS, WI, LM). The CNNDBiLSTM demonstrated strong adaptability across coastal and inland environments, showing potential for real-world use in renewable-energy resource forecasting. The wind speed analysis and forecasting show Flinders with higher and consistent wind speed as a more viable option for large-scale wind energy generation than Scottsdale in Tasmania. Full article

Accurate typhoon track forecasting is vital for disaster mitigation in East China, a region frequently impacted by landfalling typhoons. Despite advances in numerical weather prediction, uncertainties remain high, especially within 48 h of landfall, due to complex interactions among tropical cyclones, the subtropical high, and mesoscale systems. This study applies Ensemble-based Sensitivity Analysis (ESA) within a high-resolution regional ensemble prediction system (Shanghai Weather And Risk Model System-Ensemble Prediction System, SWARMS-EN) to investigate forecast uncertainties of three representative typhoons—Gaemi, Bebinca, and Kong-rey—that made landfall in East China in 2024. Our results reveal consistent sensitivity patterns across diverse large-scale environments, particularly around the western flank of the subtropical high and in proximity to nearby low-pressure systems. Track uncertainty was closely tied to fluctuations in the steering flow, notably its zonal component. Moreover, binary typhoon interactions emerged as key drivers of forecast divergence. ESA effectively identified sensitive regions where small initial perturbations exert significant downstream influence on typhoon tracks. This study demonstrates the operational value of ESA for diagnosing forecast error sources and guiding targeted observations. By linking forecast uncertainty to physical mechanisms, this research enhances our understanding of typhoon predictability and supports the development of more adaptive and accurate regional forecasting systems. Full article

Modern target tracking systems rely on radar as a sensor to detect targets and generate raw track points. These raw track points are affected by the radar’s own noise and the asymmetric non-Gaussian noise resulting from the nonlinear transformation from polar coordinates to Cartesian coordinates. Without effective processing, such data cannot directly support highly reliable situational awareness, early warning decisions, or weapon guidance. Track filtering, as a core component of target tracking, plays an irreplaceable foundational role in achieving real-time, accurate, and stable estimation of moving target states. Traditional deep learning filtering algorithms struggle with capturing long-term dependencies in high-dimensional spaces, often exhibiting high computational complexity, slow response to transient signals, and compromised noise suppression due to their inherent architectural asymmetries. In order to address these issues and balance the model’s high accuracy, strong real-time performance, and robustness, a new trajectory filtering algorithm based on a temporal convolutional network (TCN), Residual Gated Recurrent Unit (ResGRU), and multi-head attention (MHA) is proposed. The TCN-ResGRU-MHA hybrid structure we propose combines the parallel processing advantages and detail-capturing ability of a TCN with the residual learning capability of a ResGRU, and introduces the MHA mechanism to achieve adaptive weighting of high-dimensional features. Using the root mean square error (RMSE) and Euclidean distance to evaluate the model effect, the experimental results show that the RMSE of TCN-ResGRU-MHA is 27.4621 (m) lower than CNN-GRU, which is an improvement of 15.99% in the complex scene of high latitude, and the distance is 37.906 (m) lower than CNN-GRU, which is an improvement of 18.65%. These results demonstrate its effectiveness in filtering and tracking tasks in high-latitude complex scenarios. Full article

This study confronts the significant challenges inherent in Traffic State Estimation (TSE) for rural arterial networks, where sparse sensor coverage and complex, dynamic traffic flows complicate effective management and safety assurance. Traditional TSE methodologies, often dependent on single-source data streams, fail to accurately model the intricate spatiotemporal dependencies present in such environments. This fundamental limitation precipitates critical safety hazards, including pervasive over speeding and dangerous queue spillback phenomena at intersections. To address these deficiencies, we introduce a novel hybrid intelligence framework that synergistically combines a Graph Attention Temporal Convolutional Network (GAT-TCN) with advanced Kalman Filter variants, specifically the Extended, Unscented, and Sliding Window Kalman Filters. The GAT-TCN component is engineered to excel at learning complex, non-linear correlations across both space and time through multi-source data fusion. Empirical validation conducted on a real-world rural toll corridor demonstrates that our proposed model achieves a statistically significant superiority over conventional benchmarks, as rigorously quantified by substantial reductions in both Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Beyond mere predictive accuracy, the framework delivers transformative safety enhancements by facilitating the proactive identification of hazardous events, enabling earlier detection of over speeding and queue spillback compared to existing methods. Consequently, this research provides a scalable and robust framework for proactive rural traffic management, fundamentally shifting the paradigm from achieving incremental predictive improvements to generating decisive, safety-actionable insights for infrastructure operators. Full article

Space-based gravitational wave detection imposes extremely high requirements on displacement measurement accuracy, with its core measurement components being laser interferometers and inertial sensors. The laser interferometers detect gravitational wave signals by measuring the distance between two test masses (TMs) housed within the inertial sensors. Spatial alignment errors of the TMs relative to the laser interferometers can severely degrade the interferometric performance, primarily by significantly amplifying tilt-to-length (TTL) coupling noise and reducing interferometric efficiency. This paper presents a systematic analysis of the coupling mechanisms between TM alignment errors and TTL coupling noise. We first establish a comprehensive TTL noise model that accounts for alignment errors, then verify and analyze it through optical simulations. This research ultimately clarifies the coupling mechanisms of TM alignment errors in the context of space-borne gravitational wave missions and determines the allowable alignment tolerance specifications required to meet the gravitational wave detection sensitivity requirements. This work provides critical theoretical foundations and design guidance for the ground alignment procedures and on-orbit performance prediction of future space-based gravitational wave detection missions. Full article

To address the core challenges of multivariate nonlinear coupling and long-term temporal dependency in 4D UAV trajectory prediction, this study proposes an innovative model named KAN-Former. On a 21-dimensional multimodal UAV dataset, KAN-Former achieves statistically significant improvements over all baseline models, reducing the mean squared error (MSE) by 8.96% compared to the standard Transformer and by 2.66% compared to the strongest physics-informed baseline (PITA), while decreasing the mean absolute error (MAE) by 7.43% relative to TimeMixer/PatchTST. The model adopts a collaborative architecture with two key components: first, a “vertical–horizontal” cross-dimensional attention mechanism—where the vertical branch models physical correlations among multivariate variables using hierarchical clustering priors, and the horizontal branch employs a blockwise dimensionality reduction strategy to efficiently capture long-term temporal dynamics; second, it represents the first application of Kolmogorov–Arnold decomposition in trajectory prediction, replacing traditional feedforward networks with learnable combinations of B-spline basis functions to approximate high-dimensional nonlinear mappings. Ablation studies verify the effectiveness of each module, with the KAN module alone reducing MSE by 6.59%. Moreover, the model’s feature clustering results align closely with UAV physical characteristics, significantly improving interpretability. The demonstrated improvements in accuracy, interpretability, and computational efficiency make KAN-Former highly suitable for real-world applications such as real-time flight control and air traffic management, providing reliable trajectory forecasts for decision-making systems. This work offers a new paradigm for trajectory prediction in complex dynamic systems, successfully integrating theoretical innovation with practical value. Full article

Cognitive radio networks (CRNs) rely heavily on spectral sensing to detect primary user (PU) activity, yet detection at low signal-to-noise ratios (SNRs) remains a major challenge. Hence, a novel “Deep Wavelet Cyclostationary Independent Gaussian Markov Fourier Transform Dempster–Shafer Network” is proposed. When the signal waveform is submerged within the noise envelope and residual correlation emerges in the noise, it violates white Gaussian assumptions, leading to misidentification of signal presence. To resolve this, the Adaptive Continuous Wavelet Cyclostationary Denoising Autoencoder (ACWC-DAE) is introduced, in which the Adaptive Continuous Wavelet Transform (ACWT), Cyclostationary Independent Component Analysis Detection (CICAD), and Denoising Autoencoder (DAE) are introduced into the first hidden layer of a Deep Q-Network (DQN). It restores the bursty signal structure, separates the structured noise, and reconstructs clean signals, leading to accurate signal detection. Additionally, bursty and fading-affected primary user signals become fragmented and dip below the noise floor, causing conventional fixed-window sensing to fail in accumulating reliable evidence for detection under intermittent and low-duty-cycle conditions. Therefore, the Adaptive Gaussian Short-Time Fourier Transform Dempster–Shafer Model (AGSTFT-DSM) is incorporated into the second DQN layer, Adaptive Gaussian Mixture Hidden Markov Modeling (AGMHMM) tracks the hidden activity states, Adaptive Short-Time Fourier Transform (ASFT) resolves brief signal bursts, and Dempster–Shafer Theory (DST) fuses uncertain evidence to infer occupancy, thereby detecting an accurate user signal. The results obtained by the proposed model have a low error and detection time of 0.12 and 30.10 ms and a high accuracy of 97.8%, revealing the novel insight that adaptive wavelet denoising, along with uncertainty-aware evidence fusion, supports reliable spectrum detection under low-SNR conditions where existing models fail. Full article

To address the industry pain points of high seed breakage rate and uncontrollable miss-filling rate, multiple-filling rate in traditional rapeseed roller-type precision centralized seed metering devices—while breaking the adaptation limitation of existing empirical hole designs for different small-particle-size crops—this study innovatively proposes a hole optimization scheme based on the Bezier curve and develops a roller-type precision centralized seed metering device suitable for rapeseed and small-particle-size crops. First, combined with the physical properties of rapeseed seeds (particle size 1.5~2.5 mm, high sphericity, strong fluidity) and agronomic requirements for precision seeding, a multi-mechanical coupling model for seed filling and dropping (synergistic effect of gravity–centrifugal force–air blowing force) was established. The regulatory mechanism of hole geometric parameters (wrap angle, width, height) on seeding performance was clarified, and the enhancement mechanism of the Bezier curve’s curvature continuity on seed movement stability was revealed from the theoretical level. On this basis, a three-factor quadratic orthogonal combination experiment of hole wrap angle, width, and height was conducted using EDEM discrete element software. The optimal hole parameter combination was obtained through multi-objective optimization (minimizing miss-filling rate, multiple-filling rate and maximizing seed-filling qualification rate): wrap angle 2.271° (error ± 0.2°), width 3.407 mm (error ± 0.1 mm), and height 2.254 mm (error ± 0.02 mm). Simulation results showed that under this parameter combination, the seed-filling qualification rate reached 99.122%, with the miss-filling rate and multiple-filling rate as low as 0.448% and 0.416%, respectively. Further bench test verification indicated that when the roller speed was in the range of 10~30 r/min, the seed breakage rate was consistently below 0.5%, and the seed-filling qualification rate remained above 94%. Among them, the comprehensive seeding performance was optimal at a speed of 15 r/min, with a miss-seeding rate of 0.65%, a multiple-seeding rate of 2.06%, and a breakage rate of 0.12%, fully meeting the agronomic requirements for rapeseed precision seeding, providing a theoretical basis and engineering reference for the digital and universal design of key components of precision seeders for small-particle-size crops. Full article

Investor sentiment has increasingly been recognized as a behavioural factor influencing asset prices beyond traditional rational asset pricing models, yet evidence from South Africa’s property remains limited. This study investigates the short-run and long-run relationship between investor sentiment and FTSE/JSE-listed property indices, to determine the influence of sentiment on property index pricing within the South African context. Using monthly data for selected JSE/FTSE property indices, a composite investor sentiment index was constructed through a principal component analysis (PCA) of multiple market-based sentiment proxies. Consequently, a Vector Error Correction Model (VECM) was estimated to examine both the long-run and short-run relationships, integrated with the VEC Granger causality tests to determine the direction of influence between variables. The findings report a novel relationship between investor sentiment and the FTSE/JSE property indices, as they provide new insights at the disaggregated level, which is overlooked in the literature. In the short run, the findings suggest that market psychology drives short-term property price adjustments. Moreover, in the long run, the relationship remains significant, indicating that this effect persists, underscoring the enduring influence of sentiment on market valuation. Additionally, the Granger causality results indicate uni-directional relationships, where investor sentiment drives listed property pricing and macroeconomic variables, reinforcing its predictive role. The study concludes that investor sentiment is a key determinant of South Africa’s listed property market, consistent with the rationale of behavioural finance theory, and underscores that investment decisions within this market are substantially influenced by investor psychology, contributing to property market volatility. Full article

Motion cueing algorithm (MCA) aims to reproduce the dynamic motion experience of real vehicles for users of driving simulators. Under rough or irregular road conditions, vehicles are subjected to severe shocks and vibrations. However, due to the inherent response delay and limited capability of motion platforms in reproducing high-frequency components, conventional MCA often suffers from slow response and poor tracking accuracy. This mismatch leads to dynamic inconsistency between the visual feedback and the motion cues provided to the driver, which can easily induce discomfort or even aggravate simulator sickness. To address these issues, this study proposes a route-preview MCA based on adaptive model predictive control (RPAMPC). A CNN–LSTM-based vehicle trajectory prediction model is developed by integrating convolutional and recurrent neural networks to exploit forward terrain information. Subsequently, a motion cueing prediction model incorporating actuator stroke and velocity states is formulated, and an AMPC-based MCA is designed to optimize the simulator platform motion under physical constraints. Experimental results on a Stewart motion simulation platform demonstrate that, compared with traditional MCA, the proposed algorithm achieves higher-quality motion cues and significantly reduces sensory errors under complex road conditions. Full article

Water resources are increasingly scarce, with groundwater overexploitation causing major declines in quantity and quality. Effective water accounting is essential for sustainable management, which requires measuring irrigation water use despite limited metering. Traditional modeling approaches suffer from errors when there are narrow spatial coverages. Digital agriculture and remote sensing offer alternatives by enabling large-scale, cost-effective, and near-real-time monitoring. However, issues of accuracy, methodological consistency, and integration with governance frameworks still restrict operational use. This review followed the PRISMA protocol, screening 1485 documents and selecting 79 studies on remote sensing for irrigation water accounting. A structured labeling process classified papers into Technological Readiness, Management Impact, Implementation Barriers, Policy Integration, and Innovation/Gaps. Findings show a strong focus on management benefits and technological innovation, while institutional and policy aspects remain limited. Although many studies addressed multiple themes, governance integration and real-world barriers were often overlooked. Research is concentrated in digitally advanced regions, with limited attention to water-scarce areas in the Global South. The review concludes that although remote sensing improves efficiency and data availability, adoption is challenged by institutional, regulatory, and methodological gaps. Interdisciplinary work, stronger validation, and stakeholder engagement are essential for transitioning these tools into operational components of integrated water management. Full article

Detecting underwater debris is important for monitoring the marine environment but remains challenging due to poor image quality, visual noise, object occlusions, and diverse debris appearances in underwater scenes. This study proposes UDD-YOLO, a novel detection framework that, for the first time, applies a diffusion-based model to underwater image enhancement, introducing a new paradigm for improving perceptual quality in marine vision tasks. Specifically, the proposed framework integrates three key components: (1) a Cold Diffusion module that acts as a pre-processing stage to restore image clarity and contrast by reversing deterministic degradation such as blur and occlusion—without injecting stochastic noise—making it the first diffusion-based enhancement applied to underwater object detection; (2) an AMC2f feature extraction module that combines multi-scale separable convolutions and learnable normalization to improve representation for targets with complex morphology and scale variation; and (3) a Unified-IoU (UIoU) loss function designed to dynamically balance localization learning between high- and low-quality predictions, thereby reducing errors caused by occlusion or boundary ambiguity. Extensive experiments are conducted on the public underwater plastic pollution detection dataset, which includes 15 categories of underwater debris. The proposed method achieves a mAP50 of 81.8%, with 87.3% precision and 75.1% recall, surpassing eleven advanced detection models such as Faster R-CNN, RT-DETR-L, YOLOv8n, and YOLOv12n. Ablation studies verify the function of every module. These findings show that diffusion-driven enhancement, when coupled with feature extraction and localization optimization, offers a promising direction for accurate, robust underwater perception, opening new opportunities for environmental monitoring and autonomous marine systems. Full article

Extracellular vesicles (EVs) are increasingly recognized as promising non-invasive biomarkers for cancer and other diseases, but their clinical translation remains limited by the lack of comprehensive characterization strategies. Spectroscopic approaches such as Fourier-transform infrared (FTIR) spectroscopy can provide a global biochemical fingerprint of intact EVs, but their interpretation requires advanced analytical tools. In this study, we applied an autoencoder-based framework to attenuated total reflection FTIR (ATR-FTIR) spectra of blood-derived components, including plasma, red blood cells (RBCs), RBC-ghosts, and EVs, comprising 278 samples collected from 135 patients, to obtain latent features capable of capturing biologically meaningful variability. The autoencoder compressed spectra into 12 latent features while preserving spectral information with low reconstruction error. Unsupervised UMAP projection of the latent features separated the blood components into different clusters, supporting their biological relevance. The model was then applied to EV spectra from patients with hepatocellular carcinoma (HCC) and cirrhotic controls. Four features significantly differed between the two groups, and an elastic-net regularized logistic model evaluated with a leave-one-out cross-validation framework retained a single latent feature, achieving an out-of-fold ROC AUC of 0.785 (95% CI 0.602–0.967), with performance broadly comparable to that typically reported for AFP, the most commonly used biomarker for HCC. This study provides the first proof-of-concept that an autoencoder can be applied to FTIR spectra of EVs, extracting biologically relevant latent features with potential application in cancer detection. Full article