Search Results (1,769)

Variations in mechanical load constitute a dominant source of domain shift in data-driven fault diagnosis of rotating machinery, causing models trained under one operating condition to degrade sharply when deployed under another. This work addresses the problem through unsupervised domain adaptation (UDA)—which transfers diagnostic knowledge from a labeled source condition to an unlabeled target condition by aligning their feature distributions—and introduces Padé Approximant Neural Networks (PadéNets) as compact yet highly expressive feature extractors. One-dimensional PadéNet encoders are embedded into three established adaptation frameworks—Deep CORAL, Domain-Adversarial Neural Networks (DANNs), and Conditional Domain-Adversarial Networks (CDANs)—to learn load-invariant representations without any labeled target data. On the Case Western Reserve University benchmark, where the models operate directly on raw time-domain vibration signals, replacing conventional convolutional encoders with PadéNets consistently improves cross-load diagnostic accuracy, reaching up to 99.28% average target-domain accuracy at a low parameter count. To assess generalization to a more demanding setting, the CDAN–PadéNet configuration is further evaluated on frequency-domain representations of the Paderborn University dataset, where domain shift arises from simultaneous variation of load torque and radial force on bearings with real accelerated-lifetime damage, attaining 99.84% average accuracy across six cross-condition transfer tasks while requiring fewer parameters than competing methods. These results establish PadéNet-enhanced UDA as an accurate, broadly applicable approach for robust bearing fault diagnosis under varying operating conditions, with a reduced parameter count suited to resource-constrained embedded platforms. Full article

Rice cultivation is a major source of methane (CH₄) emission in the agricultural sector, with a significantly higher global warming potential than carbon dioxide. Accurate and scalable quantification of CH₄ from rice paddies is essential for carbon accounting. This study presents an automated framework for estimating rice CH₄ emissions from irrigated paddies in the central plain of Thailand, integrating multi-sensor Synthetic Aperture Radar (SAR) observations with the IPCC methodology. The framework combines Sentinel-1 C-band SAR time series for phenological detection, ALOS-2 PALSAR-2 L-band full-polarimetric SAR for water regime classification, and IPCC water-scaling factors corresponding to Continuous Flooding, Single Drainage, or Multiple Drainage regimes. Evaluated across five stratified holdout sets, the phenology detection algorithm achieved planting and harvesting date Mean Absolute Errors of 6.1 ± 1.4 and 8.3 ± 1.7 days, with a 97.0% ± 2.7% operational detection rate. Water regime classification employed rice growth stage-specific Support Vector Machine classifiers with Radial Basis Function kernels (SVM-RBF), achieving per-stage test Balanced Accuracy ranging from 0.59 to 0.89. End-to-end integration using a four-track counterfactual decomposition yielded a full-pipeline mean absolute error of 18.5 ± 4.5 $\mathrm{kg}{\mathrm{CH}}_4{\mathrm{ha}}^{-1}$ (21.4% of the mean ground-based CH₄ calculation) and a mean bias of 3.5 ± 5.8 $\mathrm{kg}{\mathrm{CH}}_4{\mathrm{ha}}^{-1}$. Water level classification was confirmed as the dominant algorithmic uncertainty source, while the IPCC Tier 1 emission factor structural range (−32% to +48% of the default) exceeded all algorithmic errors combined. The proposed framework provides a spatially explicit approach for integrating multi-frequency SAR data into IPCC-compliant methane estimation, supporting Monitoring, Reporting, and Verification applications. Full article

To accurately characterize the effects of polydisperse particle sizes, multimineral composition, and angular morphology on the packing structure of lunar regolith, a two-dimensional sequential packing method based on random convex octagons is proposed. The method establishes a particle parameter system using data from Chang’e-5 samples and generates polygonal particle models with controllable angular features through radial perturbation. On this basis, a sequential packing algorithm based on available arc analysis is developed. Non-overlapping particle insertion is achieved via geometric envelope constraints, and progressive filling is realized through effective arc sampling. Meanwhile, a packing control coefficient is introduced to enable continuous regulation of packing density. Results show that the proposed method can generate highly dense particle assemblies, with a maximum packing density of 0.8757 and an average coordination number of approximately 3.18, capturing the structural characteristics of “high compactness–low coordination number” in polydisperse angular particle systems. The algorithm exhibits a computational complexity of O(N^1.628), demonstrating high efficiency. Furthermore, contact area and contact strength are quantitatively characterized through contact contour extraction and an equivalent bow-shaped model. Radial distribution function and contact statistics indicate that the generated structures possess good randomness and physical consistency. The proposed method provides a high-fidelity mesoscopic structure generation approach for discrete element modeling (DEM) of lunar regolith and establishes a reliable foundation for analyzing the mechanical behavior of granular systems. Full article

Landslides are a significant natural hazard in regions with complex topographic, geological, and climatic conditions, where they can constrain sustainable territorial development and threaten infrastructure, land use, and environmental safety. This study aims to assess and map landslide susceptibility in Southern Primorye in order to support hazard-informed territorial planning and risk reduction. The analysis integrates vegetation, precipitation, geological, and topographic predictors with documented landslide occurrence data. A presence-only landslide susceptibility modeling approach was applied using the OneClassSVM algorithm with a radial basis function kernel. The results show that the highest susceptibility is associated with lower slope segments and coastal landforms composed of loose unconsolidated deposits and partly covered by sparse woodland. Surface runoff, subsurface flow, lithological conditions, and precipitation patterns were identified as the principal factors contributing to slope instability, while field observations confirmed that anthropogenic slope cutting related to road infrastructure may act as an additional local trigger. The model demonstrated moderate but acceptable predictive performance and allowed the delineation of areas with elevated landslide susceptibility. The resulting susceptibility map provides a regional-scale basis for more sustainable land-use planning, infrastructure placement, and landslide risk mitigation in Southern Primorye and in other regions with comparable environmental conditions. Full article

In cage aquaculture, precise estimation of fish biomass is critically important for determining appropriate feeding strategies and evaluating production capacity. Currently, prevailing fish counting approaches heavily rely on acoustic or optical technologies. However, the accuracy and reliability of the obtained data are largely compromised by factors such as fish occlusion and water turbidity in practical cage farming environments. To address this limitation, this study proposed a novel method for estimating fish population size deduced from dynamic feeding information, based on the model integrated environmental and biological factors, feed intake and biomass. A 10-week feeding experiment was carried out to collect multidimensional data including feed intake, growth parameters, and environmental variables to construct a dataset correlating feeding amount with primary influential factors. Herein a bioenergetics-informed radial basis function neural network, optimized via particle swarm optimization (BE-PSO-RBF), was developed based on those empirical data. Model validation using 47 independent test samples showed that the hybrid model achieved a mean absolute error (MAE) of 26.82, a root mean square error (RMSE) of 35.62, and a mean absolute percentage error (MAPE) of 4.14%, confirming its robust generalization performance. These findings suggest that feed-intake-based population estimation may provide a feasible complementary approach for fish population assessment under cage farming conditions similar to those investigated in this study. Full article

In multi-UAV bearing-only active tracking, the estimation and control performance is fundamentally determined by the target-centered bearing geometry, as passive angle-of-arrival measurements provide directional information without direct range information. To overcome this limitation, this paper formulates the problem as prescribed-shell bearing-geometry self-organization, in which the radial sensing scale is regulated to an admissible shell while the angular bearing distribution is actively reshaped on that shell. A shell-compatible moment–volume bearing-enclosure potential is first constructed directly on unit bearing directions, decoupling angular geometry improvement from unsafe range reduction and encoding directed balance, angular isotropy, and noncoplanar enclosure. To realize the resulting direction-space descent via physical UAV motion, a radius-normalized tangential lifting mechanism is derived from bearing-direction kinematics, eliminating radius-dependent angular-rate bias. The nominal radial–tangential command is then executed through a bearing-geometry-preserving ECBF-QP that embeds a predefined-time radial shell-reaching constraint, enforces target standoff, inter-UAV separation, and input constraints, and preserves shell reaching and tangential geometry improvement whenever feasible. Closed-loop analysis establishes shell reaching, practical bearing-geometry descent, safety forward invariance, and signal boundedness. Finally, improved bearing geometry, prescribed-shell convergence, preserved safety margins, reduced disruption from safety filtering, and real-world implementability are demonstrated via simulations, ablation studies, ROS-based validation, and a real-world flight experiment, which provide a promising approach for multi-UAV bearing-only active tracking. Full article

Wellbore plugging has become the primary constraint on gas production for numerous oil, gas, and geothermal wells in China. To enhance productivity in mature wells, a novel straight-swirling integrated jet (SSIJ) deplugging tool was designed, incorporating a converging-diverging jet (CDJ) nozzle. A combined approach of numerical simulation and experiments was employed to optimize the tool structure and evaluate the effects of different operational parameters on its blockage-removal performance. Structural optimization identified an impeller spinning angle of 540° and an impeller thickness of 12 mm as the optimal parameters, which significantly improve the three-dimensional velocity peaks and cavitation generation capability. Compared with the CDJ nozzle, the SSIJ tool produces substantially higher tangential and radial velocity components, with peak tangential and radial velocities reaching 22 m/s and 45 m/s, respectively, under the optimized conditions. The numerical results show that the peak impact pressure reaches 2.7 MPa at a standoff distance of 12 mm, while the optimal standoff distance, considering both impact magnitude and effective coverage area, is determined to be 16 mm (4 times the outlet diameter). Furthermore, indoor validation experiments under a pump pressure of 20 MPa demonstrate that the tool completely removes the artificial scale layer from the tubing inner wall within 2 min of continuous flushing, leaving no visible residue. This study provides a quantitative reference for the design and process optimization of jet blockage-removal tools. Full article

Rural distribution networks increasingly face voltage quality challenges due to high penetration of distributed renewable energy, heterogeneous rural load behavior, and long radial feeder structures with limited voltage regulation capability. Photovoltaic generation variability and agricultural load fluctuations can lead to voltage rise, reverse power flow, and branch congestion, particularly in weak rural grids. Conventional deterministic voltage control approaches relying on tap changers and capacitor banks often struggle to maintain stable voltage profiles under stochastic operating conditions. This paper proposes a risk-aware coordinated operation framework for rural multi-energy microgrids that integrates stochastic scenario modeling, voltage state perception, and adaptive optimization-based control. Renewable generation uncertainty and rural load variability are represented through correlated scenario generation and Wasserstein-distance-based scenario reduction, where 100 raw joint photovoltaic-load trajectories are reduced to 20 representative scenarios after convergence and distributional-fidelity tests. A stochastic optimization model is developed to coordinate photovoltaic inverters, battery energy storage systems, demand-side flexibility, and reactive compensation devices while satisfying network power-flow, voltage-security, storage, and communication-delay-aware implementation constraints. To mitigate extreme voltage deviation events, the framework incorporates a Conditional Value-at-Risk formulation that penalizes tail-risk voltage violations and maintains voltages within a preferred operating band of $0.97$–$1.03$ p.u. Case studies on a modified IEEE 33-bus rural distribution system with $2.00$ MW of photovoltaic capacity and $2.50$ MWh of battery storage demonstrate consistent performance improvements across deterministic, risk-neutral stochastic, chance-constrained, and robust baselines. The proposed strategy reduces peak branch loading from $0.95$ in the deterministic benchmark to $0.72$, while the $95\mathrm{th}$ percentile voltage deviation risk decreases from $0.0071$ p.u.${}^2$ to $0.0020$ p.u.${}^2$. Sensitivity, scenario-convergence, scalability, and seasonal representative-day analyses further confirm that the CVaR layer suppresses rare but severe voltage excursions without imposing excessive curtailment or computational burden. Full article

For radial distribution systems (RDSs) to operate efficiently, reliably, and sustainably, distributed generation (DG), capacitor banks (CBs), and network reconfiguration (NR) must be optimally allocated and sized. The main objectives considered in this research are minimizing real power losses, improving voltage profiles, enhancing energy utilization efficiency, and strengthening the operational reliability of distribution networks. To address this challenge, an Improved Frilled Lizard Optimizer (IFLO) is proposed to determine the optimal placement and sizing of DGs, CBs, and NR while satisfying system operational constraints. FLO is inspired by the adaptive survival and movement characteristics of frilled lizards in their natural ecosystem. The optimization mechanism of FLO is driven by hunting behavior for broad exploration and tree-climbing behavior for localized movement, enabling effective search and exploitation of promising regions. The IFLO introduces a defensive strategy phase, mimicking the lizard’s survival responses, and an adaptive local search phase, which models agile movement and stabilization behaviors. These enhancements improve the algorithm’s capability to reduce power losses, improve voltage regulation, increase network efficiency, and facilitate the effective integration of distributed energy resources into modern power distribution infrastructures. Comprehensive simulations on the IEEE 69-bus and the practical large-scale 141-bus RDS evaluate the impacts of DG and CB installation under practical operating constraints. This study investigates six scenarios involving different combinations of DG, CB, and NR to support efficient network planning and operation. Furthermore, recent optimization techniques, including Bezier Curve-Based Optimization (BCO), Horned Lizard Optimization Algorithm (HLOA), Whale Optimization Algorithm (WOA), Jaya Algorithm, and Particle Swarm Optimization (PSO), are implemented on the studied systems, and their results are compared with those of the proposed IFLO. The findings demonstrate that the suggested strategy outperforms existing optimization approaches in terms of convergence speed, solution quality, and network performance enhancement. The IFLO algorithm achieves an active power loss reduction of 92.69% for the large-scale system, while significantly improving voltage stability and operational efficiency. These outcomes contribute to the development of resilient, energy-efficient, and intelligent distribution infrastructures capable of supporting increased penetration of distributed energy resources under diverse operating conditions. Full article

Background/Objectives: Transradial access (TRA) is a standard approach in the management of ST-segment elevation myocardial infarction (STEMI); however, evidence on using a single catheter for both diagnostic angiography and percutaneous coronary intervention (PCI) is limited. This study evaluates the feasibility and clinical outcomes of using a single Judkins Left (JL) 3.5 guiding catheter via TRA in STEMI patients. Methods: A total of 1139 patients undergoing radial access PCI with a single JL 3.5 catheter were included. Procedural success was defined as completing both diagnostic coronary angiography and PCI without catheter exchange. Procedural characteristics and access-site complications were evaluated. Results: The success rate of completing diagnostic angiography and PCI using a single JL 3.5 guiding catheter without catheter exchange was 91.1%. Compared to procedures requiring multiple catheters, the single-catheter group had significantly lower total contrast volume (200 vs. 250 mL), procedure time (20 vs. 30 min), fluoroscopy time (10.3 vs. 17.6 min), radiation dose (358 vs. 545 mGy), and needle-to-balloon time (6 vs. 9 min), all with p < 0.001. Access-site complications were also lower (8.2% vs. 15.8%; p = 0.010), primarily due to reduced radial artery spasm (4.0% vs. 12.9%; p = 0.001). Conclusions: A single JL 3.5 catheter strategy via transradial access is a safe, efficient, and effective method for STEMI intervention, offering significant procedural and clinical advantages. Full article

The integration of digital formats into undergraduate medical education offers a promising approach to enhance procedural skill acquisition. This study investigates the efficacy of a short, structured instructional video as part of a blended learning curriculum for teaching radial artery puncture to final-year medical students. A single-center, randomized, cross-over trial was conducted at the University Medical Center Mainz. Seventy-eight final-year medical students were randomized into an exposure group (instructional video based on Peyton’s four-step approach) and a control group. Performance was assessed using a 14-item checklist (maximum 16 points; pass threshold: 60%). At T1, the exposure group achieved significantly higher scores (p < 0.001, r = 0.735). Only 2.5% of the exposure group failed compared to 26.3% in the control group. After one week, the exposure group showed no significant performance decline (p = 0.101). The control group improved significantly after viewing the video (p < 0.001). At T2, no statistically significant difference remained between groups (p = 0.013, adjusted α = 0.0125). A short, structured instructional video significantly enhances initial performance and short-term retention of radial artery puncture skills in a blended learning setting. Full article

This study reports cellulase production by Bacillus paralicheniformis using Plumeria alba leaf powder under submerged fermentation with a focus on systematic bioprocess optimization. Physical parameters were first optimized using a one-factor-at-a-time (OFAT) approach, followed by optimization of yeast extract, MgSO₄ and (NH₄)₂SO₄ via a central composite design (CCD) and response surface methodology (RSM). An artificial neural network (ANN) with a 5:3:1 network trained by the Levenberg–Marquardt algorithm further improved prediction of carboxylmethylcellulase (CMCase) and filter paper cellulase (FPase) activities. This study is the first to exploit Plumeria alba leaf powder as an untapped, low-cost lignocellulosic substrate for cellulase production by B. paralicheniformis and uniquely benchmarks RSM against ANN-based modeling to identify superior predictive frameworks for bioprocess optimization. Under optimized conditions (24 h, 4% w/v substrate, 1% v/v inoculum), the maximum FPase and CMCase activities reached 60.53 IU/mL/min and 332.10 IU/mL/min respectively. Partial characterization showed optimum FPase and CMCase activities at 50 °C and 70 °C, respectively, at pH 7.5. Enzymes also showed activation by NaCl and some select solvents while tolerating a broad range of metal ions. The enzymatic hydrolysis of P. alba biomass released 59.42 mg/mL total reducing sugars after 8hr, confirming efficient saccharification from a low-cost feedstock. The ANN model (R² = 97.59% for CMCase; 85.95% for FPase) outperformed RSM (R² = 85.95% and 78.25%, respectively), while radial basis function optimization reached 99.99%. These findings highlight B. paralicheniforms cellulase as a promising biocatalyst for biorefinery applications and demonstrate the value of integrating RSM and ANN for process optimization. Full article

Transbronchial lung cryobiopsy (TBLC) is increasingly used as a minimally invasive approach for tissue acquisition in the evaluation of interstitial lung disease (ILD), serving as an alternative to surgical lung biopsy (SLB) within multidisciplinary diagnostic pathways. Despite its growing adoption, variability in diagnostic yield and complication rates highlight the importance of procedural technique, probe selection, and freezing parameters. This narrative review summarizes the current landscape of TBLC, with emphasis on factors that influence diagnostic performance and safety, including procedural considerations involving endobronchial balloon blockade (EBB), radial probe endobronchial ultrasound (RP-EBUS), and cone-beam computed tomography (CBCT) for biopsy localization and airway management. Much of the existing experience is based on conventional cryoprobes, including 2.4 mm and 1.9 mm devices, typically used with freezing times of several seconds. While these approaches have defined the current role of TBLC in ILD, outcomes remain variable across centers, prompting continued refinement of procedural strategies to improve consistency. More recently, attention has expanded to include a broader range of smaller cryoprobe sizes—1.7 mm and 1.1 mm. Overall, this review provides a framework for understanding contemporary TBLC practice and highlights key areas where further study is needed to better define optimal technique and improve consistency in clinical outcomes. Full article

Roadside LiDAR is a key sensing technology for intelligent transportation systems (ITSs) due to its high-precision spatial information and reliable monitoring of traffic environments. However, extracting traffic information from LiDAR point cloud data remains challenging because measurements are produced through angular sampling, causing the spacing between adjacent points to depend on radius and beam distribution. This study proposes a geometry-aware framework that incorporates LiDAR sampling geometry into the neighborhood criterion used to determine point-to-point association. The formulation defines neighborhood tolerance as a function of radial distance and vertical angular separation, enabling clustering decisions that are consistent with the sensing mechanism. In addition, the approach integrates deployment constraints based on sensor mounting height and region-of-interest limits to maintain physically meaningful connectivity under roadside sensing conditions. A systematic calibration procedure is conducted to estimate the scaling factor and radial spacing parameters and evaluate the method using both controlled and real-world datasets. Experimental results reveal that the proposed approach improves clustering accuracy and stability by reducing false negatives in sparse regions while avoiding excessive cluster merging in dense areas. The method demonstrates robust performance across varying sensing conditions and achieves higher accuracy than baseline approaches without parameter retuning, while introducing negligible computational overhead. Full article

We present a novel method for deriving radially resolved dynamical chronometers from galaxy rotation curves, allowing galaxy assembly histories to be reconstructed directly from kinematic data. In the Nexus Paradigm, the baryonic Tully–Fisher relation is used to estimate the dynamical mass profile. We compare this profile with independently derived intrinsic baryonic mass distributions obtained from stellar Sérsic fits and gas surface-density measurement yields. This yields a radial ratio that maps to formation redshift with radial resolution. Inverting this ratio within a standard cosmological framework produces a radial lookback-time profile, representing the time since each radial shell last experienced dynamical reconfiguration. Applying the method to a pilot sample of seven SPARC galaxies, including both high- and low-surface-brightness systems as well as the Milky Way, reveals diverse age structures: stratified profiles associated with inside-out growth and flatter profiles consistent with coherent disk assembly. The method requires no dark-matter halo fitting and offers a kinematic chronometer that complements stellar population and chemical evolution approaches. The NP rotation-curve parameters were determined by minimizing the chi-squared statistic between the observed and predicted velocities using a two-stage optimization consisting of a global differential-evolution search followed by nonlinear least-squares refinement. Observational uncertainties were taken from the published rotation-curve data, supplemented by a 5 km s⁻¹ systematic error floor added in quadrature to account for non-circular motions and other unresolved systematics. We also show that the governing dynamical equation admits a gravitoelectromagnetic interpretation, in which a velocity-dependent term generates disk-wide torques that regulate angular momentum transport. This leads to a unified stability framework in which galaxy morphology emerges from a single parameter regime: balanced conditions favor a coherent spiral structure, whereas dynamically hot regimes naturally produce diffuse and ultra-faint systems. The cosmological scaling of the effective gravitomagnetic field further suggests that the spiral structure is partly regulated by cosmic time. Although the inferred ages depend on the accuracy of the baryonic mass reconstruction and on the local validity of the evolving baryonic Tully–Fisher relation, our results show that rotation curves encode time-resolved dynamical information. This establishes the radial dynamical chronometer as a new observable for studying galaxy evolution and testing gravitational frameworks. Full article