Search Results (176,453)

Lightweight design of the skeletal structure is a critical challenge in the development of Blended-Wing-Body Underwater Gliders (BWBUGs). However, existing studies often rely on empirically derived configurations for parameter optimization, which limits the potential to fully exploit structural performance. To address this issue, this paper proposes a design approach for BWBUG skeletal structures that integrates topology optimization with data-driven optimization, termed the TD-Method. Specifically, the TD-Method first applies topology optimization to identify load transfer paths within the BWBUG structure, thereby generating an initial configuration and a parameterized model for subsequent optimization. On this basis, data-driven optimization is employed to extensively explore the design space, enabling lightweight structural design under specified constraints. Finally, a comparative analysis with existing methods demonstrates that the TD-Method achieves superior skeletal structures with enhanced performance, confirming both the effectiveness and advantages. Full article

Optimizing the structural stability of foundations is challenging in modern geotechnical engineering. This study investigated the mechanism of geocell-reinforced foundations through discrete element modeling based on transparent soil model tests. A three-dimensional particle flow code (PFC^3D) model was developed to investigate the micromechanical soil–geocell interactions in both unreinforced and geocell-reinforced foundations under strip loading. Particle displacement, contact force distribution, and structural deformation within the foundation system were analyzed to quantify the performance of geocell reinforcement. The results show that geocell inclusion enhances structural performance by 2.1 times compared to an unreinforced foundation, increasing the bearing capacity from 60.6 to 126.8 kPa at a defined bearing capacity criterion. The geocell walls act as rigid physical boundaries that microscopically intercept the lateral migration and horizontal extrusion of soil particles. The kinematic trajectories of soil particles beneath the loading plate are forced into a downward realignment, decreasing the displacement vector rotation angle from 42° in the unreinforced soil to 27° in the reinforced soil and effectively mitigating the heave of adjacent surfaces. Furthermore, the quasi-rigid three-dimensional network completely interrupts the continuous steep contact force chains inherent in unreinforced foundations. Concentrated vertical stresses are converted into horizontal components through interfacial friction and mechanical interlocking, resulting in the lateral redistribution of the applied load by a distance of approximately 0.06 m. The geocell–soil composite considered as a flexible raft foundation extends load dispersion and reduces average subsoil pressure. A coupled tension and compression stress state in the horizontal plane is developed within the geocell structure. Forces are channeled along rigid paths by elevated bending moments and stress concentrations at the cell junctions. These findings provide micromechanical insights into the performance of geocell-reinforced-foundation systems. Full article

Next-generation unmanned aerial vehicles require compact propulsion systems capable of providing efficient vertical lift, rapid thrust vectoring, and improved maneuverability. Cyclorotors represent a promising alternative to conventional propellers, but their aerodynamic behavior is governed by highly unsteady blade–wake interactions, making performance prediction challenging. This study investigates a four-bladed cyclorotor equipped with NACA 0012 airfoils using transient computational fluid dynamics simulations and a calibrated semi-analytical blade-element model. The numerical analysis was performed over a rotational-speed range of 368–2305 rpm and for several pitch-amplitude configurations, including 5°, 7.5°, 10°, 12.5° and 15°. The results showed that the favorable pitch amplitude decreases with increasing rotational speed, shifting from larger amplitudes at low RPM to approximately 5° at higher RPM values. The semi-analytical model reproduced the main CFD trends for lift, drag, moment, and power, providing a reduced-order tool for preliminary cyclorotor performance estimation. The comparison confirmed that pitch-amplitude selection strongly influences aerodynamic loading and efficiency and should therefore be adapted to the operating regime. The proposed CFD-based methodology, supported by semi-analytical modelling, provides a useful framework for the aerodynamic characterization and early-stage optimization of cyclorotor propulsion systems for UAV applications. Full article

To address hydrogen separation from hydrogen-blended natural gas, this work develops a mathematical model for a novel thermal-transpiration-effect-based circulating-flow gas separator according to the Navier–Stokes equations, following the joint modification with velocity-slip and temperature-jump boundary conditions, and a binary gas diffusion model derived from the Maxwell–Stefan equations. The model is then used to investigate the component transport and flow of a CH₄-H₂ mixture at the slip flow regime. The average hydrogen mole fraction in the component enrichment zone increases monotonically as the temperature difference increases, reaching 0.429 at a hot channel temperature of 400 K. An optimum inlet gas velocity of 0.93 m/s is identified to achieve the maximum average hydrogen mole fraction in the enrichment zone. In addition, decreasing the microchannel diameter enhances the hydrogen enrichment performance, with the average hydrogen mole fraction reaching 0.578 at a microchannel diameter of 1 μm whereas increasing the microchannel diameter improves the product gas flow rate, indicating a trade-off between separation performance and processing capacity. These insights provide guidance for understanding the component transport mechanism and for the preliminary design of this type of gas separator for hydrogen separation applications. Full article

This study investigates the key determinants of bank stability and profitability in commercial and Islamic banks listed on the Amman Stock Exchange (ASE) in Jordan, with a focus on credit risk and capital adequacy during the period 2018–2024. Using panel data from 15 banks, the study applies fixed effects regression models with clustered standard errors. Liquidity is proxied by the loan-to-deposit ratio (LDR), credit risk by the loans loss provisions-to-total loans ratio, and capital strength by the equity-to-assets ratio, alongside a COVID-19 dummy and an interaction term between liquidity and credit risk. Financial performance and stability are measured using return on assets (ROA), return on equity (ROE), and the logarithmic Z-score. The findings indicate that credit risk has a significant negative effect on both bank performance and financial stability, whereas capital adequacy exerts a positive and significant effect. The COVID-19 pandemic negatively affected financial performance and stability, while liquidity (LDR) shows no significant direct effect. The interaction between liquidity and credit risk was statistically insignificant across all estimated models, suggesting that credit risk remains the dominant determinant regardless of liquidity conditions. The study highlights the importance of effective credit risk management and strong capital buffers in enhancing bank resilience. It contributes to the literature by providing recent evidence from the Jordanian banking sector and by incorporating multiple performance measures, a pandemic shock variable, and risk interaction effects to better understand bank stability within a unified empirical framework for an emerging banking market. Full article

This paper presents a comprehensive evaluation of control strategies for a highly energy-efficient plus-energy terraced housing complex equipped with photovoltaic generation, modulating ground-source heat pumps, electrical and thermal energy storage systems, and activation of building thermal mass. The study combines long-term monitoring data, annual simulations, and hardware-in-the-loop (HiL) experiments to assess modulating heat-controlled operation (HC), PV-controlled (PVC), and predictive control strategies, including simple predictive control (SPC) and model predictive control (MPC). The simulation results show that the baseline HC operation already achieves a high load cover factor (LCF), defined as the fraction of total electrical demand covered by local PV generation (direct use + battery discharge) of 65.6% and a seasonal performance factor (SPF) of the central heat pumps of 5.8. PVC increases LCF (71.0%) by shifting heat pump operation toward PV-rich periods but leads to elevated storage temperatures up to 5 K and a reduced SPF of 4.8. MPC further enhances LCF by 4–7 percentage points in simulated and HiL environments. However, its real-world performance is strongly influenced by forecast quality and the limited controllability of the heat pump system. In addition, building thermal mass activation is investigated as a complementary flexibility option. Simulation and monitoring results demonstrate that moderate room temperature set-point (2 K) increases during PV availability significantly improve LCF from 20% to 55% while maintaining thermal comfort. Overall, the findings indicate that in highly efficient plus-energy buildings, robust rule-based strategies combined with thermal mass activation can achieve a large share of the attainable benefits, while the added complexity of MPC must be carefully weighed against practical limitations. Full article

Aortic stenosis is predominantly treated through transcatheter bioprosthetic heart valve implantation. However, the materials used in these devices are prone to premature failure. Polymer heart valves provide an alternative to current commercial devices, offering materials with greater durability and customisation through fibre reinforcement. Given the wide range of available materials and structures, there is a need for a systematic and efficient approach to designing and optimising novel bioinspired polymeric leaflets. This work presents a framework that employs computational modelling and Design of Experiments (DOE) tools to optimise bioinspired, 3D-printed, fibre-reinforced polymer leaflets made using melt electrowriting (MEW). Here, finite element (FE) models are created to represent MEW fibre-reinforced polymer leaflets for application in a transcatheter aortic heart valve. The behaviour of this valve under physiological loading conditions is modelled to predict valve performance and leaflet material response. These models were first used to investigate the impact of fibre orientation on valve performance and leaflet response, thereby demonstrating the benefits of a bioinspired fibre reinforcement structure. Using a DOE approach, the structural combination of MEW fibre reinforcement and an elastomeric matrix was optimised to improve valve performance and reduce leaflet stress and strain. Overall, the framework offers an efficient and versatile methodology for optimising fibre-reinforced polymer leaflets using an in silico approach, thereby reducing the need for physical prototyping and testing of these next-generation devices during early product development. Full article

A comprehensive multiregional characterization of the spectral response of cassava leaves across different ontogenetic stages was performed. For this, ultraviolet (UV), visible (VIS) and shortwave near-infrared (UV-VIS-NIR; 200–900 nm) regions were used to identify spectral signatures and indices for their potential use as biomarkers of leaf development and physiological status of plants under induced senescence conditions. Manihot esculenta Crantz (HMC-1 variety) was used as a model. Spectral signatures were obtained from leaves at two phenological stages (4 and 6 months after planting) using UV-VIS-NIR spectroscopy by the diffuse reflectance technique. Classical and experimental spectral indices were evaluated, and their discriminatory power through different ontogenies was assessed using ANOVA/Kruskal–Wallis and post hoc tests. Senescence effects were further examined by postharvest monitoring (1–20 days), with temporal, ontogenetic, and interaction effects validated using linear mixed models (LMMs), while multivariate structure and spectral convergence were explored via principal component analysis and hierarchical clustering (PCA-HCA). Functionally Enhanced Derivative Spectroscopy (FEDS), comparative analysis, and spectral correlation mapping allowed signal’s selective enhancement and the identification of phenolic compounds, photosynthetic pigments, and structural molecular components. Results showed high ontogenetic stability of UV-associated phenolic signals (~210–220 nm), whereas the VIS region (420–600 nm) clearly differentiated young leaves. The NIR region was stable across ontogeny but highly sensitive to temporal degradation, reflecting changes in water status and internal structure. UV-VIS-NIR indices effectively differentiated young leaves and changes by stress. It is concluded that multiregional characterization of the spectral response supported by FEDS allows the extraction of robust indices with strong potential as biomarkers of leaf maturation and senescence in cassava. Full article

Aiming at problems such as the reduced positioning accuracy and insufficient dynamic response caused by the inherent hysteresis nonlinearity of piezoelectric actuators, this paper proposes a hysteresis modeling and compensation method for piezoelectric actuators based on nonlinear transformation. The proposed NT hysteresis model utilizes the nonlinear characteristics of activation functions to describe the complex nonlinearity in the hysteresis response of piezoelectric actuators. On this basis, a feedforward compensation controller is further designed based on the proposed inverse NT hysteresis model. Moreover, a composite controller is constructed by combining it with PID feedback to improve the dynamic response speed and trajectory tracking accuracy of piezoelectric actuators. Based on experimental data, the fitting performance of the proposed model is compared with that of several common hysteresis models using evaluation indicators including RMSE, MAPE and SMAPE. Finally, the performance of the proposed control method is verified through step response and sinusoidal trajectory tracking experiments. Experimental results show that the proposed NT hysteresis model performs best in characterizing the hysteresis characteristics of piezoelectric actuators, and the feedforward compensation controller constructed based on its inverse model exhibits superior control performance. Full article

Aircraft skin defect recognition is a safety-critical visual inspection task in which lightweight models must maintain high diagnostic accuracy while suppressing false alarms caused by complex surface textures, illumination variations, and weak defect patterns. This study proposes HQCA-Net, a simulation-based hybrid quantum-classical channel attention network for reliable aircraft skin defect recognition. The core component, termed Residual Quantum Channel Attention (RQCA), embeds a 10-qubit variational quantum circuit into a classical ResNet-18 backbone to perform compact and structured nonlinear feature recalibration, introducing only 30 trainable quantum-gate parameters. The quantum circuit is evaluated using state-vector simulation, and this study focuses on model-level feature recalibration, reliability, and robustness within the evaluated dataset rather than implementation on physical quantum hardware. Experiments on a six-class aircraft skin defect dataset show that HQCA-Net achieves 97.93% classification accuracy and a global false positive rate of 0.49%, outperforming ResNet-18 and classical lightweight attention mechanisms including SE, ECA, and SimAM. Additional analyses using confidence calibration, Grad-CAM visualization, Gaussian noise perturbation, few-shot training, and circuit-depth ablation further indicate that the proposed RQCA module improves feature discrimination and false-alarm suppression under compact parameter constraints. These results suggest that the hybrid quantum-classical attention module can serve as a parameter-efficient nonlinear feature recalibration strategy for reliable visual defect inspection under the tested experimental conditions. Full article

Background/Objectives: Diabetes mellitus is a chronic metabolic disorder characterized by hyperglycemia, dyslipidemia, and oxidative stress, leading to severe systemic complications. Medicinal plants rich in polyphenolic compounds have gained increasing attention as complementary therapeutic agents. This study aimed to comparatively evaluate the chemical composition, as well as the antidiabetic, hypolipidemic, and antioxidant effects of Polygonum persicaria and Vaccinium myrtillus in a streptozotocin-induced diabetic model. Although Vaccinium myrtillus has been more extensively investigated for its antidiabetic potential, the pharmacological relevance of Polygonum persicaria in diabetes remains insufficiently characterized, particularly in direct comparison with a recognized phytotherapeutic comparator. Methods: Hydroalcoholic tinctures prepared from Polygonum persicaria L. herb and Vaccinium myrtillus L. leaves were subjected to phytochemical analysis using High-Performance Thin-Layer Chromatography (HPTLC) for the identification of flavonoids and phenolcarboxylic acids, alongside spectrophotometric determination of total polyphenol and flavonoid content. Experimental diabetes was induced in CD1 mice by streptozotocin administration. Animals were treated orally for 35 days, and glycemic parameters, lipid profile, body weight, food and water intake, and oxidative stress markers (MDA, SOD, TAC, and GPx) were evaluated. Results: HPTLC/CSS screening indicated the presence of rutin, chlorogenic acid, and caffeic acid in Polygonum persicaria, while Vaccinium myrtillus showed stronger densitometric signals for phenolcarboxylic acid-type compounds, particularly chlorogenic and caffeic acids. Total polyphenol and flavonoid content were also higher in Vaccinium myrtillus (433.89 ± 8.67 mg/L GAE; 154.38 ± 3.08 mg/L QE) compared to Polygonum persicaria (269.28 ± 5.25 mg/L GAE; 132.75 ± 2.65 mg/L QE). Functionally, Vaccinium myrtillus demonstrated a significant antihyperglycemic effect from day 14 (p = 0.009) and improved lipid parameters, while Polygonum persicaria showed a delayed glycemic effect, significant only at day 35 (p = 0.014), without significant hypolipidemic activity. In contrast, Polygonum persicaria exerted a marked antioxidant effect, significantly increasing GPx activity (p = 0.025) and reducing MDA levels (p = 0.053). Conclusions: Vaccinium myrtillus showed stronger antihyperglycemic and hypolipidemic effects, while Polygonum persicaria was mainly associated with antioxidant-related biochemical changes. These differences may be influenced by phytochemical composition, but they cannot be attributed solely to total polyphenol or flavonoid content. Full article

Quantifying the impact of land use changes on the threat of flash-floods is a critical consideration in flood hazard planning and risk reduction, and is an area of active research. Here, a coupled Weather Research and Forecasting model hydrological extension package (i.e., WRF-Hydro) modeling approach is applied to simulate flash-flooding processes for short-duration, localized, intense precipitation events. To better understand the effect of urbanization on flash floods, a series of numerical experiments is performed surrounding Ellicott City, Maryland, a location which has experienced both significant heavy rainfall events and suburban development over the past several decades. Two intense rainfall events occurring on 30 July 2016 and 27 May 2018 are investigated, respectively, to first calibrate the hydrologic model performance and then quantify the sensitivity of flash flooding to varying degrees of urbanization. Performing the same experiments using observed historical land use states is of more limited insight, as the thrust of suburban development in the Ellicott City region significantly predates satellite-derived land use datasets. Results confirm that urbanization produces larger river streamflow, higher water stages, faster hydrologic responses to achieve peak flow discharge, and shorter recession limbs, even for very intense, short-duration events. The collective findings suggest that WRF-Hydro is applicable for both watershed flash flood prediction and hypothesis testing, and demonstrates potential utility to urban development decision-makers in locations such as Ellicott City, which could face future increases in catastrophic flooding. Full article

This study presents a gate-to-gate life cycle assessment (LCA) of an industrial sweet cherry sorting and packing facility in Greece, directly addressing environmental sustainability in agri-food supply chains through data-driven impact quantification and improvement pathways in post-harvest operations. The assessment focuses on a gate-to-gate system boundary encompassing all processes inside the cherry sorting and packing facility, while upstream cherry production and downstream waste management are modeled and reported separately to provide system-level context. Core-stage hotspots are then analyzed in detail in the Results section, highlighting the dominant role of electricity use compared with packaging materials. The functional unit is defined as 1 kg of packed, market-ready cherries at the factory gate. Primary data are obtained from high-resolution, batch-level measurements of mass flows, energy use, water consumption, packaging materials and waste streams over a full processing season, structured as virtual sensor outputs. These sensor-informed operational data are combined with secondary life cycle inventory information from established databases to quantify climate change impacts and identify environmental hotspots across materials, energy, water, and waste, thereby delivering a quantified picture of environmental performance in the post-harvest stage. The results show that corrugated cardboard and associated packaging components are among the main contributors within the facility-level, gate-to-gate system, while the Core stage accounts for 28.43% of total GWP100. Upstream cherry production dominates the overall Upstream–Core–Downstream climate footprint with 70.61% of total impacts. Moreover, practical mitigation scenarios are modeled, including packaging optimization, partial substitution of grid electricity with photovoltaic generation, and increased water recirculation. Ιn the combined mitigation scenario, where packaging optimization, low-carbon electricity and improved water management are implemented simultaneously, total GWP100 decreases from 114,207.32 to 92,500.27 kg CO₂-eq (−19.0%) relative to the baseline, providing actionable sustainability improvements for industry stakeholders and supporting Sustainable Development Goals (SDGs) related to climate action and resource efficiency. In addition, the proposed virtual sensor architecture and data workflow support continuous monitoring, eco-efficiency management and near-real-time LCA implementation in post-harvest agri-food systems, enabling operational sustainability. Full article

High-resolution, long-term gridded meteorological datasets from in situ observations are crucial for ecosystem monitoring, soil diagnostics, hydrological modelling, and Earth system model evaluation. This study presents two enhanced real-time adaptations of Thornton’s Daymet V4 interpolation method. Daymet4-r1 uses a traditional calibration strategy with exhaustive parameter search, while Daymet4-r2 applies a global optimization algorithm (find_min_global from the dlib library) to adjust parameters automatically at each time step. Both methods were tested over Tuscany using high-resolution terrain and a dense observation network. Validation with leave-one-out method was carried out for the period 1995–2011 for both versions, while Daymet4-r2 underwent extended evaluation from 1991 to 2024 to assess seasonal dynamics and long-term variability. Results show that Daymet4-r2 outperforms Daymet4-r1 and the original Daymet V4 for all variables (mean absolute error of 1.24 mm, 1.06 °C, 1.29 °C, 6.26%, 0.78 m/s, and 2.04 hPa for precipitation, maximum and minimum temperature, relative humidity, wind speed, and sea level pressure, respectively). The largest improvement was observed in minimum temperature due to an enhanced approach for detecting and modelling thermal inversions. The high performance, flexibility, and ability of Daymet4-r2 to operate without prior calibration highlight its potential for model verification, real-time environmental monitoring, and integration into climate services. Full article

The positioning and clamping process in aircraft assembly exhibits pronounced long-term temporal correlations and intense human–machine interactions. Consequently, assembly quality depends heavily on operator compliance and consistency. Capturing long-term, strongly correlated features in complex industrial environments remains a significant challenge. To overcome this, this study proposes a Long-Term Strongly Associated Action Recognition Network (LTSA-Net) tailored for aircraft assembly positioning and clamping tasks. Based on the C3D backbone, the model first incorporates the SimAM attention mechanism and BN modules to significantly enhance focus on critical spatiotemporal features. To address the challenge of capturing long-term temporal dependencies, LTSFEM is designed to extract global temporal information accurately. Furthermore, to balance structural lightweight design with real-time inference requirements, the CWSTB module is integrated to achieve substantial parameter compression. In addition, a dedicated aircraft assembly positioning and clamping dataset was constructed, and a robust training framework was established using the AdamW optimizer and Mixup data augmentation. Experimental results demonstrate that LTSA-Net achieves a recognition accuracy of 98.82% on the LTSA-Dataset, with a per-frame inference time of 42 ms, successfully meeting the dual requirements of high precision and real-time performance in industrial scenarios, and providing a practical technical solution for intelligent monitoring of aircraft assembly processes. Full article