Search Results (18,482)

Vertical-axis wind turbine (VAWT) clusters have been extensively investigated owing to their positive aerodynamic interactions. However, accurate predictions of the flow field and power output of each rotor in VAWT clusters using high-fidelity computational fluid dynamics (CFD) remain computationally expensive. In this study, we propose a fast computation method for the flow field and operating state of each rotor of VAWT clusters using temporally and spatially averaged velocity data compressed from an unsteady velocity field obtained via a 3D-CFD simulation of an isolated rotor. First, the unsteady 3D flow field in the 3D-CFD simulation is time-averaged over several revolutions. Next, the temporally averaged velocity is spatially averaged in the vertical direction to obtain spatially compressed data. Based on a previously developed fast computation framework, a wind-farm flow field is constructed using condensed two-dimensional velocity data obtained from a single turbine. The proposed method is applied to three-rotor configurations, and the rotational speeds of the turbines are compared with the wind-tunnel measurements. The results show that the proposed method substantially improved the prediction accuracy while maintaining a low computational cost. In addition, it can be used to efficiently design and optimize turbine layouts in VAWT wind farms. Full article

Crumb rubber-modified asphalt concrete (CMAC) has gained increasing attention as a sustainable pavement material capable of improving mechanical performance while utilizing waste tire resources. This study investigates the rutting resistance and fatigue behavior of CMAC using a combined experimental and mechanistic–empirical modeling approach. Asphalt mixtures containing 0–25% crumb rubber by binder weight were prepared and evaluated through Marshall stability and indirect tensile fatigue tests, whereas Fourier-transform infrared spectroscopy (FTIR) was used to examine binder–rubber interactions. The results indicate that crumb rubber significantly influences both the volumetric and mechanical properties of asphalt mixtures. Mixtures containing 10–15% crumb rubber exhibited optimal performances, achieving up to 36% higher Marshall stability and improved fatigue life compared with conventional asphalt mixtures. FTIR analysis revealed that rubber particle swelling and limited chemical interactions enhanced binder elasticity and improved binder–aggregate compatibility. However, excessive rubber content (≥20%) resulted in reduced stability owing to increased binder absorption and decreased effective binder film thickness. A mechanistic–empirical model incorporating viscoelastic, viscoplastic, and fatigue damage parameters successfully reproduced the experimental trends and identified the same optimal rubber content range. The findings demonstrate that CMAC with a moderate rubber content can enhance pavement durability and structural performance while promoting environmentally sustainable road construction through the reuse of waste tires. Full article

During the installation of prefabricated vertical drains (PVDs) in soft soil foundations, the smear effect induced by mandrel shoe penetration can severely damage the soil structure and reduce permeability, thereby becoming a key factor restricting foundation consolidation efficiency. Previous studies have generally neglected the smear disturbance caused by the geometry of the mandrel shoe. Although existing studies have conducted numerical and theoretical analyses on the smear effect induced by PVD installation, most of them are still based on equivalent circular simplifications and are therefore unable to characterize the anisotropic disturbance induced by a triangular mandrel shoe. To address this limitation, a three-dimensional CEL penetration model considering the real triangular geometry was established, and the traditional cavity expansion theory was directionally modified. The effects of penetration rate, geometric angular structure, and soil type of the triangular mandrel shoe on the smear zone were systematically investigated. The results show that, with increasing penetration rate, the near-field peak stress and far-field displacement increase simultaneously; from slow penetration to fast penetration, the near-field peak stress increases by approximately 42%. By quantitatively defining the critical threshold corresponding to a sharp 50% attenuation in radial displacement as the boundary of the strong smear zone, it was found that increasing the size of the mandrel shoe significantly amplifies the geometric corner effect, and the near-field disturbance range increases by about 21% compared with that of the small-sized case. The larger the size, the more pronounced the anisotropic disturbance characteristics become: the stress concentration effect and displacement splitting in the vertex direction are further enhanced, causing the disturbance range in that direction to far exceed that in the side direction. Soil properties are the key medium parameters controlling the smear zone. Owing to its relatively high stiffness index and skeleton strength, Clayey Silt shows the largest displacement range, whereas Common Clay exhibits the smallest smear zone because of its stronger structural constraint. The modified theoretical model agrees well with the CEL numerical simulation results, verifying its effectiveness under conditions that consider the geometric characteristics of the mandrel shoe. This study provides a theoretical basis and numerical support for the structural design of mandrel shoes in soft-ground PVD construction. Full article

The energy-dependent population of fine quantum states in single electron capture (SEC) reflects the intrinsic collision dynamics. Here we report experimental studies of Ar⁷⁺ ions colliding with He in the energy range of 1.05–17.5 keV/u. Owing to the high resolution of a recoil-ion momentum spectrometer, the n-, l-, and spin-state electron capture populations are well resolved, and a strong energy dependence of the SEC cross sections is observed. Most importantly, a clear inversion of the cross-section ratio between the spin-resolved triplet and singlet 3s3d configurations is found, demonstrating a breakdown of spin statistics. Together with recent spin-resolved studies of C³⁺-He collisions (PRL 133, 173002 (2024)), these results suggest that the breakdown of spin statistics is likely a general feature of charge exchange in open-shell highly charged ion systems. Full article

Pedestrian gender recognition plays an important role in pedestrian analysis and intelligent video applications, for example, in demographic statistics, soft biometric analysis, and context-aware person retrieval. However, it remains a challenging task owing to viewpoint variations, illumination changes, occlusions, and low image quality in real-world imagery. To address these issues, an effective adaptive decision fusion framework, termed the Decision Fusion Learning Network (DFLN), is proposed in this paper. The key novel aspect of DFLN is that it effectively explores both an appearance-centered view that emphasizes detailed texture and clothing information and a structure-centered view that captures rich contour and structural information for pedestrian gender recognition. To realize DFLN, a Parallel CNN Prediction Probability Learning Module (PCNNM) is first constructed to independently learn modality-specific probabilities from color image and edge maps. Subsequently, a learnable Decision Fusion Module (DFM) is designed to fuse the modality-specific probabilities and explore their complementary merits for realizing accurate pedestrian gender recognition. The DFM can be easily coupled with the PCNNM, forming an end-to-end decision fusion learning framework that simultaneously learns the feature representations and carries out adaptive decision fusion. Experiments on two pedestrian benchmark datasets, named PETA and PA-100K, show that DFLN achieves competitive or superior performance compared with several state-of-the-art pedestrian gender recognition methods. Extensive experimental analysis further confirms the effectiveness of the proposed decision fusion strategy and its favorable generalization ability under domain shift. Full article

Background: Neoadjuvant chemoradiotherapy (nCRT) followed by surgery has become the standard treatment for oesophageal cancer. However, data on the outcomes of neoadjuvant immunochemotherapy (nICT) in geriatric patients (≥70 years) who face higher perioperative risks are limited. Objective: This study aimed to compare the perioperative outcomes of nICT versus nCRT in elderly patients with locally advanced oesophageal squamous cell carcinoma (ESCC). Method: This retrospective cohort study included 132 geriatric patients (median age: 72 years) treated with nICT (n = 51) or nCRT (n = 81) followed by esophagectomy at Sichuan Cancer Hospital (2021–2024). Intraoperative outcomes, postoperative pathologic stages, and complications, including pneumonia and anastomotic leakage, were assessed. Propensity score matching (PSM), overlap weighting (OW), and inverse probability of treatment weighting (IPTW) were used to adjust for baseline covariate imbalances in the sensitivity analysis. Results: Pathologic ypT0 stage tended to be higher in the nCRT group (p = 0.014), whereas ypN0 was higher in the nICT group (p = 0.035). No significant differences in intraoperative or postoperative outcomes between the two groups, except for pulmonary complications (p > 0.05). Compared with nCRT patients, nICT patients had significantly lower pulmonary complication rates (13.7% vs. 32.1%, p = 0.030), and multivariable analysis confirmed these findings (adjusted OR = 0.26; 95% CI: 0.08–0.85; p = 0.026). Sensitivity analyses showed consistent results. Conclusions: The safety of nICT is comparable to that of nCRT in geriatric ESCC patients, with significantly fewer pulmonary complications. These findings support nICT as a valuable alternative for elderly populations. Full article

Aluminium alloys are extensively considered in aviation and automobiles owing to their lightweight properties and favourable specific strength-to-weight ratio. Generally, the poor surface properties of these alloys limit their application, particularly in sliding conditions. To enhance the surface qualities, particularly the material’s wear resilient features, a unique surface modification process using electro-discharge coating (EDC) has been employed. This work investigates the optimisation of coating variables produced by the EDC technique utilising green compact electrodes composed of 50 wt.% tungsten disulfide (WS₂) and 50 wt.% copper (Cu) powder. The substrate material utilised was AA7075 alloy. The Taguchi–TOPSIS approach was employed to determine optimal EDC process variables, with pulse-on time (T_on), current (I_p), and pulse-off time (T_off). Wear rate (WR), surface roughness (SR), and friction coefficient (CoF) were used to assess the coating features. A wear study was performed with a pin-on-disc device with an undeviating sliding speed (0.25 m/s) and a 25 N load. The results revealed that the supreme features derived from the linear plots were I_p (4 A), T_on (80 µs), and T_off (5 µs). The ANOVA found that I_p had the utmost significant impact, accounting for 44.09%; T_off, 28.01%; T_on, 20.33%; and minimum error, 8.58%. A validation trial with perfect parameters returned values of 0.000179 mm³/Nm (WR), 0.204 (CoF), and 2.818 µm (SR). These findings are significantly better than those of the other coatings. The discrepancy among the estimated and experimental relative closeness in optimal settings is 6.34%, demonstrating that the Taguchi–TOPSIS method is more appropriate for multi-criteria optimisation. Full article

In this study, a non-typical luminescent organosilicone was synthesized through a click reaction and used as a cross-linker to cure hydroxyl-terminated dimethylsilicone oil at room temperature via the sol–gel process, followed by application as a coating on a glass surface. This organosilicone film functions effectively as a luminescent down-shifting (LDS) material. Additionally, the presence of methyl groups and voids in the structure imparts a low refractive index, allowing it to serve as an anti-reflective (AR) layer. Optical and structural analyses on organosilicone-coated glass samples were conducted, and the dual-functional layer was applied to the glass cover of a perovskite solar panel to evaluate its performance. The coating not only enhanced light transmission as an AR layer but also converted UV light into blue light, which was absorbed by the solar cell. The results indicated improved solar panel performance, particularly in short-circuit current (Isc), external quantum efficiency (EQE) in the UV wavelength range, and overall efficiency. Overall, this material is a promising candidate for solar panel applications owing to maximized UV absorption for LDS, preserved transparency of the top cover glass, and room-temperature gelation, which facilitates repair of the dual-functional coating. Full article

Background: Owing to significant clinical heterogeneity, the achievement of accurate survival forecasting for individuals with colorectal cancer and peritoneal metastasis continues to be a complex undertaking. We aimed to transcend traditional prognostic limitations by evaluating machine learning boosting models against standard regression-based methods in terms of estimating overall survival (OS). Methods: Utilizing a multi-institutional registry of 150 patients diagnosed with synchronous peritoneal metastasis of colorectal cancer, we integrated 124 clinicopathological variables to refine our predictive models. Beyond standard preprocessing—including standardization and median imputation—we rigorously compared XGBoost and LightGBM against Ridge, Lasso, and linear regression via five-fold cross-validation. To specifically address right-censoring, an XGBoost Cox model was implemented and validated using Harrell’s C-index, with SHAP and LIME providing essential model interpretability. Results: Boosting models consistently outperformed linear alternatives, which struggled with high error rates and negative R2 values. Specifically, XGBoost achieved an MAE of 475 ± 60 and an RMSE of 585 ± 88. The XGBoost Cox model reached a C-index of 0.64 ± 0.06. SHAP analysis highlighted inflammatory markers and peritoneal disease extent as the most influential prognostic drivers. Conclusions: While boosting models offer a clear accuracy advantage over linear methods, their prognostic power remains moderate. These findings underscore the potential of ensemble learning in oncology, yet mandate external validation before these tools can be integrated into clinical decision-making. Full article

The energy sector and its technological landscape are rapidly changing, driven by the global need to minimize the reliance on non-renewable resources. The energy transformation over the past five years has resulted in sustainable energy initiatives involving innovative adaptations of energy technologies by regional local authorities. In this context, and as local action will eventually decide global sustainability, this article explores the ways that sustainable strategies and energy actions were comprehended and adopted by regional public authorities. The focus area is the island of Crete in Greece. Owing to its geographical position and the nearly autonomous institutional structure of the broader state apparatus, it serves as a microcosm of a state, rendering it an effective imitation of the Greek state. The methodology of this study is derived from both the relevant literature outcomes and the national legislative framework. A document review served as a preliminary tool to investigate national and regional policy frameworks. This was followed by in-depth interviews with regional stakeholders to collect primary data on implementation. This study’s originality derives from addressing the gap between the proposed measures imposed by the state, along with various sustainable activities from a holistic perspective, and their actual uptake in Crete. The analysis of the results provides insights regarding their effectiveness based on the regional authorities’ approach in a developed South Mediterranean country. The article confirms that municipalities’ heterogeneity and structural differentiation are critical for sustainable energy transition and concludes with future research directions worthy of thorough examination, towards energy transition maturity of an insular region. Full article

Cyclodextrins (CDs) have gained increasing attention as versatile platforms for enhancing drug delivery to the central nervous system, particularly in overcoming the restrictive properties of the blood–brain barrier (BBB). Owing to their unique cyclic oligosaccharide structure, CDs are capable of forming inclusion complexes with a wide range of therapeutic agents, thereby improving their solubility, stability, and bioavailability. In addition to their role as excipients, growing evidence indicates that CDs can actively modulate biological processes, including membrane fluidity and cholesterol homeostasis, which are critical factors in neurological disorders. This review explores the application of CDs in facilitating drug transport across the BBB through multiple mechanisms, including carrier-mediated transport, receptor-mediated transcytosis, and nanoparticle-based delivery systems. Special emphasis is placed on their use in the treatment of neurodegenerative and neurological diseases, such as Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, Niemann–Pick type C disease, and other central nervous system disorders. In these contexts, CD-based formulations have demonstrated the ability to enhance brain targeting, reduce pathological protein aggregation, and improve therapeutic outcomes in preclinical models. This review uniquely integrates cyclodextrin’s physicochemical properties with specific blood–brain barrier transport mechanisms, proposing a structure–transport–therapy framework that enables a more predictive understanding of brain-targeted drug delivery. Full article

Owing to the high stability and low cost of nanozymes, they have been extensively investigated and reported. In this work, highly active CeO₂ nanoflowers were first prepared and then different metal elements were doped into the CeO₂ nanoflower matrix via a novel synthesis method to fabricate M-CeO₂ (M = Cu, Fe, Co, Mn, La) nanomaterials. Mn-CeO₂ with the highest peroxidase-like activity was selected via systematic screening, the as-prepared Mn-CeO₂ nanocomposites exhibited enhanced enzyme-like activity due to the strong metal-support interaction. This article explored the effects of doping ratio, pH, temperature, reaction time, and material concentration on its activity. A simple sensitive and selective colorimetric method was established and successfully used to detect hydrogen peroxide and ascorbic acid sensitively. When the hydrogen peroxide (H₂O₂) concentration is within the 2.0–120.0 μM range, the UV-visible absorbance at 652 nm was associated linearly with the H₂O₂ concentration, R² = 0.9959, LOD = 1.7 μM (S/N = 3). The absorbance of the reaction system showed a good linear relationship with the ascorbic acid (AA) concentration (1.0–40.0 μM, R² = 0.992), LOD = 0.98 μM (S/N = 3). This study provides an effective way to construct efficient nanozymes and their potential applications in sensing and detection. Full article

An important challenge for materials researchers in the modern era is the fabrication of high-performance electrodes with novel designs and structures to enhance electrochemical sensing and energy storage performance. Recently, perovskite-structured bimetallic hydroxide materials, owing to their high conductivity, decent surface area, abundant redox activity, and good stability, have emerged as promising candidates for bifunctional electrochemical applications. In this study, we designed perovskite-type CuSn(OH)₆ microspheres via a facile coprecipitation method for nifedipine (NFD) sensing and supercapacitors (SCs). Various characterization techniques were employed to confirm the successful synthesis of CuSn(OH)₆. The uniform formation and distribution of CuSn(OH)₆ within the sphere structure provide rich reactive sites and enhance structural stability, thereby improving electrochemical activity. This architecture also induces a synergistic effect between Cu and Sn, which increases conductivity and accelerates redox kinetics. Consequently, the electrode modified with CuSn(OH)₆/GCE exhibited a wide linear concentration range of 0.4–303.3 µM and a low detection limit of 0.44 µM for NFD detection. This sensor further demonstrated superior analytical reliability, with selectivity of <5%, cycling stability of 84.79%, reproducibility of 3.3%, and recovery rates of 99.2–99.8% in the serum sample. Concurrently, the CuSn(OH)₆/NF showcased a high specific capacitance of 514 F g⁻¹ at 1 A g⁻¹, good longevity of 83.05% retention after 5000 cycles, and low charge transfer resistance of 6.56 Ω and solution resistance of 1.04 Ω, validating fast ion–electron transport. These results underscore that perovskite-based CuSn(OH)₆ is an efficient dual-functional electrocatalyst for sensitive electrochemical detection and high-performance SCs. Full article

Detecting small objects in drone imagery remains challenging because of extreme object scale variations, dense scenes, and limited pixel information. Although recent YOLOv8 variants provide multiple model scales and architectural options, systematic guidance on their practical use in UAV-based detection remains limited. Rather than proposing novel network architectures, this study provides a quantitative cost–benefit analysis and empirical deployment guidelines by comprehensively evaluating the complete YOLOv8 family on the VisDrone dataset to assess the effects of the model capacity, input resolution, and architectural modifications on the small-object detection performance. The results showed that increasing the model capacity exhibited diminishing returns: YOLOv8l achieved the best overall accuracy (15.9% mAP50), while the larger YOLOv8x model exhibited a substantial performance degradation (7.32% mAP50) owing to training instability under data-constrained conditions. Scaling the input resolution from 640 to 1280 yielded a 25% improvement in detection performance, substantially exceeding the gains obtained through architectural modifications, such as adding a P2 detection layer (+6%). The optimal configuration (YOLOv8l @ 1280) achieved a 488% improvement compared to the YOLOv5 baseline. These findings demonstrate that, for UAV-based small-object detection, prioritizing an appropriate model capacity and input resolution is more effective than increasing the architectural complexity. Full article

Cavity ring-down spectroscopy has significant potential for detecting trace volatile organic compounds, owing to its long absorption path and high sensitivity. However, in practical measurements, noise severely decreases the accuracy of decay curves and the reliability of concentration retrieval. To address this, we developed a deep learning-based denoising model called decay-upsampling FC-Net. Experimental results showed that the model improved the signal-to-noise ratio from 13.86 dB to 26.79 dB and processed a single decay curve in only 0.000207 s on average. Moreover, under high-noise conditions, it determined the ring-down time more accurately than conventional methods. This study provides an effective signal processing solution to enhance the practical reliability of Cavity ring-down spectroscopy gas detection systems. Full article