Search Results (63,825)

Tendon injury, tendon–bone interface disruption, and rotator cuff pathology involve inflammatory signaling, oxidative stress, extracellular matrix turnover, cellular senescence, and impaired differentiation. Plant-derived bioactive compounds, including curcumin, quercetin, and other flavonoids and botanical formulations, have been investigated across these pathways in preclinical tendon and enthesis models, but interpretation is complicated by heterogeneous models, formulations, delivery platforms, and endpoints. This review distinguishes preclinical and formulation-specific tissue-response signals from evidence sufficient to support patient-facing clinical or nutraceutical claims. It synthesizes 31 articles using an evidence-tiering framework separating preclinical plausibility (Tier 3), limited human-facing translational evidence (Tier 2), and robust clinical efficacy (Tier 1). The corpus was predominantly Tier 3, with two Tier 2 articles providing human tissue or clinically oriented evidence and no Tier 1 evidence identified. An evidence-calibrated translational proximity map and an overclaim prevention checklist are provided to guide interpretation. The proposed proximity map reflects relative translational position within a focused narrative corpus and was not based on systematic review methods, formal risk-of-bias assessment, or clinical intervention evidence. The limited human-facing evidence does not represent patient-intervention evidence and does not support clinical, supplementation, or treatment recommendations. These compounds remain candidates for staged translational investigation rather than established interventions. Full article

Bioanalytical methods to quantitate conjugated payloads are essential for assessing antibody-drug conjugate (ADC) stability and pharmacokinetics (PK). Dual-payload ADCs present analytical challenges; different linker chemistries can require complex digestion conditions to perform the cleavage. Developing separate methods for each linker combination can be time and resource demanding. Rat tritosomes—purified lysosomal fractions from Triton-treated rat liver—provide a comprehensive enzymatic mixture that mimics the lysosomal environment. The presented bioanalytical method combines immunoaffinity purification with tritosome-mediated digestion for simultaneous quantitation of dual-conjugated payloads. The method was applied to a model dual-payload ADC containing two different cytotoxic payloads, conjugated using different enzymatically cleavable linkers, with an unrelated DAR (drug-to-antibody ratio). Method validation in mouse plasma demonstrated excellent accuracy (bias ± 20%, LLOQ and ULOQ ± 25%) and precision (coefficient of variation CV% ≤ 20%, LLOQ and ULOQ ± 25%) across all concentration levels (lower to upper limit of quantitation, LLOQ to ULOQ) for both payloads, with 100% of quality control samples (QCs) meeting acceptance criteria for hybrid LC-MS/MS quantitation methods. This tritosome-based approach provides a unified, efficient platform for multi-payload ADC bioanalysis, eliminates linker-specific method optimization, and enables robust support for preclinical studies. The method has been tested for accuracy and precision on 4 different model ADCs and employed to quantify the conjugated payloads in in vivo samples from a homozygous hFcRn transgenic mouse model (Tg32) PK study, resulting in reliable data in accordance with total antibody measurements. Full article

Reliable and rapid forecasting of wind turbine output power is vital for operators, particularly day-ahead and intraday market scheduling and reserve allocation. However, the inherent unpredictability, intermittency, and volatility of wind turbine output make forecasting processes difficult. To address this challenge, this study proposes a Sinusoidal Representation Network (SIREN)-based forecasting model for high-accuracy, rapid direct multi-horizon forecasting of wind turbine output power. SIREN is selected due to the periodic and symmetrical mathematical structure of its sinusoidal activation function, which allows the model to represent both low-frequency trends and high-frequency sudden changes in wind energy data. To improve data quality, compensate for asymmetric fluctuations in wind data, and provide more suitable inputs for SIREN training. Several preprocessing steps are utilized before feeding the data into the model. The proposed preprocessing step includes a moving median filter, robust scaling based on median and interquartile range, Winsorizing clipping, and a Hampel filter to reduce the effects of instantaneous noise, outliers, and local peaks without disrupting temporal continuity. Subsequently, a Savitzky–Golay smoothing is applied to attenuate high-frequency measurement noise while preserving curvature, local peaks, and physically meaningful short-term dynamics in the data. The sliding-window approach is used to formulate the multi-horizon forecasting problem directly, and a direct h-step-ahead forecasting architecture is designed, preserving structural symmetry in the time series. The SIREN is trained and tested using MATLAB with the help of two different datasets: Dataset-1 has a 10 min resolution for 1 year, and Dataset-2 has a 1 h resolution for 15 years. The forecast horizon parameter h is considered separately for each step, and the proposed SIREN is independently trained, validated, and tested for each target horizon while maintaining chronological order. The results demonstrate that the proposed model is able to yield high forecast performance for a wide spectrum of horizons ranging from 10 min to 15 days. The accuracy of the proposed model for Dataset-1 is R² of 99.6%, MSE of 0.085%, MAE of 1.7%, and MAPE of 12%, while for Dataset-2, the accuracy is R² of 98.8%, MSE of 0.3%, MAE of 3.6%, and MAPE of 23%. Ablation and sensitivity analyses are conducted to evaluate the impact of the basic components used in the proposed model on forecasting performance. In addition, combative experiments are performed using traditional time series, ML, and DL forecasting techniques to better assess the contribution of the model. The obtained results show that the SIREN-based direct forecasting approach provides strong learning capability, as well as high forecasting accuracy, for both high-resolution and low-resolution wind power data. Overall, its ability to capture the symmetric and periodic characteristics inherent in wind turbine power data makes it a promising alternative for multi-horizon wind power forecasting applications. Full article

A recently developed high-strength nickel-based corrosion-resistant alloy has attracted increasing interest for drilling and production operations in unconventional oil and gas fields. Owing to its high resistance to media containing H₂S, CO₂ and chloride ions, together with its ultra-high strength and favorable strength–toughness balance, this alloy is suitable for demanding service conditions. During hot working, dynamic recrystallization (DRX) governs deformation softening, grain refinement and the subsequent microstructural state, and thus has a direct influence on final properties. In this work, isothermal compression experiments were conducted on this ultra-high-strength nickel-based corrosion-resistant alloy using a Gleeble thermal simulator at 1000–1150 °C and strain rates of 0.01–10 s⁻¹. Electron backscatter diffraction (EBSD) was used to quantify grain size, grain-boundary misorientation, kernel average misorientation (KAM) and the DRX volume fraction. The results indicate that higher deformation temperature generally accelerates DRX, lowers the KAM value and increases the recrystallized-grain fraction. Under a constant deformation temperature, the DRX volume fraction changes non-monotonically with strain rate, showing an initial increase followed by a decrease. Based on the EBSD-derived DRX fractions, linear and quadratic single-parameter models using the Zener–Hollomon parameter were examined first, but neither provided satisfactory fitting accuracy. A two-variable empirical model was therefore formulated for a fixed true strain of ε = 0.92 by considering deformation temperature and strain rate separately. The predicted values agree well with the experimental data, giving R² = 0.91278 and an average relative error of 8.53%. The proposed model captures the main variation tendency of the DRX volume fraction within the studied processing window and provides a useful basis for microstructure control and hot-working parameter design for ultra-high-strength nickel-based corrosion-resistant alloys. Full article

Bovine milk represents a highly complex colloidal system whose physicochemical stability depends on the organization of milk fat globules, casein micelles, membrane-associated phospholipids, and somatic cellular components. Mechanical separation procedures such as centrifugation induce redistribution of dispersed colloidal fractions and structural perturbations within the milk matrix, potentially enabling fraudulent reduction of somatic cell count while preserving bulk compositional parameters. In the present study, we investigated whether advanced deep learning architectures could identify centrifugation-associated structural alterations in bovine milk using microscopy image representations. A total of 16,472 microscopy images obtained from centrifuged and non-centrifuged milk samples were analyzed using Swin Transformer V2 and ConvNeXt-Base architectures. Both models successfully detected centrifugation-associated structural perturbations and substantially outperformed the previously analyzed InceptionC baseline. ConvNeXt-Base achieved 87.30% classification accuracy together with 86.85% balanced accuracy and 86.59% harmonic average of recalls following totalogit aggregation. Importantly, Swin Transformer V2 demonstrated strong monotonic relationships between logit metrics and centrifugation ratio (r = 0.640–0.651, p < 0.01), indicating sensitivity to progressive image-level changes associated with increasing centrifugation ratio. Collectively, the obtained findings demonstrate that microscopy-derived deep learning representations capture structural information associated with centrifugation-induced changes in bovine milk, supporting the applicability of AI-assisted microscopy for detecting processing-related alterations in complex dairy systems. Full article

The Droplets Triboelectric Nanogenerator (DTENG) possess distinctive merits in harvesting ambient hydropower into usable electricity. Nevertheless, droplet spreading, contact separation behavior, and dynamic interfacial interactions on insulating film surfaces are extremely sensitive to external environmental factors, giving rise to complicated nonlinear output characteristics. Herein, this work reports a droplet-driven TENG based on fluorinated ethylene propylene (FEP) thin films. We systematically explore how electrode geometry, droplet falling height, substrate inclination angle, and droplet flow rate modulate electrical output performance, and further clarify the fluid-triboelectric electron transfer between droplet hydrodynamic evolution and electric signal generation. Notably, we identify the retraction current during droplet recession, a signal largely neglected in previous solid–liquid TENG research, which complements the fundamental mechanism of interfacial charge transfer. This work not only provides a systematic experimental basis for understanding the working mechanism of DTENG, but also lays a theoretical and practical foundation for developing efficient and controllable water energy collection and self-powered sensor systems. Full article

Existing radio environment map(REM)-based emitter-detection methods suffer from high false positives and missed detections in blurred or conjoined structures, or require large annotated datasets and heavy computation. We propose an unsupervised method, persistent homology with agglomerative clustering (PH-AC), based on an improved persistent-homology algorithm. A simulated spaceborne-REM dataset is constructed via synthetic-aperture passive interferometric imaging, covering isolated, adjacent-pair, and complex-emitter distributions. Persistent homology tracks the birth, death, and merging of zero-dimensional connected components as the intensity threshold varies. To address missed detections for spatially proximate emitters, multidimensional topological features are constructed via feature-contribution analysis. Agglomerative clustering with Ward linkage then adaptively separates emitters from noise without supervision. Experimental results show that PH-AC achieves a perfect F1 score of 1.000 in isolated scenarios; for adjacent emitters, it improves F1 by 15.7% over the best image-processing method and stays within 4% of supervised deep learning methods, while requiring no annotations. In complex environments, it attains an F1 of 0.937, outperforming all compared methods. Its computational complexity is only $2.25\times {10}^6$ FLOPs, three orders lower than YOLO-based detectors. This work offers a lightweight, annotation-free topological paradigm for spaceborne-REM-emitter detection. Full article

Objective: Volleyball athletes require well-developed balance control during spiking, blocking, rapid movement, and landing. Joint range of motion (ROM) may also influence limb extension, support adjustment, and center-of-mass control. Previous studies have usually examined balance ability and joint ROM as separate factors related to volleyball performance. However, the associations between dynamic balance, static balance, and multi-joint ROM in the upper and lower limbs remain insufficiently understood. This study therefore aimed to examine the relationship between balance performance and upper- and lower-limb joint ROM in female college volleyball athletes. Methods: Thirty-five female college volleyball athletes were included. Dynamic balance of the upper and lower limbs was assessed using the Y-Balance Test, and static balance was evaluated under eyes-open and eyes-closed conditions using a static balance platform. Upper- and lower-limb ROM was measured using an electronic goniometer and the knee-to-wall test. Paired-sample t-tests were used to compare bilateral differences and differences between visual conditions. Pearson correlation analysis was performed to examine associations between joint ROM and balance performance, and false discovery rate (FDR) correction was applied to account for multiple comparisons. Results: (1) No significant bilateral difference was observed in upper-limb YBT-UQ performance (p > 0.05); for lower-limb YBT-LQ performance, a significant difference was found only in the anterior direction, with the right side showing higher values than the left side (p < 0.01). (2) Static balance parameters under the eyes-closed condition were significantly poorer than those under the eyes-open condition (p < 0.01); under the same visual condition, only the total sway path length of the right foot was significantly shorter than that of the left foot (p < 0.05). (3) The ranges of motion of right shoulder flexion, shoulder horizontal adduction, shoulder external rotation, elbow flexion, and knee-to-wall distance were significantly greater than that of the left side (all p < 0.05), and right hip internal rotation ROM was also significantly greater than that of the left side (p < 0.01). (4) Dynamic balance was correlated with selected joint ROM measures. Specifically, the anterior reach direction of the right YBT-LQ was positively correlated with hip flexion ROM (r = 0.593, p < 0.01) and knee-to-wall distance (r = 0.653, p < 0.01), and these correlations remained statistically significant after FDR correction. (5) Static balance parameters were correlated with selected lower-limb joint ROM measures in the original correlation analysis; however, these correlations did not remain significant after FDR correction. Conclusions: Female college volleyball athletes demonstrated a certain degree of bilateral asymmetry in dynamic balance and a pronounced dependence on visual input during static balance tasks. After FDR correction, the associations between the anterior reach direction of the right YBT-LQ and both hip flexion ROM and knee-to-wall distance remained stable, suggesting that these ROM measures may be related to anterior dynamic balance performance. These findings may provide a reference for postural control assessment and the development of sport-specific training programs for female volleyball athletes. Full article

This study examines the impact of public attention on financial markets during the Israel–Hamas conflict, focusing on the TA35 stock index and the Israeli Shekel (ILS) exchange rate over the period October 2023 to April 2025. By distinguishing between global and domestic Google search activity, the analysis investigates whether the origin of attention differentially affects market performance and currency dynamics. Public attention is treated as a real-time proxy for investor sentiment and perceived risk. Methodologically, the study combines Google Trends data with EGARCH(1,1) models to capture both return effects and asymmetric volatility responses. To enhance robustness, Principal Component Analysis (PCA) is applied separately to global and domestic search datasets, generating latent indices that reflect conflict-related and humanitarian narratives. These indices are subsequently incorporated into the empirical models. The findings reveal that global search intensity related to conflict topics exerts a significant negative effect on stock returns and contributes to currency depreciation, reflecting heightened uncertainty and risk aversion. In contrast, domestic search activity is associated with stabilizing or positive effects, suggesting local resilience and confidence. PCA-based models improve explanatory power and confirm that the geographical origin of attention plays a crucial role in shaping financial outcomes. Additionally, the results indicate that attention-driven shocks influence volatility asymmetrically, amplifying downside risk during periods of intensified global concern. Overall, the study contributes to the literature by integrating behavioral indicators into financial risk modeling and providing a novel, real-time framework for assessing how digital attention transmits geopolitical risk into asset prices. Full article

The shock wave/boundary layer interaction (SWBLI) is a common flow phenomenon in high-speed aircraft flow fields. It is important to control the separation caused by SWBLI. This paper investigates the influence of spanwise periodic-motion micro-vortex generators (MVGs) on SWBLI. A combination of particle image velocimetry (PIV), high-frequency Schlieren and fluorescent oil-film visualization was employed to analyze the interaction region of a flat plate compression ramp model. The incoming flow Mach number was 6, and the MVGs oscillation frequencies were 10 Hz, 30 Hz and 50 Hz, respectively. The results reveal that neither the presence nor the spanwise oscillation in the MVGs fundamentally altered the separation–reattachment flow structure. Nonetheless, both factors contributed to an increase in boundary layer thickness and an expansion of the absolute size of the separation region. The trailing vortices generated by the MVGs exerted a stabilizing influence on near-wall turbulent structures, resulting in a reduction in surface friction drag. However, the drag reduction effect diminished as the oscillation frequency increased, corresponding to a weakening of the trailing vortex strength. Additionally, the MVGs and their spanwise oscillation modulated the low-frequency energy distribution of the flow, amplifying the low-frequency oscillation peak associated with the separation shock and raising the time-averaged oscillation position. Full article

Multifunctional membranes capable of simultaneously separating oil–water emulsions and removing organic dyes from complex aqueous systems have garnered considerable attention in recent years. However, the facile fabrication of high-performance membranes that integrate both separation and adsorption functions remains a significant challenge. Herein, we report the fabrication of a polyacrylonitrile/polyvinyl alcohol–chitosan (PAN/PVA-CS) bilayer membrane with asymmetric wettability via electrospinning. The micro/nanostructures and surface wettability of the as-prepared membranes were precisely tailored by modulating the chitosan (CS) concentration. The resultant PAN/PVA-CS membrane exhibited an overall separation efficiency exceeding 97.5% for mechanically emulsified samples. Notably, the PVA-CS layer demonstrated superhydrophilicity and excellent underwater oleophobicity, enabling the gravity-driven simultaneous separation of oil-in-water emulsions and adsorption of water-soluble Congo red dye without requiring external pressure. The maximum adsorption capacity for Congo red reached 61.3 mg g⁻¹, surpassing that of numerous reported membrane-based and adsorbent materials. Concurrently, the hydrophobic PAN layer in the bilayer structure enabled the separation of water-in-oil emulsions. Overall, this work provides a promising strategy for the rational design of asymmetrically wettable multifunctional membranes with great potential for practical application in the purification of complex industrial wastewater containing both emulsified oils and soluble organic dyes. Full article

The determination of optimal geometric characteristics of a catamaran that minimize vessel motion responses under prescribed design and operational conditions remains insufficiently addressed in existing engineering practice. This study presents a systematic methodology for the evaluation of catamaran seakeeping performance through the structured parametric comparison of principal geometric parameters. The proposed methodology comprises the identification of relevant geometric variables, the specification of their admissible variation ranges in accordance with design constraints, the selection of appropriate numerical evaluation tools, and the quantitative analysis of resulting motion responses. The objective is to determine parameter combinations that yield minimum motion amplitudes. The methodology presented in this article is partly a complex methodology for evaluation of seakeeping and total resistance, and partly selection of the most favorable combinations of geometrical parameters satisfying the design task parameters across both above-mentioned hydrodynamic qualities. The resistance part of the methodology is presented in previous works with links and description provided in this article. A graphical system for presenting simulation results is developed, allowing arrangement of the calculation results on one horizontal axis, representing catamaran length variations, grouped by the speed and demihull separation values and including catamaran demihull symmetry considerations. Aligned under each other, the graphs provide an intuitive interpretation of total resistance trends and seakeeping across various geometric configurations and operational speeds. This method, the seakeeping part of which is illustrated in the results paragraph, enables a comprehensive comparison of multiple design variants within a clear visual framework. The methodology is applied to a representative catamaran configuration by parametrically varying key geometric characteristics, including vessel length, demihull separation, and hull symmetry. The corresponding seakeeping responses are evaluated using the Maxsurf Motions computational framework. The results demonstrate that systematic variation and analysis of geometric parameters enable the identification of configurations with significantly reduced motion amplitudes. Pitching RAO amplitudes for different catamaran lengths can vary 45–50%, for demihull separation—25–50% and for asymmetry 27–50%. Heaving RAO amplitudes for different catamaran lengths can vary 45–50%, for demihull separation—32–65% and for asymmetry 30–60%. The findings indicate that demihull separation, hull-form symmetry, and overall vessel length each play a significant role in determining catamaran seakeeping performance. The proposed approach provides a robust basis for the early-stage design structured parametric comparison of catamarans, facilitating the selection of geometric configurations that minimize projected vessel motions and improve overall seakeeping performance. Full article

The primary control mechanism for synaptic uptake of GABA is through γ-aminobutyric acid transporter 1 (GAT-1, SLC6A1), a known target for anti-epileptic drugs. Although there is a clinically used GAT-1 inhibitor, tiagabine, the development of a new ligand with an advanced pharmacological profile is desirable. For this purpose, a multi-tiered virtual approach to screening has been created, involving pharmacophore-based search; application of the Informational Spectrum Method for Small Molecules, followed by EIIP/AQVN filtering (ISM-SM); molecular docking using an ensemble of several experimentally obtained structures of GAT-1; and ADMET predictions. Pharmacophore-based screening of the ZINC database of natural products, combined with ISM-SM/EIIP filtering, yielded 237 candidate compounds. Structural separation analysis discriminated between the positives and negatives, enabling enrichment-based prioritization. The use of a composite normalized rank score based on docking affinity and structural similarity allowed for the identification of the top candidates: ZINC03643214 and ZINC67840571. Collectively, these refinements establish a more sophisticated computational model for identifying novel GAT-1 inhibitors and highlight promising candidates for future experimental evaluation. Full article

The increasing power density of modern electronics demands more efficient thermal management. Heat sinks with sinusoidal fins remain understudied, and the combined effect of perforations and variable fin spacing on transient performance has not been systematically quantified. This numerical study, conducted using ANSYS Fluent 2025 R2, analyzes three sinusoidal fin configurations under forced convection (3–5 m/s): solid fins (Case A), perforated fins (Case B), and perforated fins with alternating spacing of 2 mm and 4.5 mm (Case C). The base was maintained at 60 °C during a 20 s transient period. A mesh with an average skewness of less than 0.25 ensured numerical convergence. Case B showed remarkable uniformity in the base temperature (variations < 1 °C), in contrast to Case A (variations of up to 14.17 °C), due to a thermal boundary layer restart effect induced by the perforations. Case C reached the highest heat dissipation temperatures (up to 54.64 °C at 3 m/s), representing a 47.2% increase compared to Case A, indicating more effective heat extraction with this type of separate fin. The critical transient window occurs within the first 5 s (>85% of the total temperature rise). A vertical temperature gradient of 1.19 °C/mm was observed near the base. Although the perforations reduced the heat transfer area by 5.94%, the induced turbulence compensated for this loss. Sinusoidal fins with perforations and variable spacing significantly improve convective heat removal. Full article

Selective separation of quartz and feldspar is vital for high-purity silicate raw materials but is challenging due to similar surface chemistries. Conventional flotation typically requires high reagent dosages and hazardous chemicals, raising environmental and economic issues. This study proposes a sustainable flotation strategy using green, bio-derived reagents to improve quartz–feldspar separation by eco-friendly bio-derived reagents. Sodium oleate, a fatty acid collector, was used with low-toxicity modifiers to create synergistic systems. Flotation performance was tested by reagent dosage and pH, with mineral characteristics analyzed via X-ray Fluorescence (XRF) and Particle Size Distribution (PSD). Results showed that the investigated reagent systems improved the differential flotation response between quartz and K-feldspar. Under the optimized flotation conditions (pH 9.24), quartz recovery reached 84.01%, demonstrating that environmentally friendly reagent combinations can achieve favorable flotation performance while reducing chemical consumption. Response Surface Methodology (RSM) was used to optimize flotation variables like pH and reagent dosage, developing a model to predict conditions for favorable flotation response, enabling systematic process improvement. These findings highlight reagent-system optimization as an eco-friendly method for mineral beneficiation, aligning with green chemistry and sustainable practices. Full article