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Oxidative stress imbalance and cellular damage mediated by the ND4 G11778A mutation
The influence of sequential drilling and machining parameters on uncut fiber formation and delamination damage in fiber-reinforced composites
Abstract Drilling is crucial in composite manufacturing, but often induces delamination and uncut fibers, degrading structural integrity. This study examines the combined influence of sequential drilling (using drills of progressively increasing diameter) and machining parameters (feed rate and cutting speed) on damage in fiberglass composites. Sequential drilling was performed at a feed rate of 1000 mm min⁻¹ and spindle speed of 500 rpm, while the effects of machining parameters were investigated over feed rates ranging from 50 to 2000 mm min⁻¹ and spindle speeds from 500 to 3000 rpm. Results show that sequential drilling effectively reduced delamination at both the entrance and exit of drilled holes, while simultaneously decreasing exit-side uncut fibers and increasing entrance-side uncut fibers. Furthermore, increasing cutting speed significantly mitigated damage, while higher feed rates worsened it. Three-point bending tests revealed that modified drilling conditions increased the fracture load of drilled specimens by up to approximately 18%, with fracture loads rising from about 956 N to 1130 N depending on feed rate and spindle speed. Unlike previous studies that primarily examine drilling strategies or machining parameters in isolation, this work provides a unified experimental framework that links sequential drilling methodology, quantitative damage characterization (via image processing), and mechanical performance (via three-point bending tests). Quantitative damage assessment reveals the complex interplay between drilling method and parameters, offering valuable insights for optimizing composite drilling processes.
High intensity functional training versus traditional resistance training effects on inflammatory, metabolic, and physical outcomes in overweight men a randomized controlled trial
Performance analysis of a three-dimensional micromixer with baffles using a flexible physics-informed neural network
Abstract This study introduces a Flexible Physics-Informed Neural Network (FlexPINN) to overcome the limitations of standard PINNs in modeling fully three-dimensional, geometrically complex micromixers with internal baffles. The mesh-free framework integrates parallel-network architecture, first-order dimensionless governing equations, adaptive loss weighting, and a novel global conservation penalty to prevent trivial solutions and ensure robust convergence. Employing transfer learning reduces the training time for new baffle shapes by ~ 35%, requiring approximately 3.5 h versus 5.5 h for a base case on a single GPU. Validated against conventional Computational Fluid Dynamics, FlexPINN achieves high-fidelity predictions, with maximum relative errors of 3.25% for the pressure drop coefficient and 2.86% for the mixing index. A comprehensive parametric study evaluates three baffle shapes (rectangular, elliptical, triangular) across four configurations and Reynolds numbers (Re = 5, 20, 40, 80). Results demonstrate that rectangular baffles in a double-unit, staggered configuration (C) at Re = 40 yield the peak mixing efficiency of 1.63, significantly outperforming other designs. This work successfully bridges a critical gap in PINN applications by providing a validated, efficient tool for the analysis and optimization of intricate 3D passive micromixers.
A real-time detection framework for road cracks in noisy and morphologically complex environments
A minimal recurrent neural network models the robustness of interleaved practice on motor sequence learning
Resistance of vegetation sensitivity to climate and human activities under short-term drought in subtropical humid region: a case study of Guangdong, China
Abstract Understanding how the changes of vegetation sensitivity to climate and human activities under drought is essential for evaluating ecosystem resistance in subtropical humid regions. This paper focused on Guangdong Province, China, and used SPEI-3 (Standardized Precipitation Evapotranspiration Index) to identify short-term drought events (< 6 months). LMG (Lindeman-Merenda-Gold method) and XGBoost (Extreme Gradient Boosting) were used to quantitatively analyze the contributions of temperature, precipitation and nighttime light (NTL) to the NDVI of evergreen forest, grassland and urban region. LMM (Linear Mixed Model) with month and year as random effects were applied. The conclusions revealed that: (1) Short-term drought significantly reduced the sensitivity of vegetation to temperature in the south of the Tropic of Cancer, while vegetation in the north of the Tropic of Cancer maintained a stable relationship with temperature. (2) Under drought, the temperature contribution to evergreen forest decreased the most (10–13%), followed by urban region (6–15%) and grassland (7–12%). The precipitation contribution to evergreen forest increased by 7–10% and 2–7% for grassland. (3) Drought weakened the positive temperature-NDVI correlation in the west located in the south of the Tropic of Cancer, while the positive temperature-NDVI correlation persisted in the north located in the north of the Tropic of Cancer. (4) Under drought, vegetation maintained a stable positive response to climate. The temperature-NDVI relationship in more than half of the cities shifted from positive to negative.
Intracranial LPS injection induces cerebral palsy-like motor and behavioral deficits in immunodeficient mice
Investigation of direct welding of organza textile and PET mesh for designing free-form lighting
Human and environmental controls on soil contamination in a dust-prone region revealed by random forest and Shapley additive explanations analysis
Abstract Accurate prediction of the spatial distribution of potentially toxic elements (PTEs) and identification of the most important environmental drivers are essential for reducing their adverse effects on human health and the environment. In this regard, the present study was conducted to predict the spatial distribution of arsenic (As), cadmium (Cd), cobalt (Co), chromium (Cr), and lead (Pb) in a dust-prone area of central Iran using the RF model under 11 scenarios constructed based on human activity-based factors (HAF), land-based factors (LSF), physicochemical soil properties) PSP), meteorological factors (MF), and remote sensing auxiliary data (RSAD). The overall contribution of the influencing factors in predicting soil PTEs was determined using the SHapley additive exPlanations (SHAP) analysis. Soil PTEs and some other properties of 107 surface soil samples were measured in the laboratory. The best performance of RF in predicting As, Co, and Cr was observed under scenario VI (HAF + PSP) with the R 2 value of 0.59, 0.60, and 0.58, respectively. The RF under Scenario X (PSP + LSF+HAF+RSAD) showed the best performance in predicting Cd (R 2 = 0.67). The performance of RF for predicting Pb was weak in all scenarios (R 2 < 0.38). The contributions of HAF, LSF, PSP, and RSAD in predicting Cd were 54.9, 21.5, 18.3, and 5.2%, respectively. On average, the contributions of HAF and PSP to the prediction of the other three PTEs were 55.6 and 44.4%, respectively. Among these categories, distance to industries, calcium, magnesium, magnetic susceptibility, terrain ruggedness index, and distance to rivers were identified as the most important predictors. Our findings are useful for improving soil management to reduce the adverse effects of PTEs in arid environments.
Analytical wave families and stability dynamics in a modified complex Ginzburg–Landau model via the modified extended direct algebraic method
Abstract This study investigates the Modified Complex Ginzburg–Landau Equation, a fundamental nonlinear partial differential equation that plays a central role in modeling complex wave dynamics, pattern formation, and dissipative phenomena in systems such as nonlinear optics, Bose–Einstein condensates, superfluids, and plasmas. Despite its importance, obtaining exact analytical solutions and understanding their stability properties remain challenging problems with significant theoretical and practical implications. To address this challenge, the Modified Extended Direct Algebraic Method is employed to construct exact analytical solutions in a systematic and efficient manner. By transforming the governing nonlinear equation into an algebraically solvable system, a broad and unified family of exact solutions is derived. These solutions include bright and dark solitons, singular solutions, periodic and singular periodic waves, as well as solutions expressed in exponential, Weierstrass elliptic, and Jacobi elliptic function forms. In addition, a comprehensive stability analysis is carried out to examine the response of these wave structures to small perturbations and to assess their long-term dynamical behavior. The physical characteristics and dynamical features of the obtained solutions are illustrated through detailed two-dimensional and three-dimensional graphical representations for selected parameter values. The results demonstrate the effectiveness of the Modified Extended Direct Algebraic Method in analyzing complex nonlinear models and provide deeper insight into wave propagation and stability mechanisms in dissipative systems governed by the Modified Complex Ginzburg–Landau Equation.
Causal inference shapes crossmodal postdiction in multisensory integration
Abstract In our environment, stimuli from different sensory modalities are initially processed within a temporal window of multisensory integration spanning several hundred milliseconds. During this window, stimulus processing is influenced not only by preceding and current information, but also by input that follows the stimulus. The computational mechanisms underlying crossmodal backward processing, which we refer to as crossmodal postdiction, are not well understood. We examined crossmodal postdiction in the Illusory Audiovisual (AV) Rabbit and Invisible AV Rabbit Illusions, in which postdiction occurs when flash-beep pairs are presented shortly before and shortly after a single flash or a single beep. We collected behavioral data from 32 participants and fitted four competing models: Bayesian Causal Inference (BCI), forced-fusion, forced-segregation, and non-postdictive BCI. The BCI model fit the data well and outperformed all other models. Building on previous findings that demonstrate causal inference during non-postdictive multisensory integration, our results show that the BCI framework can also explain crossmodal postdiction phenomena. Our findings suggest that the brain performs causal inference not only across concurrent sensory inputs but also across temporal windows, integrating information from past, present, and subsequent events across modalities to construct a unified percept.