Search Results (136,691)

Background/Objectives: Young adulthood is a critical developmental period during which lifestyle behaviors may shape intrinsic brain network dynamics that support cognition. This pilot longitudinal intervention study examined whether variability in physical activity and sedentary behavior during an 8-week exercise and/or cognitive intervention protocol was associated with changes in intrinsic brain dynamics and cognitive and mood outcomes in undergraduate young adults. Methods: Participants (n = 32) completed resting-state functional magnetic resonance imaging (rs-fMRI) at baseline (T1) and post-intervention (T2). Dynamic functional network connectivity (dFNC) was estimated from 53 intrinsic connectivity networks derived using spatially constrained independent component analysis (ICA). Ten recurring dynamic connectivity states were identified and individualized using constrained dynamic double functional independent primitives (c-ddFIPs). State occupancy and dynamic convergence and divergence metrics were computed to characterize network flexibility. Results: Greater moderate-to-vigorous physical activity was modestly but consistently associated with increased occupancy of integrative higher-order states, particularly States 6 and 7, and reduced occupancy of more segregated configurations. More physically active individuals also demonstrated greater divergence between integrative and low-engagement states, whereas greater sedentary time corresponded to increased similarity among segregated configurations. Working memory performance showed parallel associations with more integrative and better-differentiated dynamic patterns. Conclusions: These findings suggest that dynamic functional network reconfiguration may represent a neurobiological mechanism linking lifestyle behaviors and cognitive health in young adulthood. Furthermore, they highlight the translational promise of engagement-driven, low-burden programs for college-aged young adults, showing that even modest variability in habitual physical activity corresponds to greater engagement and differentiation of integrative connectivity states linked to executive and broader cognitive functions. Full article

Background: Early vascular aging (EVA) reflects accelerated arterial stiffening and is closely linked to cardiovascular and renal target organ damage. The Early Vascular Aging Ambulatory score (EVAAs) estimates EVA using ambulatory blood pressure monitoring (ABPM) and routinely available clinical parameters. We aim to investigate the association between EVAAs-defined early vascular aging and markers of kidney involvement—particularly albumin-to-creatinine ratio (ACR)—in a hypertensive population. Methods: Fifty treated hypertensive adults undergoing 24 h ABPM were enrolled. All participants underwent laboratory evaluation, including serum electrolytes and 24 h urine collection for albumin, creatinine, sodium, and potassium. EVAAs was calculated using ABPM-derived parameters and established cardiovascular risk factors. Results: EVAAs was positively correlated with ACR (r = 0.276, p = 0.049). In addition, inverse correlations were observed between EVAAs and serum potassium (r = −0.290, p = 0.038) and serum sodium (r = −0.284, p = 0.046). Participants with moderately increased albuminuria tended to exhibit higher EVAAs values, although this difference did not reach statistical significance. Conclusions: EVAAs is associated with early markers of renal involvement in hypertensive patients, supporting its potential role as a non-invasive indicator of subclinical target organ damage. Larger studies are warranted to confirm these findings and to further validate EVAAs as a clinically useful marker of EVA. Full article

Greenhouse gas (GHG) emissions from sanitary waste (SW) are not usually quantified in institutional inventories, which limits the ability to assess its management and associated carbon footprint. This study establishes emission factors (EF) for SW generated in a higher education institution (HEI), focusing on toilet paper. In 2022, 19 sanitary waste sources were monitored, obtaining a per capita generation of 3.02 g person⁻¹ day⁻¹ and an annual total of 356.87 kg of SW. Samples were characterized through proximate and elemental analyses, applying stoichiometric calculations for two disposal-site degradation pathways: Aerobic: 841.95 kg (total climate indicator) t⁻¹ SW, and Anaerobic: 7041.97 kg (total climate indicator) t⁻¹. The arithmetic mean of the aerobic and anaerobic EFs was 3941.96 kg (total climate indicator) t⁻¹ SW. Based on an estimated annual mass of 1.12 t yr⁻¹, emissions ranged from 0.35 to 6.71 t yr⁻¹ (total climate indicator: CO₂ + CH₄-derived CO₂e) depending on the scenario. Emissions could be reduced by over 90% when aerobic degradation or controlled methane capture predominates. The results suggest that separating SW at its point of generation and ensuring that it undergoes aerobic or energy-recovery treatment processes can limit its contribution to institutional GHG inventories. Having material-specific EF enables quantitative comparison among management strategies and guides continuous-improvement decisions. Full article

In this paper, we introduce new two-sided Ostrowski-type inequalities on arbitrary time scales. Using the delta derivative and delta integral operators, we obtain explicit bounds for the deviation of a function value from the mean delta integral, under the assumption that the delta derivative is bounded between two integrable functions. With additional monotonicity conditions on the bound functions, further refinement of the obtained estimates is possible. The results recover the classical continuous Ostrowski inequality, as well as its discrete and quantum counterparts on $\mathbb{Z}$ and $q^{\mathbb{Z}}$, as special cases. Full article

Effluents from industries, manufacturing companies, textile looms, and floodwater contaminate the surface water reservoirs. This endangers the quality of water for use by humans. Wastewater remediation is one of the ways to recycle the dirty water and make it suitable for use. Photocatalysis is the most common method for wastewater remediation, especially using Titanium dioxide (TiO₂) nanoparticles. However, chemical synthesis and direct addition of nanoparticles may cause toxicity to the flora and fauna present in the water body. To address this limitation, we have green-synthesized TiO₂ nanoparticles using a horticulture waste, Calendula officinalis dried flower extract and entrapped them in a natural polymer, chitosan (CTS-TiO₂-CO nanocomposite). The polymer entrapment ensures biocompatibility as well as reduced aggregation of nanoparticles. The synthesized CTS-TiO₂-CO nanocomposite was characterized using UV-visible spectrophotometry, dynamic light scattering, zeta potential, Fourier Transformed Infrared Spectroscopy (FTIR), X-ray diffractometry (XRD), scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDAX) analysis. The absorption peak was found at 302 nm, and the hydrodynamic diameter at 490 nm. SEM images show flower-like morphology with 326 nm average particle diameter. The non-toxic dose of the nanoparticles was estimated by MTT assay and zebrafish embryo developmental studies. More than 82% fibroblast cells were viable after treatment with 100 μg/mL of CTS-TiO₂-CO nanocomposite. 85% embryos hatched after treatment with 50 μg/mL of CTS-TiO₂-CO nanocomposite. Further, the textile dye remediation assessment was done using the dye crystal violet, exhibiting 69.19% dye degradation after 4 h of sunlight exposure. Altogether, the results demonstrate that the CTS-TiO₂-CO nanocomposite was effective in the remediation of crystal violet without causing any toxicity up to a dose of 100 μg/mL. Full article

Background: Accurate dietary assessment is vital for preventing malnutrition in aging populations, particularly in home-care settings. Although Large Multimodal Models (LMMs) for nutrient estimation are evolving, their nutrient-specific accuracy requires rigorous validation. Methods: Fifteen standardized hospital meals were photographed under controlled conditions (90-degree angle, 500 lux). Ground truth values were determined by direct weighing. Estimates for energy and macronutrients were performed by 10 registered dietitians (RDs) and 10 AI models (including ChatGPT-4o and Gemini 1.5 Pro). Accuracy was assessed using Pearson’s correlation, Mean Absolute Error (MAE), and Bland–Altman analysis to quantify systematic bias. Results: For energy and carbohydrates, RDs and top-performing AI models (notably ChatGPT-4o and Gemini 1.5 Pro) demonstrated practical accuracy (r > 0.8, frequently within ±10% range). However, accuracy for protein and lipids was significantly lower across all AI models. Specifically, all AI models exhibited a substantial systematic overestimation of lipids (Mean Bias > +20%, p < 0.01), highlighting a critical “invisible nutrient” bias. Conclusions: Current AI tools show potential for caloric and carbohydrate monitoring but struggle with lipid and protein density. These findings emphasize the need for human–AI collaboration (“human-in-the-loop”) and the integration of cooking metadata to improve clinical utility in geriatric nutrition. Full article

Background: Recent advancements in heart failure (HF) therapy have significantly enhanced the management of patients across all phenotypes of left ventricular ejection fraction. However, these multidrug regimens frequently induce alterations in renal function by influencing intrarenal hemodynamics, thereby modifying glomerular capillary pressure. This phenomenon could result in a mild to moderate decline in estimated glomerular filtration rate (eGFR), often classified as “worsening kidney function.” This nomenclature stems from consistent observations of eGFR reductions recorded during HF treatment in clinical trials. This narrative review aims to elucidate why the observed eGFR declines in clinical practice may represent either loss of functioning glomeruli or pharmacologically mediated reductions in intraglomerular pressure that ultimately safeguards long-term renal and cardiovascular outcomes. Methods: By a comprehensive re-examination of data from HF clinical trials conducted with various classes of medications, all affecting eGFR, we sought to provide evidence that the decline in eGFR is associated with the activation of specific mechanisms that collectively contribute to a reduction in glomerular filtration pressure, a prominent factor in maladaptive neurohormonal responses. Results: From the investigation of angiotensin-converting enzyme inhibitors to the more recent non-steroidal mineralocorticoid receptor antagonist, the renal effects of these therapeutic regimens correlate with improvements in patient outcomes. The data consistently indicate that an early decline in eGFR, when coupled with an enhancement in HF outcomes, is associated with a more gradual decline in eGFR during long-term follow-up. Conclusions: Clinicians should recognize early declines in eGFR as indicators of favorable intraglomerular hemodynamic adjustments that mitigate maladaptive neurohormonal responses and contribute to improved long-term outcomes in patients with HF. Full article

The present study demonstrates a step-by-step method for optimizing the outflow valve geometry and maximizing thrust generation. In this system, the skin-mounted OutFlow Valve (OFV) acts as a convergent–divergent nozzle and, as such, the De Laval nozzle equations are considered as guidance for the shape optimization. The performance of the skin-mounted flapped OFV optimized designs is assessed with a combination of analytical equations and Computational Fluid Dynamics (CFD) methods. The three-dimensional Reynolds-Averaged Navier–Stokes (RANS) yield reliable thrust recovery estimates and reveal key aspects of the aerodynamic flow behaviour through the valve, highlighting the interaction between the skin-mounted flapped OFV components. The results compare well with the analytical approach, providing a basis upon which a skin-mounted flapped OFV can be tailored for a specific mission. Full article

Multimodal fusion methods leveraging various sensors provide strong support for 3D object detection. However, under adverse weather conditions such as rain, fog, snow, and intense glare, complex environmental factors can degrade sensor data quality, leading to increased false positives and missed detections. In addition, sensor modalities (e.g., LiDAR and cameras) inherently vary in information density, and directly fusing them can cause critical details in high-density data to be diluted by low-density data, thereby increasing errors. To address these issues, we propose a Semantic-Enhanced Bidirectional Multimodal Fusion (SeBFusion) framework. By introducing a semantic enhancement mechanism and a bidirectional fusion strategy, SeBFusion mitigates the impact of noise under adverse weather and alleviates information dilution in multimodal fusion. Specifically, SeBFusion first employs a virtual point generation and camera semantic injection module to selectively map image semantic features into 3D space, producing semantically enhanced LiDAR features to compensate for the sparsity of the raw LiDAR point cloud. Then, during cross-modal interaction, we design a bidirectional cross-attention fusion module. This module estimates the confidence of each modality and adaptively reweights the bidirectional information flow, thereby reducing the risk of noise propagation across modalities and improving the robustness and accuracy of 3D object detection in complex environments. Experiments on adverse-weather versions of datasets such as KITTI-C and nuScenes-C validate the effectiveness and superiority of the proposed method. On the nuScenes-C dataset, it achieves 66.2% mAP and 66.6% mAP under fog and snow conditions, respectively. Full article

The longitudinal study investigated the impact of age at first mating (AFM) on milk yield (MY) across the productive lifespan of Alpine goats born between 2005 and 2018. Data from 740 animals across three herds and 3200 lactations were analyzed. The AFM of the studied population ranged from 7 to 23 months. The impact of AFM on MY was estimated using a linear mixed model, accounting for the fixed effects of parity, litter size, season, herd, and suckling and milking durations, with the individual goat included as a random effect to control for repeated measures. The impact of AFM on lifetime production was estimated by regressing total milk yield (TMY) and number of lactations (TNL) on AFM, while accounting for herd effect. The study revealed a notable shift in productivity patterns across the animal’s life. Every additional month of AFM significantly increased milk yield in the first lactation (13.28 kg; p < 0.001), but this influence vanished in subsequent parities (p > 0.05). These higher initial yields were insufficient to compensate for the losses caused by a shortened productive lifespan. Specifically, each month of mating delay resulted in a loss of ~0.08 TNL and 34 kg TMY, totaling ~1 lactation and ~400 kg of milk for a 12-month delay. Results suggest that earlier mating may improve lifetime productivity under intensive production systems. Full article

Purpose: HER2-targeted therapy has been incorporated into the standard neoadjuvant treatment (NAT) regimen for HER2-positive early-stage breast cancer, yet a subset of patients have shown a limited pathological response. This study aimed to evaluate clinicopathological factors associated with NAT sensitivity and to develop a predictive model. Methods: This retrospective study included 13,004 HER2-positive breast cancer patients from the National Cancer Database (2010–2022) who received neoadjuvant chemotherapy plus HER2-targeted therapy. Pathological complete response (pCR) was defined as no residual invasive carcinoma in the breast and axillary lymph nodes (ypT0/is, ypN0). NAT sensitivity was additionally defined using clinical-to-pathologic stage migration according to the AJCC 8th edition criteria. Baseline characteristics and overall survival (OS) were compared between NAT-sensitive and NAT-insensitive groups. A multivariable logistic regression model was developed based on age, clinical T stage, clinical N stage, histologic subtype, tumor grade, and hormone receptor (HR) status. Model performance was assessed using the area under the receiver operating characteristic curve and calibration curves. Results: Among the patients included, 3660 (28.1%) achieved pCR. Based on the predefined stage-based criteria, 10,451 (80.4%) were classified as NAT-sensitive and 2553 (19.6%) as NAT-insensitive. NAT-insensitive patients were older and more likely to present with clinical T1c and node-negative disease, whereas NAT-sensitive patients more frequently had higher clinical T and N stages. HR-positive and lower tumor grades were significantly associated with treatment insensitivity. NAT-insensitive patients demonstrated significantly worse OS compared with NAT-sensitive patients (p < 0.001). The predictive model showed acceptable discrimination with AUCs of 0.762 in the training cohort and 0.776 in the validation cohort, demonstrating good calibration. Conclusions: NAT sensitivity in HER2-positive early-stage breast cancer exhibited substantial biological and clinical heterogeneity in real-world practice. A younger age, higher clinical stage, invasive ductal histology, higher tumor grade, and HR-negative status were associated with improved responses. A predictive model based on routinely available baseline variables demonstrated reasonable performance for estimating treatment sensitivity, supporting its potential utility for baseline risk stratification pending external validation. Full article

The use of fiber-reinforced polymers (FRPs) for strengthening existing reinforced concrete (RC) structures has significantly improved structural rehabilitation processes, providing efficient, durable, and non-invasive solutions. This study presents an advanced deep learning-based predictive model specifically developed to estimate the shear strength of concrete beams strengthened externally with carbon fiber-reinforced polymer (CFRP) composites. Using a comprehensive dataset of 216 experimentally tested CFRP-wrapped concrete beams drawn from existing research, a deep neural network model was rigorously optimized with the Optuna hyperparameter tuning framework and k-fold cross-validation to ensure robustness and generalizability. Model validation involved a thorough comparative analysis against established international design codes (ACI PRC-440.2-17, CSA-S806-12, JSCE) and a parametric study examining the sensitivity of shear strength predictions to key influencing factors, including concrete compressive strength, beam depth, and CFRP wrap thickness. Results demonstrated superior prediction accuracy and reliability of the deep learning approach compared to traditional empirical design models. Consequently, this research significantly enhances the precision of shear strength predictions for CFRP-strengthened concrete beams, supporting the development of more efficient and accurate structural rehabilitation and design guidelines. Full article

Background: Gamma Knife radiosurgery (GKS) delivers highly conformal intracranial irradiation, yet clinical decision-making still relies predominantly on physical dose metrics that do not account for fractionation, dose rate, treatment time, or DNA repair. Classical radiobiological models—including the linear–quadratic (LQ) formula and the Jones–Hopewell single-session repair model—do not extend naturally to 3- and 5-fraction GKS. Meanwhile, growing evidence suggests that biologically effective dose (BED) may better capture radiosurgical response in selected pathologies. A unified, biologically grounded, multi-fraction GKS framework has been lacking. Methods: We developed AI-BED-Fx, the first multi-fraction extension of the Jones–Hopewell radiobiological model capable of computing fraction-resolved BED for 1-, 3-, and 5-fraction GKS. The framework incorporates α/β ratio, dual-component repair kinetics, isocentre geometry, beam-on–time structure, and lesion-specific biological parameters. Four synthetic pathology-specific cohorts—arteriovenous malformation (AVM), meningioma (MEN), vestibular schwannoma (VS), and brain metastasis (BM)—were generated using distinct radiobiological signatures. Machine-learning models were trained to quantify the predictive value of physical dose versus BED for local control or obliteration. Additional experiments included Bayesian estimation of α/β and a neural-network surrogate for fast BED prediction. An exploratory comparison with a 60-lesion clinical brain–metastasis dataset was performed to assess whether key trends observed in the synthetic BM cohort were consistent with real radiosurgical outcomes. Results: AI-BED-Fx produced realistic pathology-specific BED distributions (AVM 60–210 Gy_2.47; MEN 41–85 Gy_3.5; VS 46–68 Gy₃; BM 37–75 Gy₁₀) and biologically coherent dose–response relationships. Predictive modeling demonstrated strong pathology dependence. In AVM, the three models achieved AUCs of 0.921 (Model A), 0.922 (Model B), and 0.924 (Model C), with corresponding Brier scores of 0.054, 0.051, and 0.051, with BED-based models performing best. In meningioma, BED was the dominant predictor, with AUCs of 0.642 (Model A), 0.660 (Model B), and 0.661 (Model C) and Brier scores of 0.181, 0.177, and 0.179, respectively. In vestibular schwannoma, the narrow BED range resulted in minimal BED contribution, with AUCs of 0.812, 0.827, and 0.830 and Brier scores of 0.165, 0.160, and 0.162, with physical dose and tumor volume determining performance. In brain metastases, outcomes were driven primarily by volume and physical dose, with AUCs of 0.614, 0.630, and 0.629 and Brier scores of 0.254, 0.250, and 0.253, showing negligible improvement from BED. AI-BED-Fx also accurately recovered the true α/β from synthetic outcomes (posterior mean 2.54 vs. true 2.47), and a neural-network surrogate reproduced full radiobiological BED calculations with near-perfect fidelity (R² = 0.9991). Conclusions: AI-BED-Fx provides the first unified, biologically explicit framework for modeling single- and multi-fraction Gamma Knife radiosurgery. The findings show that the predictive usefulness of BED is pathology-specific rather than universal, and that radiobiological dose provides additional predictive value only when repair kinetics and dose–response biology support it. By integrating mechanistic radiobiology with machine learning, AI-BED-Fx establishes the conceptual and computational foundations for biologically adaptive, AI-guided radiosurgery, and cross-pathology comparison of treatment response. This work uses large radiobiologically grounded synthetic cohorts for methodological validation; limited real-patient data are included only for exploratory consistency checks, and full clinical validation is planned. Full article

To reduce vehicular emission pollution in cold regions and maximize sustainable development of transportation, AC self-heating of electric vehicles is acknowledged as an efficient approach to mitigate the decline in Li-ion battery performance under low-temperature conditions. This paper introduces a novel battery self-heating approach based on reconfiguration of the drive circuit, which is compatible with both preheating and on-route heating. The undesired torque generated by the heating current can be inherently nullified regardless of the rotor position. The control of heating and driving currents is entirely decoupled, facilitating straightforward adaptation to a range of heating strategies. Furthermore, a battery electro-thermal model is proposed and integrated with the drive system model to estimate the battery temperature evolution. Comprehensive experiments are designed to validate the operating principle and the accuracy of battery temperature estimation under various working conditions. The results present a high fidelity between the experimental data and the simulation outcomes. The root mean square errors of the predicted battery temperature under all the constant and combined driving conditions are less than 1 °C. Full article

Lithium-ion batteries are widely used across diverse applications due to their high energy density, long cycle life, and fast charging capabilities. As battery-powered systems become increasingly critical, accurate estimation of the Remaining Useful Life (RUL) is essential for ensuring reliability, safety, and effective maintenance planning. This work investigates Evidential Deep Learning (EDL) for data-driven RUL estimation and introduces a novel risk-aware loss function designed to enhance both predictive accuracy and uncertainty quantification in the End-of-Life (EoL) region, where precise and trustworthy predictions are most needed. Using a publicly available dataset of lithium iron phosphate (LFP) cells, we benchmark the proposed approach against a baseline Conv–LSTM model, Monte Carlo (MC) Dropout, and Deep Ensembles. The results show that integrating the risk-aware loss into the EDL framework substantially improves the calibration of predictive uncertainty while achieving state-of-the-art accuracy near EoL. Unlike MC Dropout and Deep Ensembles, which exhibit increasing or unstable uncertainty as degradation accelerates, the proposed EDL model demonstrates a consistent reduction in uncertainty and significantly higher reliability in late-stage predictions. The findings indicate that the risk-aware evidential framework offers a reliable and computationally efficient solution for battery RUL estimation, enabling more informed decision-making in both safety-critical and consumer-oriented applications. Full article