Search Results (3,798)

Background/Objectives: Frailty is common among rural Chinese older adults despite relatively high daily physical activity, a phenomenon known as the “rural frailty paradox.” Conventional moderate-to-vigorous physical activity (MVPA) metrics rely on absolute cut-points and are often highly correlated with activity volume, limiting their ability to distinguish the roles of activity volume and activity intensity distribution. We therefore applied a cut-point-free accelerometer approach using average acceleration (AvAcc) and intensity gradient (IG) to distinguish activity volume from activity intensity distribution and to examine whether activity intensity distribution, together with diet quality, could help explain the rural frailty paradox beyond total activity volume alone. Methods: In this cross-sectional analysis of the Healthy Aging and Lifestyle Enhancement study, 1203 rural older adults were included. Physical activity (PA) was objectively measured using triaxial accelerometers to derive AvAcc and the IG. Diet quality was assessed using the China Prime Diet Quality Score (CPDQS), and frailty was assessed using the Fried frailty phenotype adapted for rural Chinese older adults. Multiple linear regression, joint effect models, and restricted cubic spline analyses were conducted after adjustment for age, sex, chronic disease status, total energy intake, and related covariates. Results: In mutually adjusted models, higher IG and CPDQS were independently associated with lower frailty scores, whereas AvAcc was not. In the fully adjusted model, IG (β = −0.14, p < 0.001) and CPDQS (β = −0.10, p < 0.001) were inversely associated with frailty score, while AvAcc showed no significant association (p = 0.665). In joint analyses, compared with the low-IG/low-CPDQS group, participants with high IG/high CPDQS had the lowest frailty scores (β = −0.28, p < 0.001), followed by those with low IG/high CPDQS (β = −0.20, p = 0.002). Restricted cubic spline analyses indicated a non-linear association between IG and frailty and an approximately linear inverse association for CPDQS. Conclusions: These findings suggest that, among rural older adults, frailty may be more strongly associated with activity intensity distribution than with total activity volume alone. Together with diet quality, this may help explain the rural frailty paradox. Full article

In Coiled Tubing (CT) acoustic telemetry, the reliability of surface signal reception is severely challenged by the “contact dead zone” of traditional probes and complex nonstationary environmental noise. To address these issues, this paper proposes a hardware-software integrated solution for high-fidelity signal extraction. In terms of hardware, a novel pickup probe based on the micro-lever principle is developed. By utilizing a pivoted lever structure with an optimized arm ratio of 2.6 to 1 and a full pressure-balanced mechanism, the design physically overcomes the contact dead zone inherent in traditional pressure-compensating probes and effectively isolates low frequency common-mode interference through a lateral floating architecture. In terms of software, a joint denoising model combining Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and wavelet thresholding is proposed. A cross-correlation coefficient criterion is introduced to adaptively screen intrinsic mode functions and eliminate residual fluid turbulence noise. Field experiments on a 1500 ft full-scale circulation loop demonstrate that the proposed probe improves the detection sensitivity of the radial breathing mode by approximately 20.6 dB compared to the baseline, while effectively eliminating stick-slip friction noise during dynamic tripping. Furthermore, the joint algorithm increases the Signal to noise Ratio by an additional 16.9 dB under typical pumping conditions of 0.5 bpm, with a normalized cross-correlation exceeding 0.96. These results verify that the proposed method effectively solves the bottleneck of weak signal detection in deep wells, providing robust technical support for CT telemetry operations. Full article

Climate change presents a challenge for global viticulture due to rising temperatures and water stress, which accelerate grape ripening, increase sugar levels, and reduce acidity. This compromises wine quality and microbial stability, resulting in higher reliance on sulfur dioxide (SO₂). However, SO₂ can inhibit desirable fermentations, including those carried out by non-Saccharomyces yeasts, which are key biotechnological tools for climate adaptation due to their ability to modulate acidity, aroma, and ethanol. Therefore, alternative disinfection methods are needed to control wild microbiota without hindering inoculated yeasts. This review critically analyzes ozone (O₃) as a non-thermal disinfection technology for winemaking. It examines the antimicrobial mechanism of ozone, its efficacy against wine-related microorganisms, its impact on the physicochemical and aromatic parameters of grapes, and its practical viability. Ozone effectively reduces spoilage-causing microbiota, achieving inactivation of approximately 3–4 log CFU/mL for yeasts, while preserving crucial grape compounds and providing a favorable environment for novel fermentation biotechnologies. Compared to other emerging technologies and SO₂, ozone offers a balanced profile: effective disinfection, minimal residues, cost-effectiveness, and compatibility with sustainable winemaking. Ozone is emerging as a promising alternative to facilitate controlled fermentations and improve wine quality among the current climatic and oenological challenges. Full article

Accurate discretization of the orientation distribution function (ODF) is essential for reliable microstructural modeling of polycrystalline aggregates. This work proposes a novel texture discretization method that achieves high-fidelity ODF approximation even with a small number of orientations using only grain volume information. The core idea is to extend conventional inverse transform sampling by reconstructing the source samples before inversion. This reconstruction suppresses discretization errors induced by random sampling fluctuations and improves adaptability to non-uniform grain size distributions (GSDs). To preserve texture diversity under the same ODF, spatial shuffling and subsequent unscrambling of grain positions are introduced. The total variation distance (TVD) is adopted as a global metric to quantify discretization errors, and key influential factors are systematically analyzed, particularly the binning strategies. Error comparisons demonstrate that, within the typical range of grain numbers (10²–10³), the TVD of the proposed method is one order of magnitude lower than that of the conventional method, with its standard deviation two orders of magnitude smaller. The randomness and periodicity of discretized textures are further investigated, thereby elucidating the underlying mechanisms for the newly introduced advantages. This method provides a robust and efficient framework for texture modeling with consideration of GSDs. Full article

This paper presents an engineering framework for smart grid virtual power plant (VPP) day-ahead scheduling using fractional calculus-enhanced particle swarm optimization, targeting practical deployment in energy management systems. A fractional calculus-enhanced particle swarm optimization algorithm was developed and validated for day-ahead scheduling in virtual power plants using authentic market data and rigorous statistical analysis. The algorithm incorporates Grünwald–Letnikov fractional derivatives with adaptive memory into particle velocity updates, enabling trajectory-aware search that leverages historical exploration patterns. A factorial experiment across 500 independent test cases (50 dates × 10 trials) with controlled random seeds demonstrated that fractional particle swarm optimization increased mean daily profit by $205, representing a 4.1% improvement over standard particle swarm optimization. Wilcoxon signed-rank tests confirmed statistical significance (p < 0.0001, Cohen’s d = 1.08), with superior performance observed in 89.4% of cases. The factorial design identified fractional calculus as the primary performance driver, while advanced scenario generation provided no significant additional benefit. Sensitivity analysis indicated that wind generation variability was the primary predictor of performance variance, with profit difference standard deviations ranging from $34 to $325 depending on meteorological conditions, supporting the use of adaptive computational strategies. Computation required approximately two minutes per optimization on standard hardware. These findings establish fractional calculus as a credible enhancement for operational energy systems and demonstrate that the quality of optimization algorithms outweighs the complexity of forecast uncertainty modeling. The results extend fractional calculus applications from benchmark functions to practical infrastructure scheduling, with projected annual value exceeding $74,000 for a 50-megawatt system. The three-stage optimization architecture is designed for integration with standard energy management systems and SCADA platforms, offering a deployable pathway for smart grid operators. Full article

Reconfigurable intelligent surfaces (RIS) have emerged as a promising technology for enhancing wireless communication by intelligently shaping the propagation environment. However, non-line-of-sight (NLoS) blockage between the access point (AP) and user equipment (UE) can still significantly degrade communication performance. This paper investigates the channel degradation caused by NLoS blockage in a single-antenna AP and multi-antenna UE system and proposes a joint power allocation and phase optimization scheme based on RIS and deep reinforcement learning (DRL). Under a composite channel model with direct and RIS-reflected links, the objective is to maximize the weighted sum rate subject to total power constraints, unit-modulus constraints on RIS elements, and quality of service (QoS) requirements. Due to the coupled variables and the non-convex unit-modulus constraint, conventional alternating optimization (AO) and convex approximation methods usually incur high complexity and yield suboptimal solutions. To address this issue, a DRL algorithm based on an Actor–Critic architecture is developed to learn adaptive power allocation and reflection coefficient adjustment policies through interaction with the environment, without requiring full global channel state information (CSI). Simulation results demonstrate that the proposed method achieves higher signal-to-interference-plus-noise ratio (SINR) and throughput while providing faster convergence and better generalization than existing methods. Full article

Arsenic (As)-enriched soils in abandoned mining areas pose persistent environmental risks, yet the temporal evolution of remediation is rarely evaluated. In this study, a two-year field monitoring program was conducted at a severely As-contaminated abandoned gold mine in Guangdong Province, China, to examine the temporal dynamics of soil properties and As behavior under different remediation strategies. Three representative slopes were investigated: slope A (slope reshaping and revegetation), slope B (terraced engineering interception), and slope C (an area influenced by acidic water bodies). The results showed that both total and available As at slopes A and B exhibited a similar pattern of initial increase followed by decline and stabilization, indicating a clear temporal scale for remediation effects. Slope A exhibited greater spatial variability, whereas slope B showed relatively minor fluctuations, suggesting that terraced engineering measures contributed to enhanced As stability. In contrast, slope C had lower total As but a higher proportion of available As prior to remediation due to the acidic conditions. Following remediation, both total and available As at slope C decreased markedly and remained stable for about six months; however, a rebound trend was observed after approximately 1.5 years, indicating the time-limited effectiveness of passivation treatments. Specifically, total As at slope C decreased from 22,916 to 4011 mg·kg⁻¹, accompanied by a 65–85% reduction in available As. Meanwhile, soil pH, soil organic matter, and cation exchange capacity exhibited pronounced non-linear variations, with an overall tendency to recover toward pre-remediation conditions. These findings underscore the importance of long-term monitoring for evaluating remediation effectiveness and periodic assessments (e.g., semiannual monitoring of soil As and nutrient status) to support adaptive environmental management and optimization of remediation strategies. Full article

Globally, the combined pollution of fine particulate matter (PM_2.5) and ground-level ozone (O₃) poses severe challenges to public health and sustainable urban development. Recent data indicate that the annual average PM_2.5 concentration in the vast majority of cities worldwide fails to meet World Health Organization safety standards, with air pollution causing millions of premature deaths annually. As a nature-based solution, the purification efficacy of vegetation remains poorly quantified due to unclear coupling mechanisms with local meteorological conditions. This study systematically reviewed and synthesized 229 empirical studies published between 2000 and 2025 from Web of Science and China National Knowledge Infrastructure (CNKI), aiming to clarify the quantitative relationships and regulatory mechanisms of plant–meteorological synergistic purification of PM_2.5–O₃. Following double-blind independent screening (κ = 0.85) and data extraction, a quantitative minimal feasible synthesis approach was adopted due to high data heterogeneity. The results indicated the following. (1) The median canopy purification efficiency of urban vegetation for PM_2.5 was 18.2% (IQR: 12.5–30.1%, n = 17), with a median dry deposition velocity (Vd–PM) of 0.05 cm s⁻¹ (0.02–30 cm s⁻¹, n = 15). The median dry deposition velocity (Vd–O₃) for O₃ was 0.55 cm s⁻¹ (0.12–1.82 cm s⁻¹, n = 8), with non-stomatal deposition contributing approximately 35%. (2) Meteorological factors exhibit nonlinear regulation: relative humidity (RH) > 70% significantly enhances PM_2.5 adsorption, wind speeds of 1.5–3.0 m s⁻¹ are optimal for PM_2.5 deposition, and temperatures > 30 °C generally inhibit plant uptake of both pollutants (n = 7). (3) Functional traits strongly correlate with purification efficacy: species with high leaf roughness (R² = 0.8), high stomatal conductance, and low BVOC emissions (e.g., Ginkgo biloba, Platycladus orientalis) exhibit optimal synergistic purification potential. Species with high BVOC emissions (Populus przewalskii, Eucalyptus robusta) can increase daily net O₃ pollution equivalents by up to 86 g and must be strictly avoided. Based on quantitative evidence, a green space planning decision matrix indexed by climate zone and pollution type was developed, specifying vegetation configuration patterns, functional group selection, and key design parameters (canopy closure, green belt width, etc.) for different scenarios. This study provides an actionable scientific basis for precision planning and climate-adaptive management of urban green infrastructure. Full article

The rotary steerable system (RSS) is the core equipment for precise wellbore trajectory control in deep oil and gas drilling, and its performance is directly determined by the coordination and adaptability of the tool’s offset actuator and control platform. To overcome the limitations of complex control architectures and low positioning accuracy of conventional offset actuators for rotary steering drilling tools, a novel three hydraulic cylinder synchronous steering offset actuator driven by a drilling fluid rotary valve distributor, along with its dedicated control strategy, is proposed. Laboratory experiments and numerical simulations are performed to analyze the piston displacement characteristics of the three hydraulic cylinder under different drilling fluid flow rates and rotary valve rotational speeds. The results demonstrate that the proposed actuator exhibits controllable piston displacement behavior. The simulated and experimental data show consistent variation tendencies with a relative error of less than 8%, thus validating the reliability of the proposed numerical model. Increasing the flow rate from 1 to 1.5 L/s increases the cycle-averaged peak-to-peak piston displacement by 14.5 mm, while raising the rotational speed from 60 rpm to 120 rpm reduces it by 25.3 mm, corresponding to a dogleg severity variation of approximately 1.9–3.1°/30 m. Piston displacement deviations are mainly attributed to valve port machining tolerance, drilling fluid compressibility, pipeline pressure loss, and internal leakage, and these discrepancies are exacerbated as the rotary valve speed or flow rate increases. Finally, optimization strategies for improving synchronization performance are proposed, thereby providing theoretical and technical support for the engineering implementation and parameter optimization of the proposed actuator. Full article

Traditional ready-to-wear garments can mostly not conform to different body shapes because of the adoption of the generic sizing system, which leads to the local strain of concentration and morphological misfit. Auxetic structures, which have a negative Poisson’s ratio, permit enhanced redistribution of stress and geometry and allow deformation. Two auxetic knitted structures were developed by using 100% polyester and 100% nylon yarns with a fabric density of 41 Wales and 40 courses per inch. Characterization of the initial fabrics involved checking the behavior of negative Poisson’s ratio (NPR) where the polyester line (P1) structure shows the highest auxeticity, with a NPR of approximately −0.4 and peak strain reductions of 80–90%, as well as air permeability, moisture management, bend test, compression, roughness, friction properties and stiffness tests to check the mechanical and comfort-related performances. The standardized tunic garment was modeled in CLO 3D on three female body shapes—hourglass, pear and rectangle—with a constant size of 34. The fit map showed a strain of 91.49% in auxetic and 509.75% in single-jersey fabric at the hip area of the pear body shape when measuring fabric and body interaction. The findings indicate lower peak strain levels, which ascertain that increased adaptability is possible and support its use in the development of adaptive ready-to-wear garments. Full article

Deep learning has emerged as an effective tool for automatic waste classification, supporting cleaner cities and more sustainable recycling systems. Because environmental protection is central to the United Nations Sustainable Development Goals (SDGs), improving the sorting and processing of everyday waste is a practical step toward this broader objective. In many real-world settings, however, waste is still sorted manually, which is slow, labor-intensive, and prone to human error. Although convolutional neural networks (CNNs) can automate this task with high accuracy, many state-of-the-art models remain too large and computationally demanding for low-cost edge devices intended for deployment in homes, schools, and small recycling facilities. In this work, we investigate lightweight waste-classification models suitable for TinyML deployment while preserving competitive accuracy. We first benchmark multiple CNN architectures to establish a strong baseline, then apply complementary compression strategies including quantization, pruning, singular value decomposition (SVD) low-rank approximation, and knowledge distillation. In addition, we evaluate an RL-guided multi-teacher selection benchmark that adaptively chooses one teacher per minibatch during distillation to improve student training stability, achieving up to 85% accuracy with only 0.496 M parameters (FP32 ≈ 1.89 MB; INT8 ≈ 0.47 MB). Across all experiments, the best accuracy–size trade-off is obtained by combining knowledge distillation with post-training quantization, reducing the model footprint from approximately 16 MB to 281 KB while maintaining 82% accuracy. The resulting model is feasible for deployment on mobile applications and resource-constrained embedded devices based on model size and TensorFlow Lite Micro compatibility. Full article

Toxic aluminum (Al) and iron (Fe) alter the hormonal balance of plants, leading to metabolic disorders and growth inhibition. Plants adapt to abiotic stress by optimizing phytohormone biosynthesis. However, the impact of toxic Al and Fe on plant hormonal status is poorly understood. Pea cultivar Sparkle and its mutant E107 (brz), accumulating Al and Fe due to disfunction of metal transporter gene OPT3, were cultivated in hydroponics supplemented or not with 80 µM of AlCl₃ or 300 µM of FeCl₃. Root and shoot biomass of E107 decreased due to Al or Fe treatments approximately by 30%, whereas growth of Sparkle was not affected. The Al and Fe content in the roots and shoots of the metal-treated mutant was circa twice that of Sparkle. Treatment with Al and Fe reduced the content of nutrients (Ca, K, Mg, S) in roots and/or shoots in both genotypes. Compared with Sparkle, untreated E107 possessed lower IAA and higher ethylene and tZR contents in roots but lower GA3, DHZ and tZ content in shoots. Mutant E107 had: lower GA3 and ethylene but higher DHZ, tZ and tZR contents in Al-treated roots; higher ABA, SA, IAA, GA3, DHZ, and tZ contents in Al-treated shoots; lower ABA and SA but higher JA, GA3, DHZ and ethylene contents in Fe-treated roots; higher ABA, SA, IAA, GA3, DHZ, and tZ contents in Al-treated shoots; higher ABA, JA, and GA3 but lower ethylene and tZR contents in Fe-treated shoots. Metal toxicity mainly reduced the content of phytohormones in roots and increased it in shoots. Hormonal disturbances were more significant in E107 than in Sparkle, and the effect of Al was stronger than Fe. Thus, toxic Al and Fe lead to complex, metal- and organ-specific changes in the hormonal status of E107. Hormonal changes might be associated with both defense reactions and the toxic effects of metals on plants. Full article

Background/Objectives: This paper investigates the application of radial basis function (RBF) interpolation to adapt the Toyota Human Model for Safety (THUMS) version 6 finite element (FE) models to diverse anthropometric profiles using ANSUR II data. The research focuses on generating personalized human body models (HBMs) across 50th, 80th, and 98th percentiles for both sexes in standing and seated postures, evaluating mesh quality with quantitative metrics, and assessing posture-dependent transformations. Methods: The geometric accuracy for the standing configuration was quantified using DICE similarity coefficients and the 95th percentile Hausdorff distance (HD95). Results: While global whole-body DICE similarity averaged approximately 0.40 due to an inherent variability in distal limb positioning, regional analysis demonstrated strong volumetric overlap in the critical chest and torso regions with DICE values ranging from 0.80 to 0.88. Regional HD95 values were within 20–30 mm across most of the surface area. Surfaces distance analyses showed that more than 95% of the nodes were within $\pm 20$ mm of the target surfaces with the distribution centered near zero across all the percentiles. The mesh quality for both standing and seated morphs demonstrated low violation rates with the aspect ratio being 28% to 30%, while warpage, skewness and, Jacobian determinants were less than 15%. The seated morphs preserved anatomical alignment and posture despite mesh density differences between the postures. Conclusions: These findings indicate that the morphing process preserves anatomical fidelity while highlighting the need for further optimization to mitigate localized distortions in dynamic simulations. Full article

Photovoltaic greenhouses (PVGs) are emerging as a key pathway for integrating renewable energy generation with protected horticulture, particularly in the Mediterranean region where high solar irradiance coincides with increasing pressure on water, land, and energy resources. This study presents a structured narrative review with a qualitative comparative synthesis of 24 peer-reviewed case studies, published from 2014 to 2025, identified through structured searches in Scopus and Web of Science and selected based on predefined relevance and eligibility criteria. Results indicate that crop yield responses to PV coverage are highly crop, season and configuration dependent. Yield stability is most consistently reported at lower coverage levels (approximately 10–20%), while higher coverage ranges (30–50%) show more variable outcomes. Mediterranean PVGs generate between 5 and 55 kWh/m² annually, depending on system configuration and PV coverage, supporting partial to high levels of energy autonomy. Economic analyses, based on a limited number of studies, report payback periods of 7.2–14.4 years and internal rates of return (IRR) of 6–14%, particularly under supportive policy frameworks. This review identifies indicative design thresholds, and crop-specific sensitivities while outlining technological and agronomic knowledge gaps and research priorities for optimizing PVG deployment in high-irradiance Mediterranean regions. Overall, PVGs demonstrate strong potential as climate-adaptive, low-carbon solutions for sustainable protected agriculture, although conclusions are constrained by a limited and methodologically heterogeneous evidence base. Full article

Pregnancy in dairy cattle is characterized by marked metabolic adaptations that may influence oxidative balance. In this study, oxidative stress markers and thiol–disulfide homeostasis were evaluated in transported and non-transported Holstein heifers during the last trimester of gestation. Clinically healthy 2-year-old heifers were divided into transported pregnant (n = 21) and non-transported pregnant (n = 9) groups. Blood samples were collected from the jugular vein approximately 90 days (3 months) after the mid-gestation transport event. These samples were analyzed for total antioxidant capacity (TAC), total oxidant status (TOS), oxidative stress index (OSI), malondialdehyde (MDA), native thiol (NTL), total thiol (TTL), and disulfide levels. Total oxidant status and oxidative stress index values were significantly higher in the non-transported group (p < 0.05). However, no significant differences were observed between groups in total antioxidant capacity, malondialdehyde, or thiol–disulfide parameters (p > 0.05). These findings suggest that metabolic adaptations specific to late gestation may influence systemic oxidant levels independently of transport exposure. Under the conditions of this study, transport did not induce a marked redox imbalance in pregnant Holstein heifers. Full article