Search Results (9,005)

Intranasal administration is a promising drug delivery route enabling precise and rapid central nervous system targeting. In our previous work, twelve hybrid colloidal dispersions were developed, consisting of synthetic poly(ethylene-oxide)-b-poly(ε-caprolactone) (PEO-b-PCL) block copolymers with an increasing proportion of the hydrophobic PCL segment, Tween 80 (Tw80) and β-cyclodextrin derivatives (βCD), either methyl-β-CD (MβCD) or hydroxy-propyl-β-CD (HPβCD) for IN delivery of ropinirole hydrochloride (RH). Colloidal dispersions were prepared at different weight ratios (system/RH equal to 10:1 and 10:5), characterized and evaluated in vitro. The aim of this study is to evaluate the ex vivo permeation through rabbit nasal mucosa and determine the pharmacokinetic parameters of RH, when administered intranasally as a colloidal dispersion, compared with oral and intranasal RH solutions in C57BL/6J mice. Ex vivo permeation studies showed that all formulations significantly enhanced RH permeation compared to the pure RH solution (0.5 mg/mL, pH 5.6). Among them, F4 [(PEO-b-PCL₁/Tw80/HPβCD)/RH 10:5] was selected for further investigation. Pharmacokinetic analysis showed that F4 significantly enhanced both systemic and brain exposure of RH, achieving higher serum AUC and C_max values, despite a 3-fold lower administered dose compared to the oral dose. It showed high systemic (F_rel(Serum) = 1815%) and brain (F_rel(Brain) = 363%) relative bioavailability compared with oral administration, underscoring its potential as an intranasal delivery system for efficient CNS targeting. Full article

Proportional, fair scheduling in OFDM-based vehicle-to-everything (V2X) uplink causes the resource-block allocation of each vehicle to vary from slot to slot, yet conventional semantic encoders produce a fixed number of output tokens regardless of the instantaneous channel capacity. When the encoder output exceeds the slot budget, transmitted features are truncated and the resulting federated learning gradient is corrupted—a problem that affected 23% of training rounds for non-line-of-sight vehicles in our experiments. The difficulty is worsened by a spatial pattern common in urban deployments: vehicles at congested intersections suffer the poorest propagation conditions while carrying the training data most relevant to safety, and throughput-driven client selection excludes them in favor of vehicles with strong channels but uninformative scenes. We address both issues within a single framework for OFDM-based V2X federated learning. On the transmission side, a Sensing-Guided Adaptive Modulation (SGAM) module derives a per-slot token budget from the current resource-block allocation and selects tokens through differentiable Gumbel-TopK pruning with a hard capacity clip, so the transmitted token count stays within the slot budget. On the scheduling side, a Channel-Decoupled Federated Learning (CDFL) module partitions clients independently by channel quality and data complexity, selects diverse representatives per partition via facility location optimization, and corrects for partition-size imbalance through inverse propensity weighting during model aggregation. Experiments on NuScenes with 20 non-IID vehicular clients under realistic OFDM channel simulation demonstrate a Macro-F1 of 0.710 (+8.7 points over the Oort-adapted baseline), zero budget violations throughout training, and a 75% reduction in training variance; the worst-class F1 more than doubles relative to FedAvg. Full article

The sustainable renovation of ageing industrial buildings presents both a challenge and an opportunity to enhance energy efficiency while preserving architectural and structural integrity. This study develops an integrated methodological framework for assessing and optimising multilayer wall systems in such conversions, combining thermal, environmental, and durability analyses. Six composite wall configurations were designed and numerically evaluated using steady-state 2D heat conduction and vapour-diffusion models. The results reveal substantial thermal improvement compared to the reference uninsulated brick wall (U = 1.41 W/m²·K). The proposed systems achieved U-values between 0.351 and 0.172 W/m²·K, meeting or surpassing European energy standards. The BP–EPS wall exhibited the lowest U-value (0.172 W/m²·K), while the FC–EPSR configuration achieved superior corner performance with a 2D surface temperature (Tsi) of 17.99 °C and the highest surface temperature factor (fRsi = 0.943), along with a reduced condensation risk, indicating more balanced overall performance. Weight and thickness reductions of up to 80.5% and 52%, respectively, were observed, enhancing retrofit feasibility and space efficiency. Life Cycle Assessment results indicated that optimised wall configurations reduced embodied carbon (A1–A3) by up to 78% and total life cycle emissions (A1–A3 + B6) by over 86% relative to the reference case. Vapour-diffusion analysis confirmed the FC–EPSR wall’s lowest condensation fraction, indicating excellent hygrothermal durability. Multi-criteria evaluation using the simple additive weighting method and Monte Carlo robustness analysis verified FC–EPSR as the most balanced and reliable system. Overall, the findings present a validated and replicable pathway for the sustainable renovation of industrial buildings, supporting the goals of European carbon neutrality and the circular economy. Full article

In the process of extracting and transporting waxy crude oil, pipeline blockages resulting from wax deposition significantly impede production efficiency and lead to substantial economic losses. Ethylene vinyl acetate copolymer (EVA) is a widely used chemical wax inhibitor; however, its performance is influenced by multiple factors, including its molecular structure, concentration, and the carbon number distribution of the wax system. A systematic elucidation of its mechanism of action and associated phase changes is therefore necessary. In this study, differential scanning calorimetry (DSC) was employed to systematically investigate the thermal behavior of a wax system with a broad carbon number distribution (C5–C50). The objectives were to analyze the influence of EVA concentration, vinyl acetate (VA) content, and molecular weight on the phase transition parameters, to elucidate the wax inhibition mechanism, and to construct a phase prediction model based on the Flory–Huggins theory. The results demonstrate that the wax appearance temperature (WAT), phase transition temperature, and phase transition enthalpy of the wax systems increase monotonically with carbon number. Furthermore, the addition of EVA was found to significantly reduce both the WAT and the amount of wax precipitated. The optimal structural parameters were identified as a VA content of 10%, a number average molecular weight of 20,000, and an optimal concentration of 800 ppm. The medium-carbon wax system (C16–C30) was found to be the most sensitive to the EVA response. The established phase model exhibited high predictive accuracy, with a mean relative error of less than 4%, a root mean square error (RMSE) of 0.32%, and a coefficient of determination (R²) of 0.987, thereby providing preliminary insights and a practical tool for optimizing EVA wax inhibitor formulations under simplified conditions and guiding their potential engineering applications. Full article

Proppant flowback during the flowback phase after hydraulic fracturing in coal reservoirs critically impacts fracture conductivity and wellbore integrity. However, experimental studies on its critical conditions and controlling mechanisms within coal’s complex fracture networks are scarce compared to sandstone or shale. This study conducted physical simulation experiments using outcrop coal samples from the XD block in China and a modified fracture conductivity system. By establishing a determination method for the critical backflow rate (Q_c), the dynamic evolution process of proppant backflow—characterized by the stages of initial stability, critical instability, severe backflow, and re-equilibration—was revealed. The influences of proppant size, flowback fluid viscosity, proppant concentration, and effective stress on Q_c were systematically analyzed, and the relative weight of each influencing factor was quantified through orthogonal experimental design. Results show that proppant backflow initiates and concentrates preferentially at the fracture outlet region, implying a higher risk of proppant failure in the near-wellbore fracture section. The Q_c decreases with reducing proppant size, increasing flowback fluid viscosity, increasing proppant concentration, and decreasing effective stress, among which effective stress is identified as the dominant controlling factor. Furthermore, no necessary correlation is observed between Q_c and the critical backflow ratio, suggesting that the initiation threshold and post-instability flowback intensity are governed by different mechanisms. This work provides experimental data and a quantitative basis for optimizing flowback strategies in coal reservoir fracturing operations. Full article

Honeycomb composites are extensively utilized in critical applications where weight is a concern in a structure, due to their high efficiency in stiffness-to-weight ratio. In this study, the effective elastic orthotropic behavior of honeycomb composites is analytically expressed as a function of the elastic properties of the constituent sheet material and the geometric parameters of the representative unit cell. Closed-form expressions based on classical beam theory and plate theory are evaluated and systematically validated against a high-fidelity finite element analysis FE-based homogenization benchmark constructed from a representative unit cell with in-plane periodic kinematic constraints. The analytical predictions exhibit generally good agreement with the FE results, with plate-theory-based formulations capturing most elastic constants with higher accuracy. To further support the fidelity of the numerical benchmark, the predicted normalized in-plane moduli are additionally compared with published experimental measurements for aluminum honeycombs, demonstrating close agreement for representative specimens. To quantify the influence of the geometric parameters, a Taguchi-style design-of-experiments (DOE) study reveals that relative density and internal cell angle jointly govern the majority of elastic moduli and Poisson’s ratios, while cell height plays a minor role. Furthermore, dedicated parametric studies confirm the cubic thickness-scaling of in-plane moduli (E₁, E₂, G₁₂), demonstrating the dominant role of bending-controlled deformation. Together, these results establish a validated, high-fidelity FE homogenization benchmark for assessing analytical formulations and providing design-level constitutive data for optimizing honeycomb core sandwich structures. Full article

Large-scale population datasets are rarely generated via simple random sampling; instead, they reflect complex designs involving stratification, clustering, and unequal inclusion probabilities. While survey weights are provided to recover population-representative estimates, standard Bayesian Kernel Machine Regression (BKMR), a flexible nonlinear model for high-dimensional exposure mixtures, does not explicitly accommodate these design features. We present a simulation-based framework that evaluates performance under complex sampling by comparing two analytic strategies applied to identical survey-like data: (i) a naïve, unweighted BKMR implementation and (ii) a design-aware workflow that can be executed using existing software without modifying the BKMR algorithm itself. Finite populations are generated with correlated exposures and a known nonlinear data-generating function. Stratified two-stage cluster samples are then drawn under both non-informative and exposure-dependent (informative) selection mechanisms, with controlled intra-class correlation (ICC). The design-aware approach incorporates sampling weights through resampling of the dataset while preserving primary sampling unit structure, followed by standard BKMR fitting. Methods are evaluated using bias, interval width, and empirical 95% coverage relative to the known truth. Across simulation scenarios, naïve BKMR exhibits bias and systematic under-coverage under informative sampling, with empirical 95% coverage often dropping to approximately 0–40%, whereas the design-aware workflow improves coverage to approximately 40–60%, moving results closer to nominal levels. These findings provide a practical, implementation-ready strategy for integrating survey design considerations into BKMR analyses and delineate conditions under which accounting for sampling design affects inference. While the proposed approach improves inferential performance relative to naïve BKMR, it does not fully achieve nominal coverage, indicating that further methodological development is required for fully valid uncertainty quantification under complex survey designs. Full article

With the rapid expansion of rural tourism in China, high-quality development has become a key concern for academics and policymakers. Existing studies have focused primarily on economic and industrial growth, with limited attention paid to development quality from the perspective of resident well-being. Using panel data from 30 Chinese provinces from 2012 to 2022, this study establishes a multidimensional evaluation framework for high-quality rural tourism. We employed the entropy weight method, Theil index, and quadratic assignment procedure analysis to examine its level, regional differences, and driving factors. The findings revealed that: (1) the overall level of rural tourism development remained relatively low but rose steadily from 0.064 (2012) to 0.150 (2022) (134.38% cumulative growth), driven by supply-side improvements and demand-side expansion. (2) Pronounced regional inequalities existed: eastern provinces had higher overall levels but larger internal gaps, whereas central/western provinces had lower overall levels but smaller internal differences, with intra-regional disparities accounting for over 66% of the national inequality. (3) The tourism market and transportation were universal key drivers, but the underlying mechanisms differed: the ecological environment exerted greater influence in the east, while public services and living standards were more critical in the central/western regions. By incorporating resident well-being into a systemic analytical framework, this study reconceptualizes high-quality rural tourism as an adaptive socio-ecological system shaped by multilevel interactions among the economy, society, and the environment. The results provide empirical evidence and systemic governance insights for promoting balanced and sustainable rural tourism development. Full article

The imbalance of urban public transport networks often leads to traffic congestion. Traditional planning prioritizes system optimization and single-mode travel, neglecting interactions between different modes. From an economic perspective and based on passenger travel behavior, this paper constructs a reasonable path set for multimodal networks. Using information entropy, it establishes multidimensional indicators including site path diversity entropy, destination regional entropy vectors, and weighted comprehensive entropy. Regional aggregation and coefficient of variation analyze internal balance, while scatter plots and the Gini coefficient measure global resource allocation equity. ArcGIS Pro 3.4.3 is employed for spatial analysis and visualization. An empirical study of Beijing’s six central districts reveals significant spatial heterogeneity in path distribution across functional zones: working areas exhibit concentric patterns, commercial areas form corridor agglomerations, residential areas have the highest entropy values, and transport hubs are relatively balanced. Cluster analysis based on entropy vectors effectively identifies commuter, residential, and hub station types. Some hubs show an ideal “high richness, low imbalance” state, while areas like Beijing Railway Station exhibit “low richness, high imbalance.” The Gini coefficient of 0.1864 indicates relatively balanced public transport resources overall. The “route-region-demand” collaborative analysis framework constructed in this study achieves a paradigm shift from static network structure to dynamic human-oriented evaluation, providing methodological support for equity assessment, network optimization, and resource allocation in multimodal public transport networks, and can contribute to the equitable and balanced sustainable development of public transport. Full article

Rapid urbanization in China has reshaped the coupling coordination between pollution and carbon emissions. However, existing studies largely rely on linear approaches and lack multidimensional and nonlinear assessments of urban growth patterns. Using panel data for 289 prefecture-level cities from 2010 to 2023, including built-up land, nighttime lights, CO₂ emissions, and PM_2.5 concentrations, this study develops three indicators: Urban Expansion Intensity (UEI), Urban Sprawl Index (USI), and Urban Compactness (UC). By integrating a coupling coordination model, K-means clustering, Geographically and Temporally Weighted Regression (GTWR), and interpretable XGBoost-SHAP analysis, four urban growth patterns are identified: High-Speed Low-Efficiency Expansion (HLE), Low-Speed Low-Efficiency Expansion (LLE), High-Speed High-Efficiency Compact (HHC), and Low-Speed High-Efficiency Compact (LHC). Results indicate that: (1) USI and UC exhibit significant nonlinear threshold effects on CCD; moderate expansion and higher compactness enhance synergy, whereas excessive dispersion or over-compactness weakens coordination. (2) UEI plays a relatively indirect and spatially heterogeneous role. (3) HHC and LHC cities achieve the highest CCD levels, while HLE cities perform the lowest. (4) Urban expansion shows an overall contraction trend, yet substantial regional disparities persist. These findings highlight nonlinear and spatially heterogeneous mechanisms linking urban growth patterns and pollution–carbon coupling coordination, providing implications for differentiated spatial governance. Full article

Background/Objectives: This secondary and exploratory meta-analysis re-evaluated 30 randomized controlled trials on free and added sugars (FS) detailed in the European Food Safety Authority’s (EFSA) report on the tolerable upper intake level for dietary sugars, focusing on the influence of food source (beverages, foods, or mixed) on cardiometabolic and anthropometric health. Methods: The EFSA’s method of analyzing the relative FS intake (difference between treatment and comparator arms, Δ%E^fs) was used, with further adjustment for the reported intake of all sources of FS and energy. The EFSA’s “high vs. low” random-effects meta-analysis comparing groups with the highest and lowest FS intake was replicated, and additional exploratory dose–response meta-regressions (linear and non-linear) were performed, stratified by food source. Given the secondary and observational nature of the analysis, all source-stratified findings should be interpreted as hypothesis-generating, rather than causal. Results: There were no interactions between Δ%E^fs and food source for any outcome, and within a source there were linearly positive and statistically significant regressions for body weight (mixed), low-density lipoprotein cholesterol (LDL-C, foods), and uric acid (beverages). Across 13 outcomes, Δ%E^fs was positively and linearly related to greater fasting glucose, high-density lipoprotein cholesterol (HDL-C), and LDL-C, and non-linearly to body weight. However, the data were limited in their representation of FS intake at typical population levels, and there were insufficient data to investigate the effect of FS from foods on most anthropometric outcomes. Conclusions: Meta-regressive dose–responses revealed little relationship between Δ%E^fs from specific food sources and health outcomes, but such effects might be masked by confounding factors. Future trials that test realistic intakes of FS across diverse food matrices and account for dietary compensation would help to overcome limitations in the body of evidence. Full article

The incorporation of the Internet of Things (IoT) in indoor agricultural systems has become an essential tool for monitoring and analyzing environmental variables, contributing to more efficient decision-making. This article presents the design and implementation of an IoT-based digital agriculture system applied to a Plant Factory (PF) for hydroponic vegetable cultivation using the Nutrient Film Technique (NFT). The objective of this study was to develop a system capable of effectively monitoring and controlling the environmental variables that directly influence the microclimate of a closed agricultural environment. The proposed system integrates a four-layer IoT architecture based on a MODBUS RS-485 communication bus, which allows for continuous data acquisition and the operation of multiple sensors and controlled devices. Additionally, user-oriented tools such as a human–machine interface (HMI), a web application, a mobile application and an automatic alert module were incorporated, enhancing accessibility and remote supervision. Experimental results showed stable control performance of ambient temperature (TA), relative humidity (RH), photoperiod, and photosynthetic photon flux density (PPFD), along with continuous monitoring of CO₂ concentration. A 30-day validation experiment using Swiss chard (Beta vulgaris L. var. cicla) under controlled conditions was conducted. The results showed progressive plant development, with leaf area increasing from 15.17 cm² to 690.39 cm², plant height from 7 cm to 31 cm, fresh weight from 23 g to 171 g, and the number of leaves from 9 to 20. These results support the functional validity of the proposed system as a reliable platform for environmental monitoring and control in controlled-environment agriculture. Full article

Parkinson’s disease (PD) may benefit from non-pharmacological motor–cognitive rehabilitation, but sensitive neuroimaging markers of training-related brain changes remain limited. This study investigated whether 4 weeks of daily Quadrato Motor Training (QMT) modulate resting-state functional connectivity (FC) in PD and secondarily explored whether whole-brain radiomic features derived from T1-weighted and fractional anisotropy (FA) images could detect pre–post differences over this short intervention interval. Fifty patients with idiopathic PD were randomized to QMT or a SHAM repetitive stepping condition, and 48 completed the protocol (25 SHAM, 23 QMT). MRI was acquired at baseline and after 4 weeks and included resting-state fMRI, 3D T1-weighted imaging, and diffusion-derived FA maps. Resting-state fMRI was analyzed using independent component analysis and dual regression, whereas an IBSI-compliant radiomics workflow and machine-learning models were used for exploratory scan-level classification. Compared with baseline, the SHAM group showed reduced synchronization across several resting-state networks, whereas the QMT group showed increased synchronization in the right sensorimotor and frontoparietal networks and no significant reductions. Between-group analyses showed lower delta-FC in SHAM than QMT in the cerebellar and sensorimotor networks. In contrast, radiomics showed limited discrimination between pre- and post-QMT scans; the best model achieved a ROC-AUC of 0.65 with near-chance accuracy, and no selected predictor remained significant after multiple-comparison correction. These findings suggest that QMT may support short-term functional network stability or task-relevant reorganization in PD relative to the SHAM condition, whereas whole-brain structural radiomics appears less sensitive for detecting early training-related effects in this setting. Full article

This study was conducted to evaluate the effect of varying dietary energy levels on milk production, feed intake, nutrient digestion and metabolism, and antioxidation function of lactating donkeys, and integrating 16S rRNA gene sequencing, metabolomics, and proteomics to comprehensively reveal the underlying regulatory networks. A single-factor, completely randomized design was used in this study. Twenty-four Dezhou donkeys with similar milk yield (3.25 ± 0.46 kg/d), lactation days (29 ± 4.34 d), parities (4.17 ± 1.17), and body weight (256 ± 34 kg) were randomly divided into three dietary treatments (n = 8), and either a fed high-energy diet (DE = 13.1 MJ/kg, HED), medium-energy diet (DE = 12.4 MJ/kg, MED), and low-energy diet (DE = 11.7 MJ/kg, LED). The experiment period included 2 weeks for adaptation and 8 weeks for data and sample collection. Orthogonal polynomial contrasts were used to evaluate the linear and quadratic effects of increasing dietary energy. There were no significant interaction effects between dietary energy level and lactation week on any milk production and quality variables (p > 0.05). Increasing dietary energy level increased DMI, milk production, milk production efficiency, and milk components (linear and quadratic; p < 0.05). Increasing dietary energy improved the digestibility of DM and neutral detergent fiber (linear; p < 0.05), and crude protein digestibility, energy digestibility and metabolism, and nitrogen metabolism (quadratic; p < 0.05). However, it decreased BHBA and NEFA concentrations (linear; p < 0.05). Furthermore, increasing dietary energy first increased then decreased the activities of GSH-PX, SOD, and T-AOC (linear and quadratic; p < 0.05), while increasing the MDA content (linear; p < 0.05). Compared with HED and MED, LED increased the relative abundance of the genera unclassified_f_Syntrophomonadaceae, Christensenellaceae_R-7_group and Treponema_2. Compared with HED, MED increased the relative abundance of the genera Ruminiclostridium_5, Ruminiclostridium_1, Family_XIII_UCG-001, unclassified_o__Clostridiales and norank_f__PL-11B10. Thyroid hormone synthesis, tyrosine metabolism, and glutathione metabolism pathways are critical metabolic routes; these pathways can enhance energy metabolism and antioxidant function, thereby improving the milk production performance of lactating donkeys. In conclusion, the digestible energy of 12.40 MJ/kg was optimal for the milk performance of lactating donkeys, whereas excessively high dietary energy (13.1 MJ/kg) may reduce milk performance. Full article

Gonadectomy is performed in bitches to prevent unwanted reproduction and reduce the risk of sex hormone-related conditions. However, growing evidence suggests that the timing of spaying may influence long-term susceptibility to non-reproductive diseases. This retrospective case–control study (2013–2023) evaluated the association between timing of spaying and the development of orthopedic and neoplastic disorders in a Belgian referral-hospital population. Cases included bitches diagnosed with cranial cruciate ligament rupture, hip dysplasia, elbow dysplasia, lymphoma, mast cell tumor, osteosarcoma, or hemangiosarcoma, while disease-free bitches served as controls. Associations between disease occurrence and spaying status were assessed using multivariable logistic regression adjusted for age, weight category, and body condition score. Age at gonadectomy (<12 vs. ≥12 months) and timing relative to the first estrus were evaluated in separate models. Spaying <12 months of age was associated with increased odds of all conditions compared with intact females. Spaying ≥12 months of age was associated with lower odds of several orthopedic and neoplastic outcomes compared with early spaying, although odds were not always comparable to those in intact females. Large body size and higher body condition score were independently associated with increased odds of orthopedic outcomes. These findings support individualized spaying strategies rather than a universal age threshold. Full article