Search Results (4,674)

Background: This study examined the associations of dietary animal (AP) and plant protein (PP) with all-cause mortality in an Italian population and assessed the potential effect modification by sex, smoking status, and overweight/obesity (defined as BMI ≥ 25 kg/m²). Methods: This longitudinal population-based study included 1350 adults (50.2% females), aged 40–74 years, recruited between 1991 and 1995, who were followed for all-cause mortality through the regional mortality registry until 2015. Dietary data were collected using a food frequency questionnaire, and protein composition was analysed within a compositional data analysis framework, modelling the balance of AP and PP within the overall macronutrient composition. The associations of protein balances with all-cause mortality were estimated using Cox proportional hazards models adjusted for potential confounders. Effect modification was evaluated through stratified analyses. Results: During follow-up, 405 deaths occurred. A greater AP relative to other macronutrients was associated with higher mortality overall (hazard ratio (HR): 1.37; 95% confidence interval (CI): 1.00–1.87) and in men (HR: 1.57; 95% CI: 1.05–2.33). In stratified analyses, these associations were restricted to ever smokers overall (HR 2.06, 95% CI 1.32–3.20), men (HR 1.90, 95% CI 1.18–3.06), women (HR 3.29, 95% CI 1.03–10.54), and to participants with normal weight (HR 1.91, 95% CI 1.07–3.41). No overall association was observed for PP. Among women, PP was associated with lower mortality in those with normal weight. Conclusions: The associations between AP and PP and mortality differed by sex, smoking status, and adiposity, supporting more tailored dietary recommendations. Full article

In this study, we investigate the optimization of ring spinning parameters affecting key yarn quality characteristics, including yarn tension, cop diameter, and end breakage. Experiments were conducted on cotton–polyester yarn using three process variables: traveler mass (60, 67.5, and 75 mg), spindle speed (12,900, 13,300, and 13,700 min⁻¹), and doff stage (43, 111, and 179 mm). A two-stage optimization method was applied: we used the Taguchi method to optimize individual responses, while a normalization-based composite scoring approach was used to integrate them to determine globally optimal ring spinning parameters under differing response-specific conditions. The results show that traveler mass is the dominant factor influencing yarn tension, contributing 65.48% and 73.29% of variation at the bottom and top ring rail positions, respectively. Cop diameter is primarily governed by doff stage, contributing 89.43% of total variance (ANOVA), with the intermediate level (111 mm) yielding the highest mean diameter and the most favorable S/N ratio. The yarn breakage rate is mainly affected by doff stage (57.26%) and spindle speed (41.89%), with minimum breakage observed at moderate spindle speed and mid-level doff stage. The global optimal parameter combination (60 mg traveler mass, 12,900 min⁻¹ spindle speed, and 111 mm doff stage) achieved balanced multi-response performance. The framework demonstrates strong predictive capability (R² > 0.991) and enables optimization. Full article

Forest carbon assessment requires methods that capture the combined effects of stand structure, site conditions, carbon pools, operational emissions, and circular-economy processes. This study aimed to develop and optimize hybrid machine learning models for predicting the gross CO₂e (carbon dioxide equivalent) balance of Polish forest stands using measurable stand- and site-related variables. The research was based on a primary dataset describing forest management in major Polish macroregions in 2020–2024. After data cleaning and preprocessing, multiple machine learning algorithms, including ensemble, boosting, neural, and hybrid models, were trained, validated, and tested. Model performance was assessed using standard regression metrics, overfitting diagnostics, learning curves, and SHAP (Shapley Additive Explanations). Most models achieved high predictive accuracy, with six of ten algorithms reaching R² values above 0.90 on the test set. The reduction in strongly correlated variables helped limit multicollinearity and excessive overlap between predictors and the target variable, supporting a more reliable interpretation of model performance. The CatBoost algorithm achieved the highest predictive performance on the test set (R² = 0.948), while also recording the lowest root mean squared error (RMSE = 152.242). However, the Decision Tree demonstrated the weakest generalization performance (R² = 0.806) on the test set. SHAP analysis identified tree height as the most influential predictor, followed by tree age, number of trees, species composition, and selected habitat features. The novelty of the study lies in integrating hybrid machine learning, interpretable modelling, and circular-economy-related carbon balance components into a single framework for rapid and operational forest carbon assessment in Polish forest stands. Full article

Shoot density is a key viticultural factor modulating canopy microclimate, berry composition, and wine quality, although yield–quality relationships are strongly influenced by environmental conditions. Saignée, a winemaking technique involving partial juice removal prior to fermentation, increases the skin-to-juice ratio and may enhance phenolic extraction. This study assessed the combined effects of shoot density (33 [T1], 20 [T2], and 15 [T3] shoots/m) and saignée (20% vs. control) on yield, grape composition, and wine chemical and sensory properties in Malbec across two vintages (2021–2022). At harvest, the pruning weight, yield components, general maturity parameters, and phenolic composition were measured. The wines were analysed for their phenolic and elemental composition, polysaccharides and volatile compounds, colour, and sensory attributes. T1 exhibited the highest yields and vegetative imbalance, whereas T2 and T3 achieved optimal Ravaz indices. The general grape maturity parameters were unaffected; however, T3 had increased berry phenolic content in 2022. T2 and T3 had enhanced wine tannins, total phenols, and polymeric pigments, particularly in 2022. Saignée increased the pH, potassium, total phenols, tannins, and acylated anthocyanins. Targeting yields near 4 kg/vine (≈10,500 kg/ha) improved vine balance and phenolic composition, although the responses were strongly modulated by interannual variability. Full article

Hyperspectral image (HSI) stripe noise removal is essential for downstream interpretation tasks. However, most existing methods exhibit incomplete joint modeling of spatial structures and inter-band spectral correlations, lack direction-aware modeling for stripe noise, and lack differentiated processing of high- and low-frequency components. To tackle these limitations, we propose a spatial-spectral attention-enhanced multi-level wavelet-informed network (SAMWNet). Its dual-branch module extracts spatial and spatial-spectral features from each band and its adjacent bands. Afterward, a discrete wavelet-informed progressive denoising (MDWPD) module conducts multi-level Haar wavelet decomposition and progressive reconstruction. Within this module, the low-frequency hybrid enhancement (LFHE) module preserves low-frequency spectral structures, while the high-frequency enhancement (HFME) module suppresses directional stripe artifacts in high-frequency subbands. We further adopt a composite loss function to balance pixel fidelity, noise estimation, structural similarity, and spectral consistency. Experimental results on simulated and real-world HSIs demonstrate that SAMWNet achieves competitive or superior performance compared with several representative HSI denoising methods. Full article

This study evaluated the effects of monolaurin intake per finishing feedlot cattle on growth performance, metabolic status, ruminal environment, and meat fatty acid profile. Twenty-four castrated Holstein males (379 ± 8.5 kg; 12 months old) were randomly assigned to two treatments: basal diet (control) or basal diet with α-monolaurin (treated: 0.762 g/kg dry matter intake; ≈6.63 g/animal/day) for 79 days. Feed intake, body weight, and feed efficiency were recorded, and blood and ruminal samples were collected during the trial. Ruminal fermentation parameters, protozoa counts, hematological and biochemical variables, oxidative status biomarkers, ruminal microbiota composition (16S rRNA sequencing), and Longissimus dorsi fatty acid profile were analyzed. Monolaurin feed did not affect dry matter intake or final body weight, but increased total weight gain, average daily gain, and feed efficiency (p ≤ 0.05), indicating improved nutrient utilization. Hematological and serum biochemical variables were largely unchanged, although total leukocyte counts were lower in treated cattle. Animals receiving monolaurin showed reduced reactive oxygen species and lower superoxide dismutase activity, suggesting improved oxidative balance without changes in lipid peroxidation. During the adaptation phase (day 14), treated cattle exhibited lower acetate, propionate, valerate, and total volatile fatty acid concentrations and higher protozoa counts, but these differences disappeared by day 79, indicating ruminal adaptation. Microbiota diversity was not altered overall, although specific genera differed in relative abundance between treatments. In meat, monolaurin increased lauric, linoleic, and arachidonic acids, reduced palmitic and heptadecanoic acids, decreased total saturated fatty acids, and increased polyunsaturated fatty acids (p ≤ 0.05). Overall, dietary monolaurin improved feed efficiency, modulated oxidative status, induced transient ruminal microbial adjustments, and enhanced the nutritional quality of beef lipids without compromising metabolic health. Full article

The rapid development of high-density cities has triggered severe ecological challenges, including habitat fragmentation, urban heat island (UHI) effects, and conflicting demands for public recreation. Traditional ecological networks (ENs) often focus only on “source” landscapes while neglecting degraded “sink” areas. This bias limits the ability of planners to resolve complex spatial conflicts. Therefore, the primary aim of this study is to develop a robust spatial planning framework that mitigates urban ecological conflicts and enhances regional resilience. To achieve this, we constructed a composite ecological network (CEN) for the high-density city of Guangzhou that harmonizes bird habitat conservation, thermal regulation, and cultural recreation. We combined the MaxEnt model, morphological spatial pattern analysis (MSPA), and circuit theory to identify functional “sources” and “sinks” across these three dimensions. Next, using complex network theory, we optimized the CEN and evaluated its structural robustness using low degree addition (LDA) and low betweenness addition (LBA) strategies. The results indicate the following: (1) The CEN effectively captured the complex mosaic landscape of the city. (2) Single-objective networks displayed distinct spatial differences—the recreational network formed a dispersed web of 242 corridors, while habitat and climate networks remained highly clustered. (3) The integrated CEN generated 1137 multi-layered corridors, creating a vital green skeleton to support species dispersal, mitigate UHI effects, and improve cultural access. (4) Optimization simulations verified that the LBA strategy provided the highest stability against targeted attacks by balancing network connectivity with local aggregation. Ultimately, this framework offers a highly adaptable planning tool for dense cities, providing precise spatial guidance to overcome ecological bottlenecks and harmonize urban growth with ecosystem resilience. Full article

Water and sanitation governance sits at the intersection of global development ambitions and highly localized service realities. While SDG6 sets universal targets for clean water and sanitation, the institutional and fiscal arrangements that translate those targets into actual service outcomes operate primarily at the subnational level. The discrepancy between globally defined objectives and locally executed delivery creates a structural research gap: how do the fiscal architectures of local governments influence progress towards SDG6? This study addresses this question for a panel of OECD countries by developing a deep learning-based estimation framework that combines bidirectional long short-term memory (BiLSTM) networks with Tianji’s horse racing optimization (THRO) algorithm. Three distinct operationalizations of fiscal decentralization are tested against SDG6 outcomes: subnational expenditure share (EFDM), subnational revenue share (RFDM), and a composite index balancing both dimensions (CFDM). Model adequacy is assessed using a layered diagnostic protocol involving regression fit, country-level residual patterns, error density profiles, Bland–Altman limits of agreement and inter-annual error trajectories. Among the three configurations, CFDM consistently records superior performance (; ; ), while even the weakest specification clears , attesting to the overall robustness of the proposed architecture. The margin by which CFDM outperforms its alternatives highlights a key finding: neither spending authority nor revenue capacity alone accurately reflects the fiscal reality of local water and sanitation governance; it is their combined effect that is important. The expenditure dimension is further proven to be the more influential of the two unidimensional proxies, consistent with the capital-intensive and maintenance-heavy nature of water infrastructure. On the other hand, coefficient findings show that fiscal decentralization is positively associated with SDG6 achievement for all models. Beyond its empirical contributions, the study introduces a methodological template for applying hybrid AI optimization to policy-relevant sustainability panels. It also connects two largely parallel bodies of scholarship, fiscal federalism and SDG research, that have rarely been examined together. Full article

Background/Objectives: Red beetroot (Beta vulgaris L.) is recognized for its antioxidant, anti-inflammatory, and metabolic properties. This study evaluated the effects of two beetroot cultivars (Boldor and Wodan) on blood serum parameters, body composition, and organ weights in male WKY rats fed a low-protein diet (LPD, 8.8% protein). Methods: Five-week-old male rats were maintained on an LPD for 8 weeks and subsequently continued on the LPD diet supplemented with 4% dried beetroot for 45 days. The experimental diets included beetroot from the Boldor and Wodan cultivars, either treated or untreated with a plant growth stimulator during cultivation. Results: Foliar application of the selenium-based plant growth stimulator did not significantly increase selenium or other element concentrations in beet roots. Elemental analysis showed higher levels of Fe, Zn, Cu, Cr, Pb, As, Cd, and Sb in the Wodan group, while Boldor increased Cr, Pb, and As; Ni and Se remained unchanged. Beetroot supplementation significantly affected 14 of the 30 measured biochemical parameters, including biomarkers of liver function (ALT, ALP, total bilirubin, albumin, and total protein), renal function (uric acid), pancreatic activity (amylase and lipase), electrolyte balance (sodium, potassium, and chloride), mineral metabolism (calcium), inflammatory status (CRP), and nutritional metabolism (iron). Conversely, no significant effects were observed on lipid profile parameters or biomarkers of cardiac and skeletal muscle injury. Among the beetroot cultivars evaluated, Wodan exerted distinct effects relative to Boldor, resulting in higher circulating total bilirubin and potassium concentrations, alongside reduced uric acid and lipase levels in treated rats. Boldor supplementation significantly increased body weight gain and fat mass, with a trend toward higher lean mass, and increased kidney weight. Wodan did not significantly affect body weight but increased kidney and spleen mass. Feed intake was similar across groups. No changes in cardiovascular function were observed ex vivo. Conclusions: Beetroot supplementation modulated multiple metabolic and physiological biomarkers in rats fed a low-protein diet, with distinct cultivar-specific effects, underscoring the importance of cultivar selection for optimizing functional dietary interventions. Full article

During pregnancy, hormonal, metabolic, and immunological changes influence the composition and function of maternal microbial communities. Increasing evidence suggests that the maternal microbiota—particularly in the vaginal, gut, and oral environments—plays a significant role in maintaining pregnancy homeostasis and supporting fetal development. In healthy pregnancies, the vaginal microbiota is typically dominated by Lactobacillus species, which help maintain a low vaginal pH and protect against ascending infections. However, disruption of this balance (vaginal dysbiosis) has been associated with obstetrical complications such as intrauterine infection and preterm birth. Similarly, the maternal gut microbiota undergoes trimester-specific changes that contribute to metabolic adaptations required for fetal growth, while alterations in microbial composition have been linked to metabolic disorders including gestational diabetes mellitus and preeclampsia. Changes in oral microbiota and periodontal disease have also been associated with adverse pregnancy outcomes through systemic inflammatory pathways and potential microbial translocation to the placenta. Recent advances in sequencing technologies have improved the understanding of host–microbiome interactions in pregnancy, although the existence of a placental microbiome remains controversial. Overall, maternal microbiota plays an important role in pregnancy physiology, and its dysregulation may contribute to obstetrical complications. Understanding these mechanisms may facilitate the development of microbiome-based diagnostic and therapeutic strategies in maternal–fetal medicine. Full article

To address the application bottlenecks of organic phase change materials characterized by low thermal conductivity and susceptibility to liquid leakage, this study utilized natural poplar wood as a raw material to construct a three-dimensional carbon/silver heterogeneous porous skeleton via delignification, gradient carbonization, and in situ electroless silver plating. Polyethylene glycol (PEG) was then vacuum-encapsulated within this structure to prepare form-stable composite phase change materials (CPCMs). The regulatory effects of carbonization temperature and metal interface modification on the microscopic morphology and thermophysical properties of the materials were systematically investigated. The results indicate that the skeleton carbonized at 800 °C achieves an optimal balance between pore distribution and skeleton rigidity, ensuring the uniform conformal growth of silver nanoparticles and endowing the material with excellent anti-leakage performance. The thermal conductivity of the optimal sample reaches as high as 0.683 W/(m·K), with the melting latent heat maintained at 133.9 J/g, while also demonstrating an agile and stable photothermal conversion response. Non-equilibrium molecular dynamics (NEMD) simulations further confirm that the silver nanoparticle modification layer smooths the phonon vibration frequency mismatch between the carbon substrate and organic segments, significantly reducing the interfacial thermal resistance. This research provides an important reference for the structural design and microscopic heat transfer mechanism analysis of high-performance phase change energy storage materials. Full article

Bio-based coatings enriched with essential oils (EOs) represent a promising alternative to synthetic preservatives to extend strawberries’ shelf-life. This study evaluated the effects of chitosan (CHT) formulations containing three selected EOs (Illicium verum, Citrus sinensis, and Citrus limon) on the volatile profile, sensory quality, and antifungal activity of strawberry fruits. Volatile emissions were characterized by Headspace Solid Phase Micro-Extraction/Gas Chromatography-Mass Spectrometry, while sensory properties were assessed using Quantitative Descriptive Analysis. Antifungal activity was evaluated both in vitro and in vivo against Botrytis cinerea. Chitosan alone slightly modified the volatile profile, while EO-enriched coatings induced marked and concentration-dependent changes, reflecting the chemical composition of the incorporated EOs. Among the tested formulations, CHT combined with 1% C. sinensis EO provided the best balance between preservation of the characteristic strawberry aroma and overall sensory acceptance. In vitro assays showed that EO volatiles, particularly from C. sinensis and I. verum, significantly inhibited fungal growth, while diffusible compounds were less effective. In vivo, EO-containing coatings reduced disease incidence and severity by approximately 50%. These findings highlight the potential of CHT–EO coatings as sustainable options for postharvest preservation, although optimization of EO type and concentration is crucial to balance sensory quality and antimicrobial efficacy. Full article

Magnesium–rich environments are frequently encountered in cementitious systems, including the use of high–Mg raw materials in clinker production, cement–clay interfaces relevant to nuclear waste disposal, and exposure of cement–based materials to seawater, where progressive decalcification can substantially alter the structure and durability of calcium aluminosilicate hydrate (C–A–S–H) phases. In this study, density functional theory (DFT) calculations were employed to investigate the combined effects of interlayer and intralayer partial decalcification, Mg²⁺ substitution, and reinforcement with epoxy– and hydroxyl–functionalized reduced graphene oxide (rGO) on the structural stability and elastic properties of alkali charge–balanced C–A–S–H under dry and hydrated conditions. Adsorption–energy calculations reveal thermodynamically favorable interactions between functionalized rGO and silicate hydrate species in the presence of Mg²⁺, with hydroxyl/rGO promoting stronger interfacial stabilization and epoxy/rGO preserving greater graphene lattice integrity. The results demonstrate that Mg²⁺ substitution together with rGO intercalation generally enhances the mechanical response of partially decalcified structures through structural densification and interfacial cohesion. Relative to dry systems, hydration further improves elastic performance, increasing Young’s modulus and bulk modulus by 1–11% and 4–19%, respectively, for interlayer decalcified nanocomposites, while intralayer configurations exhibit stronger but model–dependent enhancements of up to ≈22% and ≈33%. Compared with untreated systems, rGO–treated nan–composites exhibit enhanced stiffness, with Young’s modulus and bulk modulus increasing by up to ≈22% and ≈15%, respectively. Overall, these findings provide atomistic insights into stabilization mechanisms in partially decalcified alkali charge–balanced C–A–S–H systems and identify Mg²⁺–rGO incorporation as a promising strategy for mitigating decalcification–induced degradation in durable low–carbon cementitious nanocomposites. Full article

The increasing concentration of atmospheric CO₂ has intensified the urgent need for efficient and sustainable carbon capture technologies. Covalent organic frameworks (COFs) have emerged as a highly promising class of porous crystalline materials for CO₂ adsorption and separation owing to their structural tunability, high surface area, and precisely designable pore environments. This review summarizes recent advances in COF-based CO₂ capture systems, covering pristine COFs, functionalized frameworks, composite materials, and membrane-based architectures. In pristine COFs, CO₂ adsorption is mainly governed by micropore confinement and physisorption within well-defined channels, where surface area and pore size distribution play key roles. Functionalized COFs introduce additional active sites, including amine groups, heteroatoms, ionic functionalities, and alkali metal centers, which significantly enhance CO₂ affinity through stronger electrostatic and acid–base interactions, often leading to mixed physisorption–chemisorption behavior. Composite COFs and mixed-matrix membranes further improve performance through synergistic effects, interfacial engineering, and enhanced mass transport. Despite these advantages, challenges remain in achieving an optimal balance between capacity, selectivity, and regenerability under realistic conditions such as humidity, low CO₂ partial pressure, and multicomponent gas streams. Issues related to scalable synthesis, structural stability, and processability also limit practical applications. Overall, this review highlights key structure–property relationships and outlines future directions, including humid-stable COFs, direct air capture, computational design strategies, and advanced membrane technologies, for next-generation CO₂ capture materials. Full article

This study investigated the effect of MoS₂/graphite lubricant composition on the high-temperature compaction behavior, local mechanical uniformity, and microstructural characteristics of Fe–5.0 wt.%Si SMCs. Nine lubricant compositions were prepared by varying MoS₂ and graphite contents, and their friction behavior, Vickers hardness, and compaction behavior were evaluated experimentally and by FEA. One-way ANOVA confirmed that lubricant composition significantly affected the Vickers hardness response (F = 4.245, p = 0.000273). The measured friction coefficients were applied as interface friction conditions in FEA, and the relative density, effective strain, and absolute hydrostatic stress distributions were compared. Among the investigated compositions, C3, containing 1.0 wt.% MoS₂ and 0.3 wt.% graphite, showed the lowest friction coefficient and Vickers hardness standard deviation. In FEA, C3 also showed balanced relative density, effective strain, and hydrostatic stress distributions. XRD confirmed the α-Fe-based bcc Fe–Si matrix, while SEM-EDS indicated locally distributed lubricant-derived residual regions. Therefore, C3 was selected as the most balanced lubricant composition within the investigated range. Future studies will evaluate electromagnetic properties, including core loss and magnetic permeability. Full article