Search Results (17,029)

Mastitis represents one of the most economically devastating diseases in dairy production, causing billions of dollars in annual losses through reduced milk quality and quantity. Recent advances in microbiome research have unveiled a critical gut–mammary axis that fundamentally influences mastitis susceptibility and pathogenesis in dairy cattle. Through comprehensive analysis of microbial communities across multiple anatomical sites, we demonstrate that mastitis development involves systematic disruption of both mammary and gastrointestinal microbiomes, characterized by reduced beneficial bacterial populations and increased pathogenic species. Healthy animals maintain balanced microbial ecosystems dominated by protective taxa including Firmicutes, Bacteroidetes, and beneficial Lactobacillus species, while mastitis-affected animals exhibit dysbiotic shifts toward Proteobacteria dominance, elevated Streptococcus and Staphylococcus populations, and compromised microbial diversity. Mechanistic investigations reveal that gut microbiota disruption compromises systemic immune competence, alters metabolite production including short-chain fatty acids and bile acids, and influences inflammatory mediators that circulate to mammary tissue. Therapeutic interventions targeting this axis, including probiotics, prebiotics, and plant-derived compounds, demonstrate significant efficacy in restoring microbiome homeostasis and reducing mastitis severity. These findings establish the gut–mammary axis as a fundamental regulatory mechanism in mastitis pathogenesis, opening new avenues for microbiome-based prevention and treatment strategies that could significantly enhance dairy health management while addressing antimicrobial resistance concerns. Full article

With the acceleration of urbanization, environmental degradation is increasingly restricting the improvement of residents’ quality of life, and promoting the transformation of old communities has become a key path for sustainable urban development. However, existing buildings generally face challenges, such as the deterioration of the performance of the envelope structure and the rising energy consumption of the air conditioning system, which pose a serious test for the realization of green renovation. Inspired by the application of bionics in the field of architecture, this study innovatively designed five types of bionic envelope structures for outdoor air conditioning units, namely scales, honeycombs, spider webs, leaves, and bird nests, based on the aerodynamic characteristics of biological prototypes. The ventilation performance of these structures was evaluated at three scales—namely, single building, townhouse, and community—under natural ventilation conditions, using a CFD simulation system. The study shows the following: (1) the spider web structure has the best comprehensive performance among all types of enclosures, which can significantly improve the uniformity of the flow field and effectively eliminate the low-speed stagnation area on the windward side; (2) the structure reorganizes the flow structure of the near-wall area through the cutting and diversion of the porous grid, reduces the wake range, and weakens the negative pressure intensity, making the pressure distribution around the building more balanced; (3) in the height range of 1.5–27 m, the spider web structure performs particularly well at the townhouse and community scales, with an average wind speed increase of 1.1–1.4%; and (4) the design takes into account both the safety of the enclosure and the comfort of the pedestrian area, achieving a synergistic optimization of function and performance. This study provides new ideas for the micro-renewal of buildings, based on bionic principles, and has theoretical and practical value for improving the wind environment quality of old communities and promoting low-carbon urban development. Full article

Urbanization and the growing scarcity of surface land resources have highlighted the strategic importance of underground space as a critical component of sustainable urban infrastructure. This study presents a multi-objective optimization framework for underground infrastructure planning around transit hubs, aligning with the principles of Transit-Oriented Development (TOD). By integrating an agent-based model (ABM) with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and incorporating the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), the framework forms a unified evaluation and optimization tool that accounts for user behavior while addressing competing objectives, including minimizing evacuation time and functional conflicts, maximizing functional efficiency, and reducing layout deviations. Using Qingdaobei Railway Station in China as a case study, the method yields notable improvements: a 15% reduction in evacuation time, a 16% increase in development benefits, and a more balanced spatial configuration. Beyond technical gains, the study also discusses station planning and governance under the TOD policy context, highlighting how integrated layouts can alleviate congestion, strengthen functional synergy, and support sustainable urban development. Full article

This research examines the relationship between corporate social responsibility (CSR) and perceived financial performance (FP) in the Saudi Arabian banking industry using the mediating variables of employee engagement (EE) and green creativity (GC). This study is based on the Social Identity Theory and considers CSR as an engine to produce ethical and social results and promote environmental innovation and sustainable competitiveness. According to a survey of 650 banking employees and structural equation modeling (SEM), the results show that CSR significantly and positively affects EE, GC, and FP, with EE having the strongest mediating role. These conclusions highlight the strategic consequence of CSR in advancing sustainability by balancing financial performance, employee welfare, and environmental innovation. This study adds value to the existing body of research because it provides information on the CSR-FP relationship in a developing economy, where such information is scarcely available. Consistent with the definition of sustainability, this study indicates how CSR activities combine social, environmental, and economic aspects to foster long-term organizational sustainability and sustainable development. Full article

The construction industry’s reliance on Portland cement (PC) significantly contributes to global CO₂ emissions, driving the search for sustainable binder alternatives. This study develops and evaluates novel mineral binder systems for wood wool acoustic panels with a reduced carbon footprint. Alternative binders, including calcium aluminate cement (CAC), magnesium oxychloride cement (MOC), and gypsum–cement–pozzolan (GCP) hybrids, were combined with additives such as metakaolin and liquid glass. Mechanical testing demonstrated that 20–30% metakaolin and liquid glass composites achieved flexural strengths of up to 2.65 MPa and densities above 490 kg/m³. The GCP system showed synergistic improvements in flexural and compressive strengths by nearly 50%, along with enhanced dimensional stability and water resistance. Life cycle assessment indicated substantial CO₂ emission increases, particularly for the MOC and CAC formulations, compared to conventional Portland cement-based panels. The carbon footprint of the binder system consisting of GCP is approximately 5.644 kg of CO₂ equivalent per functional unit compared to magnesium chloride binder systems, which reach up to 10.84 kg CO₂ eq., and white Portland cement systems, which are around 6.19 kg CO₂ eq. The three-component GCP binder system offers the best balance of mechanical performance and minimised environmental impact. Key raw material contributors to the ecological load are cement (various types), MgO, MgCl₂, and metakaolin, highlighting the importance of optimising binder formulations to reduce carbon emissions. The GCP system, in particular, demonstrates unprecedented synergistic improvements in flexural and compressive strengths, dimensional stability, and water resistance while minimising CO₂ emissions. Current work sets a new benchmark for sustainable building materials by offering an eco-innovative pathway towards low-carbon, high-performance wood wool acoustic panels, aligning with global decarbonisation goals. Full article

The expanding development of graphene-based materials (GBMs) requires immediate and balanced environmental assessment balancing two key areas: investigating the risk of graphene oxide toxicity to ecosystems and evaluating GBMs’ potential to act as solutions for challenges like heavy metal stress mitigation. This study analyzed the effects of reduced graphene oxide (rGO) on copper (Cu) and nickel (Ni) toxicity in Lemna minor. Our findings reveal that rGO’s protective effects are metal-specific. L. minor demonstrated significant sensitivity to nickel, but rGO offered no mitigation; growth parameters, pigment content, and nickel accumulation showed no significant improvements with rGO co-exposure compared to Ni-plants. This suggests that rGO does not enhance L. minor’s ability to tolerate or absorb nickel, especially after 14 days (T14). In contrast, rGO showed a partially protective effect against copper toxicity. At T14, the presence of rGO significantly improved plant performance under copper stress, resulting in a 17% increase in biomass, a 19% increase in relative growth rate, and enhanced pigment content, including a 40% increase in chlorophyll when compared to Cu-plants. The protective effect of rGO was directly tied to a 37% reduction in copper accumulation, providing strong evidence that rGO reduces copper’s bioavailability, thereby limiting plant uptake. The divergent effects on Cu and Ni uptake suggest differing affinities of these metals for rGO. Future research, including large-scale experiments with various GBMs and Lemna clones, is crucial to fully assessing their phytoremediation potential. Full article

Combustion at the meso-scale is constrained by large surface-to-volume ratios that shorten residence time and intensify wall heat loss. We perform steady, three-dimensional CFD of two asymmetric vortex combustors: Model A (compact) and Model B (larger-volume) over inlet-air mass flow rates $\dot{m}$ (40–170 mg s⁻¹) and equivalence ratios ϕ (0.7–1.5), using an Eddy-Dissipation closure for turbulence–chemistry interactions. A six-mesh independence study (the best mesh is 113,133 nodes) yields ≤ 1.5% variation in core fields and ~2.6% absolute temperature error at a benchmark station. Results show that swirl-induced CRZ governs mixing and flame anchoring: Model A develops higher swirl envelopes (S up to ~6.5) and strong near-inlet heat-flux density but becomes breakdown-prone at the highest loading; Model B maintains a centered, coherent Central Recirculation Zone (CRZ) with lower $u_{\theta }$ (~3.2 m s⁻¹) and S ≈ 1.2–1.6, distributing heat more uniformly downstream. Peak flame temperatures (~2100–2140 K) occur at ϕ ≈ 1.0–1.3, remaining sub-adiabatic due to wall heat loss and dilution. Within this regime and $\dot{m}$ ≈ 85–130 mg s⁻¹, the system balances intensity against flow coherence, defining a stable, thermally efficient operating window for portable micro-power and thermoelectric applications. Full article

This study investigates the influence of 3D printing process parameters on the mesoscopic structure of polylactic acid/graphene nanoplatelet (PLA/GNP) composites. A computational fluid dynamics (CFD) multiphase flow model was developed to simulate the deposition, flow, and solidification behavior of the molten composite during the printing process. The effects of nozzle temperature (180–220 °C) and printing speed (30–50 mm/s) on the filament morphology, porosity, surface roughness, dimensional accuracy, and tensile strength of the printed parts were systematically examined. The accuracy of the model was validated by comparing simulation results with experimental data from scanning electron microscopy (SEM) observations and mechanical tests. The findings reveal that a higher nozzle temperature and a lower printing speed result in a flatter filament cross-section, which effectively reduces porosity and surface roughness, thereby enhancing print quality. Furthermore, a skewed deposition configuration achieves a denser structure and superior surface quality compared to an aligned configuration. The research uncovered a critical trade-off between dimensional accuracy and mechanical properties: low-temperature, low-speed conditions favor dimensional accuracy, whereas high-temperature, high-speed conditions improve tensile strength. A comprehensive analysis identified an optimal processing window at a nozzle temperature of 210–215 °C and a printing speed of 30–35 mm/s. This window balances performance, enabling the fabrication of composite parts with both high tensile strength (approximately 56 MPa) and excellent dimensional accuracy (root mean square deviation below 0.18 mm). This study provides a theoretical basis and process guidance for the application of 3D printing for high-performance PLA/GNP composites. Full article

The application of Artificial Intelligence (AI) in river basin pollution control shows great potential to improve governance efficiency through real-time monitoring, pollution prediction, and intelligent decision-making. However, its rapid development also brings regulatory challenges, including data privacy, algorithmic bias, responsibility definition, and cross-regional coordination. Based on the SETO loop framework (Scoping, Existing Regulation Assessment, Tool Selection, and Organizational Design), this paper systematically analyzes the regulatory needs and pathways for AI in watershed water pollution control through typical case studies from countries such as China and the United States. The study first defines the regulatory scope, focusing on protecting the ecological environment, public health, and data security. It then assesses the shortcomings of existing environmental regulations in governing AI, such as their inability to adapt to dynamic pollution sources. Subsequently, it explores suitable regulatory tools, including information disclosure requirements, algorithmic transparency standards, and hybrid regulatory models. Finally, it proposes a multi-tiered organizational scheme that integrates international norms, national legislation, and local practices to achieve flexible and effective regulation. This study demonstrates that the SETO loop provides a viable framework for balancing technological innovation with risk prevention and control. It offers a scientific basis for policymakers and calls for establishing a dynamic, layered regulatory system to address the complex challenges of AI in environmental governance. Full article

Dental caries remains one of the most prevalent chronic diseases worldwide, driven by complex interactions among dietary, hygienic, and biological factors. This study introduces a hybrid predictive framework that integrates mathematical modelling and artificial intelligence (AI) to estimate individual caries risk based on daily sugar intake, oral hygiene index, salivary pH, fluoride exposure, age, and sex. A first-order balance differential equation was applied to simulate demineralisation–remineralisation dynamics, while a feed-forward artificial neural network (ANN) was trained on simulated and literature-derived datasets. The hybrid model demonstrated strong predictive performance, achieving 91.2% accuracy and an AUC of 0.98 in classifying individuals into low-, moderate-, and high-risk categories. Sensitivity analysis identified sugar intake and oral hygiene as dominant determinants, while fluoride and salivary pH showed protective effects. These findings highlight the feasibility of combining mechanistic and data-driven approaches to enhance early risk assessment and support the development of intelligent, personalised screening tools in preventive dentistry. Full article

The aim of this study was to develop a dynamic factorial model for predicting amino acid requirements in Hy-Line Gray laying hens during critical early growth stages (0–84 days), addressing the need for precision feeding in modern poultry production systems. Methods: Four sequential trials were conducted. In Trial 1, growth curves and protein deposition equations were developed based on fortnightly body composition analyses, with parameters evaluated using the Akaike and Bayesian information criteria (AIC and BIC). In Trial 2, the carcass and feather amino acid profiles were characterized via HPLC. And established the amino acid composition patterns of chicken feather protein and carcass protein (AAF and AAC). In Trial 3, maintenance requirements were quantified through nitrogen balance studies, and in Trial 4, amino acid patterns of feather protein (APD) and apparent protein digestibility (ADD) were established using an endogenous indicator method. These datasets were integrated through factorial modeling to predict age-specific nutrient demands. Results: The developed model revealed the following quantitative requirements (g/day) for 18 amino acids across developmental stages: aspartic acid (0.1–0.863), glutamic acid (0.170–1.503), serine (0.143–0.806), arginine (0.165–0.891), glycine (0.258–1.279), threonine (0.095–0.507), proline (0.253–1.207), alanine (0.131–0.718), valine (0.144–0.737), methionine (0.023–0.124), cysteine (0.102–0.682), isoleucine (0.086–0.458), leucine (0.209–1.067), phenylalanine (0.086–0.464), histidine (0.024–0.133), lysine (0.080–0.462), tyrosine (0.050–0.283), and tryptophan (0.011–0.060). The model demonstrated strong predictive validity throughout the 12-week growth period. Conclusion: This integrative approach yielded the first dynamic requirement model for Hy-Line Gray layers during early development. The factorial framework enables precise adjustment of amino acid provisions to match changing physiological needs and has high potential value in optimizing feed efficiency and supporting sustainable layer production practices. Full article

Amid the rapid evolution of the digital economy reshaping global competitiveness, China has advanced regional coordination through the Digital China initiative and the “Data Elements ×” Three-Year Action Plan (2024–2026). To further integrate digital transformation with high-quality growth in the urban agglomerations of the middle and lower Yellow River, this study aims to strengthen regional competitiveness, expand digital industries, foster new productivity, refine the development pathway, and safeguard balanced economic, social, and ecological progress. Taking the Yellow River urban clusters as the research object, a comprehensive assessment framework encompassing seven subsystems is established. By employing a mixed-weighting approach, entropy-based TOPSIS, hotspot analysis, coupling coordination models, spatial gravity shift techniques, and grey relational methods, this study investigates the spatiotemporal dynamics between the digital economy and high-quality development. The findings reveal that: (1) temporally, the coupling–coordination process evolves through three distinct phases—initial fluctuation and divergence (1990–2005), synergy consolidation (2005–2015), and high-level stabilization (2015–2022)—with the average coordination index rising from 0.21 to 0.41; (2) spatially, a persistent “core–periphery” structure emerges, while subsystem coupling consistently surpasses coordination levels, reflecting a pattern of “high coupling but insufficient coordination”; (3) hot–cold spot analysis identifies sharp east–west contrasts, with the gravity center shift and ellipse trajectory showing weaker directional stability but greater dispersion; and (4) grey correlation results indicate that key drivers have transitioned from economic scale and infrastructure inputs to green innovation performance and data resource allocation. Overall, this study interprets the empirical results in both temporal and spatial dimensions, offering insights for policymakers seeking to narrow the digital divide and advance sustainable, high-quality development in the Yellow River region. Full article

As alternative remote communication interfaces become increasingly common, ensuring that they seamlessly integrate into daily life has become a pressing design challenge. In this context, what should a huggable communication device look like—should it have arms or a face, or resemble a conventional pillow? This study investigates users’ preferences and personalities regarding the appearance of such interfaces for remote emotional interaction. As a case study, we present HugBits, a round, cushion-like device that transmits hugs through visual and tactile feedback. Drawing on the prior literature and a participatory design workshop, we developed seven shape variations and evaluated them through an online survey with 79 Polish participants. The results reveal a consistent preference for less anthropomorphic designs, with users valuing comfort, simplicity, and intuitive affordances such as areas to rest the head or wrap the arms around. Although personality traits did not significantly predict preferences, the findings highlight broader design criteria: huggable communication interfaces, intended to remain visible and available in shared spaces, must balance emotional expressiveness with social acceptability. These insights provide guidelines for designing emotionally engaging, user-centered mediated touch technologies. Full article

Regenerative agriculture (RA) is an alternative approach in combating climate change adaptation; however, its effective implementation at scale depends on the development and adoption of standardized metrics. The methodology of this systematic review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, in order to maintain a high level of transparency and rigor throughout the process of selecting and evaluating the included studies. This research identified the challenges and opportunities associated with implementing a robust monitoring, reporting and verification (MRV) framework, which combines direct measurements, proximal sensors and remote sensing to balance accuracy and costs. An innovative aspect of this work is the integration of both social and economic indicators for assessment of RA performance, highlighting the importance of incentives based on verifiable outcomes to support the long-term adoption of regenerative practices. In addition, innovations that can facilitate the scaling and validation of these metrics are explored, which encompasses the use of open and interoperable digital infrastructures to enhance connectivity and integration. This systematic approach contributes to the development of an integrated and adaptable setting for the evaluation and monitoring of RA, serving as a cornerstone for policy formulation and sustainable management strategies. Full article

Operationalizing Earth Observation (EO)-based Machine Learning (ML) algorithms (or GeoML) for ingestion in environmental Digital Twins remains a challenging task due to the complexities associated with balancing real-time inference with cost, data, and infrastructure requirements. In the field of GHG monitoring, most GeoML models of land use CO₂ fluxes remain at the proof-of-concept stage, limiting their use in policy and land management for net-zero goals. In this study, we develop and demonstrate a Digital Twin-ready framework to operationalize a pre-trained Random Forest model that estimates the Net Ecosystem Exchange of CO₂ (NEE) from drained peatlands into a biweekly, field-scale CO₂ flux monitoring system using EO and weather data. The system achieves an average response time of 6.12 s, retains 98% accuracy of the underlying model, and predicts the NEE of CO₂ with an R² of 0.76 and NRMSE of 8%. It is characterized by hybrid data ingestion (combining non-time-critical and real-time retrieval), automated biweekly data updates, efficient storage, and a user-friendly front-end. The underlying framework, which is part of an operational Digital Twin under the UK Research & Innovation AI for Net Zero project consortium, is built using open source tools for data access and processing (including the Copernicus Data Space Ecosystem OpenEO API and Open-Meteo API), automation (Jenkins), and GUI development (Leaflet, NiceGIU, etc.). The applicability of the system is demonstrated through running real-world use-cases relevant to farmers and policymakers concerned with the management of arable peatlands in England. Overall, the lightweight, modular framework presented here integrates seamlessly into Digital Twins and is easily adaptable to other GeoMLs, providing a practical foundation for operational use in environmental monitoring and decision-making. Full article