Search Results (4,291)

The gut microbiome plays a central role in regulating nutrient utilization, immune function, and disease resistance, thereby directly influencing growth performance and feed efficiency. Existing microbiome modulation strategies, including probiotics, prebiotics, dietary interventions, and antibiotic alternatives, are critically evaluated. Despite their reported benefits, the effectiveness of these approaches often remains inconsistent across production systems. Evidence suggests that this variability is largely driven by complex interactions among microbial communities, host factors, and environmental and management conditions, which are frequently overlooked in conventional intervention-based approaches. To address this gap, this review proposes an integrated microbiome–host–environment framework that links microbial ecology with host physiology and production conditions. The framework provides a systems-level perspective for understanding the factors governing microbiome stability and production responses, offering a basis for more targeted and reliable microbiome management strategies. Finally, current challenges and future research priorities are discussed, including the integration of multi-omics technologies, precision nutrition, and data-driven approaches to support next-generation poultry production systems. By emphasizing the interconnected nature of microbiome regulation, this review contributes a conceptual foundation for improving broiler productivity and sustainability through more consistent and effective microbiome optimization. Full article

Rapid urbanization, uncoordinated land-use growth, and insufficient integration of public transport have led to severe traffic congestion and declining mobility in Peshawar, Pakistan, even after the implementation of a Bus Rapid Transit (BRT) system. The core research problem addressed in this study is the mismatch between growing travel demand and the limited capacity, coverage, and operational efficiency of the existing urban transport network. This research aims to evaluate the current performance of Peshawar’s transport system and to identify integrated, evidence-based strategies to alleviate congestion and enhance urban mobility. Specifically, the objectives are to assess roadway level of service on major corridors, examine public transport user satisfaction with the BRT system, and propose targeted infrastructure and operational improvements. A mixed-methods approach was employed, combining traffic volume and level-of-service (LOS) analysis, public transport user surveys, and field observations at critical intersections. The findings indicate that several key arterial roads operate at LOS E–F during peak hours, and future traffic projections indicate widespread capacity failures under existing road geometries. Survey results reveal significant dissatisfaction with the BRT system, particularly due to limited spatial coverage, inadequate feeder routes, overcrowding, and excessive travel times. Based on these results, the study proposes integrated interventions, including road widening and auxiliary lanes, geometric and signalized junction improvements, expansion of BRT feeder services, development of new arterial and ring roads, and enhanced pedestrian and parking infrastructure. This study links quantitative traffic performance measures with user-perceived service deficiencies. It provides practical, data-driven guidance for policymakers and planners to support a more efficient, accessible, and sustainable urban transport system in Peshawar. Full article

European sustainability transitions are increasingly defined by the convergence of blue, green, and circular economy agendas. Traditionally analysed and governed in isolation, these domains generate important interdependencies, trade-offs, and coordination challenges that remain insufficiently understood. Drawing on the multi-level perspective (MLP) and recent advances in multi-system dynamics, this article introduces the Resource Infrastructure Economy (RIE) as a novel integrative framework. The RIE differs from existing multi-system frameworks by explicitly integrating marine governance as a full socio-technical regime, theorising regulatory-driven regime coupling as a distinct transition pathway, and foregrounding the constitutive role of shared physical and digital infrastructures in shaping value creation, path dependencies, and distributional outcomes. The RIE conceptualises contemporary European transitions as processes of deep regime coupling and infrastructural integration, whereby energy, marine, and material regimes become tightly coordinated through shared physical and digital infrastructures and assertive regulatory steering. Through a systematic integrative literature review (58 core publications selected from over 450 records following PRISMA guidelines, analysed using abductive thematic analysis with MAXQDA 26 software) and comparative analysis of six countries—Portugal, Spain, Denmark, Germany, the Netherlands, and Norway—the study reveals persistent structural gaps between the three agendas alongside emerging patterns of pairwise and triadic regime coupling. While Northern and Central European frontrunners demonstrate more advanced infrastructural coordination, Southern peripheral regions face greater difficulties in governance integration and just transition outcomes. The RIE framework advances sustainability transitions theory in three ways: (1) systematically integrating blue economy scholarship into multi-system analysis; (2) theorising regulatory-driven regime coupling as a distinct transition pathway; and (3) foregrounding the constitutive role of physical and digital infrastructures and environmental data systems in shaping value creation, path dependencies, and distributional outcomes. By reframing European sustainability transitions through the lens of the Resource Infrastructure Economy, this article provides a new conceptual lens to understand uneven transition geographies and offers actionable insights for more integrated and just policy coordination across the European Green Deal. Full article

This study examines the transformation of environmental governance processes in recreational tourism in Turkey and Lithuania through artificial intelligence (AI)-supported waste management applications. The research focuses on the contributions of AI-based applications to sustainable destination management, environmental sustainability, and data-driven governance processes. A case study design was used within the framework of qualitative research methods. The dataset was obtained through semi-structured interviews with a total of 40 experts from Turkey and Lithuania. The data were analyzed using content analysis with the NVivo 14 program. The research findings reveal significant differences between the two countries in terms of digital infrastructure, institutional coordination, governance structures, and AI integration capacity. In Turkey, AI-supported waste management applications are still in their development phase; processes are largely shaped by managerial initiative, project-based approaches, financial constraints, and lack of institutional coordination. In contrast, Lithuania exhibits a more systematic and institutionalized digital governance structure thanks to EU-supported environmental and digitalization policies. However, data security, system sustainability, and high technology costs in small-scale recreation areas stand out as significant problem areas for Lithuania. This study addresses an underexplored intersection between artificial intelligence applications and environmental governance within recreational tourism contexts, contributing to the emerging literature on digital transformation in sustainable destination management. The findings reveal that AI-supported environmental management systems have significant potential to strengthen sustainable tourism management, increase operational efficiency, and support data-driven sustainable destination strategies. These findings offer practical implications for destination managers and policy makers by highlighting how AI-enabled environmental governance systems can enhance sustainability-oriented decision-making and improve operational efficiency in recreational tourism areas. Full article

This mixed-methods study explores the effectiveness of a semester-long academic development programme in addressing Imposter Phenomenon among Early-Career Academics. This intervention introduced low-technology simulations, allowing consideration of authentic challenges of practice. While experiences of Imposterism in academia are often institutionally driven, most coping strategies remain individualistic. This study responds to a paucity of research, offering an original contribution by providing evidence from a pilot evaluation. Participants (n = 19) completed the Clance Imposter Phenomenon Scale pre- and post-intervention, with those reporting moderate to intense Imposterism (scores 41–80) interviewed (n = 10). Quantitative analysis revealed that n = 3 reported less frequent imposter feelings, n = 2 reported more frequent imposter feelings, and n = 14 indicated no change. Qualitative analysis of interview data revealed that perceptions of simulation-based pedagogies were shaped by bi-directional intersections between three domains: understandings of simulation for professional learning; interactions/collaboration with peers; and personal identity/professional context. Findings indicated that sustained peer-interaction within psychologically safe and supportive environments was particularly valued, reducing isolation, enhancing professional belonging, and improving confidence–dimensions closely associated with academic wellbeing. However, contextual factors, including role ambiguity and unclear progression pathways, sometimes intensified imposter feelings, highlighting structural conditions shaping professional identity and educator wellbeing. Full article

Hydraulic supports in coal mining faces require continuous pressure monitoring to detect anomalies indicative of roof instability or equipment failure. Existing reconstruction-based methods rely on standard convolutional or recurrent encoders whose limited receptive fields or coarse temporal representations restrict detection accuracy; static per-window thresholding further discards temporal continuity during online deployment. This study proposes a temporal convolutional network autoencoder (TCN-AE) coupled with a Cumulative Sum (CUSUM) control chart for online anomaly detection in hydraulic support pressure data. The TCN encoder uses dilated convolutions with symmetric padding and residual connections, producing an exponentially expanding receptive field that captures temporal patterns at multiple scales. The CUSUM chart accumulates sustained positive deviations in the reconstruction error sequence, improving detection sensitivity while suppressing isolated false alarms. Component analysis experiments on synthetic anomalies show TCN-AE achieves an AUC of 0.811, outperforming CNN, LSTM, GRU, and fully connected autoencoder variants, along with Isolation Forest and One-Class SVM. On a manually curated real fault test set, where per-window reconstruction scores carry negligible discriminative information (AUC = 0.586, near chance), the CUSUM strategy exploits temporal continuity to improve F1 from 0.213 to 0.905 for TCN-AE. This +0.692 gain is driven entirely by temporal accumulation rather than model discriminability, demonstrating that the CUSUM framework is most valuable precisely when per-window signals are weakest. Full article

Soil erosion in arid watersheds poses a significant threat to land productivity, water resources, and long-term sustainability, necessitating spatially explicit and data-driven prioritization frameworks for targeted conservation. This study developed an integrated GIS-based multi-criteria decision-making (MCDM) framework to assess soil erosion susceptibility and prioritize twelve sub-basins (SB) of the Wadi Numan basin (683 km²), Makkah Region, Saudi Arabia. Morphometric analysis was conducted using sixteen parameters derived from a 10 m Digital Elevation Model (DEM), and Land Use/Land Cover (LULC) data were obtained from the Esri Sentinel-2 10 m dataset. Four MCDM techniques—additive ratio assessment (ARAS), complex proportional assessment (COPRAS), multi-objective optimization by ratio analysis (MOORA), and technique for order preference by similarity to ideal solution (TOPSIS)—were applied under the criteria importance through inter-criteria correlation (CRITIC) objective weighting, and their consistency was evaluated using the Spearman correlation coefficient test (SCCT) and the Kendall Tau correlation coefficient test (KTCCT). MOORA achieved the highest consistency for morphometric analysis (SCCT: 0.982; KTCCT: 0.958), while TOPSIS performed best for LULC analysis (SCCT: 0.800; KTCCT: 0.731). The final combined prioritization used MOORA for morphometric analysis and TOPSIS for LULC analysis, with proportional weighting of 72.7% and 27.3%, respectively. The scheme categorized the sub-basins into five levels of soil erosion priority. The composite ranking classified SB-9 and SB-1 under very high priority (25.94%); SB-2 and SB-3 under high priority (6.40%); SB-5, SB-6, and SB-10 under medium priority (36.37%); SB-4 and SB-8 under low priority (18.11%); and SB-11, SB-12, and SB-7 under very low priority (13.18%). This integrated method provides a practical decision-support tool for identifying and managing sub-basins susceptible to soil erosion, thereby promoting the long-term sustainability of land and water resources. Full article

This review examines the relationship between climate-driven heat stress (HS) and bovine mastitis in the Po Valley, a key European dairy region characterized by intensive production systems and increasing climatic vulnerability. It aims to contextualize how rising temperature–humidity index (THI) levels influence animal health and productivity. This study synthesizes the current literature on biometeorological conditions, epidemiological trends, and physiological mechanisms linking HS to mastitis. Evidence indicates that prolonged exposure to elevated THI impairs thermoregulation, disrupts endocrine and metabolic balance, and weakens immune function, thereby increasing susceptibility to intramammary infections. Epidemiological data reveal a clear seasonal pattern, with mastitis incidence peaking during summer months and a growing predominance of environmental pathogens. Additionally, HS negatively affects milk yield and quality, amplifying economic losses in dairy systems. The findings highlight that mastitis in this context is not merely an infectious disease but a multifactorial condition shaped by environmental, physiological, and management factors. Overall, this review underscores the need for integrated mitigation strategies, including improved housing, nutrition, genetic selection, and precision monitoring, to enhance resilience. In the face of ongoing climate change, adapting dairy production systems will be essential to safeguard animal welfare, maintain productivity, and ensure the long-term sustainability of the Po Valley dairy sector. Full article

The global sugar industry is facing increasing challenges due to climate variability, sustainability requirements, and the need for improved operational efficiency. These pressures are driving the search for advanced technological solutions to enhance productivity and resource management. Artificial intelligence (AI) has already demonstrated significant potential across various agricultural sectors; however, a comprehensive evaluation of AI applications across the entire sugar industry value chain from crop cultivation to industrial processing and supply chain management remains limited. This review provides a detailed assessment of the current state of AI and internet of things (IoT) implementation in the sugar beet industry. It examines key applications, including precision agriculture for sugarcane and sugar beet cultivation, intelligent monitoring systems for early disease detection, and AI-driven decision support tools for resource optimization. In addition, the study explores the role of AI in sugar manufacturing processes, where machine learning and data-driven models are used to optimize milling operations, improve product quality control, and enable predictive maintenance of industrial equipment. AI technologies are also shown to enhance supply chain efficiency through improved demand forecasting, logistics optimization, and real-time data analytics. Monitoring volatile organic compounds (VOCs) is becoming increasingly important in sugar beet and sugarcane storage. Microbial activity during storage and fermentation can release VOCs such as ethanol, which act as early indicators of crop degradation and spoilage. Detecting these gases using modern gas sensors enables continuous monitoring of storage conditions and crop health. When sensor data is integrated with AI and IoT systems, it can be analyzed in real time to identify early signs of microbial activity, improve storage management, and optimize processing decisions. Such intelligent monitoring systems have the potential to reduce losses and enhance overall efficiency in the sugar production chain. Full article

Bike-sharing systems (BSS) are a key component of sustainable urban mobility. However, their performance is strongly influenced by urban topography and socio-economic conditions. This study analyzes Barcelona’s public BSS, Bicing, to examine how altitude and neighborhood income affect bicycle availability, departures, and electric bicycle adoption. The main objective is to determine whether the observed “electric divide” is driven by income or by topographical necessity. The analysis uses 2023 data from 511 Bicing stations and income information from 62 neighborhoods obtained from Open Data Barcelona and the Spanish National Statistics Institute. Three indicators were constructed: bike availability ratio, departures ratio, and electric bicycle ratio. Results show a strong negative correlation between altitude and bike availability (r = −0.71) and a strong positive correlation between altitude and electric bicycle use (r = 0.78). High-altitude stations show lower availability and fewer departures, while electric bicycles dominate uphill trips. Although high-income neighborhoods initially appear to use more electric bicycles, regression results show that income becomes insignificant once altitude is controlled for. Therefore, electric bicycle adoption is driven mainly by physical necessity rather than socio-economic preference. Full article

Background/Objectives: Exposure to high and sustained levels of non-esterified fatty acids (NEFA) in the peripartal period is the main cause of fatty liver disease in dairy cows. Rumen-protected choline is often fed as part of the nutritional management of peripartal cows, with in vivo and in vitro data indicating positive effects of this nutrient on alleviating liver lipid accumulation. Although hepatic molecular mechanisms associated with choline supply have been studied using a target gene, protein, or metabolite approach, application of high-throughput technologies could vastly enhance fundamental knowledge on the functional role of choline. The main objective was to challenge isolated hepatocytes with a mixture of NEFA and determine proteome- and metabolome-wide effects in response to choline supply. Methods: Three healthy female calves (1 d old, 30–45 kg) were sacrificed to harvest hepatocytes. During a 12 h incubation, isolated hepatocytes were challenged without NEFA (control), 1.2 mM NEFA (c9-18:1, 18:2, 16:0, 18:0, and c9-16:1 at 43.5%, 4.9%, 31.9%, 14.4%, and 5.3% of total NEFA, respectively), or NEFA for 6 h followed by 10 μM choline chloride for another 6 h (NEFA + Chol). iTRAQ labeling-based protein profiling and GC/MS-based metabolomics profiling were used to determine changes in proteins and metabolites. Differentially abundant proteins for each group comparison were determined at a threshold of 1.4-fold change. Differences in metabolite profiles were assessed via pairwise comparisons. A subset of differentially abundant proteins was validated via qRT-PCR and Western blotting. Results: Compared with the control, there were 90 proteins and 22 metabolites in the NEFA group, and 83 proteins and 29 metabolites in the NEFA + Chol. Compared with NEFA, there were 49 proteins and 17 metabolites in the NEFA + Chol group. Greater abundance of hexokinase-1 (HK1), fructose-bisphosphate aldolase (ALDOA), mitochondrial pyruvate carrier 1 (MPC1), and increased concentrations of lactate with high NEFA treatment alone suggested greater glycolytic and TCA cycle activity. Accumulation of triacylglycerol in the NEFA group was associated with lipotoxicity and markers of inflammation, such as greater abundance of prostaglandin reductase 1 (PTGR1), serious cell autophagy processes, such as greater abundance of cell division cycle 42 (CDC42), and NFκB-related proteins. Choline supplementation reduced TAG partly due to greater VLDL secretion driven by greater abundance of diacylglycerol acyltransferase (DGAT1), perilipin 3 (PLIN3), and apolipoprotein C-III (APOC3). In addition, a greater abundance of carnitine O-palmitoyltransferase 1b (CPT1B) with choline suggested enhanced mitochondrial β-oxidation. Activation of the CDC42/JNK pathway and ROS/NFκB axis-related proteins, along with depressed PI3K/AKT/RAC-related proteins, indicated enhanced mitochondrial autophagy in response to NEFA. Conclusions: Overall, data confirmed published effects of choline on TAG accumulation, VLDL secretion, and fatty acid oxidation, while highlighting negative effects of NEFA on the respiratory electron transport chain, autophagy, and inflammatory processes. Full article

This study applies a synoptic seasonal climatological framework, extended vertically through the troposphere, to investigate the South Asian monsoon using daily mean data (1948–2024) from the NCEP–NCAR Reanalysis. A seasonal synoptic circulation framework was developed using self-organizing maps (SOMs) to classify four distinct seasons—winter, pre-monsoon, monsoon, and post-monsoon—and their transitional phases. Diagnostics including temperature and moisture advection and vertically integrated moisture transport (VIMT) were incorporated to examine circulation–environment interactions. The results highlight the pre-monsoon-to-monsoon transition as the most critical seasonal shift, marked by rapid land heating, steep pressure gradients, and northward ITCZ migration that initiates southwesterly monsoon winds. Classical land–sea thermal contrasts initiate the low-level monsoon wind reversal, while vertical circulation assessment suggests that mid- to upper-tropospheric thermal gradients, supported by latent heating and Hadley-type overturning, help organize and sustain monsoon circulation strength. Additionally, South Asian monsoon circulation is shifting from well-defined seasonal regimes toward more transitional states. The results reveal widespread warming, weakened VIMT during major monsoon-related phases, and uneven moisture redistribution, suggesting that climate change is reshaping the monsoon seasonal cycle through both thermodynamic and circulation-driven processes. Taken together, the findings demonstrate that monsoon dynamics arise not from a single mechanism but from interconnected processes operating across atmospheric layers. This vertically integrated synoptic circulation approach thus provides a more comprehensive framework for understanding monsoon processes. Full article

In mountainous river basins, groundwater systems are sustained by complex recharge processes and geological heterogeneity, making groundwater flow simulation challenging in data-scarce regions where hydrological inputs are often assumed to be spatially uniform. This study developed a heterogeneous geological model of the Kofu Basin, Japan, using multiple boreholes and simulated the groundwater flow by integrating MODFLOW with climate-driven recharge outputs from SWAT+. Simulated groundwater flow was evaluated against findings from previous stable isotope studies to assess the plausibility of the simulated recharge system. After calibration, the model performance improved substantially: RMSE decreased by 91.28%, MAE decreased by 84.38%, and NSE increased from 0.9530 to 0.9996. Independent validation showed good regional agreement between observed and simulated groundwater heads (R² = 0.9307; NSE = 0.9254), although RMSE and MAE remained relatively high at 32.70 m and 19.76 m, respectively, suggesting remaining uncertainty in local-scale groundwater head simulation. Simulated velocity vectors indicated localized shallow flow and more coherent regional basinward flow in the deeper aquifer. This pattern is consistent with the interpretation that mountain-derived recharge contributes to the deeper regional groundwater system. The results highlight the value of combining hydrogeological models and geochemical evidence to support recharge-process interpretation in complex mountainous basins. Full article

Restaurants combine long opening hours, catering demand, kitchen ventilation, DHW, and mixed-fuel cooking loads, making their decarbonisation different from generic commercial retrofit. For small- and medium-sized enterprise (SME) hospitality premises, this makes the transition to net-zero operation a distinct sustainability challenge because a large, process-driven share of demand lies outside conventional building-fabric and building-services retrofit. This single-case study develops a whole-building utility-calibrated OpenStudio/EnergyPlus model for Beit El Zaytoun, a 655.82 m² restaurant in Park Royal, London. Monthly electricity and gas data for June 2024–May 2025 were used to calibrate the baseline at whole-building level. Standalone and cumulative scenarios tested insulation, low-emissivity double glazing, LED lighting and controls, ASHP service scenarios, and an 11 kWp PV array. Baseline demand was 413,895 kWh/yr, equivalent to 631.1 kWh/m²·yr and 75,020 kgCO₂e/yr. The lowest-net-energy analytical package reduced net imported energy to 314,734 kWh/yr and operational carbon to 56,700 kgCO₂e/yr, a retained 24.0% reduction on the source reporting basis; this package is treated as an analytical bound rather than as a final design recommendation because it excludes cooling. The model-derived residual process load, kitchen and catering gas plus kitchen, and back-of-house electricity remained 233,920 kWh/yr across building-focused scenarios. The Residual-Load Index (RLI) rose from 0.57 to 0.74; with ±15% process-load allocation uncertainty, the optimised RLI range was 0.63–0.85, so the post-retrofit balance remained process-load dominated. The case demonstrates a practical decarbonisation ceiling likely to recur in similar high-process-load hospitality premises: fabric, lighting, heat electrification, and PV are necessary but insufficient without catering-equipment, cooking-fuel, kitchen-ventilation, refrigeration-control, sub-metering, and demand-response strategies. The paper contributes whole-building utility-calibrated quantitative evidence and a transferable RLI metric for sub-sector-specific sustainable retrofit policy, and the net-zero transition of SME food-service premises. Full article

Renewable integration is increasingly important for sustainable off-grid computing. The inherent variability of solar output frequently produces unusable midday surpluses. Leveraging recent Artificial Intelligence (AI) advances and established literature, we evaluate an AI-driven demand-response framework for scaling Large Language Models (LLMs) training servers using real-time solar energy data, Solcast forecasts, and battery storage records collected from Battery Management Systems (BMS), Maximum Power Point Tracking (MPPT) units, and smart inverters. An n8n AI Agent using the Ollama chat model gpt-oss:20b assesses surplus solar energy, activating selected servers to utilize otherwise wasted capacity. Workloads consistently align with solar availability, demonstrating 99% operational reliability, sub-second responsiveness, and accurate surplus-energy detection. This research demonstrates how Artificial Intelligence can repurpose surplus solar output into usable computational capacity, thereby contributing to a broader transition toward renewable-powered infrastructures. Full article