Search Results (1,118)

The quality of life of a host community residing in a tourist destination is significantly influenced by the consequences of tourism development in the region. Such development generates both positive impacts, such as benefits, and negative impacts, or costs, simultaneously. The main aim of the study was to analyse the relationship between residents’ perception of tourism’s dual impact (positive and negative) on the host community and their attitude towards sustainable tourism development in the Krishna-Braj Tourism Circuit in the province of Uttar Pradesh of India. The study also examined the nature of the relationship between personal benefits drawn from tourism development and residents’ perception of tourism’s dual impact on the host community. The social exchange theory served as the foundation of the study. The tourism sustainability framework, which includes three pillars, viz., economic, environmental, and socio-cultural tourism impacts, was used to analyse the dual impact of tourism on residents in the region. In total, 370 residents were identified using a proportionate quota sampling technique and interviewed. Structural equation modelling was used to test the proposed theoretical model and to examine the hypothesised relationship between study variables. The study found that residents drawing personal benefits from tourism development (such as income, job, socialisation, etc.) in the region tend to perceive tourism impacts positively and show supportive attitudes toward sustainable tourism development in the region. At the same time, the influence of personal benefits from tourism development on perceived negative impacts was found not significant. Additionally, positive perceived tourism impacts significantly shaped residents’ attitudes, while the expected negative influence of negative tourism impacts on attitudes was unsupported. In a nutshell, the study supports that the bene-fits residents derive from tourism strongly shape their perceptions and support sustainable tourism development in the region. Full article

Belatacept, a selective costimulation blocker targeting the CD28–CD80/86 pathway, represents a major innovation in solid organ transplantation immunosuppression. By providing upstream inhibition of T-cell activation without calcineurin inhibition, belatacept offers the potential for improved long-term graft and patient outcomes with reduced nephrotoxicity and metabolic adverse effects. This review summarizes the mechanistic rationale, pivotal evidence, and clinical experience supporting the use of belatacept as first-line or conversion therapy in solid organ transplantation, while addressing safety, pharmacoeconomic impact, and future research directions. A comprehensive analysis of pivotal phase II–III trials (BENEFIT, BENEFIT-EXT), recent prospective conversion studies, and ongoing trials in liver, heart, and lung transplantation was performed. Safety data and health–economic evaluations were critically appraised. In kidney transplantation, belatacept-based immunosuppression provides superior renal function and improved metabolic profiles compared with calcineurin inhibitors (CNIs), though with higher early acute rejection rates. In liver, heart, and lung transplantation, evidence remains limited, with de novo use contraindicated in liver grafts due to excess mortality and rejection. Conversion from CNI to belatacept in selected patients improves renal outcomes without compromising graft survival. Safety considerations include a higher risk of post-transplant lymphoproliferative disorder (PTLD) in Epstein–Barr virus-negative recipients. Belatacept represents a paradigm shift in transplant immunology by targeting upstream T-cell activation. While currently approved only for kidney transplantation, ongoing studies in thoracic and hepatic grafts may expand its therapeutic role. Personalized patient selection, combination regimens mitigating rejection risk, and real-world cost-effectiveness analyses will define its place in future precision immunosuppression strategies. Full article

The aim of the presented article is the comparison of external monetary environmental impacts generated by railway and road transport in Slovakia region. The Kralovany–Trstena regional line located in the northern Slovakia was selected. The monetary impacts of environmental pollution from transport on this line were analysed. Various pollutants were selected for the assessment from exhaust gases, such as fine particulate matter PM_2.5, nitrogen oxides (NO_x), sulphur dioxide (SO₂), non-methane volatile organic compounds (NMVOC), and ammonia (NH₃). The Cost–Benefit Analysis methodology was applied for the calculation of monetary impacts. The results point out that the usage of diesel multiple units on the given line has a significant impact on the monetary impacts of pollutant emissions. Full article

Background: Large positive responses to placebo are common in clinical trials and pose a major challenge when evaluating different treatments, including new foods. Standard between-group comparisons may fail to detect true effects when placebo improvements are significant. We aimed to demonstrate how a simple dose–response correlation method can help differentiate genuine positive responses from those experienced with placebo through secondary analysis of a randomized controlled clinical trial of powdered Rosa-canina fruits. Methods: Data were reanalyzed from a multicenter, double-blind, randomized, placebo-controlled trial (N = 120; ClinicalTrials.gov NCT01459939) evaluating the effects of standardized Rosa-canina powder in hip and knee osteoarthritis (OA). Participants received fixed doses, leading to variability in mg/kg exposure due to different body weights. Pearson correlations between dose/kg and changes in WOMAC pain and function at 6 and 12 weeks were calculated separately for the active and placebo groups. Standard between-group comparisons were also performed. Results: Both groups showed significant improvement, over 50%, with no statistically significant differences between them in WOMAC pain or function. However, only the active group, which received a food supplement, exhibited a consistent negative correlation between body weight and symptom improvement at 6 and 12 weeks, suggesting greater benefit with higher dose per kilogram of body weight. No dose–response relationship was observed in the placebo recipients. Therefore, weight-stratified plots revealed an exposure–response gradient in the active group. Conclusions: Dose–response correlation analysis uncovered positive effects of Rosa-canina as a nutrient that were not detectable through standard between-group comparisons. This is consistent with findings from earlier rose-hip research. This low-cost, easy-to-implement method may help distinguish active effects from placebo responses in trials with large nonspecific improvements. Incorporating such analyses could improve the identification of nutrients containing biologically active preparations and support dose selection in future clinical research. Full article

The article presents a method that supports the evaluation of design manufacturability in the area of input material selection, enabling the reduction in material diversity under single-piece and small-batch production conditions. The proposed approach combines the analysis of alternative materials with activity-based costing (ABC) and data concerning actual and planned material requirements. The method enables the assessment of the impact of semi-finished product substitution on material costs, processing costs, production organisation, and material-management costs before order execution is launched. In the conducted case study, it was demonstrated that effective management of material diversity can significantly reduce the range of materials and decrease total manufacturing costs. For the analysed period, the number of material items was reduced from 32 to 19 (a 41% reduction), resulting in cost savings of approximately 11,000 PLN. In addition to total cost, the approach supports the assessment of operational benefits associated with reduced material diversity, such as a lower number of material items, fewer suppliers, reduced inbound inspection and receipt operations, and decreased inventory levels and capital tied up in stock. Material substitution may decrease or increase direct material costs and may increase machining time when larger dimensions are used; therefore, the method jointly evaluates cost and lead-time impacts prior to order release. The results confirm that integrating design, technological, and logistics data is an effective approach to rationalising material management in machinery manufacturing enterprises. Full article

Spent garnet (SG) wastes are generated in significant quantities by several industrial activities, including abrasive waterjet cutting (AWJ), abrasive blasting, and filtration and powdered media applications. These wastes represent a promising secondary raw material for the production of sustainable construction materials, particularly green mortars and concretes, through their partial replacement of natural sand in cementitious systems. Such applications are relevant to both hydraulically setting inorganic binders (cement-based materials) and alkali-activated cementitious materials (AACMs). The valorization of SG wastes offers multiple benefits, notably a dual environmental advantage: reducing the consumption of natural aggregates and diverting industrial waste from disposal by integrating it into a new life cycle as a value-added by-product. Additional potential advantages include reduced production costs and possible improvements in the overall performance of mortars and concretes. Despite these benefits, the use of SG as an aggregate replacement remains insufficiently explored, with existing studies providing only preliminary and fragmented evidence of its feasibility. This paper presents an overview of a comprehensive four-year research program investigating SG wastes derived from single-cycle AWJ processes and their incorporation into conventional mortars as partial fine aggregate replacement in cement-based construction composites. The validation stage of the performance assessment expands the range of SG sources by including new sampling from the original suppliers, enabling verification of the repeatability and reproducibility of earlier findings. A broad set of physical, mechanical, and durability properties—particularly resistance to freeze–thaw cycles—is evaluated to achieve a robust and comprehensive material characterization. These results are further correlated with chemical and microstructural analyses, providing critical insights to support the technological transfer of SG-based construction materials to industrial applications with reduced carbon footprint. Full article

Background: Burns represent a public health problem because they generate both physical and psychological damage, especially in the child and adolescent population, and high costs, especially due to the management of scars. Advances in burn care have improved survival and quality of life for this population. New clinical trials have been conducted on the benefits of negative pressure wound therapy (NPWT), showing that it improves the healing of burns and the appearance of scars. Therefore, this study aims to analyze the efficacy of NPWT both alone and as an adjunct to conventional dressings in pediatric and adolescent patients compared with conventional treatments. Methodology: A systematic search was carried out between December 2023 and the last quarter of 2025 in databases such as PubMed, Scopus, CINAHL, and the Cochrane Library. This meta-analysis was performed following the PRISMA statement (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and was registered in PROSPERO with registration number CRD42024597293. The risk of bias 2 (RoB2) tool was used to assess the risk of bias in the studies. Quantitative meta-analyses using random-model effects were performed only for variables with sufficient comparable data among studies. For other outcomes, where meta-analysis was not feasible due to lack of comparable data or control groups, results were synthesized qualitatively. Results: A total of seven articles (three clinical trials and four retrospective studies), in which a total of 323 subjects participated, were included. The main results demonstrate the efficacy of NPWT, as it decreases the re-epithelialization time, improves the appearance of scars (MD = −1.25 (95% CI between −1.80 and −0.70)), reduces the probability of skin grafts (OR = 0.17 (95% CI between 0.06 and 0.46)), and therefore, as there is less need for surgery and fewer dressing changes, reduces costs. Conclusions: NPWT offers significant clinical benefits in the treatment of burns in children and adolescents. Although a meta-analysis could not be performed due to the lack of a control group in some studies, studies with larger samples and multicenter designs will be necessary to better assess the relevant clinical outcomes. However, the results of this study show that NPWT is effective in treating burns in children and adolescents and that its use in clinical practice may represent a promising adjunctive therapy. Full article

This critical review explores the transformative impact of artificial intelligence (AI), particularly machine learning (ML) and computer vision (CV), on scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) for metallic microstructure analysis, spanning research from 2010 to 2025. It critically evaluates how AI techniques balance automation, accuracy, and scalability, analysing why certain methods (e.g., Vision Transformers for complex microstructures) excel in specific contexts and how trade-offs in data availability, computational resources, and interpretability shape their adoption. The review examines AI-driven techniques, including semantic segmentation, object detection, and instance segmentation, which automate the identification and characterisation of microstructural features, defects, and inclusions, achieving enhanced accuracy, efficiency, and reproducibility compared to traditional manual methods. It introduces the Microstructure Analysis Spectrum, a novel framework categorising techniques by task complexity and scalability, providing a new lens to understand AI’s role in materials science. The paper also evaluates AI’s role in chemical composition analysis and predictive modelling, facilitating rapid forecasts of mechanical properties such as hardness and fracture strain. Practical applications in steelmaking (e.g., automated inclusion characterisation) and case studies on high-entropy alloys and additively manufactured metals underscore AI’s benefits, including reduced analysis time and improved quality control. Extending prior reviews, this work incorporates recent advancements like Vision Transformers, 3D Convolutional Neural Networks (CNNs), and Generative Adversarial Networks (GANs). Key challenges—data scarcity, model interpretability, and computational demands—are critically analysed, with representative trade-offs from the literature highlighted (e.g., GANs can substantially augment effective dataset size through synthetic data generation, typically at the cost of significantly increased training time). Full article

Grid-Scale Battery Energy Storage Systems (GS-BESS) play a crucial role in modern power grids, addressing challenges related to integrating renewable energy sources (RESs), load balancing, peak shaving, voltage support, load shifting, frequency regulation, emergency response, and enhancing system stability. However, harnessing their full potential and lifetime requires intelligent operational strategies that balance technological performance, economic viability, and environmental sustainability. This systematic review examines how artificial intelligence (AI)-based intelligent optimization enhances GS-BESS performance, focusing on its techno-economic, environmental impacts, and policy and regulatory implications. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we review the evolution of GS-BESS, analyze its advancements, and assess state-of-the-art applications and emerging AI techniques for GS-BESS optimization. AI techniques, including machine learning (ML), predictive modeling, optimization algorithms, deep learning (DL), and reinforcement learning (RL), are examined for their ability to improve operational efficiency and control precision in GS-BESSs. Furthermore, the review discusses the benefits of advanced dispatch strategies, including economic efficiency, emissions reduction, and improved grid resilience. Despite significant progress, challenges persist in data availability, model generalization, high computational requirements, scalability, and regulatory gaps. We conclude by identifying emerging opportunities to guide the next generation of intelligent energy storage systems. This work serves as a foundational resource for researchers, engineers, and policymakers seeking to advance the deployment of AI-enhanced GS-BESS for sustainable, resilient power systems. By analyzing the latest developments in AI applications and BESS technologies, this review provides a comprehensive perspective on their synergistic potential to drive sustainability, cost-effectiveness, and energy systems reliability. Full article

Road infrastructure underpins Nigeria’s economic competitiveness, yet Public–Private Partnership (PPP) performance is constrained not by inadequate legislation but by persistent weaknesses in enforcement and governance. Transparency deficits across procurement, design management, certification, and toll-revenue reporting have produced chronic delays, cost overruns, and declining public trust. This study offers the first empirical investigation of blockchain–Building Information Modelling (BIM) integration as a transparency-enhancing mechanism within Nigeria’s PPP road sector, focusing on Lagos State. Using a qualitative design, ten semi-structured interviews with stakeholders across the PPP lifecycle were thematically analysed to diagnose systemic governance weaknesses and assess the contextual feasibility of digital innovations. Findings reveal entrenched opacity rooted in weak enforcement, discretionary decision-making, and informal communication practices—including biased bidder evaluations, undocumented design alterations, manipulated certifications, and toll-revenue inconsistencies. While respondents recognised BIM’s potential to centralise project information and blockchain’s capacity for immutable records and smart-contract automation, they consistently emphasised that technological benefits cannot be realised absent credible institutional foundations. The study advances an original theoretical contribution: the Codify–Condition–Capacitate framework, which explains the institutional preconditions under which digital governance tools can improve transparency. This framework argues that effectiveness depends on: codifying digital standards and legal recognition; conditioning enforcement mechanisms to reduce discretionary authority; and capacitating institutions through targeted training and phased pilots. The research generates significant practical implications for policymakers in Nigeria and comparable developing contexts seeking institution-aligned digital transformation. Methodological rigour was ensured through purposive sampling, thematic saturation assessment, and documented analytical trails. Full article

Red mud (RM), an industrial byproduct generated during bauxite refining, has accumulated to more than 5 billion tons worldwide, posing serious environmental challenges. In response, substantial research over recent decades has focused on the sustainable utilization of RM, particularly in the field of construction materials. This review first summarizes the generation process and chemical composition of RM, and then systematically examines its potential applications in the production of artificial aggregates, partial replacement of cementitious materials, and synthesis of geopolymers. Existing studies demonstrate that RM exhibits considerable potential in construction applications: when used as an aggregate, it can reduce concrete porosity, enhance compressive strength, and improve overall mechanical performance. Moreover, RM can partially substitute cement or serve as a geopolymer precursor, contributing to the immobilization of toxic elements such as Pb and Cr while simultaneously improving the mechanical properties of both cementitious systems and geopolymers. The reactivity and performance of RM-based materials can be further enhanced through carbonation curing and other modification techniques. Finally, this review highlights the significant sustainability and economic benefits of RM-based concrete, supported by life-cycle assessment and cost–benefit analyses. Full article

Electric vehicles (EVs) are pivotal for enhancing the flexibility of power systems, with vehicle-grid integration (VGI) constituting the fundamental mechanism for their participation in grid regulation. VGI relies on multi-source information from EVs, charging infrastructure, traffic network, power grid, and meteorology. However, ineffective data integration mechanisms have resulted in data silos, which impede the realization of synergistic value from multi-source data fusion. To address these issues, this paper develops a quadripartite evolutionary game model that incorporates data providers, data users, government, and data service platforms, overcoming the limitation of traditional tripartite models in fully capturing the complete data value chain. The model systematically examines the cost–benefit dynamics and strategy evolution among stakeholders throughout the data-sharing process. Leveraging evolutionary game theory and Lyapunov stability criteria, sensitivity analyses were conducted on key parameters, including data costs and government subsidies, on the MATLAB platform. Results indicate that multi-source data integration accelerates system convergence and facilitates a multi-party equilibrium. Government subsidies as well as reward and punishment mechanisms emerge as critical drivers of sharing, with an identified subsidy threshold of ε_S = 0.02 for triggering multi-source integration. These key factors can also accelerate system convergence by up to 79% through enhanced subsidies (e.g., reducing stabilization time from 0.29 to 0.06). Importantly, VGI data sharing represents a non-zero-sum game. Well-designed institutional frameworks can achieve mutually beneficial outcomes for all parties, providing quantitatively supported strategies for constructing incentive-compatible mechanisms. Full article

High-quality agricultural products from the Lambayeque region have contributed to the growth of Peru’s agro-export sector and increased international trade. However, the need for agricultural exports to be more resilient and sustainable is demonstrated by the fact that markets are still concentrated, logistical costs are high, and global demand is constantly shifting. The purpose of this study is to use a gravity-based trade model and market intelligence techniques to analyse the agricultural exports from the Lambayeque region between 2015 and 2024. Using official data from the World Bank, AZATRADE, CEPII, and MINCETUR, we employed a quantitative explanatory approach. The results show that the concentration of businesses has significantly decreased while the value of exports has increased steadily. The Herfindahl–Hirschman Index increased from 6209 in 2015 to 1349 in 2024, and export destinations have become slightly more diverse. Exports are negatively impacted by geographic distance, but free trade agreements greatly benefit them. There is a lot of export potential in markets like Finland, Indonesia, Austria, Bolivia, and Vietnam. However, Israel and Hong Kong appear to be full. Overall, the findings indicate that Lambayeque’s export performance has improved, but it still runs the risk of becoming overly focused on a single sector. Long-term sustainability of the region’s agricultural exports depends on enhancing logistical infrastructure, bolstering market intelligence, and promoting regional diversity. Full article

The contemporary agricultural market is profoundly volatile, where agricultural prices are based on a complex supply chain, climatic irregularity or unscheduled market demand. Prices of crops need to be predicted in a reliable and timely manner for farmers, policy-makers and other stakeholders to take evidence-based decisions ultimately for the benefit towards sustainable agriculture and economic sustainability. Objective: The objective of this study is to develop and evaluate a comprehensive machine learning model for predicting agricultural prices incorporating logistic, economic and environmental considerations. It is the desire to make agriculture more profitable by building simple and accurate forecasting models. Methods: An assorted dataset was collected, which covers major factors to constitute the dataset of temperature, rainfall, fertiliser use, pest and disease attack level, cost of transportation, market demand-supply ratio and regional competitiveness. The data was subjected to pre-processing and feature extraction for quality control/quality assurance. Several machine learning models (Linear Regression, Support Vector Machines, AdaBoost, Random Forest, and XGBoost) were trained and evaluated using performance metrics such as R2 score, Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Results: Out of the model approaches that were analysed, predictive performance was superior for XGBoost (with an R2 Score of 0.94, RMSE of 12.8 and MAE of 8.6). To generate accurate predictions, the ability to account for complex non-linear relationships between market and environmental information was necessary. Conclusions: The forecast model of the XGBoost-based prediction system is reliable, of low complexity and widely applicable to large-scale real-time forecasting of agricultural monitoring. The model substantially reduces the uncertainty of price forecasting, and does so by including multivariate environmental and economic aspects that permit more profitable management practices in a schedule for future sustainable agriculture. Full article

In response to recent policies on sustainable finance, nature restoration, soil protection, and biodiversity conservation, it is increasingly important for projects to assess their impacts on natural capital to safeguard Ecosystem Services (ES). Nature-Based Solutions (NBSs) are recognized as strategic tools for fostering cost-effective, nature- and people-centered development. Yet, standard economic and financial assessment methods often fall short, as many ES lack market prices. Indirect, ecosystem-based approaches—such as ES monetization and environmental cost accounting—are therefore critical. This study evaluates the feasibility of investing in NBSs by estimating their economic and financial value through indirect ES valuation. An empirical methodology is applied to quantify environmental costs relative to ES delivery, using Willingness to Pay (WTP) as a proxy for the economic relevance of NBSs. The proposed ES-Cost Accounting (ES-CA) framework was implemented across major NBS categories in Europe. Results reveal that the scale of NBS implementation significantly influences both unit environmental costs and ES provision: larger interventions tend to be more cost-efficient and generate broader benefits, whereas smaller solutions are more expensive per unit but provide more localized or specialized services. The findings offer practical guidance for robust cost–benefit analyses and support investment planning in sustainable climate adaptation and mitigation from an ES perspective. Full article