Search Results (1,101)

The social integration of artificial intelligence (AI) poses fundamental challenges to value-driven domains such as education, where the adoption of new technologies raises not merely technical but also deeply rooted ethical and identity-related questions. While dominant technology acceptance models (e.g., TAM and UTAUT) primarily focus on cognitive-rational factors (e.g., perceived usefulness), they often overlook the cultural and value-based elements that fundamentally shape adaptation processes. Addressing this research gap, the present study examines how two hitherto under-researched factors—religiosity and teacher’s sense of calling—influence teachers’ attitudes toward AI and, ultimately, its adoption. The research is based on a survey of 680 Catholic secondary school teachers in Hungary. To analyse the data, we employed structural equation modelling (PLS-SEM) to examine the mechanisms of influence among religiosity, sense of calling, and AI attitudes. The results indicate that neither religiosity nor a sense of calling exerts a significant direct effect on AI adoption, and their indirect effects are also marginal. Although statistically significant relationships were found—stronger religiosity reduces a supportive evaluation of AI, while a higher sense of calling increases AI-related concerns—their practical significance is negligible. The study’s main conclusion is that successful AI integration, building on teachers’ pragmatic attitudes, is achieved not by neglecting value-based factors, but by developing critical AI literacy that treats technology as a responsible amplifier of pedagogical work. This finding suggests that value-based extensions of technology acceptance models should be approached with caution, as the role of these factors may be more limited than theoretical considerations imply. Full article

Background: By 30 June 2021, the 54 African sovereign nations had reported 5,465,790 laboratory-confirmed COVID-19 cases (including 142,171 deaths). This study aimed to estimate the monetary value of human life losses, indirect and direct costs, and the potential cost reductions due to vaccinations for advocacy use by Ministries of Health in Africa. Methods: We employed both the human capital approach to value human lives lost and an abridged total cost-of-illness methodology to estimate the indirect and direct costs of COVID-19 across 54 African countries. The secondary data analyzed was from different sources. Results: The 142,171 human lives lost had an estimated discounted total monetary value of Int$6,684,101,196, i.e., Int$47,015 per life loss and Int$4.88 per person in the population. The estimated total cost of the actual reported 5,514,709 COVID-19 cases was Int$7,155,473,174, which comprised a total direct cost of Int$3,981,927,049 (55.6%) and an indirect cost of Int$3,173,546,125 (44.4%). We projected that vaccination of all the eligible people in the population would potentially save the African continent approximately Int$41,624,735,824. The average total saving per person is approximately Int$30.4. Conclusions: The COVID-19 pandemic resulted in substantial monetary value of human life losses and indirect and direct costs. Full article

The Qinghai-Tibetan Plateau is one of the world’s most prominent biodiversity hotspots. Understanding the spatial patterns of ecological uniqueness in its plant communities is essential for uncovering the mechanisms of community assembly and informing effective conservation strategies. In this study, we analyzed data from 758 plots across 338 sites on the Qinghai-Tibetan Plateau. For each plot, the vegetation type was classified, and all plant species present, along with their respective abundance or coverage, were recorded in the database. To assess overall compositional variation, community β-diversity was quantified, while a plot-level approach was applied to determine the influence of local environmental conditions and community characteristics on ecological uniqueness. We used stepwise multiple regressions, variation partitioning, and structural equation modeling to identify the key drivers of spatial variation in ecological uniqueness. Our results show that (1) local contributions to β-diversity (LCBD) exhibit significant geographic variation—increasing with longitude, decreasing with latitude, and showing a unimodal trend along the elevational gradient; (2) shrubs and trees contribute more to β-diversity than herbaceous species, and LCBD is strongly linked to the proportion of rare species; and (3) community characteristics, including species richness and vegetation coverage, are the main direct drivers of ecological uniqueness, explaining 36.9% of the variance, whereas climate and soil properties exert indirect effects through their interactions. Structural equation modeling further reveals a coordinated influence of soil, climate, and community attributes on LCBD, primarily mediated through soil nutrient availability. These findings provide a theoretical basis for adaptive biodiversity management on the Qinghai-Tibetan Plateau and underscore the conservation value of regions with high ecological uniqueness. Full article

This paper presents a comprehensive analysis comparing the implementation of electric and diesel buses for public transport services in the low-density area of the City of Jastrebarsko in Croatia. It utilizes a multidimensional approach and incorporates direct and indirect costs, such as vehicle acquisition, operation, charging, maintenance, and environmental impact costs during the lifecycle of the buses. The results show that, despite the higher initial investment in electric buses, these vehicles offer savings, especially when coupled with significantly reduced emissions of pollutants, which decreases indirect costs. However, local contexts differ, leading to a need to revise whether or not a municipality can finance the procurement and operations of such a fleet. The paper utilizes a robust methodological framework, integrating a proposal based on real-world data and demand and combining it with predictive analytics to forecast long-term benefits. The findings of the paper support the introduction of buses as a sustainable solution for Jastrebarsko, which provides insights for public transport planners, urban planners, and policymakers, with a discussion about the specific issues regarding the introduction, procurement, and operations of buses of different propulsion in a low-density area. Full article

Frequency analysis is essential for designing hydraulic structures and managing water resources, as it helps assess hydrological extremes. However, changes in river basins can impact their accuracy, complicating the link between discharge and return periods. This study aims to comprehensively assess the probability characteristics of long-term M-day maximum/minimum discharges in the Carpathian region of Slovakia. We analyze the long-term data from 26 gauging stations covering 90 years of observation. Slovak rivers show considerable intra-annual variability, especially between the summer–autumn (SA) and winter–spring (WS) seasons. To allow consistent comparisons, we apply a uniform methodology to estimate T-year daily maximum and minimum specific discharges over durations of 1 and 7 days for both seasons. Our findings indicate that 1-day maximum specific discharges are generally higher during the SA season compared to the WS season. The 7-day minimum specific discharges are lower during the WS season compared to the SA season. Slovakia’s diverse orographic and climatic conditions cause significant spatial variability in extreme discharges. However, the estimated T-year 7-day minimum and 1-day maximum specific discharges, based on the mean specific discharge and the altitude of the water gauge, exhibit certain nonlinear dependences. These relationships could support the indirect estimation of T-year M-day discharges in regions with similar runoff characteristics. Full article

Trust evaluation has become a major challenge in the quickly developing Internet of Things (IoT) environment because of the vulnerabilities and security hazards associated with networked devices. To overcome these obstacles, this study offers a novel approach for evaluating trust that uses IOTA Tangle technology. By decentralizing the trust evaluation process, our approach reduces the risks related to centralized solutions, including privacy violations and single points of failure. To offer a thorough and reliable trust evaluation, this study combines direct and indirect trust measures. Moreover, we incorporate IOTA-based trust metrics to evaluate a node’s trust based on its activity in creating and validating IOTA transactions. The proposed framework ensures data integrity and secrecy by implementing immutable, secure storage for trust scores on IOTA. This ensures that no node transmits a wrong trust score for itself. The results show that the proposed scheme is efficient compared to recent literature, achieving up to +3.5% higher malicious node detection accuracy, up to 93% improvement in throughput, 40% reduction in energy consumption, and up to 24% lower end-to-end delay across various network sizes and adversarial conditions. Our contributions improve the scalability, security, and dependability of trust assessment processes in Internet of Things networks, providing a strong solution to the prevailing issues in current centralized trust models. Full article

The escalating global demand for fresh water, driven by urbanization and industrial growth, underscores the need for sustainable water management, particularly in the water-intensive construction sector. Although prior studies have primarily concentrated on treated wastewater, the practical viability of utilizing untreated wastewater has not been thoroughly investigated—especially in developing nations where treatment expenses frequently impede actual implementation, even for non-structural uses. While prior research has focused on treated wastewater, the potential of untreated or partially treated wastewater from diverse industrial sources remains underexplored. This study investigates the feasibility of incorporating wastewater from textile, sugar mill, service station, sewage, and fertilizer industries into concrete paver block production. The novelty lies in a dual approach, combining experimental analysis with XGBoost-based machine learning (ML) models to predict the impact of key physicochemical parameters—such as Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), and Hardness—on mechanical properties like compressive strength (CS), water absorption (WA), ultrasonic pulse velocity (UPV), and dynamic modulus of elasticity (DME). The ML models showed high predictive accuracy for CS (R² = 0.92) and UPV (R² = 0.97 direct, 0.99 indirect), aligning closely with experimental data. Notably, concrete pavers produced with textile (CP-TXW) and sugar mill wastewater (CP-SUW) attained 28-day compressive strengths of 47.95 MPa and exceeding 48 MPa, respectively, conforming to ASTM C936 standards and demonstrating the potential to substitute fresh water for non-structural applications. These findings demonstrate the viability of using untreated wastewater in concrete production with minimal treatment, offering a cost-effective, sustainable solution that reduces fresh water dependency while supporting environmentally responsible construction practices aligned with SDG 6 (Clean Water and Sanitation) and SDG 12 (Responsible Consumption and Production). Additionally, the model serves as a practical screening tool for identifying and prioritizing viable wastewater sources in concrete production, complementing mandatory laboratory testing in industrial applications. Full article

Introduction: The gut microbiota is vital for human health, and its modulation through dietary and pharmaceutical compounds has gained increasing attention. Among gut microbes, Akkermansia muciniphila has been extensively researched due to its role in maintaining intestinal barrier integrity, regulating energy metabolism, and influencing inflammatory responses. Subject: To analyze and synthesize the available scientific evidence on the influence of various bioactive compounds, including prebiotics, polyphenols, antioxidants, and pharmaceutical agents, on the abundance and activity of A. muciniphila, considering underlying mechanisms, microbial context, and its therapeutic potential for improving metabolic and intestinal health. Methods: A systematic literature review was conducted in accordance with the PRISMA 2020 guidelines. Databases such as PubMed, ScienceDirect, Scopus, Web of Science, SciFinder-n, and Google Scholar were searched for publications from 2004 to 2025. Experimental studies in animal models or humans that evaluated the impact of bioactive compounds on the abundance or activity of A. muciniphila were prioritized. The selection process was managed using the Covidence platform. Results: A total of 78 studies were included in the qualitative synthesis. This review compiles and analyzes experimental evidence on the interaction between A. muciniphila and various bioactive compounds, including prebiotics, antioxidants, flavonoids, and selected pharmaceutical agents. Factors such as the chemical structure of the compounds, microbial environment, underlying mechanisms, production of short-chain fatty acids (SCFAs), and mucin interactions were considered. Compounds such as resistant starch type 2, GOS, 2′-fucosyllactose, quercetin, resveratrol, metformin, and dapagliflozin showed beneficial effects on A. muciniphila through direct or indirect pathways. Discussion: Variability across studies reflects the influence of multiple variables, including compound type, dose, intervention duration, experimental models, and analytical methods. These differences emphasize the need for a contextualized approach when designing microbiota-based interventions. Conclusions: A. muciniphila emerges as a promising therapeutic target for managing metabolic and inflammatory diseases. Further mechanistic and clinical studies are necessary to validate its role and to support the development of personalized microbiota-based treatment interventions. Full article

High-speed machines are attractive to many industries due to their small size and light weight, but present unique cooling challenges due to their increased loss and reduced surface area. Cooling system advancements are central to the development of faster, smaller machines, and as such, are constantly evolving. This paper presents a review of classical and state-of-the-art cooling systems. Each cooling method—air cooling, indirect liquid cooling, and direct liquid cooling—has potential use in cooling high-speed machines, but each comes with unique considerations, which are discussed. An example design process highlights the interdependence of the electromagnetic and thermal design choices, illustrating the necessity of integrating the electromagnetic and thermal designs in a holistic approach. Full article

The field of metal additive manufacturing has witnessed significant growth in recent years, with technology offering the ability to produce complex geometries that are challenging to manufacture using the traditional methods. In situ monitoring and control of the manufacturing process are crucial for increasing the production capacity and improving the quality of manufactured parts. This article provides a comparative analysis of computational, indirect, and direct methods for in situ temperature monitoring during additive manufacturing of metal alloy components. Furthermore, it discusses the current status, recent improvements, and perspectives for in situ temperature measurements. The basic principles of thermal imaging, two-color pyrometry, and millimeter-wave radiometry are explored, highlighting their limitations for addressing challenges related to material emissivity and rapid changes in building material composition. Overcoming the challenges related to the inaccessibility of the chamber where the parts are formed, direct temperature measurements would allow for the integration of collected information into big data systems. Within the framework of Industry 4.0, this approach offers a viable alternative to the conventional metal shaping processes, improving the production capacity and part quality. This research aims to contribute to ongoing advancements in metal additive manufacturing and its potential to completely replace traditional metal casting practices in the Industry 4.0 era. Full article

Inland waters, serving as crucial carbon sinks and pivotal conduits within the global carbon cycle, are essential targets for carbon assessment under global warming and carbon neutrality initiatives. However, the extensive spatial distribution and inherent sampling challenges pose fundamental difficulties for monitoring dissolved organic carbon (DOC) in these systems. Since 2010, remote sensing has catalyzed a technological revolution in inland water DOC monitoring, leveraging its advantages for rapid, cost-effective long-term observation. In this critical review, we systematically evaluate research progress over the past two decades to assess the performance of remote sensing products and existing methodologies in DOC retrieval. We provide a detailed examination of diverse remote sensing data sources, outlining their application characteristics and limitations. By tracing uncertainties in retrieval outcomes, we identify atmospheric correction, spatial heterogeneity, and model and data deficiencies as primary sources of uncertainty. Current retrieval approaches—direct, indirect, and machine learning (ML) methods—are thoroughly scrutinized for their features, effectiveness, and application contexts. While ML offers novel solutions, its application remains nascent, constrained by limited waterbody-specific samples and model constraints. Furthermore, we discuss current challenges and future directions, focusing on data optimization, feature engineering, and model refinement. We propose that future research should (1) employ integrated satellite–air–ground observations and develop tailored atmospheric correction for inland waters to reduce data noise; (2) develop deep learning architectures with branch networks to extract DOC’s intrinsic shortwave absorption and longwave anti-interference features; and (3) incorporate dynamic biogeochemical processes within study regions to refine retrieval frameworks using biogeochemical indicators. We also advocate for multi-algorithm collaborative prediction to overcome the spectral paradox and unphysical solutions arising from the single data-driven paradigm of traditional ML, thereby enhancing retrieval reliability and interpretability. Full article

Livestock farming is a vital component of global food security, yet it remains a major contributor to greenhouse gas (GHG) emissions, particularly methane (CH₄), which has a global warming potential 28 times greater than carbon dioxide (CO₂). This review provides a comprehensive synthesis of current knowledge surrounding the sources, biological mechanisms, and mitigation strategies related to CH₄ emissions from ruminant livestock. We first explore the process of methanogenesis within the rumen, detailing the role of methanogenic archaea and the environmental factors influencing CH₄ production. A thorough assessment of both direct and indirect methods used to quantify CH₄ emissions is presented, including in vitro techniques (e.g., syringe method, batch culture, RUSITEC), in vivo techniques (e.g., respiration chambers, Greenfeed, laser CH₄ detectors), and statistical modeling approaches. The advantages and limitations of each method are critically analyzed in terms of accuracy, cost, feasibility, and applicability to different farming systems. We then examine a wide range of mitigation strategies, organized into four core pillars: (1) animal and feed management (e.g., genetic selection, pasture quality improvement), (2) diet formulation (e.g., feed additives such as oils, tannins, saponins, and seaweed), (3) rumen manipulation (e.g., probiotics, ionophores, defaunation, vaccination), and (4) manure management practices and policy-level interventions. These strategies are evaluated not only for their environmental impact but also for their economic and practical viability in diverse livestock systems. By integrating technological innovations with sustainable agricultural practices, this review highlights pathways to reduce CH₄ emissions while maintaining animal productivity. It aims to support decision-makers, researchers, and livestock producers in the global effort to transition toward climate-smart, low-emission livestock farming. Full article

Credit supervision of practitioners in the water conservancy construction market, a vital pillar of national infrastructure development, significantly impacts project safety and the maintenance of order in the industry. From the perspective of indirect supervision, this study constructs a tripartite evolutionary game model involving government departments, enterprises, and practitioners to analyze the dynamic evolution mechanism of credit supervision. By examining the strategic interactions among the three parties under different regulatory scenarios, we identify key factors influencing the stable equilibrium of evolution and verify the theoretical conclusions through numerical simulations. The study yields several key insights. First, while government regulation and social supervision can substantially increase the likelihood of practitioners’ integrity, relying solely on administrative regulation has an efficiency limit. Second, the effectiveness of the reward and punishment mechanism of the direct manager plays a crucial leveraging role in credit evolution. Lastly, under differentiated regulatory strategies, high-credit practitioners respond more strongly to long-term cost optimization, while low-credit practitioners are more effectively deterred by short-term, high-intensity disciplinary actions. Based on these findings, this study proposes a systematic governance framework of “regulatory model innovation–corporate responsibility enhancement–social supervision deepening.” Unlike previous studies, this framework adopts a comprehensive approach from three dimensions: regulatory model innovation, corporate responsibility enhancement, and social supervision deepening. It offers a more holistic and systematic solution for refining the credit system in the water conservancy construction market, providing both theoretical support and practical approaches. Full article

This review focuses on the synthetic methodologies for radioiodinating peptides, a crucial process for developing effective radiopharmaceuticals used in diagnostics and therapeutics. We explore direct and indirect radioiodination methods, including mechanisms, reaction conditions, and purification strategies. The focus is on the chemical approaches that enable radioiodine incorporation into peptide structures, considering the challenges of maintaining peptide integrity and biological activity. This article is intended as a detailed resource for understanding traditional approaches and recent chemical developments in radioiodination. Full article

In an effort to restrict further increases in climate change, governments and companies are exploring ways to reduce greenhouse gas (GHG) emissions. In this context, the oil industry, which contributes to indirect GHG emissions, is seeking ways to develop solutions to this issue. One such approach focuses on the connection of offshore oil production platforms to the onshore power grid via high-voltage direct current (HVDC), enabling a total or partial reduction in the number of local generators, which are generally powered by gas turbines. Therefore, this work aims to determine the technical feasibility, based on transient and dynamic stability analyses, of electrifying a system composed of six floating production storage and offloading (FPSO) units connected to a hub, which is powered by the onshore grid through submarine cables using HVDC technology. The analysis includes significant contingencies that could lead the system to undesirable operating conditions, allowing for the identification of appropriate remedial control actions. The analysis, based on real data and parameters, was carried out using PSCAD software. The results show that the modeled system is technically viable and could be adopted by oil companies. In addition to aligning with global warming mitigation goals, the proposal includes a complex system modeling approach, with the aim of enabling further study. Full article