Search Results (2,045)

This paper represents initial research with the aim to establishes a baseline for subsequent research into AI-based construction monitoring, building upon the NORMENG project in Croatia, which previously integrated photogrammetry, laser scanning, and BIM-based methods. The study tests general purpose AI’s ability to detect materials and estimate quantities, aiming to assess whether a broad, context-aware AI system can match the precision of specialized, domain-specific tools or even human work needed for productivity estimations. While the AI demonstrated potential for basic entity detection and preliminary quantity estimations, it showed significant limitations in delivering fine-grained, temporally accurate breakdowns without targeted adaptation. The findings underscore the need for domain-specific fine-tuning and human-in-the-loop validation to transform AI into a reliable tool for construction management. This initial contribution provides empirical insights and actionable recommendations for advancing automated progress monitoring in the construction sector. Full article

Background: Childhood obesity is a growing public health challenge in Poland and worldwide, associated with serious long-term health consequences. Effective prevention requires family-centered, evidence-based interventions that actively engage both children and their caregivers. This study presents the Finnish Smart Family practice—an evidence-based lifestyle counseling method developed by the Finnish Heart Association—and describes its adaptation and implementation in Poland as part of the EU Health4EUkids project. The study emphasizes the method’s practical utility for professionals working with families of children with obesity. Methods: The Smart Family approach is a structured lifestyle counseling method based on findings from the Special Turku Coronary Risk Factor Intervention Project (STRIP) that is grounded in health psychology and strength-based counseling principles. Unlike traditional counseling, which focuses mainly on information transfer, Smart Family promotes motivation, families’ active participation, and recognition of their strengths in areas such as nutrition, physical activity, sleep, and oral hygiene. The method uses practical tools including the Smart Family card, other supporting materials, and dedicated online platforms for both families and healthcare providers. These tools enable families to self-assess their lifestyle, select discussion topics during visits, and set achievable goals while supporting professionals in initiating non-judgmental, collaborative conversations. In Poland, the program was adapted using culturally appropriate materials and professional training, followed by pilot implementation in primary healthcare and educational settings that included pre-implementation planning, practical training sessions, the application of intervention tools, and outcome evaluation. Results: Pilot implementation demonstrated high usability and effectiveness. The approach enabled non-judgmental, supportive engagement with families, facilitated active participation in setting health goals, and promoted sustainable lifestyle changes in nutrition, physical activity, sleep, and other health behaviors. Evaluation highlighted the importance of supporting program objectives at the national level, standardizing child healthcare practices, and engaging media and local authorities to create a supportive ecosystem. Conclusions: The Polish experience confirms that Smart Family is an evidence-based intervention that strengthens professional competence, provides practical tools for family-centered care, and supports the long-term prevention of child-hood obesity and related non-communicable diseases. Its integration into healthcare and educational settings offers a promising strategy for improving public health outcomes. Full article

Assessment practices are central to higher education, particularly critical in theory-based programs, where they facilitate the development of conceptual understanding and higher-order cognitive skills. They also support Saudi Arabia’s Vision 2030 agenda, which aims to drive educational innovation. This narrative review examines assessment practices in theory-based programs at a Saudi public university, identifies discrepancies with learning objectives, and proposes potential solutions. A narrative review synthesised peer-reviewed literature (2015–2025) from Scopus, Web of Science, ERIC, and Google Scholar, focusing on traditional and alternative assessments, barriers, progress, and comparisons with international standards. The review found that traditional summative methods (quizzes, final exams) still dominate and emphasise memorisation, limiting the development of higher-order skills. Emerging techniques, such as projects, portfolios, oral presentations, and peer assessment, are gaining traction but face institutional constraints and resistance from faculty. Digital adoption is growing: 63% of students are satisfied with learning management system tools, and 75% find online materials easy to understand; yet, advanced analytics and AI-based assessments are rare. A comparative analysis reveals that international standards favour formative feedback, adaptive technologies, and holistic competencies. The misalignment between current practices and Vision 2030 highlights the need to broaden assessment portfolios, integrate technology, and provide faculty training. Saudi theory-based programs must transition from memory-oriented evaluations to student-centred, evidence-based assessments that foster critical thinking and real-world application. Adopt diverse assessments (projects, portfolios, peer reviews), invest in digital analytics and adaptive learning, align assessments with learning outcomes and Vision 2030 competencies, and implement ongoing faculty development. The study offers practical pathways for reform and highlights strategic opportunities for achieving Saudi Arabia’s national learning outcomes. Full article

Virtual reality (VR) has emerged as a promising tool for transforming science, technology, engineering, and mathematics (STEM) education, yet its adoption in K–12 classrooms remains uneven and often limited to short-term pilots. While prior studies highlight VR’s potential to increase engagement and support conceptual understanding, questions persist about scalability, sustainability, and equity in implementation. This paper addresses these gaps by synthesizing recent scholarship and proposing a structured framework of best practices for integrating VR into K–12 STEM education. Drawing on academic literature, U.S. policy reports, and case studies, we identify persistent challenges that include high costs, lack of teacher preparation, infrastructure disparities, and overlooked accessibility concerns. We use these findings to inform a phased implementation roadmap. Our framework emphasizes assessment and planning, technical integration, teacher preparation, student implementation, and iterative evaluation, providing actionable strategies for schools and districts. Results of this synthesis indicate that successful VR adoption requires coordinated attention to pedagogy, funding, professional development, and equity. We conclude that moving VR from isolated novelty projects to sustainable and equitable tools in STEM classrooms depends on aligning technology with curricular goals, investing in teacher pipelines, and embedding VR within long-term evaluation and improvement cycles. Full article

The field of climate modeling is undergoing a significant transformation, moving away from the traditional General Circulation Models (GCMs) and toward the use of sophisticated artificial intelligence (AI)-based prediction systems. Research has shown that AI has the potential to improve climate modeling’s regional accuracy and computing efficiency. Machine learning downscaling better captures local precipitation extremes than GCMs, while hybrid AI–physics models cut ensemble costs by reducing computational demand without sacrificing accuracy. Nevertheless, these investigations have frequently functioned in discrete settings and oversimplified situations without a thorough connection with basic physical concepts. This drawback emphasizes the necessity of a more comprehensive strategy that can handle the intricacies of climatic variability and guarantee reliable model validation. In order to assess the possibilities and challenges of hybrid models in comparison to conventional GCMs, highlighting that AI complements GCMs in regional downscaling and extremes, while GCMs provide stronger global consistency, this study synthesizes proven climate models, AI methodologies, and their accuracy in climate predictions and analyzes existing climate models to evaluate the potential and limitations of hybrid models compared to traditional GCMs. Integrated AI-driven models show notable improvements in predicting regional variations in climate and accelerating simulation processes, especially when dealing with the growing presence of extreme weather occurrences. However, it is important to have consistent datasets and open evaluation procedures in order to guarantee accuracy and deal with the difficulties that come with model benchmarking. This research highlights how crucial it is to maintain interdisciplinary cooperation in order to properly utilize what AI has to offer in climate modeling. This partnership is essential to creating more accurate and useful climate projections, which will eventually guide successful mitigation and adaptation plans for a changing global environment. In order to have a greater understanding of our climate’s future, we must keep pushing the limits of the existing modeling tools. Full article

This study aims to explore teachers’ perceptions and students’ strategies when integrating AI-mediated informal digital learning of English tools (AI-IDLE) into career ESL writing instruction. This case study involved six university instructors and over 300 students in an English writing course. Although AI-IDLE has broadened English access beyond classrooms, existing research on writing skills often neglects students’ diverse strategies that correspond to their professional aspirations, as well as teachers’ perceptions. The data included a demographic questionnaire, think-aloud protocols for real-time assessment of cognitive processes during the task, and semi-structured interviews for teachers’ validation. Findings reveal three major student strategies: (1) explicit genre understanding, (2) student-driven selection of digital multimodal tools—such as Grammarly, ChatGPT, Canva with Magic Write, and Invideo—to integrate text with images, sound, and layout for improved rhetorical accessibility, and (3) alignment with students’ post-graduation career needs. Students’ work with these AI tools demonstrated that when they created projects aligned with professional identities and future job needs, they became more aware of how to improve their writing; however, the teachers expressed hopes and doubts about the tools’ effectiveness and authenticity of the students’ work. Suggestions to use AI-IDLE to improve writing were provided. Full article

In recent years, the global energy crisis, concerns about energy security and grid parity, and the pressure to develop policies for reducing the environmental impact of anthropogenic activities have accelerated investments in renewable energy. A growing body of literature applies the real options approach (ROA) to renewable energy projects, recognizing its value in capturing irreversibility and flexibility under uncertainty. The present work provides a detailed state-of-the-art analysis on the adoption of real options to evaluate mixes of energy technologies for power generation, with a special emphasis on investments in hydropower and solar photovoltaics. The objective is to assess current applications, identify knowledge gaps, and outline priorities for advancing decision-making tools in this domain. We performed a systematic literature review following the PRISMA protocol, identifying 38 papers from the Scopus database up to February 2024. Eligible studies were peer-reviewed articles in English applying the ROA to power generation, following a technology selection process; policy evaluation or research and development studies were excluded. The selected papers were analyzed to identify trends over time and space, adopted energy technology, types of real options with valuation methods, and sources of uncertainty. The present paper also discusses the main findings and emerging gaps, providing an overview of hybrid renewable energy systems. Our analysis suggests that, despite the significant advances achieved in this area, further research is needed to exploit the potential of the ROA in investment decisions for combined renewable energy technologies, especially in cases where internal uncertainty and community perspectives need to be explicitly considered. By linking the ROA to the challenges of mixed renewable energy projects, this study enhances understanding of investment decision-making under uncertainty and identifies pathways toward more robust and adaptive project evaluation. Full article

The Diamond Open Access (OA) model—characterized by the absence of fees for both authors and readers—has gained increasing attention in recent years. A wide range of scholarly journals are using this model, as emerged while mapping the Diamond OA landscape worldwide; however, some still depend on hybrid revenue streams such as print sales, subscriptions, and marginal APCs. A number of recent initiatives underlined the need to increase quality assurance, sustainability, and cooperation within the Diamond OA ecosystem. Among them, the Diamond OA Standard (DOAS), a framework comprising detailed guidelines and a self-assessment tool to facilitate Diamond OA publishing practices, was created by the DIAMAS project, sponsored by the European Commission. Annali dell’Istituto Superiore di Sanità, the official journal of the Italian leading public health research institution, is a Diamond OA journal. To improve transparency and quality, the editorial team used the DOAS self-assessment tool to evaluate its compliance with the standards proposed by DIAMAS and to identify potential areas for improvement. This article presents the process and findings of the DOAS self-assessment tool conducted on Annali ISS, with the aim of sharing insights and support with other journals seeking to align with the DOAS framework. Full article

A new look at the concept of green chemistry from the side of the laws of production organization allows for the development of organizational solutions for achieving environmental friendliness of chemical–technological processes without capital-intensive modernization of production lines. The uniqueness of chemical production, unlike most industries, is the maximum possibility of organizing a closed resource–waste production cycle. The purpose of the article is to develop solutions for organizing chemical production based on the principles of green chemistry with an emphasis on waste recycling and resource chains using the example of rubber product production. Analysis and critical assessment of publications and literary sources showed a shortage of research on organizational tools for creating green production systems with minimal resource losses and maximum product yield. Interpretation of the laws of production organization in the projection of “green” chemistry made it possible to identify six vectors for creating sustainable production with a high strength of interrelation between the laws of production organization and the principles of green chemistry. The solutions obtained in most cases are aimed at increasing the closed nature of production and organizing circular resource chains. Using the example of rubber tire and cover production, trends of waste recycling prevalence (69%) were revealed compared to their disposal (31%). Based on artificial neural networks, a model of usefully used waste in circular resource chains was built, where three variables act as factors of sustainable production: the mass of waste returned to its own production cycle; the mass of waste returned to the production cycle of another enterprise, and the mass of waste sent for storage or disposal at landfills. The highest rate of beneficially used waste in circular resource chains is achieved in the third scenario, which prioritizes reintegrating waste back into the production cycle (57.5%). The transition from one scenario to another does not result in a polar shift in this rate, which instead varies within a range of 50–58%. The proposed solutions can be used by enterprises when choosing engineering directions and tools based on the synthesis of “green chemistry” concepts and production organization laws. Prospects for the development of the topic lie in the development of the methodology for organizing environmental engineering in the proposed directions and implementation tools as well as research into the technology of organizing closed low-waste production processes. Full article

The present paper reports the comparison of the ship motions and added resistance assessment using high fidelity RANSE simulations in virtual towing tank LincoSim, using 2D strip theory as implemented in ShipX v4.4.0 and 3D BEM potential flow software Hydrostar v8.2.1. All calculations are performed for a medium-sized RoPax ferry of Levante Ferries fleet, which operates daily routes in the Ionian Sea. Calculations by ShipX are performed in frequency domain (using strip-theory and direct pressure integration) and in time domain. The high-fidelity RANSE seakeeping modeling is based on the open-source CFD code OpenFOAM v12 using a standardized framework, tailored to take advantage of HPC facilities and based on a forcing zone formulation. The CFD simulations are performed for six wave periods in head and beam seas at the constant wave height of 3 m. Comparison of the obtained results shows that potential-flow methods are very efficient and reliable tools, suitable for the massive calculations in the first stages of the project. High-fidelity RANSE modeling seems to be more suited for selected cases such as analysis of roll and added resistance in beam waves. Full article

This paper presents a comparative study on the transdisciplinary design of resilient urban subcenters, examining the interplay between human-led and artificial intelligence (AI)-generated design approaches. By employing holistic design methods, we prepare and present revitalization projects for two areas of urban space. Our goal was to create a resilient urban subcenter that contributes to the development of a resident. The first revitalized site reflects the multicultural past of the city. The second project addresses the need to revitalize a subcenter reserved for residents. In the non-AI approach, holistic design is implemented across various universities, fields, and academic disciplines—the humanities, social sciences, engineering, and the arts. Transdisciplinary teams of sociologists, engineers, interior designers, architects, urban geographers, and acousticians transcend workshop limitations as well as cognitive boundaries, promoting the creation of new, unconventional knowledge. The AI-integrated approach employs artificial intelligence in a dual capacity: both as a generator of alternative design visions and as an analytical tool for assessing technological readiness. The findings contribute to the evolving discourse on sustainable urban development and the transformative potential of technology in transdisciplinary design practices. Full article

REDD+ has emerged as a global strategy for reducing CO₂ emissions from deforestation and forest degradation and shows great promise for the Extractive Reserves of the Brazilian Amazon (RESEX). It is essential to assess whether REDD+ projects have effectively contributed to the conservation of these areas over time. To address this issue, we analyzed land use and cover dynamics in the RESEX Rio Preto-Jacundá (Rondônia) and its surroundings from 2004 to 2020 to evaluate the impacts of a certified REDD+ project. The following two trend scenarios were simulated: (i) pre-implementation (2004–2012), projected to 2020, and (ii) post-implementation (2012–2020), projected to 2028. Historical maps were derived from the TerraClass dataset, and future projections were generated using Markov Chains combined with Cellular Automata. Forest conservation was evaluated through structural metrics such as the number, size, and shape of forest fragments, and the type, frequency, and length of boundaries with other land uses, using ArcGIS tools and Patch Analyst. Carbon sequestration was estimated from the aboveground biomass values of primary and secondary forests. The results showed that the REDD+ mechanism did not achieve the expected environmental benefits, with a decrease in carbon stocks over time and potential negative effects on the richness and composition of local flora. Full article

The primary objective of this study is to provide a more precise and beneficial mathematical model for assessing corruption dynamics by utilizing non-local derivatives. This research aims to provide solutions that accurately capture the complexities and practical behaviors of corruption. To illustrate how corruption levels within a community change over time, a non-linear deterministic mathematical model has been developed. The authors present a non-integer order model that divides the population into five subgroups: susceptible, exposed, corrupted, recovered, and honest individuals. To study these corruption dynamics, we employ a new method for solving a time-fractional corruption model, which we term the q-homotopy analysis transform approach. This approach produces an effective approximation solution for the investigated equations, and data is shown as 3D plots and graphs, which give a clear physical representation. The stability and existence of the equilibrium points in the considered model are mathematically proven, and we examine the stability of the model and the equilibrium points, clarifying the conditions required for a stable solution. The resulting solutions, given in series form, show rapid convergence and accurately describe the model’s behaviour with minimal error. Furthermore, the solution’s uniqueness and convergence have been demonstrated using fixed-point theory. The proposed technique is better than a numerical approach, as it does not require much computational work, with minimal time consumed, and it removes the requirement for linearization, perturbations, and discretization. In comparison to previous approaches, the proposed technique is a competent tool for examining an analytical outcomes from the projected model, and the methodology used herein for the considered model is proved to be both efficient and reliable, indicating substantial progress in the field. Full article

Revealing the coordination relationship between land use/land cover (LULC) and carbon storage (CS) under diverse climate scenarios is crucial for climate change adaptation in topographically complex regions. This study developed an integrated framework combining the System Dynamics (SD) model, Patch-generating Land Use Simulation (PLUS) model, and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, enabling a closed-loop analysis of driving forces, spatial simulation, and ecological feedback. This study systematically assessed LULC evolution and ecosystem CS along China’s National Highway 318 (G318) from 2000 to 2020, and projected LULC and CS under three SSP-RCP scenarios (SSP1-1.9, SSP2-4.5, SSP5-8.5) for 2030. Results show the following: (1) Historical LULC change was dominated by rapid urban expansion, cropland loss, and nonlinear grassland fluctuation, exerting strong impacts on ecosystem dynamics. Future scenario simulations revealed distinct thresholds of ecological pressure. (2) Regional CS exhibited a decline–recovery pattern during 2000–2020, with all 2030 scenarios projecting CS reduction, although ecological-priority pathways could mitigate losses. (3) Coordination between land-use intensity and CS improved gradually, with SSP2-4.5 emerging as the optimal strategy for balancing development and ecological sustainability. Overall, the coupled SD-PLUS-InVEST framework provides a practical tool for policymakers to optimize land use patterns and enhance CS in complex terrains. Full article

Research on the impact and response strategies of inter-basin water transfer projects (IBWTs) on regional hydrology, water resources, the ecological environment, the economy, and society holds significant strategic importance for the protection of the environment and long-term economic and social development throughout the entire lifecycle of IBWTs. In this study, the current state and trends in research on the comprehensive impact of IBWTs were explored using CiteSpace and HistCite, two analytical tools, to perform a bibliometric analysis on 498 studies (2002–2024) in the Web of Science Core Collection (WoSCC). The following aspects are addressed in depth: (1) The characteristics of publications on the comprehensive impact of IBWTs. (2) Critical information on the countries, institutions, and subjects engaged in research about the comprehensive impact of IBWTs. (3) The trends and hotspots of research on the comprehensive impact of IBWTs. In this study, we review and evaluate the results of research on the comprehensive impact of large-scale IBWTs, efficiently providing scholars an understanding of the existing research and new frontiers in this field. In addition, for domestic and foreign scholars who are about to delve into the assessment of IBWTs’ impacts and related research, this article can provide valuable information on hot topics and next steps in research from a global perspective. Full article