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The adult learning ecology has expanded to include digital access, requiring enhancement of theories in the specific context of low-literate learners who may not have previously benefited from educational technology. The Digital Learning Ecology (DLE) framework identifies the three dimensions of context, design, and motivation, considering theories of cognitive learning, multimedia design, and language learning ecology that help support low-literate adults. A mobile learning solution based on DLE, designed for the Barbara Bush Foundation XPRIZE Adult Literacy competition, is described. The app design utilizes context proximal to adult learners' lives to motivate and maintain active engagement, improve multiple literacies, foster engagement, and encourage persistence. DLE dimensions are mapped to specific features of the app, along with the underlying cognitive principles that support learning. Both internal evaluations by authors and external evaluations by XPRIZE demonstrate significant improvement in learning outcomes for low-literate adults. Further, many low-literate adults in this study demonstrated low digital literacy, indicating a design need for a more accessible user interface with enhanced signifiers compared to those provided by universal design principles. Carefully designed mobile solutions based on the DLE framework can enhance and expand the learning ecology for low-literate adults, supporting both formal and non-formal learning environments.

Online and virtual teaching-learning has been a panacea that most educational institutions adopted from the dire need created by COVID-19. We provide a comprehensive bibliometric study of 9523 publications on virtual laboratories in higher education covering the years 1991 to 2021. Influential bibliometrics such as publications and citations, productive countries, contributing institutions, funders, journals, authors, and bibliographic couplings were studied using the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) protocol. A new metric to complement citations called Field Weighted Citation Impact was introduced that considers the differences in research behavior across disciplines. Findings show that 72% of the research work was published between 2011-and 2021, most likely due to digitalization, with the highest number of publications in 2020-2021 highlighting the impact of the pandemic. Top contributing institutions were from the developed economies of Spain, Germany, and the United States. The citation impact from publications with international co-authors is the highest, highlighting the importance of co-authoring papers with different countries. For the first time, Altmetrics in the context of virtual labs were studied though a very low correlation was observed between citations and Altmetrics Attention Score. Still, the overall percentage of publications with attention showed linear growth. Our work also highlights that virtual laboratory could play a significant role in achieving the United Nations Sustainable Development Goals, specifically SDG4-Quality Education, which largely remains under-addressed.

COVID-19 global pandemic pushed a large number of higher educational institutions to use Online Proctored Exams (OPE) because of government-imposed lockdowns. Treating OPE as an educational technology innovation, we apply the diffusion of innovation theory in predicting factors affecting its adoption by university students which we believe is the first of its kind research study. The study presented here reviews OPE, its types, architecture, challenges, and prospects and then focuses on the student adoption experience at a large, multi-campus higher educational institution. We have used the fine-grained Aspect Level Sentiment Analysis to check the university students' attitudes towards the Online Proctored Exams. We then used linguistic features to extract the aspect terms present in the feedback comments which showed that 55% of university students having a positive attitude towards OPE. Results of our study show that innovation characteristics such as relative advantage, compatibility, ease of use, trialability, and observability were found to be positively related to acceptance of OPE.

Nickel superalloy CM247LC was fabricated by laser powder bed fusion (LPBF) and its microstructure, hardness and tribological properties were investigated. X-ray diffraction analysis showed the presence of γ, γ′ and metastable MC carbides. Microstructure revealed cellular structure with fine MC carbides at cell boundaries. Higher hardness of 408 HV was attributed to grain refinement from rapid solidification and strengthening from MC carbides. Wear testing showed lowest wear rate of 1.46x10-9 mm3/m at 10 N load and predominant wear mechanisms were abrasive and adhesive wear. This study recommends LPBF as a promising method for manufacturing CM247LC superalloy for load bearing and tribological applications.

The solidification mechanism of ductile iron is a bit complex due to the precipitation of graphite and silicon. These elements change the solidification pattern of cast iron. Density of these elements is less than iron leads to occupying more volume consequently increase the overall metal volume. There are two aspects on this increase in metal volume. One is, reducing this volume increase to reduce the creation of porosities at the earlier stage of solidification and second is, using this volume increase to remove porosity at the later stage of solidification. Proper understanding of this graphite expansion in cast iron solidification will bring insights on reducing or removing of the risers. The current study focus on correlating the net contraction and austenitic liquidus point with shrinkage. The average contraction found through this study is 1.36 % which is more than the net expansion of 0.25 % (without riser) reported in literature. The study found that properly balancing graphite precipitation, pouring temperature and mold strength can enable riserless casting of ductile iron by compensating for liquid contraction through graphite expansion.

Introduction: As ecological disasters loom, forests play a crucial role in mitigating climate change and aligning with the United Nations Sustainable Development Goals (SDGs). This study aims to systematically examine the contribution of forestry science research to the SDGs through a multidisciplinary perspective, highlighting its role in advancing global sustainability efforts. Methods: Utilizing the PRISMA protocol, we conducted a comprehensive analysis of 39,841 publications since 2015, focusing on the intersection of forestry sciences with the SDGs. Network and keyword co-occurrence analyses were performed to identify thematic clusters and interdisciplinary linkages among SDGs. Results: Our findings indicate a significant focus on SDG 15 (Life on Land) and SDG 13 (Climate Action), emphasizing the forestry sector's contributions to biodiversity conservation, climate change mitigation, and ecosystem services. The analyses revealed thematic clusters centered around ecological sustainability, sustainable energy, and social development, demonstrating the diverse ways in which forestry research supports the SDGs. Discussion: This study highlights the interdisciplinary nature of forestry sciences and identifies key areas of contribution while pointing to potential research gaps. Future research should explore advanced technologies such as AI-driven analytics and remote sensing, socioeconomic impacts of forest-based livelihoods, and innovative reforestation techniques. Strengthening urban forestry, enhancing interdisciplinary cooperation, and improving governance are recommended to ensure continued contributions of forestry sciences to global sustainability and the SDGs. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence (AI) into renewable energy and sustainability represents a transformative approach toward achieving sustainable development goals (SDGs), especially SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 13 (Climate Action). This study utilized the PRISMA framework to conduct a systematic review, focusing on the role of AI in renewable energy and sustainable development. This research utilized Scopus's curated AI research area, which employs text mining to refine AI concepts into unique keywords. Further refinement via the All Science Journals Classification system and SDG-mapping filters narrowed the focus to publications relevant to renewable energy and SDGs. By employing the BERTopic modeling approach, our study identifies major topics, such as enhancing wind speed forecasts, performance analysis of fuel cells, energy management in elective vehicles, solar irradiance prediction, optimizing biofuel production, and improving energy efficiency in buildings. AI-driven models offer promising solutions to address the dynamic challenges of sustainable energy. Insights from academia-industry collaborations indicate that such partnerships significantly accelerate sustainable-energy transitions, with a focus on AI-driven energy storage, grid management, and renewable-energy forecasting. A global consensus on the critical role of investing in technology-driven solutions for energy sustainability was underscored by the relationship between funding data and global R&D spending patterns. This study serves as a resource for practitioners to harness AI technologies for renewable energy, where for example, AI's accurate wind speed predictions can increase wind farm efficiency, highlighting the necessity of innovation and collaboration for sustainable development. [ABSTRACT FROM AUTHOR]

This study investigates the alignment of research on small island developing states (SIDSs) with sustainable development goals (SDGs), identifying key focus areas, interdisciplinary connections, research gaps, thematic clusters, and insights from case studies. By employing a mixed methods research design that combines quantitative scientometric analysis with qualitative case studies, this unique study examines the nexus between SIDS research and its contribution to SDGs. The findings reveal that SDG 13 (climate action) and SDG 14 (life below water) dominate SIDS research, emphasizing the need to address climate challenges and marine biodiversity. Economic aspects are underscored by SDG 8 (decent work and economic growth) and SDG 12 (responsible consumption and production), while urban development and inequality are addressed through SDG 11 (sustainable cities and communities) and SDG 10 (reduced inequalities). Renewable energy and food security are focal points in SDG 7 (affordable and clean energy) and SDG 2 (zero hunger). Moderate research outputs in SDG 3 (good health and well-being) and SDG 15 (life on land) suggest a holistic approach, while lower activity in SDGs 16 (peace), 6 (clean water), 4 (education), and 5 (gender) presents opportunities for further exploration. SIDS research is highly interdisciplinary, as seen through three distinct SDG networks: foundational social goals, economic and environmental objectives, and environmental preservation. The thematic clusters in SIDS research include environmental challenges and adaptation, public health, and socioeconomic development, and energy policy and environmental economics. Case studies from various SIDSs illustrate the complex interplay between environmental sustainability, economic development, and social well-being, highlighting the importance of tailored approaches, international cooperation, and local community engagement. These findings underscore the need for integrated research strategies to address the complex challenges of SIDSs, emphasizing climate action, sustainable economic practices, and comprehensive development approaches to achieve the SDGs effectively. [ABSTRACT FROM AUTHOR]

In many rural Indian schools, English is a second language for teachers and students. Intelligent tutoring systems have good potential because they enable students to learn at their own pace, in an exploratory manner. This paper describes a 3-year longitudinal study of 2123 Indian students who used the intelligent tutoring system, AmritaITS. The aim of the study was to use the students' interaction logs with AmritaITS to: (1) predict student performance, in English and Mathematics subjects, via summative and formative assessments, (2) predict students who may be at risk of failing the final examination and (3) screen students who may have reading difficulties. The prediction models for summative assessments were significantly improved by formative assessments scores, along with AmritaITS logs. The receiver operating characteristic (ROC) curve showed that students at risk of failing a class could be identified early, with high sensitivity and specificity. The models also provide recommendations for the amount of time required for students to use the system, and reach the appropriate grade level. Finally, the models demonstrated promise in identifying students who might be at risk of suffering from reading difficulties.