Abstract\nBackground: Eurycoma longifolia (commonly known as Tongkat Ali) is a medicinal plant widely recognized for its diverse pharmacological properties and therapeutic potential. Despite extensive research, a systematic understanding of global research trends and thematic focuses related to E. longifolia remains limited.\nObjective: This study aimed to perform a comprehensive bibliometric analysis to elucidate the research landscape, knowledge structure, and emerging trends in E. longifolia research, providing strategic insights for future investigations.\nMethods: Publications related to E. longifolia were retrieved from the Web of Science Core Collection database up to December 19, 2024. A total of 419 relevant documents were identified and analyzed using advanced bibliometric and visualization tools to evaluate publication output, authorship patterns, institutional contributions, and keyword co-occurrence networks.\nResults: The annual publication output demonstrated an overall increasing trajectory, albeit with periodic fluctuations. Universiti Sains Malaysia emerged as the most productive institution, while Chan Kit Lam was identified as the leading author in publication output. Keyword and thematic analyses revealed major research foci on quassinoids, root extracts, Eurycoma longifolia Jack, also known as Tongkat Ali, anti-malarial constituents, and other bioactive compounds. These findings underscore a sustained scientific interest in elucidating the phytochemical composition and pharmacological activities of E. longifolia, particularly its potential clinical applications.\nConclusion: This bibliometric assessment offers a comprehensive overview of the global research landscape on E. longifolia, highlighting key contributors, dominant themes, and emerging research directions. The insights derived from this analysis can inform future research priorities, facilitate interdisciplinary collaboration, and promote the development of evidence-based applications of E. longifolia in modern pharmacotherapy.

Cultural institutions increasingly experiment with participatory formats to connect youth audiences with local policy issues. This study investigates museum-based data storytelling workshops conducted in secondary cities in Peru. Participants co-created visual narratives from municipal datasets on mobility, waste, and neighborhood safety using guided facilitation and analog-digital tools. Pre and post workshop surveys indicated growth in confidence to discuss policy evidence and engage in community meetings. Program staff identified sustained mentorship and school partnerships as central to longer-term impact. Results highlight the role of museums as civic learning hubs that bridge cultural engagement and practical data literacy for adolescents.

School safety protocols on small islands depend on rapid communication channels that remain functional under infrastructure stress. This paper evaluates an open-source seismic alert messaging prototype deployed in island schools in Papua New Guinea. The system links local sensors, radio fallback routes, and SMS gateways to issue tiered warnings and classroom action prompts. Drill observations over one academic term showed faster evacuation initiation and better compliance with assembly protocols in participating schools. Reliability constraints were associated with power interruptions and handset turnover among staff. The findings support incremental scaling of low-cost alert ecosystems tailored to dispersed education networks.

Continuity of postpartum care remains challenging in geographically dispersed regions with limited specialist availability. This study analyzes telehealth follow-up adherence among postpartum patients across three provincial hospitals in Armenia. Appointment logs and patient interviews were used to compare participation rates before and after introducing nurse-assisted video follow-up scheduling. Adherence improved substantially in districts with coordinated reminder calls and flexible evening slots. Reported barriers included intermittent mobile internet and shared-device privacy concerns within households. Findings suggest that blended models combining local nursing teams and teleconsultation can reduce missed postpartum reviews while maintaining acceptable patient satisfaction.

Smallholder producers in flood-prone areas require timely spatial information to adapt planting decisions. This paper reports a participatory drone mapping initiative across floodplain communities in western Kenya. Local extension teams and farmer groups co-produced seasonal risk maps using low-altitude imagery and elevation modeling. Plots adjusted according to map-informed recommendations experienced lower crop loss during heavy rainfall months compared with nearby control plots. Adoption barriers included battery logistics and delayed image processing in remote zones. The project indicates that community-governed aerial mapping can improve crop planning outcomes when paired with extension support and transparent data-sharing practices.

Rural classrooms with multilingual learners face persistent gaps in early reading support and teacher feedback time. This pilot study tests an AI-supported phonics feedback workflow implemented in six primary schools in eastern Slovakia. Teachers used tablet-based prompts and weekly performance summaries to plan targeted mini-lessons in mixed-language groups. Students receiving structured feedback showed stronger decoding gains than comparison classes using standard workbook routines. Teachers reported improved confidence in identifying pronunciation patterns, though onboarding required practical mentoring. The intervention demonstrates that low-complexity AI support can strengthen foundational literacy instruction when aligned with local pedagogy and language diversity realities.

Urban heat adaptation planning often overlooks commuter micro-exposure at transit nodes. This research maps thermal hotspots along major bus corridors in two mid-sized Chilean coastal cities and estimates exposure duration by route and waiting time. Surface temperature rasters were combined with field sensor data and passenger schedule observations during summer peaks. High-exposure segments were concentrated around low-canopy transfer points and informal terminals with limited shade. Simulated mitigation using shade structures and revised stop placement reduced estimated cumulative heat burden for frequent riders. The study offers an actionable framework for transport agencies integrating climate adaptation into routine network upgrades.

Informal market traders increasingly depend on lightweight digital tools to stabilize cashflow and supplier trust. This paper examines mobile ledger adoption across three secondary markets in northern Nigeria and assesses links with inventory continuity during price volatility. Mixed methods were used, including transaction diary reviews and structured interviews with traders and cooperative managers. Users of simple ledger applications reported fewer unresolved payment disputes and faster restocking decisions than paper-only users. Constraints included inconsistent network quality and varying digital literacy among senior vendors. The study recommends cooperative-led training, offline-first design, and local language interfaces to improve sustained uptake in low-resource trade environments.

This study evaluates a low-cost biochar filtration setup designed for tube wells affected by arsenic in peri-urban and rural Bangladesh. A six-month field trial compared untreated water with two filtration designs produced from rice husk feedstock. The optimized unit reduced dissolved arsenic and improved taste acceptance among households while keeping maintenance requirements minimal. Community technicians were trained to replace media and monitor flow decline using simple checklists. Results indicate that locally fabricated filters can provide a practical transition strategy for households awaiting centralized infrastructure. The findings support policy models that combine public health surveillance with decentralized treatment maintenance.

Tourism plays an important role in the process of economic growth, poverty reduction, better local governance, biodiversity conservation, and the development of natural resource management. It is a powerful tool for achieving sustainable development goals, especially on the regional level. Among various forms of tourism, agritourism, wine tourism, and rural tourism have become increasingly important for many countries, particularly in Europe, as a means of developing sustainable tourism. The scale of wine and agritourism is growing rapidly as it provides diversified income sources. However, the correlation between business models and legislation remains a challenge for many countries. The study analyzes business models and the impact of legislation on economic direction at a regional level. The research combines an analysis of legislation, a literature review, and an evaluation of business models in different countries. The findings demonstrate that legislation plays a crucial role in the development of wine tourism and agritourism. Legislative support promotes tourism development and strengthens regional economies. This study provides valuable insights into the development of sustainable business models, highlighting the role of legislation, policies, and regulations in shaping agritourism and wine tourism.

This study examines the effectiveness of artificial intelligence (AI)-based adaptive learning systems in facilitating personalized learning in undergraduate business programs. Using ChatGPT as a personalized language learning assistant, the study employed the adaptive interaction-feedback-progress (AIFP) model to examine improvements in academic performance, motivation and self-directed learning among business administration students at Grigol Robakidze University, Georgia. 18 participants participated in a pre- and post-test quasi-experimental design that combined quantitative and qualitative methods. Results revealed a statistically significant 21% improvement in language proficiency, with the largest gains in grammatical accuracy, vocabulary use and writing fluency. Qualitative analysis revealed increased engagement, self-regulation and satisfaction with AI-generated feedback and also identified ethical concerns regarding data privacy and overreliance on automation. The results confirm that short-term AI-based personalization can replicate the cognitive and motivational benefits of individualized learning, confirming Bloom\'s ,,two sigma effect”. This study contributes to the pedagogical, technological and ethical discourse on AI in higher education and provides evidence-based ideas for integrating adaptive learning systems into business programs while taking human needs and ethical responsibility into account.