Cross-border infrastructure projects offer significant economic and social benefits for the Asia-Pacific region. If the required investment of $8 trillion in pan-Asian connectivity was made in the region’s infrastructure during 2010–2020, the total net income gains for developing Asia could reach about $12.98 trillion (in 2008 US dollars) during 2010–2020 and beyond, of which more than $4.43 trillion would be gained during 2010–2020 and nearly $8.55 trillion after 2020. Indeed, infrastructure connectivity helps improve regional productivity and competitiveness by facilitating the movement of goods, services and human resources, producing economies of scale, promoting trade and foreign direct investments, creating new business opportunities, stimulating inclusive industrialization and narrowing development gaps between communities, countries or sub-regions. Unfortunately, due to limited financing, progress in the development of cross-border infrastructure in the region is low.

This paper examines the key challenges faced in financing cross-border projects and discusses the roles that different stakeholders—national governments, state-owned enterprises, private sector, regional entities, development financing institutions (DFIs), affected people and civil society organizations—can play in facilitating the development of cross-border infrastructure in the region. In particular, this paper highlights the major risks that deter private sector investments and FDIs and provides recommendations to address these risks.

Recent times have seen significant advancements in AI and NLP technologies, poised to revolutionize logistical decision-making across industries. This study investigates integrating ChatGPT, an advanced AI language model, into strategic, tactical, and operational logistics. Examining its applicability, benefits, and limitations, the study delves into ChatGPT's capacity for strategic logistics planning, facilitating nuanced decision-making through natural language interactions. At the tactical level, it explores ChatGPT's role in optimizing route planning and enhancing real-time decision support. The operational aspect scrutinizes ChatGPT's capabilities in micro-level logistics and emergency response. Ethical implications, encompassing data security and human-AI trust dynamics, are also analyzed. This report furnishes valuable insights for the logistics sector, emphasizing AI's potential in reshaping decision-making while underscoring the necessity for foresight, evaluation, and ethical considerations in AI integration. In this publication, it is assumed that ChatGPT is not entirely reliable for decision-making in the logistics field: at the strategic level, it can be effectively used for "brainstormin" in preparing decisions, but at the tactical and operational level, the depth of the knowledge is not sufficient to make appropriate decisions. Therefore, the answers provided by ChatGPT to the defined logistic tasks are compared with real logistic solutions. The article highlights ChatGPT's effectiveness at different levels of logistics and clarifies its potential and limitations in the logistics field.

Highly nutritive and antioxidants-enriched okra (Abelmoschus esculentus) gets sub-optimal field yield due to the irregular germination coupled with non-synchronized harvests. Hence, the research aimed at assessing the combined impact of seed priming and field-level gibberellic acid (GA₃) foliar spray on the yield and post-harvest quality of okra. The lab studies were conducted using a complete randomized design (CRD), while the field trials were performed following a factorial randomized complete block design (RCBD) with three replications. Okra seeds were subjected to ten different priming methods to assess their impact on seed germination and seeding vigor. In the premier step, okra seeds were subjected to ten different priming methods, like hydro priming for 6, 12, and 18 h, halo priming with 3% NaCl at 35 ℃, 45 ℃, and 60 ℃, acid priming with 80% H₂SO₄ for 2.5, 5, and 10 min. Based on the observation, hydro priming for 12 h exhibited the best germination rate (90%), followed by halo seed priming at 60 ℃ and acid priming for 5 min. Furthermore, the halo priming at 60 ℃ demonstrated the greatest seedling vigor index (1965), whereas acid priming for 5 min resulted in favorable outcomes in terms of early emergence in 2.66 days. In addition, varying concentrations of GA₃ (0, 100, 200, and 300 ppm) were also administered to the best three primed seedlings for evaluating their field performance. The findings indicated that applying GA₃ at a concentration of 300 ppm to seedlings raised through acid priming (80% H₂SO₄ for 5 min) resulted in improved leaf length, reduced time to flowering (first and 50%) and harvest, increased pod diameter, individual pod weight, and yield per plant (735.16 g). Additionally, the treatment involving GA₃ at 300 ppm with halo priming (3% NaCl) at 60 ℃ exhibited the longest shelf life (21 days) of okra with the lowest levels of rotting (6.73%) and color change (1.12) in the polyethylene storage condition.

The increase in world carbon emissions is always in line with national economic growth programs, which create negative environmental externalities. To understand the effectiveness of related factors in mitigating CO₂ emissions, this study investigates the intricate relationship among macro-pillars such as economic growth, foreign investment, trade and finance, energy, and renewable energy with CO₂ emissions of the high gross domestic product economies in East Asia Pacific, such as China, Japan, Korea, Australia and Indonesia (EAP-5). Through the application of the Vector Error Correction Model (VECM), this research reveals the long-term equilibrium and short-term dynamics between CO₂ emissions and selected factors from 1991 to 2020. The long-term cointegration vector test results show that economic growth and foreign investment contribute to carbon reduction. Meanwhile, the short-term Granger causality test shows that economic growth has a two-way causality towards carbon emissions, while energy consumption and renewable energy consumption have a one-way causality towards carbon emissions. In contrast, the variables trade, foreign direct investment, and domestic credit to the private sector do not have two-way causality towards CO₂ emissions. The findings reveal that economic growth and foreign investment play significant roles in carbon reduction, which are observed in long-term causality relationships, while energy consumption and renewable energy are notable factors. Thus, the study offers implications for mitigating environmental concerns on national economic growth agendas by scrutinizing and examining the efficacy of related factors.

Rural sub-Saharan Africa faces limited medical access, healthcare worker shortages, and inadequate health information systems. Mobile health (mHealth) technologies offer potential solutions but remain underdeveloped in these settings. This review aims to explore the sociocultural context of mHealth adoption in rural sub-Saharan Africa to support sustainable implementation. A comprehensive Enhancing Transparency in Reporting the Synthesis of Qualitative Research (ENTREQ) search was conducted in databases like PubMed, MEDLINE, and African Journals Online, covering peer-reviewed literature from 2010 to 2024. Qualitative studies of mHealth interventions were included, with quality assessed via the Critical Appraisal Skills Program (CASP) checklist and data synthesized using a meta-ethnographic approach. Out of 892 studies, 38 met the inclusion criteria. Key findings include sociocultural factors like community trust influencing technology acceptance, local implementation strategies, user empowerment in health decisions, and innovative solutions for infrastructure issues. Challenges include privacy concerns, increased healthcare worker workload, and intervention sustainability. While mHealth can reduce healthcare barriers, success depends on sociocultural alignment and adaptability. Future interventions should prioritize community co-design, privacy protection, and sustainable, infrastructure-aware models.