The convergence of multifaceted global challenges encompassing the rise of populism, Brexit, the climate crisis, the COVID-19 pandemic, and the Russian invasion of Ukraine has catalyzed a profound reassessment of international trade policies. This article critically examines the intricate linkages between these challenges and their profound implications for the contemporary international trading system. Traditionally, globalization debates in the 1990s underscored the social and environmental dimensions of trade, yet the current landscape reveals an undeniable entwining of societal implications with trade policies. This article delves into the interconnectedness of these global challenges with trade, evaluating how each phenomenon influences and reshapes policy discourse. In particular, the rise of populism and its attendant protectionist sentiments have engendered a reevaluation of trade relationships and multilateral agreements. The seismic geopolitical event of Brexit has disrupted regional trade dynamics, signaling a paradigm shift in established trade blocs. Simultaneously, the imperatives of addressing the escalating climate crisis have spotlighted the necessity for trade policies to align with environmental sustainability goals. The COVID-19 pandemic, acting as a disruptor on a global scale, has accentuated vulnerabilities within supply chains, emphasizing the need for resilience and adaptability in trade frameworks. Additionally, the Russian invasion of Ukraine has introduced geopolitical tensions that further complicate the trade-policy landscape. By critically evaluating these intersecting challenges, this article delineates the evolving nature of trade policies and their inextricable relationship with societal and geopolitical realities. It underscores the imperative for a holistic approach in policy formulation that integrates social, environmental, and geopolitical considerations, acknowledging the integral role of trade policies in addressing contemporary global challenges.

The rising trend of tourists selecting agrotourism as a tourist destination has become an intriguing study issue. Seremban is a well-known tourist attraction that is popular among visitors. As a result, Seremban has been selected as the study site. However, river pollution may have an influence on Seremban’s natural environment and agrotourism potential. Furthermore, inadequate infrastructure, such as unauthorized parking, exacerbated the inhabitants’ problems. A growing number of young people leave Seremban to pursue employment or further education in other cities, with no desire to work as farmers. The labor scarcity has also made it difficult for farmers to grow their farms. Consequently, the study aims to examine how factors such as the natural environment, tourist infrastructure, perceived social advantages, and perceived barriers influence the attitudes of Seremban residents towards agrotourism, with a focus on its potential for driving economic growth. This study adopts quantitative research methods, employing descriptive and causal research designs. Primary data collection is conducted through questionnaires, supplemented by secondary data. Non-probability quota sampling is utilized due to the absence of a specific sampling frame, with a sample size of 385 respondents determined using G*Power software. Constructs are developed based on previous research, and the questionnaire comprises Likert-scale items to gauge attitudes and perceptions. A pilot study assesses the instrument’s reliability. Data analysis is performed using SPSS software, encompassing multiple linear regression and Pearson correlation analyses in addition to descriptive statistics. The findings provide valuable insights into the factors driving residents’ perceptions of agrotourism in Seremban, emphasizing the importance of the natural environment, tourism infrastructure, perceived social benefits, and perceived barriers in shaping attitudes. Additionally, the study highlights the resilience of residents’ positive attitudes toward agrotourism, despite potential challenges and barriers identified. Overall, these results offer implications for policymakers and stakeholders involved in tourism development in the region.

Global warming is a problem that affects humanity; hence, crisis management in the face of natural events is necessary. The aim of the research was to analyze the passage of Hurricane Otis through Acapulco from the theoretical perspective of crisis management, to understand the socio-environmental, economic, and decision-making challenges. For data collection, content analysis and hemerographic review proved useful, complemented by theoretical contrastation. Findings revealed failures in communication by various government actors; the unprecedented growth of Hurricane Otis led to a flawed crisis management. Among the physical, economic, environmental, and social impacts, the latter stands out due to the humanitarian crisis overflow. It is the first time that Acapulco, despite having a tradition in risk management against hydrometeorological events, faces a hurricane of magnitude five on the Saffir-Simpson scale. Ultimately, the city was unprepared to face a category five hydrometeorological event; institutional responses were overwhelmed by the complexity of the crisis, and the community came together to improve its environment and make it habitable again.

The rapid advancement of artificial intelligence (AI) technology is profoundly transforming the information ecosystem, reshaping the ways in which information is produced, distributed, and consumed. This study explores the impact of AI on the information environment, examining the challenges and opportunities for sustainable development in the age of AI. The research is motivated by the need to address the growing concerns about the reliability and sustainability of the information ecosystem in the face of AI-driven changes. Through a comprehensive analysis of the current AI landscape, including a review of existing literature and case studies, the study diagnoses the social implications of AI-driven changes in information ecosystems. The findings reveal a complex interplay between technological innovation and social responsibility, highlighting the need for collaborative governance strategies to navigate the tensions between the benefits and risks of AI. The study contributes to the growing discourse on AI governance by proposing a multi-stakeholder framework that emphasizes the importance of inclusive participation, transparency, and accountability in shaping the future of information. The research offers actionable insights for policymakers, industry leaders, and civil society organizations seeking to foster a trustworthy and inclusive information environment in the era of AI, while harnessing the potential of AI-driven innovations for sustainable development.

The characteristics of agricultural products are influenced by the ecosystem, from the perspective of biotic and abiotic factors, which produce in the plant physiological responses and in turn in the fruit unique physicochemical properties, which are the basis for designations of origin and strategies to add value to the product in the current market. In the present work, ten cocoa materials (Theobroma cacao L.) were selected for their outstanding productivity (FSV41, FLE3, FEAR5, FSA12, FEC2, SCC23, SCC80, SCC55, ICS95 and CCN51), which were established in the departments of Santander (931 m a.s.l.), Huila (931 m a.s.l.), Huila (931 m a.s.l.), Huila (931 m a.s.l.), Huila (931 m a.s.l.), Huila (931 m a.s.l.) and Huila (931 m a.s.l.). These were established in the departments of Santander (931 m a.s.l.), Huila (885 m a.s.l.) and Arauca (204 m a.s.l.), the main cocoa-producing areas in Colombia. For the evaluation of the physical characteristics of the collected materials, 21 quantitative descriptors were used to determine the physical variability of the fruit according to clone and place of collection. The data collected were analyzed by means of Pearson’s correlation matrix and principal component analysis, it was possible to identify those descriptors that contribute most to the variability among materials (ear index, diameter length ratio, seed weight and diameter, and fruit weight and length). In addition, it was possible to verify the effect of the place of harvest on the physical characteristics of the materials, high-lighting the importance of the adaptation study prior to the planting of the cocoa material, with the objective of guaranteeing a premium, productive and quality cocoa crop for the industry, which is competitive in the market.

The present study focuses on improving Cognitive Radio Networks (CRNs) based on applying machine learning to spectrum sensing in remote learning scenarios. Remote education requires connection dependability and continuity that can be affected by the scarcity of the amount of usable spectrum and suboptimal spectrum usage. The solution for the proposed problem utilizes deep learning approaches, namely CNN and LSTM networks, to enhance the spectrum detection probability (92% detection accuracy) and consequently reduce the number of false alarms (5% false alarm rate) to maximize spectrum utilization efficiency. By developing the cooperative spectrum sensing where many users share their data, the system makes detection more reliable and energy-saving (achieving 92% energy efficiency) which is crucial for sustaining stable connections in educational scenarios. This approach addresses critical challenges in remote education by ensuring scalability across diverse network conditions and maintaining performance on resource-constrained devices like tablets and IoT sensors. Combining CRNs with new technologies like IoT and 5G improves their capabilities and allows these networks to meet the constantly changing loads of distant educational systems. This approach presents another prospect to spectrum management dilemmas in that education delivery needs are met optimally from any STI irrespective of the availability of resources in the locale. The results show that together with machine learning, CRNs can be considered a viable path to improving the networks' performance in the context of remote learning and advancing the future of education in the digital environment. This work also focuses on how machine learning has enabled the enhancement of CRNs for education and provides robust solutions that can meet the increasing needs of online learning.