This study investigates the escalating complexity and unpredictability of global supply chains, with a particular emphasis on resilience in the agricultural sector of Antioquia, Colombia. The aim of the study is to identify and analyze the dynamic capabilities, specifically flexibility and adaptability that significantly enhance resilience within agri-food supply chains. Given the sector’s vulnerability to external disruptions, such as climate change and economic volatility, a thorough understanding of these capabilities is imperative for the formulation of effective risk management strategies. This research is essential to provide empirical insights that can inform stakeholders on fortifying their supply chains, thereby contributing to enhanced competitiveness and sustainability. By presenting a comprehensive framework for evaluating dynamic capabilities, this study not only addresses existing gaps in the literature but also offers practical recommendations aimed at bolstering resilience in the agricultural sector.

The coastal area of Bohai Bay of China has a wide distribution of salt-accumulated soils which could pose a problem to the sustainable development of the local ecology. As a result, the land remains largely degraded and unsuitable for biophysical and agricultural purposes. In this study, we characterized the soil and native plants in the area, to properly understand and identify species with satisfactory adaptation to saline soil and of high economic or ecological value that could be further developed or domesticated, using appropriate cultivation techniques. The goal was to determine the salinity parameters of the soil, identify the inhabiting plant species and contribute to the ecosystem data base for the Bay area. A field survey involving soil and plant sampling and analyses was conducted in Yanshan and Haixing Counties of Hebei Province, China, to estimate the level of salt ions as well as plant species population and type. The mean electrical conductivity (EC) of the soils ranged from 0.47 in more remote locations to 23.8 ds/m in locations closer to the coastline and the total salt ions from 0.05 to 8.8 g/kg, respectively. Each of the salinity parameters, except HCO₃⁻ showed wide variations as judged from the coefficient of variation (CV) values. The EC, as well as chloride, sulphate, Mg and Na ions increased significantly towards the coastline but the HCO₃⁻ ion showed a relatively even distribution across sampling points. Sodium was the most abundant cation and chloride and sulphate the most abundant anions. Therefore, the most dominant salinity-inducing salt that should be properly managed for sustainable ecosystem health was sodium chloride. Based on the EC readings, the most remote location from the coastline was non-saline but otherwise, the salinity ranged from slightly to strongly-very strongly saline towards the coast. There were considerably wide variations in the number and distribution of plant species across sampling locations, but most were dominated entirely Phragmites australis, Setaria viridis and Sueda salsa. Other species identified were Aeluropus littoralis, Chloris virgata, Heteropappus altaicus, Imperata cylindrica, Puccinellia distans, Puccinellia tenuiflora and Scorzonera austriaca. On average, the sampling points furthest from the coast produced the most biomass, and the point with the highest elevation had the most diverse species composition. Among species, Digitaria sanguinalis produced the highest dry mass, followed by Lolium perenne and H. altaicus, but there were considerable variations in biomass yield across sampling locations, with the location nearest the coastline having no vegetation. The observed variations in soil and vegetation should be strongly considered by planners to allow for the sustainable development of the Bahai bay area.

The digital era has ushered in significant advancements in Generative Artificial Intelligence (GAI), particularly through Generative Models and Large Language Models (LLMs) like ChatGPT, revolutionizing educational paradigms. This research, set against the backdrop of Society 5.0 and aimed at sustainable educational practices, utilizes qualitative analysis to explore the impact of Generative AI in various learning environments. It highlights the potential of LLMs to offer personalized learning experiences, democratize education, and enhance global educational outcomes. The study finds that Generative AI revitalizes learning methodologies and supports educational systems’ sustainability by catering to diverse learning needs and breaking down access barriers. In conclusion, the paper discusses the future educational strategies influenced by Generative AI, emphasizing the need for alignment with Society 5.0’s principles to foster adaptable and sustainable educational inclusion.

This research explores the advancement of Artificial Intelligence (AI) in Occupational Health and Safety (OHS) across high-risk industries, highlighting its pivotal role in mitigating the global incidence of occupational incidents and diseases, which result in approximately 2.3 million fatalities annually. Traditional OHS practices often fall short in completely preventing workplace incidents, primarily due to limitations in human-operated risk assessments and management. The integration of AI technologies has been instrumental in automating hazardous tasks, enhancing real-time monitoring, and improving decision-making through comprehensive data analysis. Specific AI applications discussed include drones and robots for risky operations, computer vision for environmental monitoring, and predictive analytics to pre-empt potential hazards. Additionally, AI-driven simulations are enhancing training protocols, significantly improving both the safety and efficiency of workers. Various studies supporting the effectiveness of these AI applications indicate marked improvements in risk management and incident prevention. By transitioning from reactive to proactive safety measures, the implementation of AI in OHS represents a transformative approach, aiming to substantially reduce the global burden of occupational injuries and fatalities in high-risk sectors.

This study provides empirical data on the impact of generative AI in education, with special emphasis on sustainable development goals (SDGs). By conducting a thorough analysis of the relationship between generative AI technologies and educational outcomes, this research fills a critical gap in the literature. The insights offered are valuable for policymakers seeking to leverage new educational technologies to support sustainable development. Using Smart-PLS4, five hypotheses derived from the research questions were tested based on data collected from an E-Questionnaire distributed to academic faculty members and education managers. Of the 311 valid responses, the measurement model assessment confirmed the validity and reliability of the data, while the structural model assessment validated the hypotheses. The study’s findings reveal that New Approaches to Learning Outcome Assessment (NALOA) significantly contribute to achieving SDGs, with a path coefficient of 0.477 (p < 0.001). Similarly, the Use of Generative AI Technologies (UGAIT) has a notable positive impact on SDGs, with a value of 0.221 (p < 0.001). A Paradigm Shift in Education and Educational Process Organization (PSEPQ) also demonstrates a significant, though smaller, effect on SDGs with a coefficient of 0.142 (p = 0.008). However, the Opportunities and Risks of Generative AI in Education (ORGIE) study did not find statistically significant evidence of an impact on SDGs (p = 0.390). These findings highlight the potential opportunities and challenges of using generative AI technologies in education and underscore their key role in advancing sustainable development goals. The study also offers a strategic roadmap for educational institutions, particularly in Oman to harness AI technology in support of sustainable development objectives.