This study’s primary objective is to determine the financial repercussions, including expenses, profits, and losses, that certain stakeholders in the Tuong-mango value chain face at various distribution stages. This was achieved through the utilisation of stakeholders cost-benefit value chain analysis. These individuals collectively contributed 849 sample observations to the dataset including 732 farmers, 10 cooperative, 32 collectors, 25 wholesalers, 30 retailers, 12 exporters and processors, and 08 grocery stores/fruit. The robust financial performance of the Tuong-mango value chain is attributable to its integrated economic efficiency, as evidenced by its over USD 1 billion in revenue and USD 98.2 million in net income. The marketing channels, specifically channels 1, 2, and 3, generate a total of USD 906.1 million in revenue, yielding a net profit of USD 81.9 million. The combined sales from domestic marketing channels 4 and 5 total USD 160 million, yielding a net profit of USD 16.2 million. The findings indicate that due to their limited scope and suboptimal grade 1, farmers are the most vulnerable link in the supply chain. This study proposes three strategies for augmenting quality, fostering technological advancement, and facilitating the spread of benefits. This study’s findings contribute to the existing literature on value chain analysis as it pertains to various tropical fruits and vegetables. The study provides empirical evidence supporting the utility of the value chain method in policy formulation.

Recently, there has been a burgeoning fascination with the influence of urban green spaces (UGS) on physical activity (PA) and health. This interest has been accompanied by a mounting body of evidence that establishes a connection between UGS and residents’ PA levels. Numerous studies have been conducted to investigate the significance of UGS and have generally agreed on their connection with health. However, there is still considerable variation in viewpoints regarding the intermediate factors contributing to this association. The primary objective of this study was to investigate the potential correlation between different qualitative factors of UGS and PA. The study involved the collection of data from four parks located in Edinburgh. Four trained observers utilised the Environmental Assessment of Public Recreational Spaces (EARPS Mini) tool to code various environmental characteristics. Additionally, the Method for Observing Physical Activity and Wellbeing (MOHAWk) observation tool was employed to code instances of on-site incivility and the characteristics and behaviours of residents engaging in UGS activities. The results of this study show that the facilities and environment, area and socioeconomic status (SES) of UGS positively affect the type of PA and the level of PA, as well as influence residents’ attentiveness to the environment and their interactions with each other. Demographics such as gender and age group are also significantly related to the level and type of PA. Significant differences in the level and type of PA, and race only differed significantly in the choice of activity type. These results suggest that the quality of the UGS environment affects the level, type, and status of PA among residents and that resident characteristics also have an impact. Future research suggests increasing data collection related to PA frequency and PA duration and considering longitudinal observations over time for refinement.

The use of autonomous weapons systems (AWS) has led to several opposing legal opinions regarding their violations of international law. The responsibility of the state, individuals, and corporations as producers, designers, and programmers is all being taken into consideration. If the decision to kill humans without “meaningful human control” is transferred to computers, it would be hard to attribute accountability for the actions of AWS to their corporations. Consequently, this means that corporate actors will enjoy impunity in all cases. The present paper indicates that the most significant problem arising from the use of AWS is the attribution of responsibility for its violation. Corporations are not subject to liability for the legitimate use of weapons under international law. The main problem with corporate responsibility, according to article 25 (4) of the Rome Statute, is that the provision only relates to individual criminal responsibility and that the ICC shall only have jurisdiction over natural persons. Nevertheless, corporations may be held accountable under aspects of international law. The paper proposes a more positive view on artificial intelligence, raising corporations’ accountability in international law by historically linking the judging of business leaders. The article identifies aiding and abetting as well as co-perpetration as the two modes of accountability under international law potentially linked to AWS. The study also explores the main ambiguity in international law relating to corporate aiding and abetting of human rights violations by presenting the confusion on determining the standards of these 2 modes of liability before the ICC and International ad doc Tribunal. Moreover, with the new age of war heavily dependent on AI and AWS, one cannot easily and precisely ascertain who must be held accountable for war crimes because of the unanticipated facts in decision-making combined with the aiding or abetting of violations of international law. International law prioritizes the goal of ending impunity for the individual and largely neglects the need to achieve the same goal for corporate complicity. In sum, progress to regulate the use of AWS by corporate actors could be enormously helpful to the cause of ending impunity.

Objective: To promote the development of China’s crop seed industry with high quality, guarantee food security and sustainable agricultural development, scientific design of the evaluation index system for high-quality development of the seed industry and conduct of metric analysis are the keys to promoting the revitalization of the seed industry and the construction of a strong agricultural country. Methods: This paper focused on the high-quality development of China’s crop seed industry as the main research object by combining previous research findings of studies based on the connotation of high-quality development of the crop seed industry and constructed the evaluation index system of high-quality development of China’s crop seed industry which covers five dimensions, namely, innovation-driven development, green and sustained development, coordinated and comprehensive development, opening-up and strengthened development, and share-and-promote development, The Entropy method, Dagum’s Gini coefficient, Kernel’s density estimation, and panel regression methods were used to comprehensively analyze the spatial and temporal evolution, regional differences, and driving factors of the level of high-quality development of the crop seed industry in 30 provinces (municipalities and autonomous regions) of China from 2011 to 2020. Conclusions: After systematic analysis, it was concluded that (1) the overall level of high-quality development in China’s crop seed industry has stabilized, and progress has been made. (2) The overall inter-regional differences among the four major regions showed a gradual upward trend, with the inter-regional differences serving as the primary source of the differences and the contribution rate of various inter-regional differences demonstrating an upward trend. (3) Innovation capacity, the cultural and educational level of rural residents, the development of rural infrastructure, national financial support, and market-oriented approach are important factors driving the high-quality development of the crop seed industry in Chinese provinces (districts and municipalities).

Metaverse technology has various uses in communication, education, entertainment, and other aspects of life. Consequently, it necessitates using some interactive mobile applications to enter the virtual world and gain real-time, face-to-face experiences, particularly among students. This research focused on the factors accelerating metaverse technology acceptance particularly, Metaverse Experience Browser application acceptance among the students under the factors proposed by the unified theory of acceptance and use of technology (UTAUT) model. Notably, lack of studies in metaverse browsers and their prevalence during the post pandemic era, indicates a strong literature gap. The researchers gathered data from n = 384 higher education students from the two cities in the United Arab Emirates and applied Structural Equation modelling (SEM) for data analysis. Results revealed that Performance Expectancy (p < 0.003) and Social Influence (p = 0.000) were significant factors affecting the Behavioral Intention of the students to consider Metaverse Experience Browser as an interactive mobile application. On the other hand, behavioural Intention significantly affects (p = 0.000) Effort Expectancy, which shows how fewer efforts and greater accessibility are associated with one’s behavioural Intention. Besides, the effect of Behavioral Intention (p = 0.000) on Metaverse Experience Browser acceptance also remained validated. Finally, Effort Expectancy (p = 0.000) also indicated its significant effect on the Metaverse Experience Browser. These results indicated that the factors proposed by UTAUT have greater applicability on the Metaverse Experience Browser as they showed their relevance to its acceptance. The present study concludes that the acceptance of Metaverse Experience Browser as an interactive mobile application is a level ahead in improving students’ experiences. Thus, the Metaverse Experience Browser is considered a modified way of creating, sharing, participating, and enjoying the virtual world, indicating its greater usage among students for different purposes, including education and learning.

This research examines three data mining approaches employing cost management datasets from 391 Thai contractor companies to investigate the predictive modeling of construction project failure with nine parameters. Artificial neural networks, naive bayes, and decision trees with attribute selection are some of the algorithms that were explored. In comparison to artificial neural network’s (91.33%) and naive bays’ (70.01%) accuracy rates, the decision trees with attribute selection demonstrated greater classification efficiency, registering an accuracy of 98.14%. Finally, the nine parameters include: 1) planning according to the current situation; 2) the company’s cost management strategy; 3) control and coordination from employees at different levels of the organization to survive on the basis of various uncertainties; 4) the importance of labor management factors; 5) the general status of the company, which has a significant effect on the project success; 6) the cost of procurement of the field office location; 7) the operational constraints and long-term safe work procedures; 8) the implementation of the construction system system piece by piece, using prefabricated parts; 9) dealing with the COVID-19 crisis, which is crucial for preventing project failure. The results show how advanced data mining approaches can improve cost estimation and prevent project failure, as well as how computational methods can enhance sustainability in the building industry. Although the results are encouraging, they also highlight issues including data asymmetry and the potential for overfitting in the decision tree model, necessitating careful consideration.