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.

This study explored the relationships between college students’ indecisiveness, anxiety, and career decision-making ability. Using the convenience sampling method, 1072 college students at a college in Hunan Province, China completed a questionnaire online that included the Indecisiveness Scale, Career Exploration and Decision Self-Efficacy Scale, and Generalized Anxiety Scale-7. Participants reported their gender and place of origin (rural or city). They indicated whether they were an only child, were left behind, and liked the major they were studying. The t-test was used to identify differences in indecisiveness, career decision-making ability, and anxiety according to demographic characteristics. Correlations were calculated between the main variables of interest. Regression analysis was conducted to test the mediation model. Participants who liked their major were significantly more indecisive than those who did not like their major. Career decision-making ability was significantly higher among men than women, participants from urban areas than those from rural areas, participants who were an only child than those with siblings, and among non-left-behind participants than those who were left behind. Anxiety was significantly lower in participants who liked their major than those who did not like their major. In addition, anxiety partially mediated the relationship between indecisiveness and career decision-making ability. College students’ indecisiveness and career decision-making ability are affected by sociocultural background, gender, family background, and career interest. Anxiety partially mediates the relationship between indecisiveness and career decision-making ability. Implications of the findings for counseling college students are discussed.

The destructive geohazard of landslides produces significant economic and environmental damages and social effects. State-of-the-art advances in landslide detection and monitoring are made possible through the integration of increased Earth Observation (EO) technologies and Deep Learning (DL) methods with traditional mapping methods. This assessment examines the EO and DL union for landslide detection by summarizing knowledge from more than 500 scholarly works. The research included examinations of studies that combined satellite remote sensing information, including Synthetic Aperture Radar (SAR) and multispectral imaging, with up-to-date Deep Learning models, particularly Convolutional Neural Networks (CNNs) and their U-Net versions. The research categorizes the examined studies into groups based on their methodological development, spatial extent, and validation techniques. Real-time EO data monitoring capabilities become more extensive through their use, but DL models perform automated feature recognition, which enhances accuracy in detection tasks. The research faces three critical problems: the deficiency of training data quantity for building stable models, the need to improve understanding of AI's predictions, and its capacity to function across diverse geographical landscapes. We introduce a combined approach that uses multi-source EO data alongside DL models incorporating physical laws to improve the evaluation and transferability between different platforms. Incorporating explainable AI (XAI) technology and active learning methods reduces the uninterpretable aspects of deep learning models, thereby improving the trustworthiness of automated landslide maps. The review highlights the need for a common agreement on datasets, benchmark standards, and interdisciplinary team efforts to advance the research topic. Research efforts in the future must combine semi-supervised learning approaches with synthetic data creation and real-time hazardous event predictions to optimise EO-DL framework deployments regarding landslide danger management. This study integrates EO and AI analysis methods to develop future landslide surveillance systems that aid in reducing disasters amid the current acceleration of climate change.

In this study, the author investigates the evolving role of women in corporate boardrooms historically dominated by men, aiming to discern whether their inclusion merely serves as symbolic representation or carries substantive impact. Using a narrative literature review methodology, the author meticulously examines the historical impediments women faced in leadership positions. The findings suggest that deep-seated societal biases, rather than a lack of capability, traditionally constrained women’s leadership trajectories. While some studies suggest that corporations with genuine gender diversity in leadership may outperform in financial outcomes and innovation, this advantage is not consistently observed across all contexts and industries, necessitating a cautious interpretation of these mixed and context-dependent findings. The study argues that women’s inclusion in boardrooms is a strategic imperative for modern corporations striving for resilience, adaptability, and sustained growth in an intricate global landscape, yet also recommends further research to fully understand the broader impacts of such diversity. Furthermore, the study offers practical strategies for enhancing gender diversity in corporate leadership.

The idea of a smart city has evolved in recent years from limiting the city’s physical growth to a comprehensive idea that includes physical, social, information, and knowledge infrastructure. As of right now, many studies indicate the potential advantages of smart cities in the fields of education, transportation, and entertainment to achieve more sustainability, efficiency, optimization, collaboration, and creativity. So, it is necessary to survey some technical knowledge and technology to establish the smart city and digitize its services. Traffic and transportation management, together with other subsystems, is one of the key components of creating a smart city. We specify this research by exploring digital twin (DT) technologies and 3D model information in the context of traffic management as well as the need to acquire them in the modern world. Despite the abundance of research in this field, the majority of them concentrate on the technical aspects of its design in diverse sectors. More details are required on the application of DTs in the creation of intelligent transportation systems. Results from the literature indicate that implementing the Internet of Things (IoT) to the scope of traffic addresses the traffic management issues in densely populated cities and somewhat affects the air pollution reduction caused by transportation systems. Leading countries are moving towards integrated systems and platforms using Building Information Modelling (BIM), IoT, and Spatial Data Infrastructure (SDI) to make cities smarter. There has been limited research on the application of digital twin technology in traffic control. One reason for this could be the complexity of the traffic system, which involves multiple variables and interactions between different components. Developing an accurate digital twin model for traffic control would require a significant amount of data collection and analysis, as well as advanced modeling techniques to account for the dynamic nature of traffic flow. We explore the requirements for the implementation of the digital twin in the traffic control industry and a proper architecture based on 6 main layers is investigated for the deployment of this system. In addition, an emphasis on the particular function of DT in simulating high traffic flow, keeping track of accidents, and choosing the optimal path for vehicles has been reviewed. Furthermore, incorporating user-generated content and volunteered geographic information (VGI), considering the idea of the human as a sensor, together with IoT can be a future direction to provide a more accurate and up-to-date representation of the physical environment, especially for traffic control, according to the literature review. The results show there are some limitations in digital twins for traffic control. The current digital twins are only a 3D representation of the real world. The difficulty of synchronizing real and virtual world information is another challenge. Eventually, in order to employ this technology as effectively as feasible in urban management, the researchers must address these drawbacks.