Research networks organized around a particular topic are built as knowledge is produced and socialized. These are parts of a seminal or initial production, to which new authors and subtopics are added until research and knowledge networks are formed around a particular area. The purpose of the research was to find this type of relationship or network between authors, institutions, and countries that have contributed to the issue of the circular economy and specifically its relationship with sustainability. This allows those interested in the said object of study to know the research advances of the network, enter their research lines, or create new networks according to their interests or needs. The study used a bibliometric-type descriptive quantitative approach using the Scopus scientific database, the R Studio data analytics application, and the Bibliometrix library. The results were found to determine a relationship building from 2006, which makes it an emerging topic. However, the growth it has achieved in recent years of more than 31% shows a strong interest in the subject. Of the subtopics that have been addressed, sustainability, recycling, solid waste, wastewater, and renewable energy. Similarly, sectors such as construction, the automotive industry, tourism, cities, the agricultural sector, the chemical industry, and the implementation of technologies 4.0 and 5.0 in their processes stood out. The most prominent country in the scientific approach to this area is Italy. The most prominent author for his citations is Molina-Moreno, the source of knowledge that stands out for his contributions is the University of Granada and different networks have been built around their knowledge.

The purpose of this study is to investigate the correlation between sponsorship and the performance and development of early career athletes transitioning from junior level to professional sports, because this issue has not been fully explored in the Czech Republic. The reason is the almost absolute absence of financial or material support for such early-career athletes, when their transition from junior categories and the entire junior category is almost always exclusively financed and supported by their parents and families. We also emphasise the absolute absence of legislative provisions that would give supporters of such athletes at least a tax or other advantage. The research is based on research of Cardenas (2023), Hong and Fraser (2023) and Moolman and Shuttleworth (2023) and aims to assess how financial and material support provided by sponsors can enhance an athlete’s performance and long-term career trajectory. A mixed method approach was adopted, combining quantitative analysis through surveys and performance data with qualitative interviews. Data from 173 early career athletes from various disciplines were analysed using t-tests and ANOVA statistical methods to assess financial stability, access to better training, and community participation. Results indicate that sponsorship significantly contributes to better performance metrics, with sponsored athletes showing a 20% improvement in competition results compared to nonsponsored athletes. Furthermore, sponsorship financial support improved training opportunities and access to elite facilities, which was shown to increase athletes’ performance by 15%. However, some challenges related to sponsorship obligations, such as marketing commitments, were highlighted by athletes, underscoring the pressures that sponsorship can introduce. The implications of this study suggest that effective sponsorship strategies can play a vital role in an athlete’s career development, offering not only financial stability but also opportunities for personal branding and increased community engagement. Another implication is a possible consideration for legislators in the context of preparing a legislative framework enabling tax or other benefits for companies and organisations sponsoring or supporting these young athletes. More research is recommended to explore the long-term impact of sponsorship on athlete mental health and career sustainability, as well as the differences in sponsorship effects across various sports disciplines.

In this review are developed insights from the current research work to develop the concept of functional materials. This is understood as real modified substrates for varied applications. So, functional and modified substrates focused on nanoarchitectures, microcapsules, and devices for new nanotechnologies highlighting life sciences applications were revised. In this context, different types of concepts to proofs of concepts of new materials are shown to develop desired functions. Thus, it was shown that varied chemicals, emitters, pharmacophores, and controlled nano-chemistry were used for the design of nanoplatforms to further increase the sizes of materials. In this regard, the prototyping of materials was discussed, affording how to afford the challenge in the design and fabrication of new materials. Thus, the concept of optical active materials and the generation of a targeted signal through the substrate were developed. Moreover, advanced concepts were introduced, such as the multimodal energy approach by tuning optical coupling from molecules to the nanoscale within complex matter composites. These approaches were based on the confinement of specific optical matter, considering molecular spectroscopics and nano-optics, from where the new concept nominated as metamaterials was generated. In this manner, fundamental and applied research by the design of hierarchical bottom-up materials, controlling molecules towards nanoplatforms and modified substrates, was proposed. Therefore, varied accurate length scales and dimensions were controlled. Finally, it showed proofs of concepts and applications of implantable, portable, and wearable devices from cutting-edge knowledge to the next generation of devices and miniaturized instrumentation.

This study aimed to determine the socio-economic poverty status of those living in rural areas using data surveys obtained from household expenditure and income. Machine learning-based classification and clustering models were proven to provide an overview of efforts to determine similarities in poverty characteristics. Efforts to address poverty classification and clustering typically involve comprehensive strategies that aim to improve socio-economic conditions in the affected areas. This research focuses on the combined application of machine learning classification and clustering techniques to analyze poverty. It aims to investigate whether the integration of classification and clustering algorithms can enhance the accuracy of poverty analysis by identifying distinct poverty classes or clusters based on multidimensional indicators. The results showed the superiority of machine learning in mapping poverty in rural areas; therefore, it can be adopted in the private sector and government domains. It is important to have access to relevant and reliable data to apply these machine learning techniques effectively. Data sources may include household surveys, census data, administrative records, satellite imagery, and other socioeconomic indicators. Machine learning classification and clustering analyses are used as a decision support tool to gain an understanding of poverty data from each village. These strategies are also used to describe the profile of poverty clusters in the community in terms of significant socio-economic indicators present in the data. Village clusters based on an analysis of existing poverty indicators are grouped into high, moderate, and low poverty levels. Machine learning can be a valuable tool for analyzing and understanding poverty by classifying individuals or households into different poverty categories and identifying patterns and clusters of poverty. These insights can inform targeted interventions, policy decisions, and resource allocation for poverty reduction programs.

This study aims to identify the causes of delays in public construction projects in Thailand, a developing country. Increasing construction durations lead to higher costs, making it essential to pinpoint the causes of these delays. The research analyzed 30 public construction projects that encountered delays. Delay causes were categorized into four groups: contractor-related, client-related, supervisor-related, and external factors. A questionnaire was used to survey these causes, and the Relative Importance Index (RII) method was employed to prioritize them. The findings revealed that the primary cause of delays was contractor-related financial issues, such as cash flow problems, with an RII of 0.777 and a weighted value of 84.44%. The second most significant cause was labor issues, such as a shortage of workers during the harvest season or festivals, with an RII of 0.773. Additionally, various algorithms were used to compare the Relative Importance Index (RII) and four machine learning methods: Decision Tree (DT), Deep Learning, Neural Network, and Naïve Bayes. The Deep Learning model proved to be the most effective baseline model, achieving a 90.79% accuracy rate in identifying contractor-related financial issues as a cause of construction delays. This was followed by the Neural Network model, which had an accuracy rate of 90.26%. The Decision Tree model had an accuracy rate of 85.26%. The RII values ranged from 68.68% for the Naïve Bayes model to 77.70% for the highest RII model. The research results indicate that contractor financial liquidity and costs significantly impact construction operations, which public agencies must consider. Additionally, the availability of contractor labor is crucial for the continuity of projects. The accuracy and reliability of the data obtained using advanced data mining techniques demonstrate the effectiveness of these results. This can be efficiently utilized by stakeholders involved in construction projects in Thailand to enhance construction project management.

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.