This article scrutinizes the multifaceted challenges inherent in intergovernmental coordination across various sectors, with a particular emphasis on sustainable development and entrepreneurial activity within the Republic of Moldova. It argues that despite the existence of intergovernmental cooperation, it often manifests as deficient, contradictory, incomplete, and inefficient. Through a meticulous analysis, this study delineates the roles of pertinent authorities and institutions in fostering the sustainable development of entrepreneurial activities, identifying critical inter-institutional coordination issues and challenges. The discourse extends to examining institutional processes and the extent to which policies, laws, and international standards are implemented to nurture and sustain business activities. Moreover, the paper explores various strategies to cultivate responsible, transparent, and effective dialogue between institutions, thereby promoting innovative practices, expanding cooperation, and fostering partnerships with national and civil society organizations, including international bodies.

Luxembourg institutions have the opportunity to reconcile environmental goals with financial stability by implementing Green Fintech solutions, as the banking sector increasingly recognizes the importance of sustainability. This study employs a quantitative approach and analyzes data collected from 150 participants working in the banking industry of Luxembourg. The research aims to assess the consequences of adopting Green Fintech on sustainable development. Banking institutions can boost their financial resilience and mitigate climate-related risks by adopting Green Fintech, which improves their sustainability. The paper emphasizes the importance of Green Fintech in the Luxembourg banking sector for advancing sustainable development goals. To effectively address the increasingly complex environmental concerns, it is crucial to embrace innovative Fintechs.

This paper aims to explore how developing countries like Indonesia have an approach to managing talent to enhance career development using an application system. The application of talent management in the career development of civil servants in Indonesia includes planning, implementing, monitoring, and evaluating career development. Talent management is essential for the government sector and can help improve employee quality, organizational performance, and the achievement of human potential. This research aims to examine the application of talent management in organizations and develop a state civil apparatus information system (SI-ASN) to support the career development process of civil servants. The research methods used include library research and field research, including interviews with competent officials in West Java Province as primary data. The qualitative data was collected in 2022–2023. The results of this study show that the application of talent management for civil servants in Indonesia is considered appropriate, as it directs employees to positions that are in line with their qualifications, competencies and performance. However, it requires an improvement in the methods used, particularly for competency tests, which may be conducted with new methods that are more efficient in terms of budget and time. The study concluded that the application of talent management in the career development of civil servants in Indonesia has a positive impact on the quality of leaders and organizations because it ensures that the appointed leaders are the most competent ones in the field and shows the importance of talent management in succession planning and the career development of civil servants.

This study examines the interaction between foreign direct investment (FDI), idiosyncratic risk, sectoral GDP, economic activity, and economic growth in ASEAN countries using structural equation modeling (SEM) performed using AMOS software. The analysis uses data from the ASEAN Statistics Database 2023 to distinguish the significant direct and indirect impacts of FDI on idiosyncratic risks, sectoral GDP, economic activity and aggregate economic growth can. ASEAN, which includes ten Southeast Asian countries, has experienced rapid economic growth and increasing integration in recent decades, making it an interesting area to study these relationships. The study covers a comprehensive period to capture trends and differences among ASEAN member states. Applying SEM with AMOS allows a detailed examination of complex relationships between important economic variables. The results show a clear link between FDI inflows, idiosyncratic risks, industry GDP performance, economic activity, and overall economic growth. More specifically, FDI inflows have a notable direct influence on idiosyncratic risks, which then impact GDP growth by sector, and the level of economic activity and ultimately contribute to economic growth trends. economy more broadly in ASEAN countries. These findings highlight the importance of understanding and effectively managing the dynamics between FDI and various economic indicators to promote sustainable economic development across ASEAN. This information can inform policymakers, investors, and stakeholders in developing targeted strategies and policies that maximize the benefits of FDI while minimizing related risks to promote strong and inclusive economic growth in the region. This study highlights the multifaceted relationships in the ASEAN economic context, emphasizing the need for strategic interventions and policy frameworks to exploit the potential of foreign investment directed at ASEAN, to the Sustainable Development Goals and long-term economic prosperity in the region.

An appraisal of the groundwater potential of Alex Ekwueme Federal University Ndufu Alike was carried out by integrating datasets from geology, geographic information system and electrical resistivity survey of the area. The study area is underlain by the Asu River group of Albian age. The Asu River Group in the Southern Benue Trough comprises of Shales, Limestones and Sandstone lenses of the Abakaliki Formation in Abakaliki and Ikwo areas. The shales are generally weathered, fissile, thinly laminated and highly fractured and varies between greyish brown to pinkish red in colour. Twenty (20) Vertical Electrical Sounding data were acquired using SAS 1000 ABEM Terrameter and processed to obtain layer parameters for the study area. A maximum current electrode spacing (AB) of 300 meters was used for data acquisition. Computer aided iterative modelling using IPI2 Win was used to determine layer parameters. In-situ Hydraulic Conductivity measurements at seven parametric locations within the study area were conducted and integrated with Electrical Resistivity measurements to determine aquifer parameters (e.g., Hydraulic conductivity and Transmissivity) in real time. This technique reduces the attendant huge costs associated with pumping tests and timelines required to carry out the technique. Accurate delineation of aquifer parameters and geometries will aid water resource planners and developers on favourable areas to site boreholes in the area. Several correlative cross-sections were generated from the interpreted results and used to assess the groundwater potential of the study area. Results show that the resistivity of the the aquifer ranges from 7.3 Wm–530 Wm while depth to water ranges from 11.4 m to 55.3 m. Aquifer thicknesses range from 8.7 m at VES 5 to 36.3 m at VES 6 locations. Hydraulic conductivity ranges from 1.55 m/day at VES 15.18, and 19 locations to 9.8 m/day at VES 3 and 4 locations respectively. Transmissivity varies from 17.48 m²/day at VES 19 to 98 m²/day at VES 3 locations respectively. Areas with relatively high transmissivities coupled with good aquifer thicknesses should be the target of water resource planners and developers when proposing sites for drilling productive boreholes within Alex Ekwueme federal University Ndufu Alike.

Breast cancer was a prevalent form of cancer worldwide. Thermography, a method for diagnosing breast cancer, involves recording the thermal patterns of the breast. This article explores the use of a convolutional neural network (CNN) algorithm to extract features from a dataset of thermographic images. Initially, the CNN network was used to extract a feature vector from the images. Subsequently, machine learning techniques can be used for image classification. This study utilizes four classification methods, namely Fully connected neural network (FCnet), support vector machine (SVM), classification linear model (CLINEAR), and KNN, to classify breast cancer from thermographic images. The accuracy rates achieved by the FCnet, SVM, CLINEAR, and k-nearest neighbors (KNN) algorithms were 94.2%, 95.0%, 95.0%, and 94.1%, respectively. Furthermore, the reliability parameters for these classifiers were computed as 92.1%, 97.5%, 96.5%, and 91.2%, while their respective sensitivities were calculated as 95.5%, 94.1%, 90.4%, and 93.2%. These findings can assist experts in developing an expert system for breast cancer diagnosis.