This study aims to identify and the implementation of ASN Management policies on career development aspects based on the merit system in the West Java Provincial Government and 6 Regency/City Governments in West Java Province. The failure of the institutionalization of the meritocratic system in ASN career development is partly triggered by the symptoms of the appointment or selection of officials in the central and regional levels not based on their professionalism or competence except for subjective considerations, political ties, close relationships and even bribery. This study uses a qualitative method with a descriptive approach. The operationalization concept in this study uses Merilee S. Grindle's Policy Implementation theory which consists of dimensions of policy content and its implementation context. The factors that cause the implementation of the policy to be less than optimal include: 1. Uneven understanding of meritocracy; 2. Slowness/unpreparedness in synchronizing central and regional rules/policies; 3. The information integration system between the center and regions has not yet been implemented; 4. Limited supporting infrastructure; 5. Limited permits for related officials; 6. Transparency; 7. Collaboration across units/agencies; 8. External intervention; 9. Use of information systems/technology. To optimize these factors, an Accelerator of Governmental Unit's Success (AGUS) model was created, which is a development of the Grindle policy implementation model with the novelty of adding things that influence implementation, including top leader's commitment and wisdom, effectiveness of talent placement, on-point human development, technology savvy, cross-unit/agency collaboration, and monitoring and evaluation processes.

This study investigates the public’s perceptions of digital innovations in pharmacy, with a focus on health informatics and medication management. Despite the rapid development of these technologies, a comprehensive understanding of how various demographics perceive and interact with them is lacking hence, this research aims to bridge this gap by offering insights into public attitudes and the factors influencing the adoption of digital tools in pharmacy practice, as KSA population and healthcare professionals after Covid-19 has observed the significant potential of digital health. A cross-sectional survey involving 1132 participants was conducted, employing SPSS for data analysis to ensure precise and reliable results. The findings indicate general optimism about the potential of digital innovations to enhance healthcare outcomes but concerns about data privacy and usability significantly affect user acceptance. The researchers recommended tailored educational programs and user-centered design to facilitate the adoption of digital pharmacy innovations. Key contributions include the identification of ‘Ease of Use’ and ‘Data Security and Privacy’ as predominant factors in the adoption of digital health tools.

Identify and diagnosis of homogenous units and separating them and eventually planning separately for each unit are considered the most principled way to manage units of forests and creating these trustable maps of forest’s types, plays important role in making optimum decisions for managing forest ecosystems in wide areas. Field method of circulation forest and Parcel explore to determine type of forest require to spend cost and much time. In recent years, providing these maps by using digital classification of remote sensing’s data has been noticed. The important tip to create these units is scale of map. To manage more accurate, it needs larger scale and more accurate maps. Purpose of this research is comparing observed classification of methods to recognize and determine type of forest by using data of Land Cover of Modis satellite with 1 kilometer resolution and on images of OLI sensor of LANDSAT satellite with 30 kilometers resolution by using vegetation indicators and also timely PCA and to create larger scale, better and more accurate resolution maps of homogenous units of forest. Eventually by using of verification, the best method was obtained to classify forest in Golestan province’s forest located on north-east of country.

The aim of this research is to determine the incidence of socioeconomic variables in migration flows from the main countries of origin that form part of the international South-North migration corridor, such as Mexico, China, India, and the Philippines, during the 1990–2022 period. The independent variables considered are GDP per capita, unemployment, poverty, higher education, and public health, while the dependent variable is migration flows. An econometric panel data model is implemented. The tests conducted indicate that all variables have an integration order of I (1) and exhibit long-term equilibrium. The econometric models used, Dynamic Ordinary Least Squares (DOLS) and Fully Modified Ordinary Least Squares (FMOLS), reveal that unemployment and poverty had the strongest influence on migration flows. In both models, within this international migration corridor, GDP per capita, higher education, and health follow in order of importance.

Fire is one of the most serious hazards, which causes many economic, social, ecological, and human damages every year in the world. Fire in forests and natural ecosystems destroys wood, regeneration, forest vegetation, as well as soil erosion and forest regeneration problems (due to the dryness of the weather and the weakness of the soil). Awareness of the extent of the zones that have been fired is important for forest management. On the other hand, the difficulty of fieldwork due to the high cost and inaccessible roads, etc. reveals the need for using remote sensing science to solve this problem. In this research, MODIS satellite images were used to detect and determine the fire extent of Golestan province forests in northern Iran. MID13q1 and MOD13q1 images were used to detect the normal conditions of the environment. The 15-year time series data were provided for the NDVI and NDMI indicators in 2000-2015. Then, the behavior of indicators in the fire zone was studied on the day after the fire. The burned zones by the fire were specified by determining the appropriate threshold and then, they were compared to long-term normals. In the NDMI and NDVI indicators, the mean of the numeric value threshold limit for determining the burnt pixels was respectively 1.865 and 0.743 of the reduction in their normal long-term period, which are selected as fire pixels. The results showed that the NDMI index could determine the extent of the burned zone with the accuracy of 95.15%.

This paper aims to explore how to build a sustainable peace and development model for China’s peacekeeping efforts through the application of data-driven methods from UN Global Pulse. UN Global Pulse is a United Nations agency dedicated to using big data and artificial intelligence technologies to address global challenges. In this paper, we will introduce the working principles of UN Global Pulse and its application in the fields of peacekeeping and development. Then, we will discuss the current situation of China’s participation in peacekeeping operations and how data-driven methods can help China play a greater role in peacekeeping tasks. Finally, we will propose a sustainable peace and development model that combines data-driven methods with the advantages of China’s peacekeeping efforts to achieve long-term peace and development goals.