Technology development in the agricultural sector is important in the development of Thailand’s economy. The purpose of this research was to study the approach of guidelines for future agricultural technology development to increase productivity in the Agricultural sector in order to develop a structural equation model. The research applied mixed-methodology. Qualitative research by in depth interview from 9 experts and focus group with 11 successful businesspersons for approve this model. The quantitative data gather from firm, in the 500 of agricultural sector by using questionnaire, using statistical tests of descriptive analysis, inferential analysis, and multivariate analysis. The research found guidelines for future agricultural technology development to increase productivity in the Agricultural sector composed of 4 latent. The most important item of each latent were as following: 1) Agrobiology Technology (= 4.41), in important item as choose seeds that for disease resistance and tolerate the environment to suit the cultivation area, 2) Environmental Assessment (= 4.37),, in important item as survey of cultivated areas according to topography with geographic information system, 3) Agricultural Innovation (= 4.30), in important item as technology reduces operational procedures, reduce the workforce and can reduce operating costs, and 4) Modern Management Systems (= 4.13), in important item as grouping and manage as a cooperative to mega farms. In addition, the hypothesis test found that the difference in manufacturing firm sizes. Medium and Small size and large size revealed overall aspects that were significantly different at the level of 0.05. The analysis of the developed structural equation model found that there was in accordance and fit with the empirical data and passed the evaluation criteria. Its Chi-square probability level, relative Chi-square, the goodness of fit index, and root mean square error of approximation were 0.062, 1.165, 0.961, and 0.018, respectively.

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.

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.

In recent times, there has been a surge of interest in the transformative potential of artificial intelligence (AI), particularly within the realm of online advertising. This research focuses on the critical examination of AI’s role in enhancing customer experience (CX) across diverse business applications. The aim is to identify key themes, assess the impact of AI-powered CX initiatives, and highlight directions for future research. Employing a systematic and comprehensive approach, the study analyzes academic publications, industry reports, and case studies to extract theoretical frameworks, empirical findings, and practical insights. The findings underscore a significant transformation catalyzed by AI integration into Customer Relationship Management (CRM). AI enables personalized interactions, fortifies customer engagement through interactive agents, provides data-driven insights, and empowers informed decision-making throughout the customer journey. Four central themes emerge: personalized service, enhanced engagement, data-driven strategy, and intelligent decision-making. However, challenges such as data privacy concerns, ethical considerations, and potential negative experiences with poorly implemented AI persist. This article contributes significantly to the discourse on AI in CRM by synthesizing the current state, exploring key themes, and suggesting research avenues. It advocates for responsible AI implementation, emphasizing ethical considerations and guiding organizations in navigating opportunities and challenges.

This article aims to describe and analyze pattern of management learning communities in frontier area Indoensia-Philippines. The relationship between Indonesia-Phlippines in frontier area represents a unique intersection culture and dynamic interplay onf interaction. The people in frontier area were relating by the historical events in the past. This article using historical methods; heuristic, critics/verification, interpretation and historiography were to emphasize the utilization of primary sources. The primary source collected from the oral tradition between Indonesia-Philippines people in frontier area. This article employs a social scientific approach to elucidate the cultural relationships within border communities. Cultural relationships are indicative of an extensive process that exerts influence on communal living practices in the management of their existence as a unique identity. This study provides a comprehensive analysis of the cultural relations in the frontier area between Indonesia and the Philippines. The findings offer insights into the intricate interplay of factors shaping cultural dynamics in border regions, contributing to a deeper understanding of cross-border interactions and the construction of cultural identities.

Liquid Metal Battery (LMB) technology is a new research area born from a different economic and political climate that has the ability to address the deficiencies of a society where electrical energy storage alternatives are lacking. The United States government has begun to fund scholarly research work at its top industrial and national laboratories. This was to develop Liquid Metal Battery cells for energy storage solutions. This research was encouraged during the Cold War battle for scientific superiority. Intensive research then drifted towards high-energy rechargeable batteries, which work better for automobiles and other applications. Intensive research has been carried out on the development of electrochemical rechargeable all-liquid energy storage batteries. The recent request for green energy transfer and storage for various applications, ranging from small-scale to large-scale power storage, has increased energy storage advancements and explorations. The criteria of high energy density, low cost, and extensive energy storage provision have been met through lithium-ion batteries, sodium-ion batteries, and Liquid Metal Battery development. The objective of this research is to establish that Liquid Metal Battery technology could provide research concepts that give projections of the probable electrode metals that could be harnessed for LMB development. Thus, at the end of this research, it was discovered that the parameter estimation of the Li//Cd-Sb combination is most viable for LMB production when compared with Li//Cd-Bi, Li-Bi, and Li-Cd constituents. This unique constituent of the LMB parameter estimation would yield a better outcome for LMB development.