The use of geotechnologies combined with remote sensing has become increasingly essential and important for efficiently and economically understanding land use and land cover in specific regions. The objective of this study was to observe changes in agricultural activities, particularly agriculture/livestock farming, in the North Forest Zone of Pernambuco (Mata Norte), a political-administrative region where sugarcane cultivation has historically been the backbone of the local economy. The region’s sugarcane biomass also contributes to land use and land cover observations through remote sensing techniques applied to digital satellite images, such as those from Landsat-8, which was used in this study. This study was conducted through digital image processing, allowing the calculation of the Normalized Difference Vegetation Index (NDVI), the Soil-Adjusted Vegetation Index (SAVI), and the Leaf Area Index (LAI) to assess vegetation cover dynamics. The results revealed that sugarcane cultivation is the predominant agricultural and vegetation activity in Mata Norte. Livestock farming areas experienced a significant reduction over the observed decade, which, in turn, led to an increase in agricultural and forested areas. The most dynamic spatiotemporal behavior was observed in the expansion and reduction of livestock areas, a more significant change compared to sugarcane areas. Therefore, land use and land cover in this region are more closely tied to sugarcane cultivation than any other agricultural activity.

This study considers the relationship between investment in the manufacturing and processing industries and economic growth in Vietnam. This study applies an autoregressive distributed lag (ARDL) model to reassess the long- and short-term relationships between industrial investment and economic growth from 1998 to 2023. It has been found that in both the long and short term, investments in this sector have a positive and significant effect on economic growth. The results further show that labor negatively affects growth in the long run, but is favorable in the short run. The verdict for the role of exports is that more evidence is required before any conclusive analysis can be conducted. Reinvestment in the manufacturing and processing industries for further economic growth is evident in the foregoing analysis. On the other hand, this research provides insight into the optimization of the utilization of resources and future sustainability by the government.

Arabic rhetoric has traditionally relied on ancient texts and human interpretation for teaching purposes. The study investigates ChatGPT’s ability to analyze and interpret Arabic rhetorical devices, specifically examining its capacity to handle cultural and contextual elements in rhetorical analysis. Drawing on institutional implementation frameworks and recent educational technology research, this study examines policy considerations for Arabic rhetoric education in an AI-driven environment, with a particular focus on sustainable digital infrastructure development and systematic reforms needed to support AI integration. The study employed the comparative approach to analyze eight rhetorical examples, including metaphors (“Zaid is a lion”), similes (“Someone is a sea”), and metonymy (“A person full of ash”), then compare ChatGPT’s interpretations with traditional explanations from classical Arabic rhetoric texts, particularly “Dala’il al-I’jaaz” by al-Jurjani. The results demonstrate that ChatGPT can provide basic interpretations of simple rhetorical devices, but it struggles with understanding cultural contexts and multiple layers of meaning inherent in Arabic rhetoric. The findings indicate that AI tools, despite their potential for modernizing rhetoric education, currently serve best as supplementary teaching aids rather than replacements for traditional interpretative methods in Arabic rhetoric instruction.

Named Entity Recognition (NER), a core task in Information Extraction (IE) alongside Relation Extraction (RE), identifies and extracts entities like place and person names in various domains. NER has improved business processes in both public and private sectors but remains underutilized in government institutions, especially in developing countries like Indonesia. This study examines which government fields have utilized NER over the past five years, evaluates system performance, identifies common methods, highlights countries with significant adoption, and outlines current challenges. Over 64 international studies from 15 countries were selected using PRISMA 2020 guidelines. The findings are synthesized into a preliminary ontology design for Government NER.

Despite having a strategic position in supporting the Indonesian economy, the productivity of SME’s is still suboptimal. The increase in the number of SME’s has not been followed by increased competitiveness due to various limitations experienced by this sector. In an effort to provide a comprehensive picture in improving the performance of food processing SME’s in developing countries such as Indonesia, the purpose of this study was to examine the function of product innovation, internet marketing, and brand identity in shaping competitive advantage having an impact on business performance. This research is focused on food processing SME’s in the city of Bogor. The number of samples used was 100 SME’s. The sampling method used the non-probability sampling method with a snowball sampling technique. The data obtained were analyzed using the Structural Equation Model (SEM). Based on the age characteristic of business actors, the majority of business actors were 40–50 years old, of which 52% had their final formal education at high school level. As many as 61% of respondents had attended business training. Based on the results of the Partially Least Square (PLS) SEM analysis, it was found that product innovation, internet marketing and brand identity all had a significant positive effect on competitive advantage and business performance. The influence of brand identity on competitive advantage had the greatest effect, with a value of 0.451. This study contributes to existing research by examining the determinants of the business performance of processed food SME’s through the holistic model offered. This research is innovative because the business raises new issues related to internet marketing by SME’s and investigates them empirically.

Surveys are one of the most important tasks to be executed to get valued information. One of the main problems is how the data about many different persons can be processed to give good information about their environment. Modelling environments through Artificial Neural Networks (ANNs) is highly common because ANN’s are excellent to model predictable environments using a set of data. ANN’s are good in dealing with sets of data with some noise, but they are fundamentally surjective mathematical functions, and they aren’t able to give different results for the same input. So, if an ANN is trained using data where samples with the same input configuration has different outputs, which can be the case of survey data, it can be a major problem for the success of modelling the environment. The environment used to demonstrate the study is a strategic environment that is used to predict the impact of the applied strategies to an organization financial result, but the conclusions are not limited to this type of environment. Therefore, is necessary to adjust, eliminate invalid and inconsistent data. This permits one to maximize the probability of success and precision in modeling the desired environment. This study demonstrates, describes and evaluates each step of a process to prepare data for use, to improve the performance and precision of the ANNs used to obtain the model. This is, to improve the model quality. As a result of the studied process, it is possible to see a significant improvement both in the possibility of building a model as in its accuracy.