Accurate demand forecasting is key for companies to optimize inventory management and satisfy customer demand efficiently. This paper aims to Investigate on the application of generative AI models in demand forecasting. Two models were used: Long Short-Term Memory (LSTM) networks and Variational Autoencoder (VAE), and results were compared to select the optimal model in terms of performance and forecasting accuracy. The difference of actual and predicted demand values also ascertain LSTM’s ability to identify latent features and basic trends in the data. Further, some of the research works were focused on computational efficiency and scalability of the proposed methods for providing the guidelines to the companies for the implementation of the complicated techniques in demand forecasting. Based on these results, LSTM networks have a promising application in enhancing the demand forecasting and consequently helpful for the decision-making process regarding inventory control and other resource allocation.

Innovation management is an organizational iterative process of seeking and selecting new opportunities and ideas, implementing them, and capturing value from the results obtained. In the defense sector, due to the increasing interdependence between military capabilities and technology, countries have adopted innovation management approaches to drive the modernization of their defense industrial bases, promoting the development and integration of advanced technologies. This study presents an original systematic literature review on innovation management approaches applied to defense in developing countries. After the phases of identification and screening, 62 documents both from academic and gray literature were analyzed and categorized into 22 distinct approaches. The advantages, disadvantages, contexts, and potential applications of each approach were discussed. The findings show that the appropriate use of these approaches can strengthen the innovation capacity and technological independence of late-industrializing countries, consolidating their position in the global defense landscape and ensuring their sovereignty and continuous technological progress.

This study examines the crucial role of digital marketing in promoting sustainable tourism in the villages of Bali. It adopts a mixed methods approach, using qualitative and quantitative data collection and analysis. The qualitative data were obtained from semi-structured interviews with management teams who have experience in implementing digital marketing strategies for village tourism. The interviewees were selected using a purposive sampling technique. The quantitative data were gathered from questionnaires distributed to domestic tourists who visited the villages. The questionnaires measured the tourists’ perceptions of digital marketing as a tool for village tourism marketing. The study found that digital marketing plays a vital role in promoting tourism villages, as most tourists learned about the villages through online media. The study also identified five dimensions of digital marketing, namely website media, social media, search engines, email marketing, and online advertising, which have potential effects on the sustainability of tourism villages. The study conducted statistical tests to examine the effects of 20 indicators of digital marketing on village tourism marketing. The results showed that 16 indicators had a significant positive effect, while four indicators had no effect. These findings suggest that digital marketing is an effective way to market tourism villages and enhance their sustainability.

This study investigates the impact of the Belt and Road Initiative (BRI) on the construction sector in Southeast Asia, focusing on Thailand, Malaysia, and Cambodia. Qualitative research approach is used to analyze the implications of Chinese investments in these countries, exploring both the opportunities and challenges faced by Chinese investors. Key research questions address the resilience of the construction sector, the obstacles encountered by investors, and the influence of policy on the construction business. Through interviews with CEOs and senior managers of major construction companies and a review of relevant documents, the study uncovers the economic and geopolitical motivations behind China’s BRI strategy. The findings reveal significant insights into the benefits and drawbacks of BRI financing, providing recommendations for overcoming challenges and leveraging future opportunities in Southeast Asian construction sectors.

A gradually detailed geophysical investigation took place on Ancient Marina territory. In that area was extended Ancient Tritaea, according to responsible Archaeological Services. The first approach had been attempted since 1988 by applied electric mapping based on a twin-probe array. Later, the survey extended to the peripheral zone under the relative request from the 6th Archaeological Antiquity. A new approach was implemented by combining three different geophysical techniques, like electrical mapping, total intensity, and vertical gradient. These were applied on discrete geophysical grids. Electric mapping tried to separate the area into low and high-interest subareas according to soil resistance allocation. That technique detected enough geometrical characteristics, which worked as the main lever for the application of two other geophysical techniques. The other two techniques would be to certify the existence of geometrical characteristics, which divorced them from geological findings. Magnetic methods were characterized as a rapid technique with greater sensitivity in relation to electric mapping. Also, vertical gradient focuses on the horizontal extension of buried remains. Processing of magnetic measurements (total and vertical) certified the results from electric mapping. Also, both of the techniques confirmed the existence of human activity results, which were presented as a cross-section of two perpendicular parts. The new survey results showed that the new findings related to results from the previous approach. Geophysical research in that area is continuing.

The economy, unemployment, and job creation of South Africa heavily depend on the growth of the agricultural sector. With a growing population of 60 million, there are approximately 4 million small-scale farmers (SSF) number, and about 36,000 commercial farmers which serve South Africa. The agricultural sector in South Africa faces challenges such as climate change, lack of access to infrastructure and training, high labour costs, limited access to modern technology, and resource constraints. Precision agriculture (PA) using AI can address many of these issues for small-scale farmers by improving access to technology, reducing production costs, enhancing skills and training, improving data management, and providing better irrigation infrastructure and transport access. However, there is a dearth of research on the application of precision agriculture using artificial intelligence (AI) by small scale farmers (SSF) in South Africa and Africa at large. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) and Bibliometric analysis guidelines were used to investigate the adoption of precision agriculture and its socio-economic implications for small-scale farmers in South Africa or the systematic literature review (SLR) compared various challenges and the use of PA and AI for small-scale farmers. The incorporation of AI-driven PA offers a significant increase in productivity and efficiency. Through a detailed systematic review of existing literature from inception to date, this study examines 182 articles synthesized from two major databases (Scopus and Web of Science). The systematic review was conducted using the machine learning tool R Studio. The study analyzed the literature review articled identified, challenges, and potential societal impact of AI-driven precision agriculture.