The article examines the role of electronic arbitration in settling commercial disputes. The article relies on the analytical approach to study legal texts and the comparative approach to examine the rules of international law and national laws in the field of electronic arbitration. In addition, the article discusses the concept of electronic arbitration and its distinction from traditional forms of arbitration. The article also explains the legal provisions related to it, especially those related to electronic arbitration agreements. Finally, the article explains the challenges related to its implementation how to take advantage of its benefits.

The objective of this work was to analyze the effect of the use of ChatGPT in the teaching-learning process of scientific research in engineering. Artificial intelligence (AI) is a topic of great interest in higher education, as it combines hardware, software and programming languages to implement deep learning procedures. We focused on a specific course on scientific research in engineering, in which we measured the competencies, expressed in terms of the indicators, mastery, comprehension and synthesis capacity, in students who decided to use or not ChatGPT for the development and fulfillment of their activities. The data were processed through the statistical T-Student test and box-and-whisker plots were constructed. The results show that students’ reliance on ChatGPT limits their engagement in acquiring knowledge related to scientific research. This research presents evidence indicating that engineering science research students rely on ChatGPT to replace their academic work and consequently, they do not act dynamically in the teaching-learning process, assuming a static role.

 This article provides an account of the tourism in Petra encompassing its development from the time of the Nabataean Kingdom until the early 20th century. It delves into the factors that sparked tourism travel routes taken, security measures implemented, and influential individuals who have shaped Petra’s tourism history. Located at a juncture in the Middle East, Petra has consistently fascinated people with its sense of adventure. The city’s historical importance as a trade hub and a melting pot for cultural exchanges during the Nabataean era laid a strong foundation for its enduring charm. The skillful navigation of trade routes and effective marketing strategies employed by the Nabataean Kingdom played a role in establishing Petra as an irresistible destination for travelers. Supported by findings and ancient records it becomes evident that extensive trade networks flourished during this period highlighting the city’s role in the region. Its allure transcended generations captivating observers from Greece to its rediscovery by Burckhardt (1818–1897).

This study conducts a comparative analysis of various machine learning and deep learning models for predicting order quantities in supply chain tiers. The models employed include XGBoost, Random Forest, CNN-BiLSTM, Linear Regression, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), Bidirectional LSTM (BiLSTM), Bidirectional GRU (BiGRU), Conv1D-BiLSTM, Attention-LSTM, Transformer, and LSTM-CNN hybrid models. Experimental results show that the XGBoost, Random Forest, CNN-BiLSTM, and MLP models exhibit superior predictive performance. In particular, the XGBoost model demonstrates the best results across all performance metrics, attributed to its effective learning of complex data patterns and variable interactions. Although the KNN model also shows perfect predictions with zero error values, this indicates a need for further review of data processing procedures or model validation methods. Conversely, the BiLSTM, BiGRU, and Transformer models exhibit relatively lower performance. Models with moderate performance include Linear Regression, RNN, Conv1D-BiLSTM, Attention-LSTM, and the LSTM-CNN hybrid model, all displaying relatively higher errors and lower coefficients of determination (R²). As a result, tree-based models (XGBoost, Random Forest) and certain deep learning models like CNN-BiLSTM are found to be effective for predicting order quantities in supply chain tiers. In contrast, RNN-based models (BiLSTM, BiGRU) and the Transformer show relatively lower predictive power. Based on these results, we suggest that tree-based models and CNN-based deep learning models should be prioritized when selecting predictive models in practical applications.

This study was designed to study the push and pull motivational factors affecting the foreign backpackers travel behavior towards Full Moon Party in Koh Phangan District, Surat Thani Province. In the sample 300 foreign backpackers aged 18 or older were included, who came to attend the Full Moon Party solely for vacation purposes and not for any work or income generating activities. The study was executed using a structured questionnaire. The statistical tools for the analysis of the data included, but were not limited to, frequency counts, computed percentages, means, standard deviations, chi-square analysis, one- way ANOVA, and Pearson correlation at the 0.05 level of significance. The research demonstrated that with respect to the first-time foreign visitors in Thailand to attend the Full Moon Party, then, they have habitually stayed at the resorts and the bungalows. It was a general observation that such visitors preferred to seek out information on the Internet, social websites as well as tourism websites. Their activities included horse riding, general activities, seeing natural sights including waterfalls and mountains, going for mountain hikes, participating in physically hard and risky outdoors activities, and nighttime activities. Tourists are sufficiently motivated to visit Thailand for its various appealing attributes, as revealed by the analysis. Furthermore, 10 motivational components were identified with 24 variables; Push Motivation Components: (1) Escape and Novelty Seeking, (2) Feel Free, (3) Open the World, and (4) Social Need. Pull Motivation Components: (1) Party, (2) Unique, (3) Only for Myself, (4) Sea Lover, (5) Diversity, and (6) Loner. Demographic characteristics for example gender, age, marital status, education level, occupation, and place of residence were also studied. The push factors, as well as the pull factors of travel, were found to co-relate with the behavior of female foreign backpackers on the other hand where both were significant.

This study aimed to assess the influence of awareness and health habituation techniques, student management activities, the role of stakeholders, and the character of healthy living on health independence. The method used in this study is quantitative with descriptive test analysis techniques, partial t statistics and F test. This research was conducted in elementary schools in East Java Province, consisting of 92 elementary schools in 5 regions at East Java. Samples were taken using purposive techniques, and the number of samples was 348 people, consisting of principals, teachers and students. The results found that awareness and health habituation techniques have a significant influence on the character of healthy life of students, student management activities have a significant influence on the character of healthy life, the role of stakeholders has a significant influence on the character of healthy life, awareness and health habituation technique have a significant influence on health independence, student management activities have a significant influence on health independence, the role of stakeholders has a significant influence on health independence, the character of healthy living has a significant effect on health independence, and student management activities and the role of stakeholders have a significant effect on the character of healthy life, and have a significant impact on health independence.