Outsourcing logistics operations is a common trend as businesses prioritize core activities. Establishing a sustainable partnership between businesses and logistics service providers requires a systematic approach. This study is needed to develop a more effective and adaptive framework for logistics service provider selection by integrating diverse criteria and decision-making methodologies, ultimately enhancing the precision and sustainability of procurement processes. This study advocate for leveraging industry-based knowledge in procurement, emphasizing the need to define decision-making elements. The research analyzes nearly 300 logistics procurement projects, using a neural network-based methodology to propose a model that aids businesses in identifying optimal criteria for evaluating logistics service providers based on extensive industry knowledge. The goal of this study is to develop and test a practical model that would support businesses in choosing most suitable criteria for selection of logistics service providers based on cumulative market patterns. The results of this study are as follows. It introduces novel elements by gathering and systematizing unique market data using developed data processing methodology. It innovatively classifies decision-making elements, allocating them into distinct groups for use as features in a neural network. The study further contributes by developing and training a predictive model based on a prepared dataset, addressing pre-defined goals, expectations related to green logistics, and specific requirements in the tendering process for selecting logistics service providers. Study is concluded by summarizing suggestions for future research in area of adopting neural networks for selection of logistics service providers.

The article presents an answer to the current challenge about needs to form methodological approaches to the digital transformation of existing industrial enterprises (EIE). The paper develops a hypothesis that it is advisable to carry out the digital transformation of EIE based on considering it as a complex technical system using model-based system engineering (MBSE). The practical methodology based on MBSE for EIE digital representation creation are presented. It is demonstrated how different system models of EIE is created from a set of entities of the MBSE approach: requirements—unctions—components and corresponding matrices of interconnections. Also the principles and composition of tasks for system architectures creation of EIE digital representation are developed. The practical application of proposed methodology is illustrated by the example of an existing gas distribution station.

Sustainable hybrid education is an educational approach that combines multiple kinds of instruction. Online education and traditional face-to-face education will be implemented in tandem to propel the educational process towards contemporary approaches, with the aim of achieving high-quality outcomes and staying abreast of scientific and technical advancements. The objective of this study is to determine the correlation between hybrid education, which is a sustainable model, and the academic performance of graduate students in select Egyptian universities, based on international quality criteria. The study employed a descriptive analytical methodology, and data was collected using a meticulously designed computerized questionnaire, whose validity and reliability were verified using proper statistical techniques. The study sample comprised 2235 postgraduate students enrolled in Egyptian universities, specifically Cairo, Helwan, and Ain Shams. The study’s findings determined that the extent of hybrid education and the efficacy of the procedure. The sample members possess a high level of education, and hybrid education has a significant positive influence on the quality of the educational process. Hybrid education mostly impacts the academic components, and there are variations among universities in implementing hybrid education, with Ain Shams University being particularly favorable towards it. The study proposed enhancing the university’s human resources for students, faculty, and staff, as well as assuring the availability of diverse gadgets and resources utilized in the hybrid education setting.

This research was conducted using a survey research method to investigate the influence of Artificial Intelligence (AI) on Nigerian students' academic performances in tertiary institutions. Nigerian tertiary institutions have an estimated population of about 2.5 million students across the universities, polytechnics, monotechnics, and colleges of education. A sample size of 509 was used. The researchers adopted an online questionnaire (Google Form) to administer questions to respondents across Nigeria to elicit responses from the respondents bordering on their awareness and the use of AI and its attendant impacts on their academic performance. Five research objectives were raised for the proper investigation of this study. From the findings of the study, the researchers found that the majority of Nigerian students use AI and that AI has positive impacts on the educational performance of Nigerian students. It was also found that Nigerian students have training on the use of AI for educational purposes and that they are more familiar with Snapchat AI and ChatGPT. Conclusively, AI is useful to students in the sense that it enhances their knowledge of their courses, improves their learning and speaking skills, and helps them to have a quick understanding of their course by way of simplifying technical aspects of their courses. The researchers therefore recommend as follows: Nigerian tertiary institutions should formally train students as well as teachers on the use of AI for academic purposes so that they can understand the ethical implications of the use of AI. Using AI for writing could be interpreted to mean examination malpractice, and this should not be condoned in the educational sector; however, at the moment, a small number of students used AI for examinations. Albeit, the appropriate use of AI should be fully integrated into Nigerian tertiary institutions' curricula.

Purpose: This research aims to unravel the intricate dynamics that connect economic status with individuals’ engagement in dance training institutes. Focusing on the affordability of classes, access to resources, awareness, cultural background, and geographic location, the study seeks to provide a nuanced understanding of how economic considerations influence various facets of engagement within the dance community. Method: Conducted through 13 semi-structured interviews, this research adopts a qualitative approach to explore the multi-faceted relationships between economic status and dance engagement. Thematic analysis, structured in three steps, is employed to uncover patterns, themes, and insights within the qualitative data. Findings: The study uncovers a myriad of findings that illuminate the impact of economic factors on dance engagement. Affordability emerges as a significant barrier, influencing access to classes and participation in competitions or performances. Access to resources, including studio space and trained instructors, proves pivotal in shaping individuals’ experiences within dance education. Awareness and exposure play crucial roles, with limited exposure hindering engagement, while the cultural background and geographic location intersect with economic considerations, shaping preferences and opportunities within the dance community. Originality/Significance: This research contributes to the field by offering a focused exploration of economic influences within the dance community. The originality lies in its holistic approach, considering the interconnected nature of affordability, access to resources, awareness, cultural background, and geographic location. From a policy and institutional standpoint, the findings have practical implications, guiding initiatives to address disparities and foster a more accessible and supportive environment within dance training institutes.

This study aims to identify the risk factors causing the delay in the completion schedule and to determine an optimization strategy for more accurate completion schedule prediction. A validated questionnaire has been used to calculate a risk rating using the analytical hierarchy process (AHP) method, and a Monte Carlo simulation on @RISK 8.2 software was employed to obtain a more accurate prediction of project completion schedules. The study revealed that the dominant risk factors causing project delays are coordination with stakeholders and changes in the scope of work/design review. In addition, the project completion date was determined with a confidence level of 95%. All data used in this study were obtained directly from the case study of the Double-Double Track Development Project (Package A). The key result of this study is the optimization of a risk-based schedule forecast with a 95% confidence level, applicable directly to the scheduling of the Double-Double Track Development Project (Package A). This paper demonstrates the application of Monte Carlo Simulation using @RISK 8.2 software as a project management tool for predicting risk-based-project completion schedules.