Periodicals of Engineering and Natural Sciences (PEN - International University of Sarajevo)
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Evaluative revolution in university education in the age of social networking
Get PDFEvaluation mechanisms are a subject of analysis to strengthen the performance of university students; this study compares two modalities: evaluation through activities on the social network Facebook and assessment through a multiple-choice test. Using a quantitative methodological design with a comparative approach between the two models, grades were collected and entered into the SPSS 26 program as a first step. The study used mean analysis tools such as ANOVA, Levene\u27s test, and Post Hoc (Games-Howell) to measure significant differences between means and variances of the grades of three academic cuts. The target sample consisted of two groups of 44 students from the Unidades Tecnológicas de Santander. The results show that the Facebook evaluation model did not present significant differences between the cuts, which is inferred as homogeneity in the grades. On the other hand, the written evaluation process showed substantial differences between the grades of each cut. As a main finding, it is concluded that the type of evaluation impacts the grades. The dialogic measurement by social networks allows for equity in the assessment of the group, while the written tests generate a differentiation in the students\u27 performance
Integration of digital technologies into the methodology of teacher training in the system of dual education
Get PDFThe problem of introducing digital resources into the dual education system is still not sufficiently covered. This study aims to analyze the effectiveness of digital technologies in training future teachers in the dual education system in the context of the Kazakh educational process. The study involved 2 groups of students: control and experimental. The experimental group was trained in the dual education system using digital technologies. The study was conducted at the following universities: Ozybekali Zhanibekov South Kazakhstan Pedagogical University (41 participants) and Shakarim University of Semey (40 participants). The study used standardized questionnaires and testing methods at two stages (before and after integrating digital tools). The following indicators were analyzed: passive and active activities and creativity. The results emphasize the importance of using LMS, online mentoring, planning and assessment resources, and cloud services. The integration of digital technologies into dual education has positively impacted the level of creativity and reduced the passivity of students in the experimental groups of both universities. However, the activity remained almost unchanged. This identified the need for additional interventions. The conclusions indicate that digital technologies in dual education contribute to the growth of creativity and reduce passivity, which was determined by the results in experimental groups
Blockchain-enabled carbon tracking in the oil Industry: A simulation-based study supporting ESG integration
Get PDFThe oil industry is increasingly being compelled to reconcile the complexity of its carbon-intensive business with Environmental, Social, and Governance (ESG) aims. ESG compliance is being handicapped by existing carbon reporting frameworks that are commonly fragmented, audit-driven, and subject to data falsification. This study explores how the use of blockchain technology can improve data integrity, traceability, and compliance costs and, as a result, transform oil supply chain carbon emissions monitoring. Through simulation-based evaluation, we are comparing systems supported by blockchain to conventional emission reporting systems through performance metrics such as cost of verification, audit lag, and traceability accuracy. To record carbon data at all locations, the simulation is combining smart contracts and decentralized ledger nodes to replicate a regional upstream midstream oil supply chain. In accordance with the study, blockchain integration enhances audit effectiveness by 91%, traceability by 36%, and lowers verification costs up to 70%. The study recognizes blockchain as a key digital infrastructure for sustainable business functioning and gives insightful recommendations to make ESG reporting easier for heavy industries
Identification of energetically critical processes for cocoa production in Santander, Colombia
Get PDFThe intensification of industrial activities in post-harvest cocoa processes has generated a disproportionate increase in energy consumption. The objective of this work is to describe those energetically critical ones that are used in the cocoa production line in Santander so that further research can evaluate the implementa-tion of alternative or renewable energy systems. Through the collection of information and data manage-ment, it is seeking to promote the efficient use of energy based on the NTC ISO 500001. For this end, a description of the processes involved in the production of cocoa was made considering the technical data. Then, the energy consumed by the equipment used in the different processes was determined for 30 days, operating 24 hours a day, assuming power factors of 0.75 and 0.9. Finally, through analysis, calculations, and the application of the Pareto methodology, it was possible to identify that the process with the highest energy consumption is the cocoa refining step, since the equipment used in this process consumes more than 30% of energy from the total energy used for cocoa production
EEG-based image classification using an efficient geometric deep network based on functional connectivity
Get PDFTo ensure that the FC-GDN is properly calibrated for the EEG-ImageNet dataset, we subject it to extensive training and gather all of the relevant weights for its parameters. Making use of the FC-GDN pseudo-code. The dataset is split into a "train" and "test" section in Kfold cross-validation. Ten-fold recommends using ten folds, with one fold being selected as the test split at each iteration. This divides the dataset into 90% training data and 10% test data. In order to train all 10 folds without overfitting, it is necessary to apply this procedure repeatedly throughout the whole dataset. Each training fold is arrived at after several iterations. After training all ten folds, results are analyzed. For each iteration, the FC-GDN weights are optimized by the SGD and ADAM optimizers. The ideal network design parameters are based on the convergence of the trains and the precision of the tests. This study offers a novel geometric deep learning-based network architecture for classifying visual stimulation categories using electroencephalogram (EEG) data from human participants while they watched various sorts of images. The primary goals of this study are to (1) eliminate feature extraction from GDL-based approaches and (2) extract brain states via functional connectivity. Tests with the EEG-ImageNet database validate the suggested method\u27s efficacy. FC-GDN is more efficient than other cutting-edge approaches for boosting classification accuracy, requiring fewer iterations. In computational neuroscience, neural decoding addresses the problem of mind-reading. Because of its simplicity of use and temporal precision, Electroencephalographys (EEG) are commonly employed to monitor brain activity. Deep neural networks provide a variety of ways to detecting brain activity. Using a Function Connectivity (FC) - Geometric Deep Network (GDN) and EEG channel functional connectivity, this work directly recovers hidden states from high-resolution temporal data. The time samples taken from each channel are utilized to represent graph signals on a topological connection network based on EEG channel functional connectivity. A novel graph neural network architecture evaluates users\u27 visual perception state utilizing extracted EEG patterns associated to various picture categories using graphically rendered EEG recordings as training data. The efficient graph representation of EEG signals serves as the foundation for this design. Proposal for an FC-GDN EEG-ImageNet test. Each category has a maximum of 50 samples. Nine separate EEG recorders were used to obtain these images. The FC-GDN approach yields 99.4% accuracy, which is 0.1% higher than the most sophisticated method presently availabl
Personal and technological determinants of electric vehicle adoption in Oman: The moderating role of government innovation capability
Get PDFThe adoption of electric vehicles (EVs) is a crucial step toward sustainable transportation, yet its expansion in emerging markets like Oman faces several challenges. Limited charging infrastructure, technological readiness, and policy support impact the adoption rate. This study investigates the interplay between personal and technological determinants influencing EV adoption in Oman, emphasizing the moderating role of government innovation capability. A structured survey was conducted among 410 decision-makers from key regulatory bodies, and data were analysed using structural equation modelling. The research confirms that social norms, together with perceived usefulness and driver IT competency and system quality, substantially boost EV adoption, but insufficient charging facilities continue as the primary impediment. The capability for government innovation acts as a moderator that alters how social norms, system quality, and charging infrastructure influence the adoption of EVs. The study shows that EV adoption requires both governmental policy intervention and infrastructure development and consumer education programs for successful implementation. This research connects government innovation capability to established models, guiding both policymakers and industry leaders who want to develop a technologically progressive EV ecosystem. Research should expand beyond this study by using multiple stakeholder perspectives and combining longitudinal timeframes and various research methods to achieve better insights into electric vehicle adoption patterns
Application of artificial intelligence in human capital management of the civil service: predicting career trajectories and personalized personnel development
Get PDFCivil service in Kazakhstan faces rigid career structures, inefficient training allocation and limited use of data-driven HR systems. It has national digitalization programs but artificial intelligence (AI) integration in human capital management (HCM) remains underdeveloped. This creates a gap for AI-driven solutions. This study examines the potential of AI and neural networks to modernize HCM through the concept of digital human capital that combines employee digital competencies with institutional AI readiness. For this purpose, current study used a mixed-methods framework by integrating a systematic literature review, qualitative case study analysis, machine learning models (LSTM and K-means) and panel econometric analysis. Results show the LSTM model achieved high predictive accuracy in forecasting career trajectories (F1-score: 0.84) and cluster analysis identified four distinct digital competency groups. Econometric findings revealed a significant positive impact of digital HR tools on employee performance. Qualitative insights indicated moderate institutional readiness, with barriers such as data quality issues and resistance to change. The study advances public administration theory by operationalizing AI-driven personalization in workforce development and recommends investment in interoperable HR systems, AI pilot programs and digital upskilling to enable scalable reform in Kazakhstan and other post-Soviet contexts
Electronic textbooks and their impact on learning in Ukrainian schools
Get PDFThe use of digital materials is a significant part of modern education. The article aims to analyze the impact of using electronic textbooks on students\u27 academic achievements, motivation, and learning levels in Ukrainian schools. The realization of the proposed goal involved using a scientific survey and questionnaire method from two groups of students: experimental (n=125) and control (n=125). The experiment took place from September to December 2024. The study involves 7th-grade students. The results show that electronic textbooks for students demonstrated better learning outcomes than students in the control group. It is indicated that modern e-textbooks hosted on modern learning platforms have simple navigation tools and provide the ability to view structural elements, highlight text, and control knowledge. The study also points to the positive effects of using electronic textbooks. The conclusions state that well-designed and well-written electronic textbooks influence students\u27 willingness to use them
Using artificial intelligence to optimize human resource management processes
Get PDFIncorporating technology into human resource management is at an advanced stage and is revolutionizing the existing environment of human resources. The study examines how human resource management processes can be optimized using artificial intelligence. A survey research design is employed by administering a questionnaire to the participants. The study population comprises employees of top HR outsourcing firms in five different cities in Ukraine. The stratified purposive sampling technique was deployed in drawing samples from the five cities using Raosoft sample size calculator, which gives a sample size of one hundred and sixteen (116) participants. Data collection was achieved with structured questionnaires to provide answers to research questions. We employed descriptive analysis of tables and regression analysis. The result revealed that efficiency at work can be improved with artificial intelligence in human resource management and decision-making. Also, artificial intelligence is germane to the process of human resource management. However, artificial intelligence suffers limitations in the areas of ethical consideration
Digital financial technologies and their impact on sustainable development of regional markets
Get PDFThis study addresses the gap in understanding how digital financial technologies (FinTech) influence sustainable development in regional markets, characterized by urban-rural disparities challenges. Employing a mixed-methods approach from 2020 to 2023, primary data from 55 stakeholders (including FinTech startups, regulators, and SMEs) and secondary sources (government reports and financial transactions) across five regions were analyzed using thematic coding and regression models. Results reveal stark contrasts: urban centers achieve 82% mobile banking penetration and 12% higher SME growth, while rural regions lag at 29% due to infrastructural deficits. In economically challenged areas, FinTech enabled 39% of SMEs to sustain their operations, yet regulatory compliance diverged sharply, with urban areas at 89% and rural areas at 31%. The study advocates for prioritizing rural 4G infrastructure, localized regulatory sandboxes, and public-private partnerships to foster inclusive solutions, rejecting one-size-fits-all models that can exacerbate inequalities