Periodicals of Engineering and Natural Sciences (PEN - International University of Sarajevo)
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Production rate optimization through discrete-event simulation: A case study at pharmaceutical industry in Iraq
Get PDFUsing Samarra Drug Industry (SDI) as a case study, this research creates and implements a discrete-event simulation (DES) to find and fix production bottlenecks in Iraq\u27s pharmaceutical manufacturing sector. The goal is to reduce cycle time, regulate work-in-process (WIP), and increase sustainable throughput without sacrificing regulatory compliance. By fitting distributions and doing goodness-of-fit tests, we can predict stochastic processing, setup, failure, and cleaning behaviors using detailed time-study observations and historical production records from critical phases such as granulation, tablet compression, coating, and packing. A two-stage experimental technique is supported by a verified baseline model that is in accordance with seen key performance metrics (throughput, WIP, cycle time, resource utilization, and overall equipment effectiveness). Prior to that, a screening design is used to find high-leverage elements in buffer capacity, setup time reductions, personnel configurations, and preventive maintenance schedules. Also, under realistic shift schedules and demand unpredictability, simulation-optimization (genetic algorithm/Pareto search) investigates the trade-offs between throughput maximization, work-in-progress limitations, and lead-time goals. The reliability is tested by conducting sensitivity studies to factors including cleaning intervals, product mix, and equipment downtime. The results show that the production rate may be significantly increased without sacrificing quality control or regulatory standards. They also show how the timing of maintenance, setup efficiency, and buffer location interact critically. By expanding DES practice through the integration of bottleneck analytics with optimization under industry-specific operational limitations, this article presents a realistic improvement roadmap for Iraq\u27s Samarra Drug Industry. For quick use, we offer managerial implications and a roadmap for gradual deployment
The impact of technological change on the transformation of global and local markets in countries with economies in transition
Get PDFThis systematic review analyzes the impact of technological change on market transformation in transition economies, focusing on Ukraine through a synthesis of 85 studies (2010–2025). Guided by PRISMA methodology, the study integrates econometric modeling, sectoral trends, and regional disparities to reveal asymmetric technological adoption. Key findings indicate digital finance and agriculture lead with 45% and 30% adoption rates, contributing 1.8% and 1.2% to GDP, respectively, while manufacturing lags at 8% adoption due to institutional and infrastructural gaps. Econometric results demonstrate that a 1% rise in technological adoption drives 0.58% GDP growth, escalating to 0.76% in high-governance regions like Lviv, where blockchain reduced land fraud by 40%, a novel quantification of governance-technology synergy. Conversely, conflict-affected Donetsk saw -1.5% GDP growth despite partial tech adoption, underscoring governance’s critical moderating role, a previously underexplored factor in transitional contexts. Rural-urban divides persist, with 60% of villages lacking 4G coverage and only 12% of workers trained in AI. The study highlights Ukraine’s dual role as a tech innovator and a cautionary tale of geopolitical and institutional constraints, offering a first-of-its-kind synthesis of crisis-driven innovation and spatial inequities. Practical recommendations include streamlining regulations, investing $500 million in rural broadband, and reskilling 100,000 workers by 2025. By validating endogenous growth theory’s emphasis on localized ecosystems and Schumpeterian disruption, this study pioneers a policy framework tailored to transitional economies, bridging theoretical rigor with actionable metrics for equitable growth
Application of machine learning algorithms to predict mechanical properties of aluminum alloys
Get PDFAlloys used in aerospace, automotive industries and structural engineering have good strength to specific weight ratio, corrosion resistance and workability. Mechanical properties like tensile and yield strengths, elongation and hardness are vital to ensure integrity of structures. However, conventional techniques in measuring these properties are time consuming, tedious and destructive. This paper providesa framework in which limited experimental data & Machine Learning algorithms are employed to evaluate the important mechanical properties of aerospace grade aluminum alloys. Data set of alloy compositions as well as processing parameters were obtained through material libraries to train and validate through techniques like Random Forest Regression (RFR), Artificial Neural Networks (ANN) and Support Vector Regression. The tensile strength was predicted with the best results for R – Squared & Root Mean Squared Errors were Coefficient of Determination R2= 0.96, RMSE = 12.4 MPa using ANN and RFR were more efficient in predicting the elongation (R2>0.93). The given approach demonstrated a better performance than the ordinary regression approaches with over 20 percent improvement and less experimental work up to about 60%. This approach provides the boosted & scalable approach to greatly increase the pace of material design and mechanical characterization of applications in the aerospace industry
Study of radiation sources of automatic control system of optical parameters of fiber-optic sensors
Get PDFThe prime objective of this experimental study is to check the influence of radiation on the performance and stability of a fiber-optic sensor’s control system. It examines the key issues caused by radiation and proposes solutions to improve the accuracy of measurements. For this test, a model of radiation exposure was created using special gamma and beta rays to observe how they influenced signal flow and the sensors’ reaction. Special ways of analyzing spectra and constant observation systems were used to detect changes and decreases in the optical signals resulting from radiation. As the radiation dose increases, it begins to degrade the quality of the data, ultimately leading to a 15% weakening of the signals at 500 Gy. Nonetheless, the use of special protective materials and programs managed to cut radiation exposure by a staggering 70%. The study demonstrates that enhancing both the radiation-hardened components and dynamic controls in fiber-optic sensors improves their reliability in areas with high radiation exposure. Such discoveries offer significant benefits for nuclear facilities, aerospace, and medical imaging, as resistance to radiation is crucial in these fields. These solutions play a role in strengthening the performance of automatic control for fiber-optic sensors
University students’ perceptions of using generative artificial intelligence tools for learning English language
Get PDFGenerative artificial intelligence (GAI) tools such as ChatGPT, Google Gemini (Bard) and Claude AI have emerged as powerful tools in different aspects of English language learning. These tools provide online learners with personalized, interactive, engaging and productive language learning experiences. Unlike traditional AI, GAI analyses users’ data to create up-to-date and consistent outputs based on previously entered data. The paper explores university students’ perceptions of using GAI tools for learning the English language. The paper examines university students’ perceptions of using GAI tools for learning English. The study was conducted at the International University of Sarajevo in Bosnia and Herzegovina. The research draws on the theoretical models of Borgmann [11], Davis [15], [16], and Shoufan [28]. An adapted survey was distributed to university students enrolled in different programs, and a total number of 226 students participated in the survey (N=226). The research investigated students’ perceptions regarding the awareness of GAI tools, their usefulness, technical usage, and both negative and positive attitudes towards GAI. The independent variables included students’ gender and their field of study. The research results offer insights into current trends in university students’ English language learning and the use of GAI tools, which have become an integral part of university education.
Keywords: Generative artificial intelligence (GAI), Higher education, English language learning, University students’ perceptions
Problems and prospects for the implementation of artificial intelligence in the educational process of Kazakhstani Universities
Get PDFIntegrating artificial intelligence into education offers great prospects that improve the learning process. Individualization of learning approaches and rationalization of resource use are very productive for learning. This study analyzes the key challenges and opportunities for implementing artificial intelligence into the educational system of Kazakhstani universities. The work focuses on analyzing the current situation and ways to improve this process. This article analyzes the problems and prospects for implementing artificial intelligence into education. The aim of the work was to identify the main challenges and opportunities for integrating artificial intelligence into the educational environment of Kazakhstan. The article uses an analytical method. It is aimed at studying the current state of the infrastructure, the readiness of teachers, and the necessary conditions for the effective use of artificial intelligence technologies. The results of the work demonstrate the significant potential of artificial intelligence to improve the educational process. Personalization of learning and simplification of complex scientific concepts are the main advantages. The work highlights key obstacles to implementing artificial intelligence in education in Kazakhstan. Among them is the lack of technical resources and specialized training of teachers. For the full integration of artificial intelligence into the education system of Kazakhstan, there is a need to modernize the infrastructure, improve teacher training, and develop national initiatives
Adaptation of pedagogical approaches to the implementation of intelligent systems in the educational process
Get PDFInteractive systems play an essential role in the modern educational system, but the issue of their optimization remains under-researched. Research purpose. The article aims to analyze the attitudes of teachers and students to the introduction of intelligent systems in the educational process, as well as to identify the main approaches to adapting pedagogical methods and key barriers. Research method. A purposive sample was used to include participants. Two groups were selected: students and teachers (60 participants in total). The main instrument in the study is a cross-sectional survey. Survey period: October – November 2024. Results. The findings show that intelligent learning technologies are known to all participants in the educational process: 32% actively use them, 52% are partially familiar with them, and only 14% determined that they rarely use them. Their effectiveness is undisputed, and their prospects are associated with increasing teaching efficiency. Among the problems in use are the lack of digital literacy, unresolved ethical issues, digital threats, and lack of necessary infrastructure. Recommendations are offered: increasing the motivational component, conducting special courses and training for teachers and students, improving technical support, and overcoming the shortcomings of the legislative framework. Conclusions. An essential potential of intelligent systems is their capabilities in gamification, blended learning, project-based learning, and the use of interactive lectures and seminars. The conclusions indicate that further adaptation of existing pedagogical approaches will require interactivity and adaptability
Modern talent management technologies: corporate analysis of practices and innovative methods in different regions of the world
Get PDFTalent management is an important component of modern management. The purpose of this article is to analyze modern talent management technologies, to study corporate practices and innovative methods in different regions of the world. To accomplish this task, the author used the scientific method of collecting and analyzing PRISMA sources and literature (53 literature items were selected and studied in total). The results revealed the most popular mechanisms of innovative talent management: People analytics, artificial intelligence, gamification, and remote work platforms. At the same time, there are some regional peculiarities in the implementation of talent management, and companies in regional labor markets are adapting to certain rules of the game. However, the gradual impact of globalization may offset such peculiarities. Among the challenges to high-quality talent management, the article examines such factors as active digitalization, socio-political or economic crises, global competition for talent, and regulatory restrictions. The conclusions note that the ways to overcome these challenges depend on the policies of companies and organizations, as well as on the ability to counter new challenges
Development of an expert system based on fuzzy logic as support for heat pipes design
Get PDFHeat pipe design and selection require specialist knowledge due to numerous possible combinations and restrictions that should be taken into account. The general objective of this work is to design a Specialist System that assists future engineers in material and working fluid selection for a suitable heat pipe applica-tion, based on the technical operating requirements. The methodology consisted of a qualitative perspective through interviews with two specialist engineers in the heat pipes area. The resulting information from the interviews was organized into a library, working as a source for the specialist system. In addition, several books from the literature completed the information in the library. Based on the operating conditions and the provided library, the program recommends suitable materials and working fluids and the necessity of porous media for the application, similar to a consult with a heat pipe specialist. The new expert system can be a tool for researchers and engineers in heat pipe design as passive control systems, providing more suitable solutions for each application
Adapting hybrid approaches for electronic medical record management and sharing using blockchain sharding
Get PDFIn the past few years, it is noticed that management and sharing medical records is a key step towards increasing healthcare provider connectivity and making the healthcare system more efficient. The scalability and sustainability issues confer to mismanagement of patient is record and also raised several issues in privacy and security. The study aims to suggest more efficient alternatives for Electronic Healthcare System. Scalability and privacy are the major limitations that existing systems contain so the goal of this study is to define alternatives about how parameters like scalability, usability and data protection could be achieved in an efficient manner for healthcare system. In the healthcare industry, providing accurate, thorough, and up-to-date information on patients is critical. Another feature that allows researchers to consider efficient EHR systems is rapid access to patient records for boosting efficiency and coordination. Blockchain sharding technique is utilized along with hyper-ledger protocols and Proof-of-Authority to carry out our model implementation