Periodicals of Engineering and Natural Sciences (PEN - International University of Sarajevo)
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Innovative concepts for designing commercial spaces: functionality, adaptability and aesthetics
Get PDFThe importance of adopting new design and architectural solutions represents a critical aspect of contemporary business. This article aims to analyze innovative approaches to commercial space design, considering modern requirements for functionality, adaptability, and aesthetics. To achieve this objective, scientific methods were examined, including the PRISMA framework for literature collection, and a survey was conducted among users and professionals. In total, 41 relevant sources published between 2019 and 2025 were analyzed. The survey included 119 participants: 42 professionals, 39 business owners, and 38 visitors. The methodology employed in this study involved certain limitations that may affect the interpretation of the findings. The sample size was relatively small, and the respondents were geographically limited, which restricted the ability to consider cultural and regional influences on design perception. The results indicated that functionality and aesthetic appeal were the most highly valued aspects of design. Business owners generally expressed satisfaction with both the functional and visual elements of their commercial spaces. Visitors preferred environments that combined modern style, comfort, and intuitive navigation. The study further highlights the growing relevance of flexible spatial solutions that can be easily adapted to evolving functional needs or changing target audiences. The conclusions emphasize the significance of modern style, comfort, intuitive navigation, and the biophilic approach as key elements of innovative commercial landscape design. Future research should investigate the role of digitalization in the development of commercial spaces, given the increasing usefulness of digital tools in optimizing time and space
Demographic and occupational factors predicting personality types among healthcare practitioners: An analysis using structural equation modeling (SEM)
Get PDFPersonality significantly influences healthcare professionals\u27 performance, interpersonal communication, and stress management. While existing literature suggests that individual personality traits may be shaped by demographic and occupational variables, there is a scarcity of studies employing robust statistical modeling in healthcare settings. This study aimed to investigate the structural impact of demographic factors (such as gender, academic qualification, and subspecialty) and professional variables (including workplace, work shifts, weekly working hours, and experience) on the Big Five personality traits among healthcare practitioners using Structural Equation Modeling (SEM). A total of 364 healthcare workers from hospitals, primary health care centers, and emergency clinics participated in the study, with data collected through a structured questionnaire utilizing the Big Five Inventory–2 Short Form (BFI-2-S). The analysis revealed that demographic variables accounted for 11% of the variance in the latent personality construct (R² = 0.11, β = 0.326, p < .001), with a statistically significant structural effect observed (β = 0.326, p < .001), particularly affecting Agreeableness and Open-Mindedness. In contrast, professional variables did not demonstrate a meaningful impact (β = -0.079, p = 0.693). Further regression analyses indicated that gender and academic qualification were significant predictors of Extraversion, Agreeableness, and Neuroticism. These findings suggest that personality is more strongly influenced by demographic background than by occupational conditions among healthcare professionals, emphasizing the importance of incorporating personality-based considerations into recruitment, supervision, and training programs within healthcare institutions. Future research should adopt longitudinal and culturally sensitive approaches to further explore these relationships in diverse clinical contexts.
Personality significantly influences healthcare professionals\u27 performance, interpersonal communication, and stress management. While existing literature suggests that individual personality traits may be shaped by demographic and occupational variables, there is a scarcity of studies employing robust statistical modeling in healthcare settings. This study aimed to investigate the structural impact of demographic factors (such as gender, academic qualification, and subspecialty) and professional variables (including workplace, work shifts, weekly working hours, and experience) on the Big Five personality traits among healthcare practitioners using Structural Equation Modeling (SEM). A total of 364 healthcare workers from hospitals, primary health care centers, and emergency clinics participated in the study, with data collected through a structured questionnaire utilizing the Big Five Inventory–2 Short Form (BFI-2-S). The analysis revealed that demographic variables accounted for 11% of the variance in the latent personality construct (R² = 0.11, β = 0.326, p < .001), with a statistically significant structural effect observed (β = 0.326, p < .001), particularly affecting Agreeableness and Open-Mindedness. In contrast, professional variables did not demonstrate a meaningful impact (β = -0.079, p = 0.693). Further regression analyses indicated that gender and academic qualification were significant predictors of Extraversion, Agreeableness, and Neuroticism. These findings suggest that personality is more strongly influenced by demographic background than by occupational conditions among healthcare professionals, emphasizing the importance of incorporating personality-based considerations into recruitment, supervision, and training programs within healthcare institutions. Future research should adopt longitudinal and culturally sensitive approaches to further explore these relationships in diverse clinical contexts
Validation of a Simulation Model in TRNSYS for an Institutional Photovoltaic System: Case Study at the University of Santander - UDES
Get PDFThe objective of this study is to develop and validate an energy simulation model in TRNSYS to evaluate the performance of the grid-connected photovoltaic system installed in the Guane building of the University of Santander (UDES), located in Bucaramanga, Colombia. A technical characterization of the system was performed, followed by the design of a model in TRNSYS using real configuration, operational, and meteorological data. Subsequently, an experimental validation was carried out based on the comparison between simulated values and measured data during the year 2024, using statistical metrics such as absolute error, relative error, and accuracy. The model achieved an average relative error of 3.3%, with an average absolute difference of 146.47 W, demonstrating an overall agreement of more than 96% between simulated and experimental results. These results show that the model reproduces, with high fidelity, the behavior of the photovoltaic system under real conditions. The validated model represents a useful tool for energy management, maintenance planning, and operational optimization of institutional solar systems, and it is replicable in similar tropical contexts. In addition, it enables the exploration of expansion scenarios and sustainable energy transitions from both academic and technical perspectives
Application of big data for the analysis and optimization of production lines
Get PDFThis research was prompted by the need for and rising interest in the expanding use of big data analytics to optimize production lines in the manufacturing business. The best method to improve productivity is to convert data into an achievable analysis. Production processes are growing increasingly complicated, arising from increased demands. Manufacturers of various sorts of goods are discovering substantial value in big data. The use of big data technologies in production lines and beyond is a recent multidisciplinary study integrating advanced computational analysis. The article gives insights into expanding real-time data analysis skills across numerous industrial industries by presenting a detailed assessment of big data technology and data analytics approaches. Using numerous case studies, this article analyses the vital role and advantages that big data analysis and optimization give manufacturers in increasing the efficiency and productivity of production lines in manufacturing
Immersive learning of the Sustainable Development Goals (SDGs) in literature from 2020 to 2024
Get PDFThis research presents an innovative approach to immersive learning, defining it as an artificial system that simulates key psychological processes, including memory, language, perception, and human intelligence. Within this framework, the Sustainable Development Goals (SDGs) serve as essential reference points for the literature review. A unique aspect of this study is its comprehensive methodology for searching, analyzing, and modeling categories related to immersive learning and the Sustainable Development Goals (SDGs). The research involved a documentary, transversal, exploratory, and retrospective analysis using a sample of abstracts published in journals indexed in international repositories, along with a keyword search conducted from 2020 to 2024. The findings indicate the presence of awareness nodes, policies, and programs that illustrate the central themes, groupings, and structures of the learning network. The study concludes with a strong recommendation to extend the model for empirical validation and to explore immersive learning scenarios connected to the SDGs in uncertain and contingent contexts, such as during the pandemic. This approach aims to provide a practical tool for educators, psychologists, and professionals in education and sustainable development
Trends in the development of web computing in e-commerce
Get PDFThis systematic review examines the role of web computing technologies, cloud technologies, web integration, and automation in transforming e-commerce. Using the PRISMA 2020 framework, 345 articles were identified, with 55 meetings the inclusion criteria for analysis. Studies included were peer-reviewed, published between 2019 and 2024, and focused on empirical applications of web computing in e-commerce. Articles were excluded if they lacked relevance, empirical data, or methodological rigor. The findings highlight the pivotal role of cloud technologies in enhancing scalability and reducing operational costs, with hybrid systems providing secure and adaptable storage solutions. Web integration, facilitated by APIs and middleware, ensures seamless customer experiences and efficient logistics and payment systems coordination. Automation technologies, including robotic process automation (RPA) and AI-driven chatbots, significantly streamline workflows, improve customer satisfaction, and reduce human error. However, challenges such as high implementation costs and cybersecurity risks persist, particularly for small and medium-sized enterprises (SMEs). This review highlights the transformative potential of web computing technologies in advancing e-commerce while addressing critical barriers to adoption. The findings provide actionable insights for researchers, policymakers, and industry leaders to optimize the use of these technologies and promote sustainable growth in the e-commerce sector
Analysis of the implementation of blockchain technologies in management to ensure transparency, efficiency and sustainability
Get PDFThis paper tackles the critical problem of how blockchain technology can boost management practices\u27 transparency, efficiency and sustainability in a transitional economy like Ukraine. The focus is to systematically evaluate blockchain\u27s impact on this outcome by analysing the roles of blockchain implementation, digital security, decentralisation, and innovation in Ukraine from 2008q1 to 2023q4. All the variables are stationary at the first difference revealed from the unit root test of Augmented Dickey-Fuller. The series cointegration is confirmed using the bound test of Augmented Dickey-Fuller. The study uses time series data and an Autoregressive Distributed Lag (ARDL) model. The long-run results indicate that decentralization and digital security are incredibly influential in enhancing efficiency and sustainability. Furthermore, ECMt−1 is negative and statistically significant, suggesting a long-term equilibrium adjustment. Such policy recommendations include a supportive regulatory framework, incentives toward sector-specific blockchain applications, secure, transparent digital ecosystems, and using blockchain to its full potential in the sectors
Machine learning algorithms for predicting air quality index: A case study in urban and industrial zones
Get PDFIn urban and industrial areas, the prediction of the air quality index (AQI) is important in order to control the air pollution and protect public health. The goal of this work is to enhance the AQI prediction by making use of the advanced machine learning (ML) and deep learning (DL) models capable of learning spatial and temporal dependencies. The main goal of this research is to examine the performance of the different ML and DL models such as Random Forest (RF), XGBoost, LSTM, Transformer and Temporal Graph Neural Networks (TGNN) for AQI prediction in urban and industrial zones. To capture the variability of data, a multi-source data collection approach is taken by using air quality data (PM2.5, PM10, SO2, NO2, CO, O3), weather data, satellite imagery, and IoT sensor data. The data were pre-processed and engineered in terms of temporal and spatial features and advanced models were used to predict AQI. And key metrics of RMSE, MAE and R2 were used to evaluate model performance. Results indicate that Transformer models achieved the best performance in urban areas, with an RMSE of 14.1 and R² of 0.89, because they can capture long-term temporal patterns. In industrial zones, TGNN models achieved an RMSE of 17.9 and an R² of 0.87 because they could capture spatial correlations and pollution dispersion. Both models exhibited high resilience to extreme pollution events and minimal performance degradation under missing data scenarios in robustness testing. We show that Transformer and TGNN models outperform traditional ML models by a large margin in AQI prediction, especially during high pollution episodes. The results are consistent with real-time air quality monitoring and dynamic policy making in urban and industrial environments. Future work should implement the models in other regions and improve data quality to increase applicability
The impact of artificial intelligence on the strategic planning of economic development of countries
Get PDFTraditional economic planning frameworks struggle to address rapid market changes and nonlinear sectoral interactions, often resulting in suboptimal policy outcomes. This study systematically analyzes how artificial intelligence (AI) transforms strategic economic development across ten countries (the UK, Japan, the USA, China, Ukraine, France, Canada, Singapore, Germany, and South Korea) from 2015 to 2024. Using a mixed-methods approach – integrating panel data regression (fixed-effects and 2SLS models) with a PRISMA-guided review of 89 studies – the research quantifies AI’s macroeconomic impacts and ethical risks. Key findings reveal that a 1-unit increase in AI adoption intensity correlates with a 0.38–0.41% GDP growth rise, driven by predictive analytics in advanced economies like the USA and Singapore. However, infrastructural gaps in Ukraine caused 31% data loss in AI models, hindering policy scalability. Ethical challenges include algorithmic bias in France’s hiring systems (13% minority recruitment disparity) and data privacy breaches in Singapore (19% corporate breach rate). For Ukraine, targeted recommendations include prioritizing AI-ready digital infrastructure (e.g., centralized data hubs) and adopting EU-style ethical audits to mitigate bias in public-sector algorithms. Policymakers globally must balance AI-driven efficiency with equitable governance to harness its full potential
Solubility of phytochemicals and challenges in in vitro studies: a literature review
Get PDFPoor solubility remains a critical barrier in the in vitro evaluation of phytochemicals, many of which are hydrophobic and difficult to dissolve in aqueous media. This review explores the physicochemical factors influencing phytochemical solubility, emphasizing the role of solvent properties such as polarity, proximity, and cytotoxicity. Commonly used solvents—including polar protic, polar aprotic, and non-polar solvents —are discussed concerning their solubilizing capacity and compatibility with biological systems. Solvent-induced changes in membrane dynamics and cytotoxic profiles are also examined, highlighting the need for cautious selection and optimization. Several advanced strategies to enhance solubility, such as co-solvent systems, pH modulation, nanocarrier encapsulation, surfactants, and deep eutectic solvents (DESs), are reviewed. A focused case study on curcumin illustrates how different solubilization methods can significantly improve in vitro performance. The review underscores the importance of standardized solvent reporting to ensure reproducibility and reliability in phytochemical research