Energy efficiency, sustainability, and renewable energy sources are becoming increasingly relevant topics in today’s world. Buildings are one of the largest consumers of energy in society, and as such, improving their energy efficiency by reducing unnecessary energy loss and utilizing solar power is crucial. This paper comprehensively analyzes a neighborhood with buildings characteristic of the researched area by applying empirical and theoretical methods and calculations that have been proven in numerous individual cases. The main contribution of this paper is its demonstration that implementing methods to increase the energy efficiency of buildings and utilizing the potential of solar power can result in significant savings in energy consumption, increase the energy sustainability of the analyzed buildings, and substantially reduce the negative environmental impact. The novelty of this study lies in the location and multiple software applications for data analysis. The data and conclusions obtained in this paper serve as a foundation and path towards sustainable development in the field of energy efficiency for buildings in this and similar areas. Heat loss was calculated by analyzing households in the urban neighborhood of Nova Kolonija. All analyzed houses exceeded the maximum allowed annual required energy for household heating (75 kWh/m2). After the reconstruction and implementation of the proposed measures to increase energy efficiency, all houses met the requirements and entered a higher energy class, C. Energy for heating was reduced from 9294.68 kWh/a to 4641.84 kWh/a, representing a reduction of 50.03%. Simulations were conducted regarding solar rooftop power plant installation of 5655 Wp capacity. Software that was used included: a Photovoltaic Geographical Information System (PVGIS), Photovoltaic System software (PVsyst 7.3.), and Photovoltaic Design and Simulation software (PV*SOL). The results of the analysis indicated that the average amount of electricity produced is 6186.98 kWh, which meets 98.12% of households’ annual electricity consumption of 6278.41 kWh. The paper contributes to the existing body of literature and provides significant insight for both practical implications and future studies.

This study examines the influence of altering hydraulic parameters on the temperature of ISO HM VG 46 hydraulic mineral oil. Hydraulic mineral oils find extensive application in industrial power transmission systems, where precise temperature control is crucial for achieving optimal performance and prolonging system lifespan. Variations in hydraulic parameters, encompassing flow rate, pressure, and viscosity, can significantly impact the thermal characteristics of hydraulic oil.In this research, experimental studies were carried out to study the influence of changing hydraulic parameters on the temperature of hydraulic mineral oil. Experiments were performed on a hydraulic press, where different working conditions were simulated.The quality of hydraulic fluid is considered, according to all research, to be the most influential factor in ensuring the reliable operation of hydraulic systems.

Amidst the globalization of markets, there has been a continuous intensification of competitiveness between enterprises. The modern business environment has caused a shift in how business is conducted. Opportunities and challenges arise, which put a tremendous pressure on enterprises regardless of size and industry. Entrepreneurship in enterprises plays an important role in obtaining a competitive edge in the market. Thus, entrepreneurial intentions in enterprises can often shape the future and survival of the enterprise. In this paper, the prediction of entrepreneurial intentions in enterprises through Internet marketing predictors is addressed. For this, several statistical methods in data mining were used. First, simpler approaches such as linear regression, logistic regression were used. Afterward, classifier decision trees QUEST (quick, unbiased, efficient, statistical tree), and CHAID (chi-squared automatic interaction detection) were used. The sample for analysis was 137 enterprises from Serbia. Furthermore, a supervised machine learning algorithm, support vector machine (SVM) was used. Finally, a feed-forward neural network (FNN) was applied. The results varied across the applied approach, thus providing significant insights into the dynamics of data mining for prediction outcomes in an enterprise setting.

This study examines how selected technological and human-centered factors affect sustainable lean performance potential (SLPP) in manufacturing enterprises within the context of Industry 4.0 transition to Industry 5.0. The relationship between SLPP and Industry 4. transition to Industry 5.0 is contextual, meaning this direct relationship is not analyzed via statistical methods. A structured survey was conducted with 128 managers (n = 128), focusing on human-centric technology design (HCTD), artificial intelligence for waste minimization (AIWM), predictive maintenance (PMAI), and IoT integration in production (IOTP). The data were analyzed using descriptive statistics, correlation analysis, linear regression analysis, Harman’s single-factor test results, multicollinearity test, and non-linear curve estimation analysis. The results show that all four independent variables are positively associated with SLPP. IoT integration and AI for waste minimization had the strongest effects, followed by predictive maintenance. Human-centric technology design showed a weaker, yet still statistically significant, relationship with SLPP. The findings support a model where digital tools contribute directly to lean and sustainable outcomes, while human-centered approaches are emerging more gradually. The research adds empirical evidence to ongoing discussions about factors affecting lean performance in the context of industrial changes.

The authors investigated the system for oil production using piston pumps, which represents a significant part of the global industry, relying on a mechanical exploitation method that requires optimization to reduce energy consumption and increase efficiency. The paper analyzes the application of SCADA (Supervisory Control and Data Acquisition) systems, which enable monitoring and visualization of key operational parameters, thereby creating predictive maintenance that can significantly improve oil production performance. Considering various variables, such as specific well conditions and reservoir properties, the implementation of modern technologies, including IoT, allows for real-time data integration, contributing to process optimization. Key elements of the SCADA system include sensors, programmable logic controllers (PLCs), and supervisory computers that facilitate data collection, processing, and analysis. Through the use of the AVEVA platform, the visualization of collected data enables specialists to respond to emergencies, reducing downtime and increasing productivity. This intelligent management system allows for analysis and predictive action, which is crucial for improving efficiency and reducing maintenance costs.