The basis of every hydraulic system is based on energy transformations, which are realized through hydraulic working fluid. Hydraulic oils are certainly subject to changes within their structure, meaning the basic characteristics and parameters of hydraulic oil, such as density, viscosity, humidity. The oils that are exploited are exposed to the process of degradation, which largely leads to significantly poorer quality of hydraulic fluid. The paper deals with the influence of changes in the characteristics of the hydraulic fluid on the hydraulic operating parameters of the gear pump installed on the hydraulic press. The parameters refer to pressure, flow, and temperature, as well as the quality of hydraulic oil, which affects the volumetric efficiency of the pump, and the results presented in the conclusion are based on the measured values of parameters before and after corrective measures. The control of parameters aims to increase the efficiency and reliability of the hydraulic system, a way of modern detection of deviations of parameters from standard, required values.

In the era of rapid technological growth, we are facing increased energy consumption. The question of using renewable energy sources is also essential for the sustainability of wireless sensor networks and the Industrial Internet of Things, especially in scenarios where there is a need to deploy an extensive number of sensor nodes and smart devices in industrial environments. Because of that, this paper targets the problem of monitoring the operations of solar-powered wireless sensor nodes applicable for a variety of Industrial IoT environments, considering their required locations in outdoor scenarios and the efficient solar power harvesting effects. This paper proposes a distributed wireless sensor network system architecture based on open-source hardware and open-source software technologies to achieve that. The proposed architecture is designed for acquiring solar radiation data and other ambient parameters (solar panel and ambient temperature, light intensity, etc.). These data are collected primarily to define estimation techniques using nonlinear regression for predicting solar panel voltage outputs that can be used to achieve energy-efficient operations of solar-powered sensor nodes in outdoor Industrial IoT systems. Additionally, data can be used to analyze and monitor the influence of multiple ambient data on the efficiency of solar panels and, thus, powering sensor nodes. The architecture proposal considers the variety of required data and the transmission and storage of harvested data for further processing. The proposed architecture is implemented in the small-scale variants for evaluation and testing. The platform is further evaluated with the prototype sensor node for collecting solar panel voltage generation data with open-source hardware and low-cost components for designing such data acquisition nodes. The sensor node is evaluated in different scenarios with solar and artificial light conditions for the feasibility of the proposed architecture and justification of its usage. As a result of this research, the platform and the method for implementing estimation techniques for sensor nodes in various sensor and IoT networks, which helps to achieve edge intelligence, is established.

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.

Industry 4.0 brings digitization and automation, increasing efficiency, but causing job loss. Industry 5.0 integrates people and machines to overcome these limitations. Entrepreneurship in Society 4.0 is shaped by digital technologies, while Entrepreneurship in Society 5.0 emphasizes the balance of technology and humanistic values. The adoption of Industry and Society 5.0 is growing, requiring further development of infrastructure and education. The focus of this paper is the analysis of the concept of entrepreneurship 5.0 in society 5.0 and the development of a theoretical model for the improvement of entrepreneurship.

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.