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Following a recycling or continuous recycling process, there is always waste with no material or market value that can be converted into energy or other fossil fuel substitutes. The present study aimed to evaluate the management of organic waste policy and to predict the trend of organic waste generation in Albania. The research used an appropriate Box–Jenkins Auto Regressive Integrated Moving Average (ARIMA) to determine the quantification of organic waste to be generated. The main results obtained can support the decision-making process in the planning, change and short-term implementation of organic waste management, and the information provided is very useful in collecting, transporting, storing and managing waste in Albanian cities (Tirana, Durrës, Kukës, Berat, Shkodra, Dibër, Gjirokastër and Elbasan). Furthermore, the high percentage of the organic waste generation until 2025 constitutes good premises to raising public awareness related to their energy recovery.

This article demonstrates, on one hand, how organizations are able to optimize their business processes to streamline their activities and, on the other hand, how performance can be enhanced by using the target cost method in a construction project. Based on the analysis of national and international literature, the authors carried out a quantitative and qualitative study based on a questionnaire containing a set of four questions aimed at highlighting the issues mentioned in the launched objectives. Business process-specific steps in a construction project are described and analyzed. The case study outlines how to optimize business processes by using Swimlane charts and how to reduce operating costs by applying the target cost method. The article concludes with our conclusions about the real benefits of optimizing business processes in a construction project by using the target cost method and increasing the performance of entities in this field.

The purpose of this study was to determine the motivation to buy eco-friendly products via machine learning techniques. With this in mind, a dataset was collected between November and December 2024 from 245 organic consumers in Maramureș County, Romania, via a questionnaire. Consumers’ main motivations to buy eco-friendly products were considered according to three categories: Health Care, Environmental Protection, and Superior Quality. In the analysis of the dataset, among the four feature selection techniques used, Random Forest was determined to be the best with the highest accuracy value. At the beginning of the study, 16 variables were thought to be important categorical factors for consumers’ eco-friendly product-buying motivations, with 5 of these being found to be the most effective with the Random Forest technique. Then, the SHAP method was applied to identify the contribution of driving factors to the buying motivation for eco-friendly products. All analyses were conducted with Python software. The results of the SHAP method indicated that while all factors perform well, consumers considering themselves as eco-friendly is the most important factor for the Environmental Protection category when buying eco-friendly products, while the most important criterion of the original certification category was found to be the Health Care category. The most effective factor for Superior Quality was determined as the high-price category, which is the main barrier to purchasing eco-friendly products.

Starting with ANAF's \"glory\" period, that is in the Ciolos Government, each year, the Ministry of Public Finance boasts that it has collected more taxes and charges than was foreseen: for example in 2017 in November, it has already cashed as foreseen for the budget year 2017 (7% over what was planned). These data are real, but with what price? ANAF has begun to verify the firms, and the present study examines macroeconomic data on the number of checks in the period 2014-2016, and for the same period we will also examine these data for Harghita County, where wages are almost the lowest in the whole country. Thus, it can be determined whether the macroeconomic trends are comparable to the Harghita County trends or not, and which will be the risk of being checked in Romania by ANAF - depending on where a company is registered.

This paper aims to identify certain factors that may influence museums’ success in adopting and implementing a sustainability policy. The research hypotheses were formulated based on an in-depth study of the literature and subsequently tested using classical linear regression methodology. The empirical research is based on 86 Romanian national questionnaires collected in October and November 2016, which tracked museums’ self-reported sustainability practices. To examine the relationships between sustainability policy and independent variables, we developed three generalized linear regression models. Our results revealed that environmental behavior and some components of social responsibility (heritage exposure and community involvement) have a positive impact on implementing sustainability policies and principles within museums. Surprisingly, economic performance did not prove to have a significant influence on sustainability policy, suggesting that financial autonomy, efficiency, and productivity are not essential elements in fostering a sustainable mindset within museums. The developed models serve as valuable tools for museum managers, providing guidance in adopting appropriate managerial decisions to achieve sustainability goals. Furthermore, the empirical research offers new insights that enhance and advance current knowledge and research in the field of sustainable management in museums.

The concept of soil management has, in fact, ancient origins. However, the first soil maps date from the 18th century, while the first soil models are known only from the end of the 19th century. Towards the end of the twentieth century, there have been considerable developments in terms of mapping techniques and soil condition modeling tools. These developments are characterized by the use of IoT in soil mapping and related activities, which consists of the use of GIS, GPS, remote sensing, etc. In the present study, we aimed to develop a synthesis of the evolution of soil resources worldwide and by continents, over the entire period for which there are official data. The study of the dynamics of the evolution of soil resources and their use was carried out by collecting raw data from official databases and processing them, while data refer to the period 1961 - 2018. Globally, between 1961 and 2018, the average soil resources used in agriculture is equal to 4,817,663 thousanrs ha, and forest resources for forestry, in the same period, amount to, on average, over 4 billion hectares.

Starting from Michael Porter's diagnosis regarding the dynamics of the competitive environment based on five forces and from the perspective of the value chain analysis in achieving the competitive advantage on a cutting edge market, this article proposes an in-depth analysis in the topic of purchasing decisions within economic organizations, whose processes need to be approached strategically in order for organizations to develop and evolve properly. Managing in a proper way the relation with the suppliers of goods, raw materials, and services will determine the costs to decrease and thus the revenues and implicitly the profit will grow, placing the entity on a good track in fighting with competition and bringing benefits for all its stakeholders. Moreover, taking into account that the environment in which organizations operate is often surrounded by uncertainty, the decisional process, especially the strategic one, is characterized by ambiguity, doubled also by the subjectivity of the human factor in decisional reasoning. In this context, choosing the right supplier according to specific selection criteria represents the most important decision of the purchasing function. Sometimes, it requests the implementation of qualitative mathematical methods such fuzzy logic, which is a powerful tool that can efficiently shape the ambiguity within decision-making process and support the organization long-term strategy and competitive market positioning.

This work examines the temperature-dependent stiffness of Ethylene Propylene Diene Monomer (EPDM) rubber using tensile testing and Dynamic Mechanical Analysis (DMA). Additionally, it provides an appropriate model for Finite Element Analysis (FEA). The tensile tests indicate that EPDM has high rigidity and minimal elongation at −40°C, intermediate characteristics at 23°C, and a decrease in stiffness with rising temperature, up to 80°C. Nevertheless, a sudden rise in rigidity is seen at a temperature of 125°C, which may be ascribed to the strengthening influence of carbon black. The DMA findings confirm this pattern, demonstrating an upward trend in storage modulus and stiffness as temperature increases. At the same time, tan delta and loss modulus fall, suggesting a reduction in viscoelastic damping. The calibration of the Finite Element Analysis (FEA) model, using the Arruda-Boyce, Yeoh 3D, and Mooney-Rivlin 5-parameter models, determines that the Arruda-Boyce model is the most precise in accurately replicating the experimental data. The results indicate that the improved resistance to heat and increased rigidity of EPDM at high temperatures are affected by its material composition. However, more investigation is required to have a comprehensive understanding of the underlying processes.