Today it is obvious that corporate social responsibility (CSR) is more than just a volunteer activity, it is also related to the operation of the firms and to competitive advantages. Many factors influence CSR and CSR-competitiveness relations; firm size could be the most crucial one. Originally CSR is related to large companies, although smaller firms can be active in CSR mainly in different ways with different background. Based on this idea the paper aims to explore the correlation between small and medium-sized enterprises’ (SMEs) corporate social responsibility (CSR) and competitive advantages. An interview research was conducted among thirty SMEs in a Hungarian city of Győr in 2021/22 to reveal how owner-managers interpret CSR, competitiveness and their relations. As SMEs cannot provide exact data on this topic the personal perception method was used to explore the CSR-competitiveness relation. A moderate relation was observed between CSR and competitiveness and the research revealed that different methodologies have to be applied for SMEs than large companies which results from the fact that SMEs’ CSR is less formal and lacks exact data.

Sustainability is a top priority for municipal administrations, particularly in large urban centers where citizens rely on transportation for work, study, and daily errands. Public transportation faces a significant challenge beyond availability, performance, safety, and comfort: balancing the cost for the city with fare attractiveness for passengers. Meanwhile, bicycles, supported by public incentives due to their clean and healthy appeal, compete with public transit. In Curitiba, the integrated transport system has been consistently losing passengers, exacerbated by the pandemic and the rise in private vehicle usage. To address this, the city is expanding bicycle infrastructure and electric bike rental services, impacting public transit revenue, and prompting the need for financial compensation to maintain affordable fares for those reliant on public transport. Therefore, this study’s objective is to analyze the bicycle’s impact on public transportation, considering the impact of public policies on economic and social efficiency, not just ecological and environmental factors. Data from six main bus lines were collected and analyzed in two separate linear regression models to verify the effects of new bicycles in circulation, bus tariffs, and weather conditions on public transportation demand. Research results revealed a significant impact of bus tariffs and fuel prices on the number of new bicycles that are diverting passengers from public transportation. The discussion may offer a different perspective on public transport policies and improve city infrastructure investments to strategically change the urban form to address social and economic issues.

In Costa Rica, there is no explicit recommendation from the competent authorities for the use of a specific phantom, so experts must explore what suppliers offer, among which the Normi Mam Digital phantom from PTW stands out. This article presents the results of the dosimetry and image quality control applied to the Normi Mam Digital phantom to validate it as equipment that complies with the recommendations of the Human Health Series No. 17. The results obtained were satisfactory, proving that the equipment complies with the tolerances recommended by international health bodies.

This article presents a comparative analysis and characteristics of the pension insurance systems of foreign countries, namely Germany, Sweden, Estonia, Poland, France with the aim of implementing their effective and noteworthy experience in Ukraine. The key indicators of the characteristics of the pension systems of these countries and Ukraine (amount of insurance contributions to the public pension fund; retirement age; insurance period; the ratio of the average pension to the average wage) have been studied and analyzed. Having examined these indicators, the authors came to the conclusion that the general trend in most countries of the world is that they guarantee only a minimum pension, which is caused by the increase in the share of pensioners in society. To solve the demographic problems affecting pension provision, the most countries increase the retirement age and provide financial incentives to stimulate citizens to work after the retirement age. To increase the pension, there are pension accumulation tools at the expense of the employer or at the expense of one’s own funds. Following the example of foreign countries, the directions for improving the pension insurance system of Ukraine were allocated.

Retinal disorders, such as diabetic retinopathy, glaucoma, macular edema, and vein occlusions, are significant contributors to global vision impairment. These conditions frequently remain symptomless until patients suffer severe vision deterioration, underscoring the critical importance of early diagnosis. Fundus images serve as a valuable resource for identifying the initial indicators of these ailments, particularly by examining various characteristics of retinal blood vessels, such as their length, width, tortuosity, and branching patterns. Traditionally, healthcare practitioners often rely on manual retinal vessel segmentation, a process that is both time-consuming and intricate, demanding specialized expertise. However, this approach poses a notable challenge since its precision and consistency heavily rely on the availability of highly skilled professionals. To surmount these challenges, there is an urgent demand for an automatic and efficient method for retinal vessel segmentation and classification employing computer vision techniques, which form the foundation of biomedical imaging. Numerous researchers have put forth techniques for blood vessel segmentation, broadly categorized into machine learning, filtering-based, and model-based methods. Machine learning methods categorize pixels as either vessels or non-vessels, employing classifiers trained on hand-annotated images. Subsequently, these techniques extract features using 7D feature vectors and apply neural network classification. Additional post-processing steps are used to bridge gaps and eliminate isolated pixels. On the other hand, filtering-based approaches employ morphological operators within morphological image processing, capitalizing on predefined shapes to filter out objects from the background. However, this technique often treats larger blood vessels as cohesive structures. Model-based methods leverage vessel models to identify retinal blood vessels, but they are sensitive to parameter selection, necessitating careful choices to simultaneously detect thin and large vessels effectively. Our proposed research endeavors to conduct a thorough and empirical evaluation of the effectiveness of automated segmentation and classification techniques for identifying eye-related diseases, particularly diabetic retinopathy and glaucoma. This evaluation will involve various retinal image datasets, including DRIVE, REVIEW, STARE, HRF, and DRION. The methodologies under consideration encompass machine learning, filtering-based, and model-based approaches, with performance assessment based on a range of metrics, including true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), negative predictive value (NPV), false discovery rate (FDR), Matthews's correlation coefficient (MCC), and accuracy (ACC). The primary objective of this research is to scrutinize, assess, and compare the design and performance of different segmentation and classification techniques, encompassing both supervised and unsupervised learning methods. To attain this objective, we will refine existing techniques and develop new ones, ensuring a more streamlined and computationally efficient approach.

Ecological environment damage events will destroy or damage the balance between animal and plant habitats and ecosystems, and even pose a threat to China’s ecological security. However, at present, there are some problems in the identification and evaluation of forest ecosystem damage, such as imperfect evaluation system, insufficient quantitative evaluation methods, imperfect damage compensation management system, and lack of analysis of the overall damage of the interaction between human activities and forest ecosystem. Based on the damaged object, the system involves a total of four first-class indicators, including physical damage, mental damage, economic forest fruit loss, forest by-products loss, processing and manufacturing loss, forest tourism loss, scientific research literature and history loss, soil conservation loss, water conservation loss, wind prevention and sand fixation loss, carbon fixation and oxygen release loss, atmospheric purification loss. There are 14 secondary indicators of emergency treatment fee and investigation and evaluation fee, as well as 22 tertiary indicators, and the value quantification method of each indicator is clarified by using market value method, alternative cost method, shadow engineering method, recovery cost method and other methods. The article also discusses the management system of forest ecosystem damage from the two aspects of forestry technology department and judicial administration department. The purpose is to provide reference for the quantification and standardization of forest ecosystem damage assessment technology and the improvement of management system.