The study examines the factors shaping inflation in 2022–2023 and explores why inflation in the Hungarian economy has increased more sharply than in neighboring countries with similar structures. The research hypothesis suggests that the inflationary surge, which is notable both globally and within the European Union, is not solely due to market economy mechanisms, but also to specific circumstances in Hungary, including the state’s radical interventions aimed at curbing inflation. The study seeks to highlight these effects and provide recommendations for economic policymakers to develop a more resilient inflation policy. Additionally, it focuses on analyzing inflation in the agricultural sector. The results indicate that, alongside global inflationary pressures, several country-specific factors have driven up the inflation rate in Hungary. Energy prices have risen sharply, and some supply chains from the East have been disrupted. The country under study is less productive, and the impact of the energy price shock on the energy-intensive food industry is higher than in surrounding countries. Consequently, the exchange rate volatility in 2022–2023, combined with short- and medium-term factors, has had a significant impact on food inflation, causing substantial deviations from long-term equilibrium. The research concludes that, in addition to increasing food self-sufficiency, special attention should be given to the domestic development of the agricultural supply chain.

The objective of the study was to determine the relationship between open government and municipal effectiveness State a region of the Peruvian jungle. The research followed a quantitative approach with a non-experimental, cross-sectional, and correlational design. The population comprised citizens of State in a region of the Peruvian jungle, with a sample of 625 individuals. A structured survey was employed as the data collection technique, using a validated questionnaire as the instrument. The results revealed a positive, high, and significant correlation between governance and municipal effectiveness (Spearman’s Rho = 0.813, p < 0.01). Furthermore, the dimensions of transparency, integrity, accountability, and citizen participation showed moderate to high correlations with municipal effectiveness, with accountability (Rho = 0.779) emerging as the most influential dimension. It was concluded that the principles of open government play a crucial role in shaping the perception of effective municipal management. This underscores the need to strengthen transparency, integrity, and citizen participation policies to enhance public services and foster trust in local authorities.

This study examines how circular economy (CE) practices contribute to energy resilience by mitigating the impacts of energy shocks and supporting sustainable development. Through a systematic literature review (SLR) of recent studies, we analyze the ways in which CE strategies—such as resource recovery, renewable energy integration, and closed-loop supply chains—enhance energy security and reduce vulnerability to energy disruptions. Our research draws on academic databases, focusing on publications from 2018 to 2024, to identify key themes and practices that illustrate the transformative potential of the circular economy. Findings reveal that CE practices at macro, mezzo, and micro levels support resilience by fostering efficient resource use, reducing dependency on non-renewable energy sources, and promoting sustainable economic growth. Additionally, we highlight the roles of foreign direct investment (FDI), research and development (R&D), and supportive policies in accelerating the adoption of circular systems. The study concludes with recommendations for future research to address identified gaps, suggesting a roadmap for advancing circular economy practices as a means to enhance energy resilience and sustainability aims to reveal how wide array of factors affect transition towards more sustainable or circular economy.

Soil salinity is a major abiotic stress that drastically hinders plant growth and development, resulting in lower crop yields and productivity. As one of the most consumed vegetables worldwide, tomato (Solanum lycropersicum L.) plays a key role in the human diet. The current study aimed to explore the differential tolerance level of two tomato varieties (Rio Grande and Agata) to salt stress. To this end, various growth, physiological and biochemical attributes were assessed after two weeks of 100 mM NaCl treatment. Obtained findings indicated that, although the effects of salt stress included noticeable reductions in shoots’ and roots’ dry weights and relative growth rate as well as total leaf area, for the both cultivars, Rio Grande performed better compared to Agata variety. Furthermore, despite the exposure to salt stress, Rio Grande was able to maintain an adequate tissue hydration and a high leaf mass per area (LMA) through the accumulation of proline. However, relative water content, LMA and proline content were noticeably decreased for Agata cultivar. Likewise, total leaf chlorophyll, soluble proteins and total carbohydrates were significantly decreased; whereas, malondialdehyde was significantly accumulated in response to salt stress for the both cultivars. Moreover, such negative effects were remarkably more pronounced for Agata relative to Rio Grande cultivar. Overall, the current study provided evidence that, at the early growth stage, Rio Grande is more tolerant to salt stress than Agata variety. Therefore, Rio Grande variety may constitute a good candidate for inclusion in tomato breeding programs for salt-tolerance and is highly recommended for tomato growers, particularly in salt-affected fields.

This research underscores the importance of enhancing the early detection of diabetic retinopathy and glaucoma, two prominent culprits behind vision loss. Typically, retinal diseases lurk without symptoms until they inflict severe vision impairment, underscoring the critical need for early identification. The research is centered on the potential of leveraging fundus images, which offer invaluable insights by analyzing various attributes of retinal blood vessels, such as their length, width, tortuosity, and branching patterns. The conventional practice of manually segmenting retinal vessels by medical professionals is both intricate and time-consuming, demanding specialized expertise. This approach, reliant on pathologists, grapples with limitations related to scalability and accessibility. To surmount these challenges, the research introduces an automated solution employing computer vision. It conducts an evaluation of diverse retinal vessel segmentation and classification methods, including machine learning, filtering-based, and model-based techniques. Robust performance assessments, involving metrics like the true positive rate, true negative rate, and accuracy, facilitate a comprehensive comparison of these methodologies. The ultimate goal of this research is to create more efficient and accessible diagnostic tools, consequently enhancing the early detection of eye diseases through automated retinal vessel segmentation and classification. This endeavor combines the capabilities of computer vision and deep learning to pioneer new benchmarks in the realm of biomedical imaging, thereby addressing the pressing issues surrounding eye disease diagnosis.

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.