Measuring the performance of healthcare organizations has become a crucial yet challenging task, which is the focus of this study. The paper’s primary goal is to identify the key factors that shape healthcare organizations’ performance management systems in Serbia, which can serve as useful guidelines for implementing sustainable solutions. Additionally, the aim is to emphasize the importance of a broad implementation of performance measurement systems to facilitate strategy implementation and enhance organizational effectiveness. The empirical research involved an online survey of 280 respondents, including managers, executives, and operational staff from both private and public healthcare organizations in Serbia. Statistical analysis was conducted using SPSS 20. The study identifies key challenges, including the lack of a developed performance measurement system, weak support from information and management systems for performance improvement, and an organizational structure that does not support performance enhancement. Furthermore, it has been found that a deeper understanding of the essence of measurement significantly contributes to identifying problems in its application in the healthcare sector. It was also observed that the more challenges identified in the measurement process, the less favourable the perception of the flexibility and adaptability of the system.

Inflammation of the lungs, called pneumonia, is a disease characterized by inflammation of the air sacs that interfere with the exchange of oxygen and carbon dioxide. It is caused by a variety of infectious organisms, including viruses, bacteria, fungus, and parasites. Pneumonia is more common in people who have pre-existing lung diseases or compromised immune systems, and it primarily affects small children and the elderly. Diagnosis of pneumonia can be difficult, especially when relying on medical imaging, because symptoms may not be immediately apparent. Convolutional neural networks (CNNs) have recently shown potential in medical imaging applications. A CNN-based deep learning model is being built as part of ongoing research to aid in the detection of pneumonia using chest X-ray images. The dataset used for training and evaluation includes images of people with normal lung conditions as well as photos of people with pneumonia. Various preprocessing procedures, such as data augmentation, normalization, and scaling, were used to improve the accuracy of pneumonia diagnosis and extract significant features. In this study, a framework for deep learning with four pre-trained CNN models—InceptionNet, ResNet, VGG16, and DenseNet—was used. To take use of its key advantages, transfer learning utilizing DenseNet was used. During training, the loss function was minimized using the Adam optimizer. The suggested approach seeks to improve early diagnosis and enable fast intervention for pneumonia cases by leveraging the advantages of several CNN models. The outcomes show that CNN-based deep learning models may successfully diagnose pneumonia in chest X-ray pictures.

Prepolymers containing isocyanates must be prevented from curing when exposed to moisture, which can be achieved by blocking the isocyanate groups with a suitable agent. The study carefully examines several blocking agents, including methyl ethyl ketoxime (MEKO), caprolactam, and phenol, and concludes that methyl ethyl ketoxime is the best choice. Spectroscopic and thermal analyses, as well as oven curing studies, are conducted with various blocking agents and isocyanate prepolymer to castor oil ratios, revealing MEKO to be the most effective blocking agent which gets unblocked at higher temperatures.

The aim of this research is to explore the relationship between remuneration, job satisfaction, and employee performance. Remuneration, in this context, refer to a system synchronization that is based on performance appraisal result. In this, regard, the research employed a descriptive quantitative method, with a population comprising all University of Padjadjaran lecturers which were a total of 2,090. Furthermore, in order to gather the research sample, a probability sampling technique was employed. This technique was selected because of its reputation as the most general strategic sampling technique in quantitative research to achieve representativeness (1). The obtained result showed that there was a positive and significant relationship between the remuneration and job satisfaction of lecturers in University of Padjadjaran. Accordingly, a significant value of 0.000 < 0.05 and a t-count value of 19.330 > 1.95 was observed, meaning the H1 hypothesis in this research was accepted. It is also expedient to acknowledge that a positive and significant relationship was found between job satisfaction and the performance of the lecturers in study area. For this relationship, a significant value of 0.010 < 0.05 and a t-count value of 5.676 > 1.95 was found. These findings led to the acceptance of the H2 hypothesis proposed in this research. Similarly, the relationship between remuneration and the performance of the observed lecturers was found to be positive and significant. The observed significant value in this regard was 0.000 < 0.05 and the t-count value was 4.057 > 1.95, indicating that H3 hypothesis was also accepted. Lastly, the relationship between remuneration and employee performance mediated by job satisfaction of lecturer in University of Padjadjaran was explored, and it was found to also be positive and significant, with a significant value of 0.000 < 0.05 and a t-count value of 5.429 > 1.95. This indicated that the H4 hypothesis proposed in the research was accepted.

This study critically examines the implications of international transport corridor projects for Central Asian countries, focusing on the Western-backed Transport Corridor Europe-Caucasus-Asia (TRACECA), the Chinese initiative “One Belt—One Road”, and the International North-South Transport Corridor (INSTC) supported by the Russian Federation, India, and Iran. The analysis underscores the risks associated with Western projects, highlighting a need for a more explicit commitment to substantial infrastructure investments and persistent contradictions among key investors and beneficiaries. While the Chinese initiative presents significant benefits such as transit participation, infrastructure development, and economic investments, it also carries risks, notably an increased debt burden and potential monopolization by Chinese corporations. The study emphasizes that Central Asian countries, though indirect beneficiaries of INSTC, may not be directly involved due to geographical constraints. Study findings advocate for Central Asian nations to balance foreign investments, promote economic integration, and safeguard political and economic sovereignty. The study underscores the region’s wealth of natural and human resources, emphasizing the potential for increased demand for goods and services with improved living standards, strategically positioning these countries in the evolving global economic landscape.