A fresh interest has been accorded to metal iodides due to their fascinating physicochemical properties such as high ionic conductivity, variable optical properties, and high thermal stabilities in making micro and macro devices. Breakthroughs in cathodic preparation and metallization of metal iodides revealed new opportunities for using these compounds in various fields, especially in energy conversion and materials with luminescent and sensory properties. In energy storage metal iodides are being looked at due to their potential to enhance battery performance, in optoelectronics the property of the metal iodides is available to create efficient LEDs and solar cells. Further, their application in sensing devices, especially in environmental and medical monitoring has been quite mentioned due to their response towards environmental changes such as heat or light. Nevertheless, some challenges are still in question, including material stability, scale-up opportunities, and compatibility with other technologies. This work highlights the groundbreaking potential of metal iodide-based nanomaterials, emphasizing their transformative role in innovation and their promise for future advancements.

This paper analyses the impact of an integrated business management system on business operations in trade in Republic of Croatia. The integration of management systems provides various benefits to a company, so the aim of this paper is to analyse the impacts of integrated management systems on the business operations of trade companies in the Republic of Croatia. The purpose of this paper is to examine and analyse, but also to adequately theoretically argue the impact of transformational leadership, quality culture, and the degree of integration on the development of integrated management systems. Empirical research investigated integrated management systems in companies in the trade sector in the Republic of Croatia. Based on the set conceptual model and research results, we conclude that companies with a highly developed quality culture have proven management system integration. Our research didn’t confirm the significance of transformational leadership in interpreting changes in the degree of management system integration, but it highlights the positive correlation between the application of quality culture and integration; confirms the substantial impact of integrated management systems on both internal and external benefits, emphasizing its strategic imperative for sustained business success.

The Circular Economy is one of the most prominent cross-disciplinary and cross-sectoral concepts to emerge in recent decades. It has permeated academia, policymaking, business, NGOs, and the general public, leading to numerous applications of the concept, some of which only partially overlap. In this article, we review recent debates and research trends in the Circular Economy, outlining the ten most common groups of its conceptualizations using the PRISMA (Preferred Items for Systematic Reviews and Meta-Analysis) method. We then propose a post disciplinary and transnational research program on the Circular Economy that would not only combine hard and soft sciences in unprecedented ways but also have important practical applications, such as developing tools to embed the Circular Economy in natural, technical, economic, and socio-cultural settings.

Digital transformation is a significant phenomenon that affects almost every business sector, particularly the telecommunications industry, which is closely intertwined with information technology. This study is grounded in McLuhan’s concept of technological determinism and Martin Heidegger’s philosophy of technology, which asserts that media and technology shape human thoughts and interactions, benefiting individuals, society, and culture alike. The primary objective of this research is to investigate the environmental factors that influence digital transformation and to assess its impact on the strategic renewal of a company. This research employs exploratory qualitative methods, collecting in-depth information through interviews with the respondents from Indonesia’s leading telecommunications operator who can provide comprehensive and contextual insights into digital transformation. The findings reveal specific environmental factors that drive digital transformation. The major identified components of strategic renewal include advancements in information technology, the role of human resources, and interactions with external parties, including customers and partners.

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.