Digital health care and smart health care are emerging as transformative paradigms for modernizing traditional healthcare infrastructure, recognized as a critical pillar of nation-building and aligned with the United Nations Sustainable Development Goals (SDG-17). The seamless integration of big data analytics with healthcare systems enables the development of advanced data-driven ecosystems capable of managing massive, complex, heterogeneous, and real-time patient-centric information. A wide spectrum of information and communication enabling technologies—including the Internet of Things, cloud and edge computing, artificial intelligence (AI), machine learning, 5G, smart biosensors, bioinformatics, biomarkers, and mathematical optimization techniques—are converging to deliver personalized and patient-centric healthcare solutions. Furthermore, AI-based drug discovery and multi-omics approaches (genomics, proteomics, metabolomics, and pharmacogenomics) accelerate innovation in precision medicine. The fusion of medical sciences, computer science, and AI technologies is reshaping innovation, healthcare delivery, and public policy, although the absence of unified global standards remains a challenge. This study explores the attractive features of digital health care and big data analytics, highlighting their potential to revolutionize healthcare innovation and policy reform for sustainable nation-building. Although this work highlights the potential of digital and smart health care in reshaping healthcare infrastructures, it remains largely descriptive and conceptual. In this review, an effort is made to assess the attractive features of digital health care and big data analytics and their impact on reshaping innovation in medical sciences and AI technologies for nation-building. Public policy is undergoing a changing phase, with active consideration and massive reforms.

Quality-related costs represent a significant concern for manufacturing organizations, as they directly influence competitiveness, efficiency, and long-term sustainability. Understanding how these costs interact is crucial for developing effective quality management strategies, particularly in emerging economies such as India. This study investigates the interactions among cost of quality (CoQ) factors in the Indian manufacturing sector using an interpretive structural modeling (ISM) approach. A comprehensive literature review identified critical CoQ components, which were then structurally interrelated through ISM to develop a hierarchical framework that clarifies the relationships between driving and dependent factors. This framework provides insight into how specific CoQ elements influence one another, offering actionable guidance for quality management. The study not only advances theoretical understanding but also contributes a practical tool for decision-makers in manufacturing. Key findings indicate that prevention-related factors act as strong drivers, whereas appraisal- and failure-related costs are more dependent. The proposed ISM-based framework provides a strategic lens for prioritizing quality improvement initiatives.

Information security remains a significant concern for the adoption of smart cities (SCs) worldwide, particularly in relation to the development and implementation of digital ecosystems. SCs entail the interconnectedness of networks and systems that collect and process huge volumes of diverse data. This study analyzes the impact of data privacy and data security issues on the citizens’ willingness to adopt smart city environments. A critical review of the existing literature was conducted regarding the relationship between data privacy and security concerns and the adoption of the smart city ecosystem. The data collected from two sample groups, experts and citizens, were analyzed using statistical techniques, including independent samples t-tests and correlation analysis. The findings indicate that citizens and experts had significantly different perceptions of the characteristics of SCs. Still, both groups exhibited a strong positive correlation between key adoption variables and citizens’ readiness to accept SCs. Based on the findings, several recommendations are proposed to increase citizens’ acceptance of SCs.

The problem of controlling and mitigating vibrations of the main structure is very important. The use of passive control devices is one solution to this problem. To ensure high efficiency of these devices, their parameters must be optimized. Many authors have addressed the problem of selecting the optimal design for such dampers, but many issues remain unresolved. This paper examines the influence of various optimal designs of the asymmetric single-sided vibro-impact non-linear energy sinks (SSVI NESs), that is, vibro-impact dampers, on system dynamics and damper efficiency in mitigating unwanted vibrations of the main structure. Nine damper designs with different masses and other optimized parameters are considered. These designs exhibit similarly high efficiency but display complex and distinct dynamic behaviors. We show that many damper designs can achieve comparable high performance. Therefore, the results of optimization procedures—which, moreover, allow a considerable degree of arbitrariness in their execution—can be ambiguous. SSVI NESs consistently display complex asymmetrical dynamics with alternating periodic modes and irregular, particularly chaotic, modes across different excitation force frequencies. Although the dynamic behavior of each optimally designed damper varies, this complexity does not affect the damper efficiency, which remains relatively stable.

A novel approach for quality assessment of potatoes using an artificial neural network (ANN) technique is developed and integrated with thermal imaging system to analyze the decay of tissues in potatoes. The merger of ANN with acquired thermal images leads to increased system efficiency, accuracy, reliability, and speed. A sample of 480 thermal images of potatoes was generated for training and testing the dataset. Categorization of potatoes as healthy or defective is performed based on seven critical parameters, such as standard deviation, energy, entropy, skewness, kurtosis, means, and variance. A classifier based on ANN is employed, incorporating two hidden layers with 200 neurons in the first layer and 50 neurons in the second layer. The input layer size is feature-dependent, while the output layer employs two neurons for binary classification. Among the seven features evaluated individually, entropy and energy achieved the best results, with classification accuracies of 79.81% and 81.27%, respectively. The highest overall performance, 94.89% accuracy with 455 correctly classified samples, was obtained by combining all features except variance. It is observed that integrating thermal imaging with ANNs is a promising approach for quality assessment. Selecting appropriate features is crucial for optimizing the classification model.

Investment decisions in extractive industries increasingly depend on the technological configuration of engineer-to-order (ETO) machinery. This study explores the role of ETO projects in reshaping investment logic within extractive industries. To strengthen conceptual framing, we explicitly distinguished between primary and secondary ETO within the multi-project investment structure. Special attention was given to secondary ETO—the adaptation of standardized equipment to specific geological conditions—which emerges as a critical component of multi-project investment strategies. We clarified that the novelty of the approach lies in treating secondary ETO as an embedded, corrective investment rather than a standalone engineering decision. In addition, we quantitatively assessed the impact of ETO-related expenditures within multi-project investment, using the net present value indicator. This study introduced a model that comprises both the base serial equipment and a custom ETO component, enabling the achievement of the designed performance parameters. To analyze sensitivity, we employed the Box-Wilson experimental design method. Results showed that ETO costs had no significant influence on the response function (i.e., the production volume required to reach breakeven), whereas the most critical factors were the marginal revenue and the project duration. Theoretically, this study reconceptualizes ETO not as an isolated manufacturing decision but as a functional component within broader investment planning. These results reinforce the conceptual distinction between primary and secondary ETO by demonstrating that corrective engineering adaptations embedded in multi-project structures do not alter the financial breakeven logic.

User-centered design addresses end users’ needs to ensure effective product deployment. Unlike previous methods, this process focuses on addressing technology-related issues without considering ethical or sociocultural factors. As a result, radical product innovation continues to capture the interest of design scholars and experts. Contemporary advancements in technology and materials have significantly influenced the development of personal protective equipment (PPE) in sports, with a growing emphasis on enhancing athletes’ well-being. Within equestrian disciplines, PPE such as safety vests for jockeys plays a critical role in mitigating physical risk. While prior research has examined the physiological, cognitive, and performance-related aspects of horse riding, a notable gap remains in understanding how the design of jockeys’ PPE can effectively reduce the severity of sustained injuries. This study investigates the current limitations and future potential of jockey safety vests through the lens of material innovation, wearable technology, and design regulation. Drawing on a case study within the Australian regulatory context, this research identifies a critical tension between safety standards and innovation, suggesting that rigid compliance frameworks may inadvertently hinder the evolution of high-performance protective equipment. The study proposes a design-led approach to rethinking jockey vests as intelligent wearable systems—incorporating flexible sensors and smart materials—to enhance injury prevention, improve post-accident outcomes, and offer actionable data for both medical professionals and insurers. The study advocates for a shift toward more dynamic product standards that support design experimentation and user-centered innovation in sports PPE, thereby serving as a foundational step for future research and applications.