The integration of blockchain technology into healthcare systems has emerged as a technical solution for enhancing data security, protecting privacy, and improving interoperability. Blockchain-based smart contracts offer reliability, transparency, and efficiency in healthcare services, making them a focal point of many studies. However, challenges such as scalability, regulatory compliance, and interoperability continue to limit their widespread adoption. This study conducts a comprehensive literature review to assess blockchain-driven health data management, focusing on the classification of blockchain-based smart contracts in health policy and the health protocols and standards applicable to blockchain-based smart contracts. This review includes 80 core studies published between 2019 and 2025, identified through searches in PubMed, Scopus, and Web of Science using the PRISMA method. Risk of bias and methodological quality were assessed using the Joanna Briggs Institute tool. The findings highlight the potential of blockchain-enabled smart contracts in health policy management, emphasizing their advantages, limitations, and implementation challenges. Additionally, the research underscores their transformative impact on digital health policies in ensuring data integrity, enhancing patient autonomy, and fostering a more resilient healthcare ecosystem. Recent advancements in quantum technologies are also considered as they present both novel opportunities and emerging threats to the future security and design of healthcare blockchain systems.

This work presents the design and implementation of an IoT enabled Endotracheal Tube Cuff Pressure Controller Device. This device, a fusion of electronics, control, and software engineering, aims to automatically regulate the cuff pressure of an Endotracheal Tube placed in a patient’s trachea, ensuring that it remains within the optimal pressure range. The ideal pressure range, established to be between 20-30 cmH2O, can be adjusted to accommodate different patients’ needs. The device is designed as an IoT device and includes an emergency button for shutting down the system in case of an emergency. The total cost of the system, which amounts to approximately 70 USD, makes it a cost-effective solution compared to other commercially available options. In order to verify the device’s capability to accurately read and supply pressure, it is benchmarked against the gold standard (Fluke 729 300G FC) using quantitative tests including Pearson’s r test, the paired t-test, and Bland-Altman analysis. The results from these assessments confirmed that the performance characteristics of the device are notably comparable to the Fluke 729 300G FC, which will be further examined in this study. These outcomes, along with the device’s economic viability, validate it as a workable and reasonable alternative. The necessity for an automated and continuous monitoring system is further reinforced by the fact that manual cuff pressure measurement is prone to error and may even put the patient through discomfort.

Falling is a major problem among the people globally, frequently due to some health problems including vision loss or balance disorder as a consequence of aging. As a result of the falling in elder people; injuries, complications, neurological problems and mortality are generally occurred. This situation also affects the patients' families psychologically. A large number of studies show that significant fractures, injuries and in some cases death are commonly encountered in elderly. Nonetheless, in the immediate intervention, the rate of damage is reduced and the life quality can be significantly restored. The purpose of this paper is to reduce fall-related problems by developing a new fall detector that we called Tesodev fall detector. In addition, a proper algorithm and a wearable electronic device attaching the sensor to patient's clothes are provided. The electronic device is an IOT device which can send and receive data wirelessly, which means that the device can inform the medical centres to improve medical attention time. Also, this device is modular and device allows keeping other medical data such as blood sugar, tension, heart rate, SPO2. Sensitivity, error rate and classification accuracy in this study are 89.8%, 23.4%, 76%, respectively.

The integration of blockchain technology into healthcare systems has emerged as a transformative solution for enhancing data security, protecting privacy, and improving interoperability. Blockchain-based smart contracts offer reliability, transparency, and efficiency in healthcare services, making them a focal point of many studies. However, challenges such as scalability, regulatory compliance, and interoperability continue to limit their widespread adoption. This study conducts a comprehensive literature review to assess blockchain-driven health data management, focusing on the classification of blockchain-based smart contracts in health policy and the health protocols and standards applicable to blockchain-based smart contracts. The systematic review includes 80 core studies published between 2019 and 2025, identified through searches in PubMed, Scopus, and Web of Science (WoS) using the PRISMA method. Findings highlight the potential of blockchain-enabled smart contracts in health policy management, emphasizing their advantages, limitations, and implementation challenges. Additionally, the research underscores their transformative impact on digital health policies, particularly in ensuring data integrity, enhancing patient autonomy, and fostering a more resilient healthcare ecosystem. Recent advancements in quantum science and technologies are also considered, as they present both novel opportunities and emerging threats to the future security and design of healthcare blockchain systems.

Artificial intelligence (AI) has become one of the most transformative technologies of recent years. By leveraging AI, businesses can enhance their environmental interaction, perform advanced analytics, and make sustainable and equitable decisions. At this point, AI is also recognized as a key driver in the advancement of green information technologies (Green IT). Green IT focuses on enabling organizations to increase productivity and efficiency while minimizing environmental impact. This study aims to identify the key research trends at the intersection of AI and Green IT and to conduct a systematic bibliometric analysis of the existing literature. Based on 246 articles retrieved from the Web of Science database (2010–2025), the study examines the most productive countries, influential journals, and thematic clusters to provide a strategic overview for future research. It was observed that AI significantly contributes to strategies such as energy efficiency, smart grid development, and climate crisis mitigation. Notably, this paper also highlights how the synergy between AI and Green IT can lay the foundation for energy-efficient and sustainable metaverse infrastructures, where immersive technologies and intelligent systems demand green and scalable computing solutions. As one of the few bibliometric studies on this emerging convergence, the paper offers strategic insights for both academia and industry to promote environmentally responsible AI-driven digital ecosystems.

The main goal of this paper is to control a mobile autonomous vehicle by studying EEG signals and their properties. EEG signals are analyzed to classify an example dataset provided by Internet resources. Different feature extraction methods were applied. Since EEG signals have high noise and information is embedded in the noise spectrum, frequency analysis was used to eliminate noise. Wavelet Transform was discussed in detail. Feature vectors were selected by Fisher Discriminate Analysis, that is an improved version of Linear Discriminant Analysis. With these data, mobile vehicle was controlled by differential driving technique. An attempt was made to avoid obstacles and to move with commands extracted from EEG signals.