People in the life sciences who work with Artificial Intelligence (AI) and Machine Learning (ML) are under increased pressure to develop algorithms faster than ever. The possibility of revealing innovative insights and speeding breakthroughs lies in using large datasets integrated on several levels. However, even if there is more data at our disposal than ever, only a meager portion is being filtered, interpreted, integrated, and analyzed. The subject of this technology is the study of how computers may learn from data and imitate human mental processes. Both an increase in the learning capacity and the provision of a decision support system at a size that is redefining the future of healthcare are enabled by AI and ML. This article offers a survey of the uses of AI and ML in the healthcare industry, with a particular emphasis on clinical, developmental, administrative, and global health implementations to support the healthcare infrastructure as a whole, along with the impact and expectations of each component of healthcare. Additionally, possible future trends and scopes of the utilization of this technology in medical infrastructure have also been discussed.

This paper examines the implementation of a digital product passport (DPP) using Hyperledger Fabric technology to enhance product lifecycle management within the European Union’s circular economy action plan. This study addresses the need for detailed product information on materials, origin, usage, and end-of-life instructions to improve recycling practices and promote sustainable consumption. The approach integrates decentralized identifier (DID) technology for unique product identification using the cheqd.io platform with an enterprise tailored Hyperledger Fabric blockchain network for DPP data management, leveraging their strengths to enhance security and efficiency. This paper details the data model for the DPP, including entities like Product, Manufacturer, Supplier, and Material. Performance tests on the Hyperledger Fabric network demonstrate the system’s efficacy, focusing on CRUD operations and scalability. Future work will extend to the development of client applications and more comprehensive performance evaluations considering scalability and network expansion.

The intelligent condition monitoring of wind turbines reduces their downtime and increases reliability. In this manuscript, a feature selection-based methodology that essentially works on regression models is used for identifying faulty scenarios. Supervisory control and data acquisition (SCADA) data with 1009 samples from one year and one month before failure are considered. Gearbox oil and bearing temperatures are treated as target variables with all the other variables used for the prediction model. Neighborhood component analysis (NCA) as a feature selection technique is employed to select the best features and prediction performance for several machine learning regression models is assessed. The results reveal that twin support vector regression (99.91%) and decision trees (98.74%) yield the highest accuracy for gearbox oil and bearing temperatures respectively. It is observed that NCA increases the accuracy and thus reliability of the condition monitoring system. Furthermore, the residuals from the class of support vector regression (SVR) models are tested from a statistical point of view. Diebold–Mariano and Durbin–Watson tests are carried out to establish the robustness of the tested models.

This paper presents the development of a cost-effective automatic system for greenhouse environment control. The architectural and functional features were analyzed in the context of the realization of a controlled-environment agricultural system through all its stages: installation, deployment of the software, integration, maintenance, crop control strategy setup and daily operation of the grower. The proposed embedded platform provides remote monitoring and control of the greenhouse environment and is implemented as a distributed sensing and control network integrating wired and wireless nodes. All nodes were built with low-cost, low-power microcontrollers. The key issues that were addressed include the energy-efficient control, the robustness of the distributed control network to faults and a low-cost hardware implementation. The translation of the supervisory growth-planning information to the operational (control network) level is achieved through a specific architecture residing on a crop planning module (CPM) and an interfacing block (IB). A suite of software applications with flows and interfaces developed from a grower-centric perspective was designed and implemented on a multi-tier architecture. The operation of the platform was validated through implementation of sensing and control nodes, application of software for configuration and visualization, and deployment in typical greenhouses.

As cities evolve into smarter and more connected environments, there is a growing need for innovative solutions to improve urban mobility. This study examines the potential of integrating blockchain technology into passenger transportation systems within smart cities, with a particular emphasis on a blockchain-enabled Mobility-as-a-Service (MaaS) solution. In contrast to traditional technologies, blockchain’s decentralized structure improves data security and guarantees transaction transparency, thus reducing the risk of fraud and errors. The proposed MaaS framework enables seamless collaboration between key transportation stakeholders, promoting more efficient utilization of services like buses, trains, bike-sharing, and ride-hailing. By improving integrated payment and ticketing systems, the solution aims to create a smoother user experience while advancing the urban goals of efficiency, environmental sustainability, and secure data handling. This research evaluates the feasibility of a Hyperledger Fabric-based solution, demonstrating its performance under various load conditions and proposing scalability adjustments based on pilot results. The conclusions indicate that blockchain-enabled MaaS systems have the potential to transform urban mobility. Further exploration into pilot projects and the expansion to freight transportation are needed for an integrated approach to city-wide transport solutions.

Advanced wind measuring systems like Light Detection and Ranging (LiDAR) is useful for wake management in wind farms. However, due to uncertainty in estimating the parameters involved, adaptive control of wake center is needed for a wind farm layout. LiDAR is used to track the wake center trajectory so as to perform wake control simulations, and the estimated effective wind speed is used to model wind farms in the form of transfer functions. A wake management strategy is proposed for multi-wind turbine system where the effect of upstream turbines is modeled in form of effective wind speed deficit on a downstream wind turbine. The uncertainties in the wake center model are handled by an adaptive PI controller which steers wake center to desired value. Yaw angle of upstream wind turbines is varied in order to redirect the wake and several performance parameters such as effective wind speed, velocity deficit and effective turbulence are evaluated for an effective assessment of the approach. The major contributions of this manuscript include transfer function based methodology where the wake center is estimated and controlled using LiDAR simulations at the downwind turbine and are validated for a 2-turbine and 5-turbine wind farm layouts.