In the field of pharmaceutical research, identifying promising pharmaceutical compounds is a critical challenge. The observance of Lipinski's Rule of Five (RO5) is a fundamental criterion, but evaluating many compounds manually requires significant resources and time. However, the integration of computational techniques in drug discovery in its early stages has significantly transformed the pharmaceutical industry, enabling further efficient screening and selection of possible drug candidates. Therefore, this study explores RO5 using algorithms of Machine Learning (ML), offering a comprehensive method to predict the druggability of pharmaceutical compounds. The study developed, evaluated, and validated the performance metrics of multiple supervised machine learning models. The best model was used to build an application that can predict and classify potential drug candidates. The findings revealed promising capabilities across all models for drug classification. Among all the explored models, Random Forest (RF), Extreme Gradient Boost (XGBoost), and Decision Tree (DT) classifiers demonstrated exceptional performance, achieving near-perfect accuracy of 99.94%, 99.81% and 99.87% respectively. This highlights the robustness of ensemble learning methods in classifying compounds based on RO5 adherence. The comparative analysis of these models underscores the importance of considering balanced accuracy, precision, F1-score, recall, and Receiver Operating Characteristics-Area Under the Curve (ROC-AUC) score, interpretability, and computational efficiency when choosing between ML algorithms in drug discovery. The DrugCheckMaster application was subsequently developed using the most predictive model and is now available on Render (https://capstone-project-dc7w.onrender.com/).

This research study aims to examine the possibilities of Hyperledger Fabric (HLF) in the healthcare sector. The study addresses the gap in the knowledge base through developing customization techniques to enable the simplicity and efficacy of Electronic Medical Records (EMR) adoption for healthcare industry applications. The focus of this research explores methods of using blockchain technology that prioritise users. The investigation of several concepts used in developing web applications has been determined. The study identified that an open-source project, known as Hyperledger Fabric, can be utilised to construct a novel method of storing EMRs. The framework provides a test network that can be customised to satisfy the need of several projects, including storing medical records. This research additionally outlines the difficulties encountered and problems that need to be resolved before Hyperledger Fabric can be successfully implemented in healthcare systems. Considering all types of blockchains available, the needs are met by Hyperledger Fabric, which offers a distributed and secure environment for healthcare systems. Blockchain has the potential to transform healthcare by putting the patient at the centre of the system and enhancing health data protection and interoperability. Also, by using grant and revoke access mechanisms, patients have complete control over their medical information as well as authorized doctors who are allowed to view records. This functionality is made possible by the chaincode defined in the blockchain platform. The research study has both practitioner and research implications for the development of secure blockchain-based EMRs.

Several heterogeneous, intelligent, and distributed devices can be connected to interact with one another over the Internet in what is termed internet of things (IoT). Also, the concept of IoT can be exploited in the industrial environment for enhancing the production of goods and services and for mitigating the risk of disaster occurrences. This application of IoT for enhancing industrial production is known as industrial IoT (IIoT). Emergent configuration (EC) is a technology that can be adopted to enhance the operation and collaboration of IoT connected devices in order to improve the efficiency of the connected IoT systems for maximum user satisfaction. To meet user goals, the connected devices are required to cooperate with one another in an adaptive, interoperable, and homogeneous manner. In this paper, a survey of the concept of IoT is presented in addition to a review of IIoT systems. The application of ubiquitous computing-aided software define networking (SDN)-based EC architecture is propounded for enhancing the throughput of oil and gas production in the maritime ecosystems by managing the exploration process especially in emergency situations that involve anthropogenic oil and gas spillages.

To account for nonstationarity, channel characterization and system design methods that employ the non-wide-sense stationary uncorrelated scattering (non-WSSUS) assumption are desirable. Furthermore, the inadequacy of the Doppler shift operator to properly account for the frequency shift in wideband channel implies that the time-frequency characterization methods that employ the Doppler shift operator are not appropriate for most wideband channels. In this article, the statistical time-scale domain characterization of the non-WSSUS wideband channel is presented. This approach employs the time scaling operator in order to account for frequency spreading, and also emphasizes on the nonstationarity of the wideband channel. The non-WSSUS statistical assumption termed local-sense stationary uncorrelated scattering (LSSUS) is presented and employed in characterizing the nonstationary property of the time-varying wideband channel. The LSSUS channel model is then parameterized to provide useful coherence and stationarity/nonstationarity parameters for optimal system design. Some application relevance of the developed model in terms of channel capacity and diversity techniques are discussed. Measurement and simulation results show that the assumption of ergodic capacity and the performance of various diversity techniques depend on the degree of channel stationarity/nonstationarity. It is shown that the quantification of this degree of stationarity through the channel parameters can provide a way of tracking channel variation and allowing for adaptive application of diversity techniques and the channel capacity.

Radio-over-fiber (ROF) technology has several advantages such as providing huge bandwidth, inherent immunity to electromagnetic interference and reduced power consumption. This paper presents a simulation to investigate the performance of orthogonal frequency division multiplexing (OFDM) as a modulation technique to transmit the basebands signal over fiber. The model contains a laser diode used as optical modulator to convert the signal from baseband to optical frequency. The QPSK-OFDM transmitted signal and power spectrum are obtained for both of AWGN and optical fiber link channels. The study reveals that the OFDM performance on the optical fiber link in terms of spectrum containment is nearly similar to that on AWGN channel. The significance of this study comes from the promising future of using optical fibers to transmit radio signal. Consequently the model of this paper can be used with different wireless communication systems such as wireless LANs and digital video broadcasting (DVB) and it is supporting to the 4th generation cellular systems. The details of design and simulated results for the proposed model are presented and discussed.