This paper introduces an improved quadratic DC-DC boost converter to provide an ultra-high level voltage gain with the switched-capacitor cell based structure. The proposed converter has the ability to excel the output voltage gain to the ultra-high level without any voltage doubler circuit. Hence, the total number of components is less compared to the conventional topologies. The proposed converter offers reduced voltage stress on the capacitors, diodes, and semiconductor switches while generating a high voltage gain effect with a low duty cycle. In addition, the continuous source current (CSC) and input-to-output side ground-sharing features are also available for renewable energy applications. The steady-state and comparative performance analysis with similar topologies and power loss calculations are presented in this paper. Furthermore, the simulation results extracted from the Matlab/Simulink are compared and analyzed with the conventional topology. Finally, the experiments are carried out by designing a 150 W laboratory prototype where the peak efficiency is found 90% while delivering 80 W power to the load side.

Blood pressure (BP) monitoring can be performed either invasively via arterial catheterization or non-invasively through a cuff sphygmomanometer. However, for conscious individuals, traditional cuff-based BP monitoring devices are often uncomfortable, intermittent, and impractical for frequent measurements. Continuous and non-invasive BP (NIBP) monitoring is currently gaining attention in the human health monitoring area due to its promising potentials in assessing the health status of an individual, enabled by machine learning (ML), for various purposes such as early prediction of disease and intervention treatment. This review presents the development of a non-invasive BP measuring tool called sphygmomanometer in brief, summarizes state-of-the-art NIBP sensors, and identifies extended works on continuous NIBP monitoring using commercial devices. Moreover, the NIBP predictive techniques including pulse arrival time, pulse transit time, pulse wave velocity, and ML are elaborated on the basis of bio-signals acquisition from these sensors. Additionally, the different BP values (systolic BP, diastolic BP, mean arterial pressure) of the various ML models adopted in several reported studies are compared in terms of the international validation standards developed by the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS) for clinically-approved BP monitors. Finally, several challenges and possible solutions for the implementation and realization of continuous NIBP technology are addressed.

Carbon–steel pipelines have mostly been utilized in the oil and gas (OG) industry owing to their strength and cost-effectiveness. However, the detection of corrosion under coating poses challenges for nondestructive (ND) pipeline monitoring techniques. One of the challenges is inaccessibility because of the pipeline structure, which leads to undetected corrosion, which possibly leads to catastrophic failure. The drawbacks of the existing ND methods for corrosion monitoring increase the need for novel frameworks in feature extraction, detection, and characterization of corrosion. This study begins with the explanations of the various types of corrosion in the carbon–steel pipeline in the OG industry and its prevention methods. A review of critical sensors integrated with various current ND corrosion monitoring systems is then presented. The importance of acoustic emission (AE) techniques over other ND methods is explained. AE data preprocessing methods are discussed. Several AE-based corrosion detection, prediction, and reliability assessment models for online pipeline condition monitoring are then highlighted. Finally, a discussion with future perspectives on corrosion monitoring followed by the significance and advantages of the emerging AE-based ND monitoring techniques is presented. The trends and identified issues are summarized with several recommendations for improvement in the OG industry.

Flexible sensors have been extensively employed in wearable technologies for physiological monitoring given the technological advancement in recent years. Conventional sensors made of silicon or glass substrates may be limited by their rigid structures, bulkiness, and incapability for continuous monitoring of vital signs, such as blood pressure (BP). Two-dimensional (2D) nanomaterials have received considerable attention in the fabrication of flexible sensors due to their large surface-area-to-volume ratio, high electrical conductivity, cost effectiveness, flexibility, and light weight. This review discusses the transduction mechanisms, namely, piezoelectric, capacitive, piezoresistive, and triboelectric, of flexible sensors. Several 2D nanomaterials used as sensing elements for flexible BP sensors are reviewed in terms of their mechanisms, materials, and sensing performance. Previous works on wearable BP sensors are presented, including epidermal patches, electronic tattoos, and commercialized BP patches. Finally, the challenges and future outlook of this emerging technology are addressed for non-invasive and continuous BP monitoring.

Pipeline corrosion is one of the leading causes of failures in the transmission of gas and hazardous liquids in the oil and gas industry. In-line inspection is a non-destructive inspection for detecting corrosion defects in pipelines. Defects are measured in terms of their width, length and depth. Consecutive in-line inspection data are used to determine the pipeline’s corrosion growth rate and its remnant life, which set the operational and maintenance activities of the pipeline. The traditional approach of manually processing in-line inspection data has various weaknesses, including being time consuming due to huge data volume and complexity, prone to error, subject to biased judgement by experts and challenging for matching of in-line inspection datasets. This paper aimed to contribute to the adoption of machine learning approaches in classifying pipeline defects as per Pipeline Operator Forum requirements and matching in-line inspection data for determining the corrosion growth rate and remnant life of pipelines. Machine learning techniques, namely, decision tree, random forest, support vector machines and logistic regression, were applied in the classification of pipeline defects using Phyton programming. The performance of each technique in terms of the accuracy of results was compared. The results showed that the decision tree classifier model was the most accurate (99.9%) compared with the other classifiers.

In this paper, a novel structure of multilayer organic photovoltaic cell has been designed and simulated. The integration of Poly(3-hexylthiophene-2,5-diyl) (P3HT) buffer layer and Poly(9,9-bis(3’-(N,N-dimethyl) N- ethylammoinium propyl-2,7-fluorene)-alt-2,7-(9,9 dioctyl fluorene)) dibromide (PFN:BR) electron transport layer (ETL) in the proposed solar cell has improved the performance significantly. The various performance measuring parameters like power conversion efficiency (PCE), short circuit current (Jsc), open circuit voltage (Voc), fill factor (FF), quantum efficiency (QE) have improved significantly. Furthermore, the effect of different layer thickness, the density of traps $N_{t}$ and temperature on the proposed solar cell has been studied and the optimum value has been obtained. It has been observed that after optimizing the different parameters of the proposed structure, the performance measuring parameters shows an improvement of 14%, 33.3%, 200% and 300% in Voc FF, Jsc and PCE respectively over the reported organic solar cells. Further, a QE of about 90% is achieved in the proposed structure.