A Strategy for Classification of “Vaginal vs. Cesarean Section” Delivery: Bivariate Empirical Mode Decomposition of Cardiotocographic Recordings

We propose objective and robust measures for the purpose of classification of “vaginal vs. cesarean section” delivery by investigating temporal dynamics and complex interactions between fetal heart rate (FHR) and maternal uterine contraction (UC) recordings from cardiotocographic (CTG) traces. Multivariate extension of empirical mode decomposition (EMD) yields intrinsic scales embedded in UC-FHR recordings while also retaining inter-channel (UC-FHR) coupling at multiple scales. The mode alignment property of EMD results in the matched signal decomposition, in terms of frequency content, which paves the way for the selection of robust and objective time-frequency features for the problem at hand. Specifically, instantaneous amplitude and instantaneous frequency of multivariate intrinsic mode functions are utilized to construct a class of features which capture nonlinear and nonstationary interactions from UC-FHR recordings. The proposed features are fed to a variety of modern machine learning classifiers (decision tree, support vector machine, AdaBoost) to delineate vaginal and cesarean dynamics. We evaluate the performance of different classifiers on a real world dataset by investigating the following classifying measures: sensitivity, specificity, area under the ROC curve (AUC) and mean squared error (MSE). It is observed that under the application of all proposed 40 features AdaBoost classifier provides the best accuracy of 91.8% sensitivity, 95.5% specificity, 98% AUC, and 5% MSE. To conclude, the utilization of all proposed time-frequency features as input to machine learning classifiers can benefit clinical obstetric practitioners through a robust and automatic approach for the classification of fetus dynamics

A novel automated demand response control using fuzzy logic for islanded battery‐operated rural microgrids

Islanded rural microgrid require continuous resource monitoring. Demand response schemes have been phenomenal in managing loads. However, urban demand response schemes are well equipped with market prices and peak time penalties to control deferrable loads. In rural microrids, regular loads such as fans, lights and water pumps are normally used that do not fall under category of deferrable loads. In addition, full liberty of utilizing regular load at any time, lack of awareness and no information of storage reserves make task of load management more complex. In this research fully automated two layered demand response scheme is designed for regular operating loads. The first layer control is load mode control. The mode of operation is decided on the state of charge (SoC) of battery. In second layer, fuzzy controller is designed on the consumer's routines, SoC and ambient temperature as membership function. Results are assessed in terms of consumers comfort and availablity of SoC. The load operation in automated demand response remained indentical to actual rutine operation as per consumer's desire with 5 to 7% deviation. In all modes of operation SoC levels remained 15% higher and heavy load operated 13.5% more compare to relevant study

Theoretical Analysis of Roll-Over-Web Surface Thin Layer Coating

This study presents the theoretical investigation of a roll-over thin layer formation under the lubrication approximation theory. The set of differential equations derived by lubrication approximation is solved by the optimal homotopy asymptotic method (OHAM) to obtain precise expressions for pressure and velocity gradients. Critical quantities such as velocity, pressure gradient, and coating layer depth are numerically estimated. The impact of parameters affecting the coating and layer formation is revealed in detail. Results indicate that the transport properties of the higher-grade fluid play an essential role in regulating velocity, pressure, and the final coated region. Moreover, couple stress effects on the properties of fluid particles to be coated on roller-surface have also been studied

Leveraging InGaN solar cells for visible light communication reception

Solar cells are increasingly being utilised for both energy harvesting and reception in free-space optical (FSO) communication networks. The authors focus on the implementation of a mid-band p-In0.01Ga0.99 N/p-In0.5Ga0.5 N/n-In0.5Ga0.5 N (PPN) solar cell, boasting an impressive 26.36% conversion efficiency (under 1.5AM conditions) as a receiver within an indoor FSO communication network. Employing a solar cell with dimensions of 1 mm in length and width, the FSO system underwent simulation using Optisystm software, while the solar cell's behaviour was simulated using SCAPS-1D. The received power from the solar cell was then compared to that of four commercially available avalanche photodiode (APD) receivers. Exploring incident wavelengths spanning 400–700 nm within the visible spectrum, across transmission distances of 5, 10, 15, and 20 m, the study presented current-voltage (IV) and power-voltage curves. Notably, the InGaN solar cell exhibited superior electrical power output compared to all commercial APDs. In conclusion, the findings underscore that augmenting received power has the potential to enhance FSO network quality and support extended transmission distances.</p

Numerical Investigation of Heat Transfer on Unsteady Hiemenz Cu-Water and Ag-Water Nanofluid Flow over a Porous Wedge Due to Solar Radiation

Nanoparticles are generally used to scatter and absorb solar radiations in nanofluid-based direct solar receivers to efficiently transport and store the heat. However, solar energy absorption in nanofluid can be enhanced by using differential materials and tuning nanofluid parameter. In this regard, theoretical investigations of unsteady homogeneous Hiemenz flow of an incompressible nanofluid having copper and silver nanoparticles over a porous wedge is carried out by using optimal homotopy asymptotic method (OHAM). Hence, a semi-analytical solver is applied to the transformed system to study the significance of magnetic field along with Prandtl number. In this work, impacts of conductive radiations, heat sink/source, unsteadiness, and flow parameters have been investigated for velocity and temperature profiles of copper and silver nanoparticles-based nanofluid. The effects of magnetic strength, volume fraction of nanoparticles, thermal conductivity, and flow parameters have also been studied on the considered nanofluids

Dynamical Study of Fokker-Planck Equations by Using Optimal Homotopy Asymptotic Method

In this article, Optimal Homotopy Asymptotic Method (OHAM) is used to approximate results of time-fractional order Fokker-Planck equations. In this work, 3rd order results obtained through OHAM are compared with the exact solutions. It was observed that results from OHAM have better convergence rate for time-fractional order Fokker-Planck equations. The solutions are plotted and the relative errors are tabulated

Antimony trisulfide with graphene oxide coated titania nanotube arrays as anode material for lithium-ion batteries

The performance of the Lithium Ion Batteries (LiBs) is significantly influenced with the synergetic chemical properties of two different materials in a composite form. The specific capacity of both titanium dioxide arrays (TNAs) and Antimony trisulfide (Sb2S3) bottleneck the performance of LiB due to the low conductivity after the implantation as anode material. Herein, a novel multifunctional composite composed of highly dispersed Sb2S3 on freestanding tubular TNAs host via chemical bath deposition method (CBD) for use as anode material in lithium-ion batteries (LIBs). The loading quantity of Sb2S3 with reduced graphene oxide (rGO) was regulated to achieve adjustable outcomes. The composite anode consisting of TNAs/ Sb2S3/G in lithium-ion batteries (LIBs) has a specific capacity that is three times greater than conventional anodes. Furthermore, this composite anode maintains stable cyclic performance even after undergoing 300 cycles. The initial coulombic efficiency of the composite electrode is 100%, whereas the bare TNAs had a coulombic efficiency of 45%. The cycle performance analysis demonstrated that the TNAs/ Sb2S3/G composite has superior specific capacity and efficiency, even under high current density conditions of 500 mA/cm2. The rate performance is greatly improved, indicating the efficacy of this innovative composite anode material for high-performance LIBs

Stress Monitoring Using Machine Learning, IoT and Wearable Sensors

The Internet of Things (IoT) has emerged as a fundamental framework for interconnected device communication, representing a relatively new paradigm and the evolution of the Internet into its next phase. Its significance is pronounced in diverse fields, especially healthcare, where it finds applications in scenarios such as medical service tracking. By analyzing patterns in observed parameters, the anticipation of disease types becomes feasible. Stress monitoring with wearable sensors and the Internet of Things (IoT) is a potential application that can enhance wellness and preventative health management. Healthcare professionals have harnessed robust systems incorporating battery-based wearable technology and wireless communication channels to enable cost-effective healthcare monitoring for various medical conditions. Network-connected sensors, whether within living spaces or worn on the body, accumulate data crucial for evaluating patients' health. The integration of machine learning and cutting-edge technology has sparked research interest in addressing stress levels. Psychological stress significantly impacts a person's physiological parameters. Stress can have negative impacts over time, prompting sometimes costly therapies. Acute stress levels can even constitute a life-threatening risk, especially in people who have previously been diagnosed with borderline personality disorder or schizophrenia. To offer a proactive solution within the realm of smart healthcare, this article introduces a novel machine learning-based system termed "Stress-Track". The device is intended to track a person's stress levels by examining their body temperature, sweat, and motion rate during physical activity. The proposed model achieves an impressive accuracy rate of 99.5%, showcasing its potential impact on stress management and healthcare enhancement