Wavelet and Spectral Analysis of Normal and Abnormal Heart Sound for Diagnosing Cardiac Disorders

Hossain, Amzad; Uddin, Sharif; Rahman, Parinda; Anee, Meratun Junnut; Rifat, Md Mehedi Hasan; Uddin, Mahtab

doi:10.1155/2022/9092346

Cited by 6 publications

(1 citation statement)

References 30 publications

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“…The fluctuation or uncertainty around the estimated response surface simulation are represented by the standard error in RSM. It shows the degree of variance that could be expected in the projected response at any specific spot inside the design area [85]. The response surface model's standard error is graphically represented by the standard error plot.…”

Section: Model Contour and Response Surface Plotmentioning

confidence: 99%

MHD Mixed Convection of Non-Newtonian Bingham Nanofluid in a Wavy Enclosure with Temperature-Dependent Thermophysical Properties: A Sensitivity Analysis by Response Surface Methodology

et al. 2023

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The numerical investigation of magneto-hydrodynamic (MHD) mixed convection flow and entropy formation of non-Newtonian Bingham fluid in a lid-driven wavy square cavity filled with nanofluid was investigated by the finite volume method (FVM). The numerical data-based temperature and nanoparticle size-dependent correlations for the Al2O3-water nanofluids are used here. The physical model is a two-dimensional wavy square cavity with thermally adiabatic horizontal boundaries, while the right and left vertical walls maintain a temperature of TC and TH, respectively. The top wall has a steady speed of u=u0. Pertinent non-dimensional parameters such as Reynolds number (Re=10,100,200,400), Hartmann number (Ha=0,10,20), Bingham number (Bn=0,2,5,10,50,100,200), nanoparticle volume fraction (ϕ=0,0.02,0.04), and Prandtl number (Pr=6.2) have been simulated numerically. The Richardson number Ri is calculated by combining the values of Re with a fixed value of Gr, which is the governing factor for the mixed convective flow. Using the Response Surface Methodology (RSM) method, the correlation equations are obtained using the input parameters for the average Nusselt number (Nu¯), total entropy generation (Es)t, and Bejan number (Beavg). The interactive effects of the pertinent parameters on the heat transfer rate are presented by plotting the response surfaces and the contours obtained from the RSM. The sensitivity of the output response to the input parameters is also tested. According to the findings, the mean Nusselt numbers (Nu¯) drop when Ha and Bn are increased and grow when Re and ϕ are augmented. It is found that (Es)t is reduced by raising Ha, but (Es)t rises with the augmentation of ϕ and Re. It is also found that the ϕ and Re numbers have a positive sensitivity to the Nu¯, while the sensitivity of the Ha and Bn numbers is negative.

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Section: Model Contour and Response Surface Plotmentioning

confidence: 99%

MHD Mixed Convection of Non-Newtonian Bingham Nanofluid in a Wavy Enclosure with Temperature-Dependent Thermophysical Properties: A Sensitivity Analysis by Response Surface Methodology

et al. 2023

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Development of an IoT-based multi-level system for real-time water quality monitoring in industrial wastewater

Nishan,

Akter,

Sony

et al. 2024

Discov Water

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Industrialization and urbanization contribute significantly to environmental pollution, particularly in water bodies. The consequences of this pollution are dire, impacting both the environment and public health. A serious hazard to life on Earth, contaminated water can cause food poisoning, diarrhea, respiratory diseases, and skin ailments, among other health problems. In countries like Bangladesh, where industrial activities, particularly in the garment sector, are prevalent, waste dumping into rivers and canals has reached alarming levels. This has resulted in a drastic decline in water quality, endangering aquatic life and ecosystems. To solve this important issue, we built an innovative real-time water quality monitoring system using the Internet of Things (IoT). Our system operates at three levels, collecting data from different layers of wastewater. Key parameters, including temperature, turbidity, total dissolved solids (TDS), and pH, are continuously monitored to assess water quality. The analysis revealed that wastewater temperature varied between 25 and 37 °C across different sites, with higher temperatures observed in industrial zones, indicating potential thermal pollution. Total dissolved solids (TDS) levels exhibited substantial variations, ranging from 170 to 360 ppm, surpassing standard thresholds and posing a threat to aquatic ecosystems. Turbidity readings ranged from 1.30 to 12.89 NTU, with higher turbidity observed in areas receiving industrial discharge, highlighting the impact of suspended solids on water clarity. pH values exceeded 7.0 across all sites, ranging from 9.5 to 10.32, indicating alkaline wastewater, particularly in regions with dyeing and textile industries. By integrating these specific metrics into our monitoring system, we provide valuable insights into industrial wastewater quality and its environmental implications. These findings underscore the urgent need for real-time monitoring and targeted interventions to address water pollution. Implementing sustainable water management strategies in industrial contexts is critical to protecting public health and preserving ecosystems for future generations.

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AI diagnosis of heart sounds differentiated with super StethoScope

Ogawa,

Namino,

Mori

et al. 2024

Journal of Cardiology

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Wavelet and Spectral Analysis of Normal and Abnormal Heart Sound for Diagnosing Cardiac Disorders

Cited by 6 publications

References 30 publications

MHD Mixed Convection of Non-Newtonian Bingham Nanofluid in a Wavy Enclosure with Temperature-Dependent Thermophysical Properties: A Sensitivity Analysis by Response Surface Methodology

MHD Mixed Convection of Non-Newtonian Bingham Nanofluid in a Wavy Enclosure with Temperature-Dependent Thermophysical Properties: A Sensitivity Analysis by Response Surface Methodology

Development of an IoT-based multi-level system for real-time water quality monitoring in industrial wastewater

AI diagnosis of heart sounds differentiated with super StethoScope

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