Hypertensive disorders in pregnancy at the Federal Medical Centre, Yenagoa, South-South Nigeria: a 5-year review

Background: Hypertensive disorders complicate 5.2%-8.2% of pregnancies, and contribute significantly to perinatal and maternal morbidity and mortality worldwide. The objective of this study is to determine the incidence, clinical characteristics, maternal and perinatal outcomes of hypertensive disorders in pregnancy at the Federal Medical Centre, Yenagoa, Bayelsa State, South-South Nigeria.Methods: This retrospective study was conducted between 1 January, 2016 and 31 December, 2020. Relevant data was retrieved, entered into a pre-designed proforma, and analysed using IBM SPSS version 25.0.Results: Out of the 4,571 obstetric patients that were managed in our Centre in the period under review, 335 of them had HDP, giving an incidence rate of 7.32%. The most common HDP were pre-eclampsia (189, 56.4%) and eclampsia (82, 24.5%), while the least common was chronic hypertension (3, 0.9%). A little more than one-half (171, 51.0%) of the women delivered preterm, with a mean gestational age at delivery of 35.5 weeks. The most common route of delivery was emergency Caesarean section (205, 61.2%). There were three maternal deaths, giving a case fatality rate of 0.9%. Two of the maternal deaths were due to eclampsia, and one, from pre-eclampsia.Conclusions: Women should be adequately counseled to embrace preconception care, early booking and regular antenatal care visits, with proper monitoring of blood pressure and urine protein. Prompt diagnosis and management are key in preventing the maternal and perinatal morbidity and mortality that are associated with these disorders

Performance Evaluation of a Nanomaterial-Based Thermoelectric Generator with Tapered Legs

A thermoelectric generator (TEG) converts thermal energy to electricity using thermoelectric effects. The amount of electrical energy produced is dependent on the thermoelectric material properties. Researchers have applied nanomaterials to TEG systems to further improve the device’s efficiency. Furthermore, the geometry of the thermoelectric legs has been varied from rectangular to trapezoidal and even X-cross sections to improve TEG’s performance further. However, up to date, a nanomaterial TEG that uses tapered thermoelectric legs has not been developed before. The most efficient nanomaterial TEGs still make use of the conventional rectangular leg geometry. Hence, for the first time since the conception of nanostructured thermoelectrics, we introduce a trapezoidal shape configuration in the device design. The leg geometries were simulated using ANSYS software and the results were post-processed in the MATLAB environment. The results show that the power density of the nanoparticle X-leg TEG was 10 times greater than that of the traditional bulk material semiconductor X-leg TEG. In addition, the optimum leg geometry configuration in a nanomaterial-based TEG is dependent on the operating solar radiation intensity

High Performance Solar Thermoelectric Generator Using Asymmetrical Variable Leg Geometries

This paper presents a computational study of the combined effects of variable geometry and asymmetry in the legs of thermocouples of thermoelectric modules used in solar thermoelectric generators (STEGs). Six different models were considered for the thermocouples in each module, namely: rectangular-rectangular legs, rectangular-trapezoidal legs, rectangular-X legs, trapezoidal-trapezoidal legs, trapezoidal-X legs, and X-X legs. Simulations of the six different modules under the same heat flux was carried out in ANSYS 2020 R2 software. Temperature and voltage distributions were obtained for each model and the results indicate significant variations due to the utilization of varying leg geometries. Results show that the X-X leg module generated the highest temperature gradient and electric voltage. In comparison, a temperature gradient and electric voltage of 297 K and 16 V, respectively were achieved with the X-X leg module as against 182 K and 8.4 V, respectively, achieved in a conventional rectangular leg module. This suggests a 63.2% and 90.5% increase in the temperature gradient and electric voltage of the conventional TE module. Therefore, this study demonstrates that X geometry gives the best performance for thermoelectric modules and STEGs

Predicting the Optimal Performance of a Concentrated Solar Segmented Variable Leg Thermoelectric Generator Using Neural Networks

The production of high-performing thermoelectrics is limited by the high computational energy and time required by the current finite element method solvers that are used to analyze these devices. This paper introduces a new concentrating solar thermoelectric generator made of segmented materials that have non-uniform leg geometry to provide high efficiency. After this, the optimum performance of the device is obtained using the finite element method conducted using ANSYS software. Finally, to solve the high energy and time requirements of the conventional finite element method, the data generated by finite elements are used to train a regressive artificial neural network with 10 neurons in the hidden layer. Results are that the power and efficiency obtained from the optimized device design are 3× and 2× higher than the original unoptimized device design. Furthermore, the developed neural network has a high accuracy of 99.95% in learning the finite element data. Finally, the neural network predicts the modified device performance about 800× faster than the conventional finite element method. Overall, the paper provides insights into how thermoelectric manufacturing companies can harness the power of artificial intelligence to design very high-performing devices while saving time and cost

Machine Learning Performance Prediction of a Solar Photovoltaic-Thermoelectric System with Various Crystalline Silicon Cell Types

Since the solar photovoltaic-thermoelectric (PV-TE) is an upcoming technology, the current literature on PV-TEs have failed to thoroughly investigate the effects of different solar cell types on the PV-TE’s performance. Such parametric study becomes necessary since the properties of the solar cell, characterized by the cell temperature coefficient and reference efficiency, determine the overall performance of the PV-TE. Further, the design information obtained from numerical solvers is minimal due to the high time and computational energy required to optimize the photovoltaic-thermoelectric (PV-TE) performance. This problem severely affects the ease with which useful design information can be drawn from current methods to design high-performance PVs. Additionally, to reduce the complexity of the existing numerical model, the previous models have introduced some principal assumptions that have significantly affected the accuracy of the numerical results. Finally, the numerical model has neglected several real-life operating conditions since they increase the model complexity. For the first time, deep neural networks are proposed to predict the photovoltaic-thermoelectric performance designed with 3 different crystalline solar cells as a perfect replacement for the inefficient numerical methods used to analyze the hybrid system. After that, the data generated by the numerical solver is fed to an optimum 3-layer deep neural network with 20 neurons per layer to forecast the hybrid system’s performance efficiently. The training of the neural network is governed by Bayesian regularization and the performance of the model is evaluated using the mean squared error, coefficient of determination, and training time. Results show that the deep neural network accurately learned the results that took the conventional numerical solver 1,600 mins to generate in just 12 min and 27 s, making the proposed network 128.51 times faster than the traditional numerical method. Furthermore, the accuracy of the network is demonstrated by the low mean squared error of and high training and testing regression of 100% and 99.98%, respectively

Harnessing solar power: Innovations in nanofluid-cooled segmented thermoelectric generators for exergy, economic, environmental, and thermo-mechanical excellence

Addressing the imperative need for advancements in thermoelectric generation, this study pioneers an analysis of nanofluid-cooled solar segmented thermoelectric generators with non-uniform cross-sections. Insights into thermal management, structural integrity, and economic efficiency in thermoelectric systems are provided, employing a numerical model that accurately represents thermoelectric effects by accounting for temperature dependencies of semiconductor materials. Through research, exploration of diverse coolant strategies, including TiO2, Fe3O4, Al2O3, and graphene, offers key insights into their impact on cooling dynamics. Findings demonstrate that graphene nanofluid, operating at a flow velocity of 2 m/s, outperforms others, achieving optimal power generation of 221.77 mW and an exergy efficiency of 8.99 %. Additionally, at a concentrated irradiance of 595 kWm−2, graphene leads in environmental benefits, with the highest CO2 savings of 0.38 kgyr−1, and demonstrates economic advantages with a cost-effective dollar per mW value of 3.79×10−6 $mW−1. Furthermore, the study verifies the structural integrity of these systems, with graphene achieving an optimal von Mises stress of 1.35 GPa at a semiconductor height of 0.2 mm. These advancements contribute to environmentally friendly and high-performance generators for sustainable energy solutions, paving the way for future innovations in thermoelectric technology

Renewable Energy Potential Estimation Using Climatic-Weather-Forecasting Machine Learning Algorithms

The major challenge facing renewable energy systems in Nigeria is the lack of appropriate, affordable, and available meteorological stations that can accurately provide present and future trends in weather data and solar PV performance. It is crucial to find a solution to this because information on present and future solar PV performance is important to renewable energy investors so that they can assess the potential of renewable energy systems in various locations across the country. Although Nigerian weather provides favorable weather conditions for clean power generation, there is little penetration of renewable energy systems in the region, since over 95% of the power is fossil-fuel-generated. This is because there has been no detailed report showing the potential of clean power generation systems due to the dysfunctional meteorological stations in the country. This paper sought to fill this knowledge gap by providing a machine-learning-inspired forecasting of environmental weather parameters that can be used by manufacturing companies in evaluating the profitability of siting renewable energy systems in the region. Crucial weather parameters such as daily air temperature, relative humidity, atmospheric pressure, wind speed, and rainfall were obtained from NASA for a period of 19 years (viz. 2004–2022), resulting in the collection of 6664 high-resolution data points. These data were used to build diverse regressive neural networks with varying hyperparameters to find the best network arrangement. In summary, a low mean-squared error of 7 × 10−3 and high regression correlations of 96% were obtained during the training

Multiobjective Optimization and Machine Learning Algorithms for Forecasting the 3E Performance of a Concentrated Photovoltaic-Thermoelectric System

Previous theoretical research efforts which were validated by experimental findings demonstrated the thermo-economic benefits of the hybrid concentrated photovoltaic-thermoelectric (CPV-TE) system over the stand-alone CPV. However, the operating conditions and TE material properties for maximum CPV-TE performance may differ from those required in a standalone thermoelectric module (TEM). For instance, a high-performing TEM requires TE materials with high Seebeck coefficients and electrical conductivities, and at the same time, low thermal conductivities (). Although it is difficult to attain these ideal conditions without complex material engineering, the low implies a high thermal resistance and temperature difference across the TEM which raises the PV backplate’s temperature in a hybrid CPV-TE operation. The increased PV temperature may reduce the overall system’s thermodynamic performance. To understand this phenomenon, a study is needed to guide researchers in choosing the best TE material for an optimal operation of a CPV-TE system. However, no prior research effort has been made to this effect. One method of finding the optimum TE material property is to parametrically vary one or more transport parameters until an optimum point is determined. However, this method is time-consuming and inefficient since a global optimum may not be found, especially when large incremental step sizes are used. This study provides a better way to solve this problem by using a multiobjective optimization genetic algorithm (MOGA) which is fast and reliable and ensures that the global optimum is obtained. After the optimization has been conducted, the best performing conditions for maximum CPV-TE energy, exergy, and environmental (3E) performance are selected using the technique for order performance by similarity to ideal solution (TOPSIS) decision algorithm. Finally, the optimization workflow is deployed for 7000 test cases generated from 10 features using the optimal machine learning (ML) algorithm. The result of the optimization chosen by the TOPSIS decision-making method generated an output power, exergy efficiency, and CO2 saving of 44.6 W, 18.3%, and 0.17 g/day, respectively. Furthermore, among other ML algorithms, the Gaussian process regression was the most accurate in learning the CPV-TE performance dataset, although it required more computational effort than some algorithms like the linear regression model