Using renewable energy sources, such as palm oil-based biodiesel, in diesel engines has demonstrated considerable potential.However, assessing their effectiveness requires comprehensive performance tests to facilitate economic evaluations, particularly in measuring fuel consumption.This study focuses on the gravimetric method for quantifying fuel consumption using a 40 kg scale.This research aims to develop a cost-effective and user-friendly method of the field and laboratory measurement technique.A load cell was integrated with an HX711 signal amplifier and a data processor to achieve this objective, complemented by an Arduino Uno data acquisition program.The study collected data during idle operating conditions at 700 rpm and New European Driving Cycle (NEDC) simulations in passenger cars fueled by biodiesel blend fuel with a volume ratio of 30% (B30).The recorded data was meticulously analyzed and presented using spreadsheet software.The results of the scale compared to the flow meter revealed fuel consumption rates in idle tests of 3.475 and 3.464 g/s, respectively.The result demonstrated a mere 0.32% difference between this developed method and conventional approaches in the idle test and a 5.8% difference in the NEDC cycle test.These findings reveal the viability and accuracy of the gravimetric technique, offering a cost-effective solution of real-world fuel consumption measurement in the pursuit of sustainable energy solutions.

The challenge of swift and reliable earthquake location prediction within earthquake early warning (EEW) systems underscore the need for innovative solutions. Existing methods provide predictions, yet there is a clear demand for enhanced accuracy and expediency in the process of resolving. The article intends in prediction of both magnitude of earthquakes and the affected areas in a specific region, focusing on California, United States, by leveraging historical data using neural networks (NNs). Neural networks outperform other algorithms such as XGBoost, linear regression, random forest, gradient boosting and support vector machine (SVM), in terms of high R2 score and low score of MAE and also mean squared error (MSE). The derived results of R2 score, MSE and MAE are 0.1607, 0.1615 and 0.2951, respectively. It seamlessly integrates geospatial visualization through Folium and GeoPandas. These tools enhance the predictive model’s capabilities by generating dynamic maps enriched with markers, each representing the anticipated impact of earthquakes. The user interface facilitates interactive input, enabling users to input earthquake parameters for real-time predictions. The resultant map not only showcases the predicted impact zone but also provides valuable insights into the severity of seismic events. It stands as a testament to the synergy between machine learning and geospatial visualization, offering a holistic solution for earthquake prediction and geographical representation.

Abstract Fast and accurate wheeling pricing has emerged as an important issue in the recent competitive power market. Embedded cost-based wheeling pricing is well accepted by power market, because it is based on actual flow of power wheeled by them. It also recovers fully the fixed cost of wheeling facility installation and operation. In this article, metaphor-less Rao-3-based ACOPF, MVA-mile method and Bialek tracing has been employed to compute wheeling prices across various generators and loads. In actual power market due to continuously varying load conditions, the computation of wheeling prices is quite a time taking process. Because for computing wheeling prices, the optimal power flow (OPF) program has to be run each time for every loading condition. In this scenario, the artificial neural network (ANN) approach has been found to be very useful, to estimate wheeling prices instantly and accurately for any unseen loading scenario. Here, a number of ANNs have been developed under parallel computing environment. This article presents a metaphor-less Rao-3-based approach to project wheeling prices in the competitive power market by developing a new radial basis function neural network (RBFNN). The present work of wheeling pricing has been demonstrated and examined on IEEE 30-bus system.

Abstract The introduction of smart meters, sensors and integrated electronic devices in the electrical secondary distribution network (ESDN) has led to the collection of massive amounts of data. Accurate prediction of faults from this data can help to improve the reliability, safety and operational efficiency of ESDN. Due to its complexity, ESDN big data are hard to process and manage using traditional technologies and tools. The difficulties posed by dataset complexity arise from issues including high dimensionality, imbalance and variability, and one current challenge is to address them simultaneously. Currently, the capability of fault prediction techniques to address this challenge remains limited. New approaches are needed to overcome it. To this purpose, this article presents a big data-based ensemble for fault prediction in ESDN (BDEFP-ESDN) on Apache Spark with gradient-boosted trees, random forest, decision tree and binomial logistic regression base models. BDEFP-ESDN is optimized for the complexity of the ESDN dataset by dimension reduction, bootstrap sampling and hyperparameter optimization approaches in the training process and a weighted voting approach in the prediction process. Our experimental results illustrate the efficiency of BDEFP-ESDN against traditional classifiers like ANN, SVM, RF and XGB, achieving an accuracy of 99.6% for both binary and multiclass classification.

The behavior of fatigue crack growth(FCG) plays a vital role in the design and performance of modern structural materials like Al 6061-T6 alloy.Al 6061-T6 is an alloy of Al-Mg-Si which is artificially aged under T6 condition.In the present study, Al 6061-T6 plates of 6 mm thickness welded by a gas metal arc welding(GMAW) technique using an ER-5356 as filler wire.The microhardness profile was obtained through Vickers microhardness test.The pre-qualification tests such as X-ray radiography, tensile, and bending tests were carried out on base metal (BM), heat-affected zone (HAZ), and weld zone (WZ)along rolling direction to study the mechanical behavior.The fatigue crack growth rate (FCGR) tests on the three zones were carried out as per ASTM E-647 standards under stress ratios of 0.1, 0.3, and 0.5.The WZ possessed highest FCGR compared to BM and HAZ.The lowest fracture toughness (K c ) was found for WZ, which is due to fusion of filler wire with BM.The microstructural transformation in HAZ from very fine needle shape b'' precipitates to coarse bar shape b' precipitates produced by the thermal effect during the welding process resulting in moderate FCGR compared to WZ and BM.Mechanical strength and microstructure regeneration during the welding thermal cycle are the two important factors that affect the FCGR in all zones.Fractographic analysis was carried out using a scanning electron microscope (SEM) to study the crack initiation, final fracture, nature of the fracture, and causes of failure under various stress ratios.

Society is shifting to an environmental basis due to threats caused by climate change, resource scarcity, and excessive generation of plastics.Biotechnology has sought alternatives to valorise waste streams, such as sludge generated in municipal wastewater treatment plants (WWTPs).Sludge bio-fermentation is suitable for sustainable volatile fatty acids (VFA) production by anaerobic digestion (AD).This biotechnological process is an excellent opportunity, as VFAs are in high demand by industry.This work aimed to study the environmental impacts generated by the bioproduction of VFA from primary sludge (PS) from the El Salitre WWTP in Bogot a using the life cycle analysis (LCA) methodology.VFA production was carried out in three semi-continuous reactors, using PS, an initial organic load of 14 gVS/L, a temperature of 25 C, and a pH of 10 for 7 and 16 d, obtaining a higher VFA production and yield for the 16-day experiment with 6048 mgCOD/L; 432 gCOD/gVS, compared to 5472 mgCOD/L; 391 gCOD/gVS produced in 7 d.An LCA was carried out to select the best retention time considering eighteen environmental categories.The study of the impact categories and the modelling of the inventory data were carried out using the ReCiPe Midpoint method and Simapro software.The results showed that energy use in the process steps is the bottleneck point for sustainability and causes significant effects in almost all the impact categories evaluated.Additionally, it was found that producing VFA from municipal WWTP sludge can be considered a sustainable process due to the indicators obtained being lower than those found in the literature.Also, the concentrations achieved allow the use of these VFAs as carbon sources for making polyhydroxyalkanoates.

Nigeria has a large deposit of clay which is the major raw material for the fabrication of porcelain insulators for high voltage applications.However, the country depends largely on imported porcelain insulators to meet its high voltage needs even in the face of the worsening exchange rate of the Nigerian currency compared to the US dollar and other foreign currencies.As a result, it becomes necessary to sort for local means of fabricating high voltage pin insulators from locally available raw materials.In this study, clay sourced from three different geographical locations (Auchi, Ikorodu, and Ota) are used to fabricate three samples of high voltage insulators.The fourth sample of insulator is also fabricated from the Ota clay with Plaster of Paris (POP) as an additive to enhance workability.The physical and electrical properties of the insulator samples are investigated and compared with imported porcelain insulators.The results show that the insulator fabricated using the clay from Auchi has the highest breakdown voltage of 5 kV and lowest leakage current of 2 mA and thus can be adopted for low-tension insulation instead of the high-tension insulation speculated from the onset of the research.

Gestational diabetes is characterized by hyperglycemia diagnosed during pregnancy.High blood sugar levels are likely to affect both the mother and child.This disease frequently goes undiagnosed due to its fewer prominent symptoms, resulting in severe unmanaged hyperglycemia, obesity, childbirth complications and overt diabetes.Artificial Intelligence is increasingly deployed in the medical field, revolutionizing and automating data processing and decisionmaking.Machine learning is a subset of artificial intelligence that can create reliable healthcare screening and predictive systems.With the advent of machine learning, detecting gestational diabetes and getting more profound insights about the disease is possible.This study explores the development of a reliable clinical decision support system for gestational diabetes detection using multiple machine learning architectures using combinations of five data balancing methods to detect gestational diabetes.An ensemble stack trained on the synthetic minority oversampling technique with edited nearest neighbor obtained the highest performance with accuracy, sensitivity and precision of 96%, 95% and 99%, respectively.Additionally, a layer of explainable artificial intelligence was added to the best-performing model using libraries such as SHapley Additive exPlanations, Local Interpretable Model-agnostic Explanations, Quantum lattice, Explain Like I'm 5 algorithm, Anchor and Feature importance.The importance of factors such as Visceral Adipose Deposit and its contribution toward the prediction of gestational diabetes is explored.This research aims to provide a meaningful and interpretable clinical decision support system to aid healthcare professionals in early gestational diabetes detection and improved patient management.

This study examines how non-covalent and covalent surface modification methods affect the stability and thermo-physical characteristics of carbon nanotubes (CNTs) and graphene platelets (GNP) when distributed in solar thermal fluids. Surfactants are employed for examining non-covalent surface modification, while acid treatment is utilized for investigating covalent modification. Nanofluid samples are created with concentrations of 0.0625%, 0.125%, 0.25%, and 0.5% by weight in pure ethylene glycol (EG) and EG–water (80:20). Zeta potential analysis was utilized to study the stability of the nanofluids. The oxidized CNT–GNP nanofluids showed outstanding stability, with zeta potential values remaining almost constant for 60 d. Experiments were conducted on pure, surfactant-dispersed, and oxidized CNT–GNP nanofluids. The thermal conductivity of oxidized CNT–GNP nanofluids increased by 16% when 0.5 wt% was dispersed in the EG–water (80:20) sample. Nanofluids with CTAB and SDS showed a 14% and 13% enhancement in thermal conductivity, respectively. Formulas were created to forecast thermal conductivity and dynamic viscosity values.

Abstract Core loss estimation in switched reluctance motor is a complex task, due to non-linear phenomena and non-sinusoidal flux density waveforms. Several methods have been developed for estimating it (e.g. empirical, and physical-mathematic models), each one with merits and limitations. This paper proposes a new method for core losses estimation based on Finite Element Method Magnetics software. The main idea is using the machine phase-current harmonics as input for estimating core losses. In addition, a comparative study is carried out, where the proposed approach is faced up to a different one, based on Fourier decomposition of the flux density waveforms in the machine sections. In order to systematically analyze and compare the applied estimation cores loss techniques, a case study of a three-phase 6/4 SRM for different simulation scenarios is introduced. The outcomes of both methods are discussed and compared, where core loss convergence is found for limited speed and load ranges.