Technologies that predict the sources of substances diffused in the atmosphere, ocean, and chemical plants are being researched in various fields. The flows transporting such substances are typically in turbulent states, and several problems including the nonlinearity of turbulence must be overcome to enable accurate estimations of diffusion-source location from limited observation data. We studied the feasibility of machine learning, specifically convolutional neural networks (CNNs), to the problem of estimating the diffusion distance from a point source, based on two-dimensional, instantaneous information of diffused-substance distributions downstream of the source. The input image data for the learner are the concentration (or luminance of fluorescent dye) distributions affected by turbulent motions of the transport medium. In order to verify our approach, we employed experimental data of a fully developed turbulent channel flow with a dye nozzle, wherein we attempted to estimate th... [more]

Generally, software developers make errors during the distributed software development process; therefore, software testing delay is a significant concern. Some of the software mistakes are minor, but others may be costly or harmful. Since things can still go wrong—individuals encounter mistakes from time to time—there is a need to double-check any software we develop in a distributed environment. The current global pandemic, COVID-19, has exacerbated and generated new challenges for IT organizations. Many issues exist for distributed software testing that prevent the achievement of successful and timely risk reduction when several of the mechanisms on which testing is based are disrupted. The environment surrounding COVID-19 is quickly evolving on a daily basis. Moreover, the pandemic has exposed or helped to develop flaws in production systems, which obstruct software test completion. Although some of these issues were urgent and needed to be evaluated early during the distributed so... [more]

Heavy metal contamination is an elevating threat to the aquatic environment of estuarine bays. In the present study, surface sediments from Shantou Bay in South China were collected and detected for the concentrations of heavy metals including copper (Cu), zinc (Zn), chromium (Cr), nickel (Ni), cadmium (Cd), and lead (Pb) using ICP-MS. Spatial distribution, temporal trend, and potential ecological risks for the metal pollution were discussed. The results showed that levels of metals generally decreased by the order of Zn > Pb > Cr > Cu > Ni > Cd. Spatial variation of metal contents was observed with an order of Rongjiang River > Niutian Bay > Shanthou Harbor for Zn and Cd. Sediments of the Niutian Bay were observed with higher levels of Cu, Cr, Ni, and Pb. Compared with reported data from the same region at different sampling periods, a low−high−low trend was observed for the concentrations of the six elements, suggesting a great improvement of sediment quality in Shantou Bay. The aver... [more]

Six heavy metals (As, Cu, Cd, Zn, Cr, and Pb) in surface sediments (0−5 cm) from the twenty selected sites of the coastal Pearl Bay (South China Sea) were analyzed to assess the distribution pattern and potential ecological risk. Overall concentrations (mg/kg, dw) in the sediment samples were: As (10.88 ± 6.50), Cu (24.16 ± 18.63), Cd (0.55 ± 0.78), Zn (48.53 ± 30.06), Cr (35.78 ± 28.66), Pb (31.28 ± 18.50). Results showed that the overall mean values of Cd concentrations exceeded the standard of China Marine Sediment Quality, caused by significantly high levels of Cd contents in five sites (S8, S11, S13, S16, and S17) at the offshore area of Pearl Bay. Generally, the metal concentrations showed a decreasing trend from the offshore area to the inner bay. Various index values such as the geo-accumulation index (Igeo), the ecological risk index (Eri), and the contamination factor (CF) demonstrated that the coastal Pearl Bay was not polluted by the examined metals except for Cd, which mig... [more]

This study included the testing of normal (Gaussian) distribution of input data and, consequently, spatially interpolating maps of chemical components and cement modules in the flysch. This deposit contains the raw material for cement production. The researched area is located in southern Croatia, near Split, as part of the exploited field “St. Juraj−St. Kajo”. There are six lithological units: (1) alternation of marls and sandstones with inclusions of conglomerates, (2) marl, (3) calcsiltite, (4) calcarenite, (5) marl with nummulites, (6) debrites, and (7) clayey marl. All of them are deposited in the (a) northern and (b) southern beds. Only debrites are divided into the (a) western and (b) eastern layers. Those lithological units were divided technologically based on their cement modules (lime saturation factor (LSF), silicate module (SM), and aluminate module (AM)). The average thicknesses were analysed, followed by normality tests (Kolmogorov−Smirnov (K−S) and Shapiro−Wilk (S−W)) o... [more]

This study is focused on the study and development of a gas-to-liquid heat pipe heat exchanger (HPHE) based on numerical and experimental analysis. Stainless steel heat pipes were installed inside the heat exchanger in the form of three equilateral triangles, staggered into a hexagonal configuration to simulate the waste heat recovery from hot exhaust gas to a water flow. The first main aim of this study was focused on 3D design and numerical analysis, which were used to create and calculate the effect of similar input conditions on the overall system. The system was tested for the overall heat transfer by measuring the temperature change in both fluids. The heat transfer and overall average temperature were used to calculate the effectiveness of the system. In the second part of this study, a test of the waste heat recovery was undertaken with this setup, using water as the cooling fluid. The study was conducted with different input velocities and temperatures of waste hot air, contro... [more]

In this paper the propulsion of elliptical objects (called squirmers) by imposed tangential velocity along the surface is studied. For a symmetric velocity distribution (a neutral squirmer), pushers (increased tangential velocity on the downstream side of the ellipse) and pullers (increased tangential velocity on the upstream side of the ellipse), the hydrodynamic characteristics, are simulated numerically using the immersed boundary-lattice Boltzmann method. The accuracy of the numerical scheme and code are validated. The effects of Reynolds number (Re) and squirmer aspect ratio (AR) on the velocity u*, power expenditure P* and hydrodynamic efficiency η of the squirmer are explored. The results show that the change of u* along radial direction r* shows the relation of u*~r*−2 for the neutral squirmer, and u*~r*−1 for the pusher and puller. With the increase of Re, u* of the pusher increases monotonically, but u* of the puller decreases from Re = 0.01 to 0.3, and then increases from Re... [more]

The present study is carried out to examine the behavior of magnetohydrodynamic Williamson nanofluid flow and heat transfer over a non-linear stretching sheet embedded in a porous medium. In the current work, the influence of heat generation and viscous dissipation has been taken into account. The considered phenomenon in the form of partial differential equations is transformed into ordinary differential equations by utilizing an appropriate similarity transformation. The reduced form is solved by using rigorous MATLAB built-in solver bvp4c. The numerical solutions for the velocity field, temperature field, and mass concentration along with the skin friction coefficient, Nusselt number, and Sherwood number are computed. The obtained solutions are shown in graphs and are discussed with physical reasoning. It is noted that by increasing Williamson fluid parameter W, the velocity decreases and concentration profile increases. It is deduced that increasing Eckert number Ec leads to a rise... [more]

Through field investigation and experimental analysis, the forms, contents and distribution of heavy metals (Zn, Pb, Cu, Cd, Ni, Cr) in rhizosphere and non-rhizosphere soils of Miscanthus floridulus growing everywhere in Tielongwei mine pond (sample plot 1), Caoduikeng tailings pond (sample plot 2), Donghua tailings pond (sample plot 3) and Small tailings pond (sample plot 4) in Dabaoshan, Guangdong Province were studied. The results showed that the main forms and distributions of heavy metals in rhizosphere and non-rhizosphere soils are basically the same, which shows that the mineral content accounts for most of the total amount of heavy metals, while the exchange content is low. Compared with non-rhizosphere soil, the proportion of exchangeable and organic heavy metals in rhizosphere soil increased significantly, in which the proportion of organic-bound Cu increased by 53.25%, the proportion of organic-bound Cd and Pb increased by more than 17%, and the proportion of Zn increased by... [more]

With the extremely rapid growth of data in various industries, big data is gradually recognized and valued by people. Medical big data, which can best reflect the significance of big data value, has also received attention from various parties. In Saudi Arabia, healthcare quality assessment is mostly based on human experience and basic statistical methods. In this paper, we proposed a healthcare quality assessment model based on medical big data in a region of Saudi Arabia, which integrated traditional evaluation methods and machine learning based techniques. Healthcare data has been accurate and effective after noise processing, and the outliers could reflect certain medical quality information. An improved k-nearest neighbors (KNN) algorithm has been proposed and its time complexity have been reduced to be more suitable for big data processing. An outlier indicator has been established based on statistical methods and the improved KNN algorithm. Experimental results showed that the p... [more]

This work deals with a numerical investigation of a hydrodynamic−elastic problem within the framework of a double enclosure solar collector technological configuration. The solar collector presents two enclosures separated by an elastic absorber wall. The upper enclosure is filled with air, whereas the lower one is filled with Fe3O4/water nanofluid. The mathematical model governing the thermal and flow behaviors of the considered nanofluid is elaborated. The effects of imposed hot temperatures, the Rayleigh number and air pressure on the nanofluid’s temperature contours, velocity magnitude distribution, temperature evolution, velocity magnitude evolution and Nusselt number evolutions are numerically investigated. The numerical results show and assess how the increase in the Rayleigh number affects convective heat transfer at the expense of the conductive one, as well as how much the Nusselt number and the nanofluid velocity magnitude and temperature are affected in a function of the im... [more]

Voltage/frequency (V/f) drive of a three-phase induction motor plays a crucial role in a flour mill for energy saving. Wheat consumption in India is increasing day by day, which reached 105,000 metric ton (MT) in 2021. India’s high wheat consumption and production increase flour mills. Thus, energy efficiency in a flour mill is a must in the present situation. Hence, V/f drives are widely used in flour mills. Apart from the advantages of V/f drive, electronic circuits in a drive induce harmonics in a power system. Power quality plays a vital role in a modern power system. Harmonics by V/f drive increase the current consumption, causing increased losses, cable overheating, and motor overheating, which necessitates a filter for harmonic mitigation. In this paper, an artificial neural network controller-based hybrid power filter is proposed for harmonic mitigation. A hybrid power filter (HPF) is presented to overcome the problems and achieve the active and passive power filter’s benefits.... [more]

In the train operation department, humans are the most important and dynamic element, and their safe behavior is directly related to the safety of railway transportation. How to accurately assess the safety behavior risk of on-site workers is an urgent problem to be solved. In risk practice, some scholars directly use the accident potential data to calculate the risk parameters, and the accuracy of the risk magnitude is greatly affected by the data quality. Second, the traditional two-dimensional matrix only considers two external factors, probability and severity, without an in-depth analysis of the inherent vulnerability of risk, resulting in low accuracy of risk assessment. With a focus on the hazard factor, this study proposes a three-dimensional risk assessment approach based on grid management to carry out a personalized risk assessment of grid events. Through the grid division, the method can accurately identify the risk events of employees in any cell grid at a certain moment i... [more]

A new supervisory training rule for a shallow fully connected neural network (SFCNN) is proposed in this present study. The proposed training rule is developed based on local linearization and analytical optimal solutions for linearized SFCNN. The cause of nonlinearity in neural network training is analyzed, and it is removed by local linearization. The optimal solution for the linearized SFCNN, which minimizes the cost function for the training, is analytically derived. Additionally, the training efficiency and model accuracy of the trained SFCNN are improved by keeping estimates within a valid range of the linearization. The superiority of the proposed approach is demonstrated by applying the proposed training rule to the modeling of a typical nonlinear pH process, Boston housing prices dataset, and automobile mileage per gallon dataset. The proposed training rule shows the smallest modeling error and the smallest iteration number required for convergence compared with several previo... [more]

This paper considers the constrained inverse problem based on the nonlinear convection-diffusion equation in the multiphase porous media flow. To solve this problem, a widely convergent homotopy method is introduced and proposed. To evaluate the performance of the mentioned method, two numerical examples are presented. This method turns out to have wide convergence region and strong anti-noise ability.

Developing a forecasting model for oilfield well production plays a significant role in managing mature oilfields as it can help to identify production loss earlier. It is very common that mature fields need more frequent production measurements to detect declining production. This study proposes a machine learning system based on a hybrid empirical mode decomposition backpropagation higher-order neural network (EMD-BP-HONN) for oilfields with less frequent measurement. With the individual well characteristic of stationary and non-stationary data, it creates a unique challenge. By utilizing historical well production measurement as a time series feature and then decomposing it using empirical mode decomposition, it generates a simpler pattern to be learned by the model. In this paper, various algorithms were deployed as a benchmark, and the proposed method was eventually completed to forecast well production. With proper feature engineering, it shows that the proposed method can be a p... [more]

A mathematical model of micropolar squeezing flow of nanofluids between parallel planes is taken into consideration under the influence of the effective Prandtl number using ethyl glycol (C2H6O2) and water (H2O) as base fluids along with nanoparticles of gamma alumina (γAl2O3). The governing nonlinear PDEs are changed into a system of ODEs via suitable transformations. The RKF (Range−Kutta−Fehlberg) technique is used to solve the system of nonlinear equations deriving from the governing equation. The velocity, temperature, and concentration profiles are depicted graphically for emerging parameters such as Hartmann number M, micronation parameter K, squeeze number R, Brownian motion parameter Nb, and thermophoresis parameter Nt. However, physical parameters such as skin friction coefficient, Nusselt number, and Sherwood number are portrayed in tabulated form. The inclusion of the effective Prandtl number model indicated that the effect of the micropolar parameter K on angular velocity h... [more]

The present study concerns investigating the two-dimensional Magnetohydrodynamics (MHD) boundary layer flow of Williamson nanofluid over a non-linear stretching sheet. The focus of this study is based on the global influence of the non-Newtonian Williamson fluid parameter (λ) rather than the local one that exists in the literature for linear and non-linear stretching cases. The mathematical model of the problem is based on the law of conservation of mass, momentum, and energy. The derived partial differential equations are transformed into ordinary differential equations by applying an appropriate similarity transformation. The subsequent equations are solved numerically by using the Shooting method. The physical quantities Skin friction coefficient, as well as the Sherwood and Nusselt numbers are computed locally. To validate the implemented shooting method, a comparison is made with the results obtained by Matlab function bvp4c, and good agreement is found. The Prandtl number, Pr, ha... [more]

African agriculture is adversely impacted by arable soil compaction, the degree of which is affected by the speed at which the tractor is maneuvered on the fields, which affects the degree of soil compaction. However, there is no reliable, existing mathematical correlation between the extent of compaction on the one hand, and the tractor speed/s and soil moisture levels on the other. This paper bridges this gap in knowledge by resorting to the artificial neural networks (ANNs) method to predict the effects of tractor speed and soil moisture on the state of soil compaction. The models were ‘trained’ with penetration resistance (CPR) and bulk density test data obtained from field measurements. The resulting correlation coefficient (R = 0.9) showed good compliance of the prediction made with the ANN models with on-field data. It follows, thereby, that the model developed by the authors in this study can be effectively used for predicting the effects of speed, soil density, and moisture co... [more]

The present research describes the design, analysis, and modeling of an air-granular particle fluidized-bed system with dimensions of 0.08 m × 0.4 m × 0.08 m. The hydrodynamic and thermal experiments are designed to verify the numerical model previously created for this purpose. The gas-solid two-phase flow is described using a three-dimensional, two-fluid model based on the Eulerian−Eulerian method. The experiment is conducted, and the numerical model is updated for the new geometry while maintaining the solution parameters. Silica sand and sintered bauxite particles are employed in both experimental and numerical investigations to examine the behaviors of these particles. The hydrodynamic validity of the numerical model is established by the use of experimental findings for pressure drop and bed expansion ratio. The thermal tests are conducted with 585 K hot sand, and the temperature distribution in the bed is measured using K-type thermocouples and compared with the simulation data.... [more]

This paper develops an incipient fault detection and isolation method using the Wasserstein distance, which measures the difference between the probability distributions of normal and faulty data sets from the aspect of optimal transport. For fault detection, a moving window based approach is introduced, resulting in two monitoring statistics that are constructed based on the Wasserstein distance. From analysis of the limiting distribution under multivariate Gaussian case, it is proved that the difference measured by the Wasserstein distance is more sensitive than conventional quadratic statistics like Hotelling’s T2 and Squared Prediction Error (SPE). For non-Gaussian distributed data, a project robust Wasserstein distance (PRW) model is proposed and the Riemannian block coordinate descent (RBCD) algorithm is applied to estimate the Wasserstein distance, which is fast when the number of sampled data is large. In addition, a fault isolation method is further proposed once the incipient... [more]

The melting effect, a type of heat transferal process, is a fascinating mechanism of thermo-physics. It is related to phase change issues that occur in several industrial mechanisms. Glass treatment, polymer synthesis, and metal processing are among these. In view of this, the current investigation explicates the flow of a dusty nanofluid through a stretching cylinder in a porous medium by considering the effect of the melting heat transfer phenomenon. Using the required similarity transformations, the governing partial differential equations (PDEs) showing the energy transference and fluid motion in both the liquid and dust phases were translated into ordinary differential equations (ODEs). The numerical solutions for the acquired ODEs were developed using the Runge−Kutta−Fehlberg method of fourth−fifth order (RKF-45) and the shooting process. Graphical representations were used to interpret the effects of the governing parameters, including the porosity parameter, the Eckert number,... [more]

The bi-reforming of methane (BRM) is a promising process which converts greenhouse gases to syngas with a flexible H2/CO ratio. As there are many factors that affect this process, the coupled effects of multi-parameters on the BRM product are investigated based on Gibbs free energy minimization. Establishing a reliable model is the foundation of process optimization. When three input parameters are changed simultaneously, the resulting BRM products are used as the dataset to train three artificial neural network (ANN) models, which aim to establish the BRM prediction model. Finally, the trained ANN models are used to predict the BRM products when the conditions vary in and beyond the training range to test their performances. Results show that increasing temperature is beneficial to the conversion of CH4. When the molar flow of H2O is at a low level, the increase in CO2 can enhance the H2 generation. While it is more than 0.200 kmol/h, increasing the CO2 flowrate leads to the increase... [more]

Ammonia as a non-carbon fuel is expected to play an important role in the future, but it is difficult to be effectively utilized at this stage due to its flame retardancy and other characteristics. Therefore, we propose to use gliding arc plasma combined with a swirl burner to enhance the combustion performance of ammonia. The electrical characteristics, electron density, gas rotational temperature and the distribution of key active species in the burner were studied via optical emission spectroscopy (OES). With the increase of equivalence ratio (EQR), the width of the Hα line decreases significantly, indicating that the electron density shows a downward trend, even as the gas rotational temperature shows an upward trend. When the equivalence ratio was 0.5, the gas rotational temperature increases by about 320 K compared with the pure air condition. During pure air discharge, there will still be obvious NO emission due to the plasma reaction, but with the addition of NH3, the NO conten... [more]

The P−U characteristic curve of the photovoltaic (PV) cell is a single peak curve with only one maximum power point (MPP). However, the fluctuation of the irradiance level and ambient temperature will cause the drift of MPP. In the maximum power point tracking (MPPT) algorithm of PV systems, BP neural network (BPNN) has an unstable learning rate and poor performance, while the genetic algorithm (GA) tends to fall into local optimum. Therefore, a novel PV fuzzy MPPT algorithm based on an adaptive genetic simulated annealing-optimized BP neural network (AGSA-BPNN-FLC) is proposed in this paper. First, the adaptive GA is adopted to generate the corresponding population and increase the population diversity. Second, the simulated annealing (SA) algorithm is applied to the parent and offspring with a higher fitness value to improve the convergence rate of GA, and the optimal weight threshold of BPNN are updated by GA and SA algorithm. Third, the optimized BPNN is employed to predict the MPP... [more]