[This corrects the article DOI: 10.1371/journal.pone.0290267.].

Many different kind of fluids in a wide variety of industries exist, such as two-phase and three-phase. Various combinations of them can be expected and gas-oil-water is one of the most common flows. Measuring the volume fraction of phases without separation is vital in many aspects, one of which is financial issues. Many methods are utilized to ascertain the volumetric proportion of each phase. Sensors based on measuring capacity are so popular because this kind of sensor operates seamlessly and autonomously without necessitating any form of segregation or disruption for measuring in the process. Besides, at the present moment, Artificial intelligence (AI) can be nominated as the most useful tool in several fields, and metering is no exception. Also, three main type of regimes can be found which are annular, stratified, and homogeneous. In this paper, volume fractions in a gas-oil-water three-phase homogeneous regime are measured. To accomplish this objective, an Artificial Neural Network (ANN) and a capacitance-based sensor are utilized. To train the presented network, an optimized sensor was implemented in the COMSOL Multiphysics software and after doing a lot of simulations, 231 different data are produced. Among all obtained results, 70 percent of them (161 data) are awarded to the train data, and the rest of them (70 data) are considered for the test data. This investigation proposes a new intelligent metering system based on the Multilayer Perceptron network (MLP) that can estimate a three-phase water-oil-gas fluid’s water volume fraction precisely with a very low error. The obtained Mean Absolute Error (MAE) is equal to 1.66. This dedicates the presented predicting method’s considerable accuracy. Moreover, this study was confined to homogeneous regime and cannot measure void fractions of other fluid types and this can be considered for future works. Besides, temperature and pressure changes which highly temper relative permittivity and density of the liquid inside the pipe can be considered for another future idea.

Two-phase liquid-gas flows are common in industries such as mining, energy, chemicals, and oil. The gamma-ray absorption technique is a non-contact method widely used to measure parameters for such flows. By analyzing signals from scintillation detectors, flow parameters can be determined and flow structures identified. This study evaluated four types of water-air flow regimes using selected computational intelligence methods. The experiments involved a water-air flow in a horizontal pipe with a 30 mm internal diameter, using two sealed Am-241 gamma ray sources and two scintillation probes type NaI(Tl). Eight features for fluid flow were extracted from the power spectral density and the cross-spectral density of the obtained measurement signals and then used as input for the classifier. Six computational intelligence methods, including k-means, a single decision tree, a support vector machine, a probabilistic neural network, a multilayer perceptron, and a radial basis function, were applied to identify the flow regime. The results showed that all of the methods provided good results of classification for the analyzed types of water-air flow.

This study explores a new electrode configuration for measuring the void fraction of two-phase flows using capacitance-based sensors. The proposed method is considered 'skewed' because of its unique geometric shape, and the performance of the proposed sensor was evaluated and improved via multiple simulations using the COMSOL Multiphysics software. The simulations encompass three different flow patterns, stratified, annular and homogeneous, whose performance themselves were verified in a previous study. The influences of geometric properties and parameters on the sensitivity of the proposed sensor were evaluated to determine an optimal configuration. Furthermore, the sensitivity distribution on different void fractions of the sensor was analysed for various void fractions in different flow patterns. Additionally, the proposed configuration was also compared alongside double-ring and concave sensors to determine the overall sensitivity. At 2.11 pF, the overall sensitivity of the proposed sensor was significantly higher than that of the other sensors. It is worth mentioning that the measurement precision of multiphase flow meters is of high importance, particularly in petroleum industry because of the oil price and also the high amount of transported products.

Measuring the volume fraction of different types of fluids with two or three phases is so vital. Among all available methods, two of them, capacitance-based and gamma-ray attenuation, are so popular and widely used. Moreover, nowadays, AI which stands for Artificial Intelligence can be seen almost in all areas, and the measuring section is no exception. In this paper, the main goal is to predict the volume fraction of a three-phase homogeneous fluid which contains water, oil, and gas materials. To opt for an optimised method, a combination of capacitance-based sensors, gamma-ray attenuation sensor and Artificial Neural Networks (ANN) is utilised. To train the proposed metering system which is a MLP type, two inputs are considered. For the first input, the concave sensor is simulated in COMSOL Multiphysics software and different combinations of three phases (different volume fractions) are applied. Then through theoretical investigations of gamma-ray sensor, Barium-133 which radiates 0.356 MeV is used. This way, the second required input is generated. Finally, to implement a new and accurate metering system, a number of networks with different characteristics are run in the MATLAB software. The best structure had a Mean Absolute Error (MAE) equal to 0.33, 3.68 and 3.75 for the water, gas and oil phases, respectively. The accuracy of the presented metering system is illustrated by the received outcomes. The novelty of this study is proposing a new combined method that can measure a three-phase homogeneous fluid's volume fractions containing water, gas and oil, precisely.

Nowadays, there is a high demand to use radiation-based equipment to increase the precision of oil products monitoring systems. In this study, the application of X-ray tubes combined with feature extraction and artificial intelligence techniques were investigated for determining the volumetric percentages in two-phase flows. Firstly, a detection system consists of an X-ray tube, a horizontal Pyrex-glass pipe, and a NaI(TI) detector was simulated by MCNP code. Two flow regimes of annular and stratified were simulated in different volume percentages in the pipe. After acquiring the needed dataset using MCNP code, ten time-domain features were extracted from the dataset and then applied as the input of the neural network model of group method of data handling (GMDH). The GMDH neural network has the ability to diagnose the efficient input for achieving the best network configuration. Among extracted time characteristics of RMS, WL, ASS, MSR, ASM, average, STD, median, skewness, and kurtosis, GMDH neural network introduced the characteristics of RMS, ASS, MSR, ASM, and average as effective characteristics. Indeed, this GMDH neural network feature helped to determine effective characteristics for predicting volume percentages. The obtained predictions with a maximum root-mean-square error of 0.62 indicate that the use of the feature extraction methods is very applicable in determining volume percentages.

Measuring the volume fraction of each phase in multi-phase flows is an essential problem in petrochemical industries. One of the standard flow regimes is stratified two-phase flow, which occurs when two immiscible fluids are present in a pipeline. In this paper, we performed several experiments on vertical concave, horizontal concave, and double-ring sensors to benchmark obtained simulation results from modeling these sensors in stratified two-phase flow using COMSOL Multiphysics software. The simulation data was confirmed by experimental data. Due to the low number of data in the experimental method in order to extract more data, the mentioned software was used to extract more data and then compare the sensitivity of different directions of concave and double ring sensors. The simulation results show that the overall sensitivity of the concave is higher than the double-ring and the momentary sensitivity of the horizontal concave is higher in higher void fractions, and the vertical one has higher sensitivity in lower void fractions.

In order to promote ecological sustainability, the issue of sulphur dioxide emissions is of increasing interest to researchers. Majority of the current research, however, focuses on the relationship between sulphur dioxide (SO2) emissions, foreign direct investment (FDI), and trade, as well as the effects of trade on SO2 emissions, thus rarely takes it into account that the greater impact of the institutional environment and economic growth on SO2 emissions. Using the 2008–2017 provincial panel data, this paper uses a fixed effects model to empirically test the institutional environment and economic growth of sulphur dioxide (SO2) emissions. The results show that GDP growth and SO2 emissions had an inverted “U”-shaped relationship. The institutional environment and the higher level of government intervention in the region led to SO2 emissions decreasing significantly, and the institutional environment and the level of government intervention on economic growth and SO2 emissions form a negative regulatory role. In this paper, environmental governance research, specified by the regional environmental governance, and government environmental performance audit policy provide empirical evidence, thus promoting sustainable ecological and environmental development.

Biological systems in case of real-time state and also large-scale simulation approach are interesting and challenge-based due to different aspects of nonlinear mathematical modeling that can describe the interactions of biological blocks. Thus, hardware circuit designing of these basic blocks in the Central Nervous System (CNS) can be an important field in case of achieving high performance neuromorphic system emulator. This paper presents a high-speed, low-cost, and efficient digital circuit for emulating the plausible calcium-dynamic-based model of astrocyte which has spontaneous oscillations. The nonlinear high-cost functions of the complex astrocyte model are reformulated using the power-2 based low-cost terms using optimized exhaustive search algorithm. Subsequently, the proposed model is simulated in case of validating the presented model and new optimized functions. Finally, the proposed model is physically realized in hardware case using Virtex 4 FPGA platform to test and validate final circuits. FPGA implementation results confirmed the ability of the design to emulate biological cell behaviours in detail with high accuracy. The proposed hardware consumes maximum 2% of the all resources of a Virtex 4 board. Additionally, timing analysis and synthesize report represent that the proposed model works in a high frequency of 371.56 MHz. Moreover, to validate the results of implementation, the proposed model is compared with the original model and other similar works in terms of accuracy, speed-up, and maximum number of implemented astrocyte.

The air kerma is a key parameter in medical diagnostic radiology. Radiologists use the air kerma parameter to evaluate organ doses and any associated patient hazards. The air kerma can be simply described as the deposited kinetic energy once a photon passes through the air, and it represents the intensity of the radiation beam. Due to the heel effect in the X-ray sources of medical imaging systems, the air kerma is not uniform within the X-ray beam's field of view. Additionally, the X-ray tube voltage can also affect this nonuniformity. In this investigation, an intelligent technique based on the radial basis function neural network (RBFNN) is presented to predict the air kerma at every point within the fields of view of the X-ray beams of medical diagnostic imaging systems based on discrete and limited measured data. First, a diagnostic imaging system was modeled with the help of the Monte Carlo N Particle X version (MCNPX) code. It should be noted that a tungsten target and beryllium window with a thickness of 1 mm (no extra filter was applied) were used for modeling the X-ray tube. Second, the air kerma was calculated at various discrete positions within the conical X-ray beam for tube voltages of 40 kV, 60 kV, 80 kV, 100 kV, 120 kV, and 140 kV (this range covers most medical X-ray imaging applications) to provide the adequate dataset for training the network. The X-ray tube voltage and location of each point at which the air kerma was calculated were used as the RBFNN inputs. The calculated air kerma was also assigned as the output. The trained RBFNN model was capable of estimating the air kerma at any random position within the X-ray beam's field of view for X-ray tube voltages within the range of medical diagnostic radiology (20-140 kV).

In addition to affecting people's bodily and mental health, the Covid-19 epidemic has also altered the emotional and mental well-being of many workers. Especially in the realm of institutions and privately held enterprises, which encountered a plethora of constraints due to the peculiar circumstances of the epidemic. It was thus anticipated that the present study would use a group method of data handling (GMDH) neural network for analyzing the relationship of demographic factors, Coronavirus, resilience, and the burnout in startups. The test methodology was quantitative. The research examined 384 startup directors and representatives, which is a sizable proportion of the limitless community. The BRCS, the MBI-GS, and custom-made assessments of stress due to the Coronavirus were all used to collect data. Cronbach's alpha confirmed the polls' dependability, and an expert panel confirmed the surveys' authenticity. The GMDH neural network's inherent potential for self-organization was used to choose the most useful properties automatically. The trained network has a three-layered topology with 4, 3, and 2 neurons in each of the hidden layers. The GMDH network has significantly reduced the computational load by using just 7 parameters of marital status, stress of covid-19, job experience, professional efficiency, gender, age, and resilience for burnout categorization. After comparing the neural network's output with the acquired data, it was determined that the constructed network accurately classified all of the information. Among the achievements of this research, high accuracy in predicting job burnout, checking the performance of neural network in determining job burnout and introducing effective characteristics in determination of this parameter can be mentioned.

The goal of the present investigation is to assess the applicability of the Gig Economy Framework (GEF) to the nursing workforce in Saudi Arabia. In order to learn more about the viability of the gig economy paradigm for the nursing profession, this study employed a cross-sectional survey technique. The survey asked questions specific to the nursing profession in Saudi Arabia and the GEF, while also taking into account other relevant variables. This nurse survey was sent to 102 Saudi Arabian hospitals’ HR departments. After removing invalid and missing data, 379 responses remained. The gig economy’s impact on everyday living and professional growth differed significantly between groups. After processing the data, we inputted them into a multi-layer perceptron (MLP) neural network to find relationships between responses to surveys and compatibility with the GEF. There were 20 inputs to this neural network and four possible outputs. The results of the network are the answers to questions about how the gig economy might affect four areas—life, financial management, and personal and professional comfort and development. Outputs 1–4 were predicted with 96.5%, 96.5%, 99.2%, and 99.2% accuracy, respectively. The primary issues with the nursing workforce in Saudi Arabia may be addressed with the use of gig economy elements. As a result, it is crucial to provide a trustworthy, intelligent strategy for foreseeing the gig economy’s framework’s alignment.

There was an error in the original publication [...]

What is presented in this research is an intelligent system for detecting the volume percentage of three-phase fluids passing through oil pipes. The structure of the detection system consists of an X-ray tube, a Pyrex galss pipe, and two sodium iodide detectors. A three-phase fluid of water, gas, and oil has been simulated inside the pipe in two flow regimes, annular and stratified. Different volume percentages from 10 to 80% are considered for each phase. After producing and emitting X-rays from the source and passing through the pipe containing a three-phase fluid, the intensity of photons is recorded by two detectors. The simulation is introduced by a Monte Carlo N-Particle (MCNP) code. After the implementation of all flow regimes in different volume percentages, the signals recorded by the detectors were recorded and labeled. Three frequency characteristics and five wavelet transform characteristics were extracted from the received signals of each detector, which were collected in a total of 16 characteristics from each test. The feature selection system based on the particle swarm optimization (PSO) algorithm was applied to determine the best combination of extracted features. The result was the introduction of seven features as the best features to determine volume percentages. The introduced characteristics were considered as the input of a Multilayer Perceptron (MLP) neural network, whose structure had seven input neurons (selected characteristics) and two output neurons (volume percentage of gas and water). The highest error obtained in determining volume percentages was equal to 0.13 as MSE, a low error compared with previous works. Using the PSO algorithm to select the most optimal features, the current research’s accuracy in determining volume percentages has significantly increased.

One of the most severe problems in power plants, petroleum and petrochemical industries is the accurate determination of phase fractions in two-phase flows. In this paper, we carried out experimental investigations to validate the simulations for water–air, two-phase flow in an annular pattern. To this end, we performed finite element simulations with COMSOL Multiphysics, conducted experimental investigations in concave electrode shape and, finally, compared both results. Our experimental set-up was constructed for water–air, two-phase flow in a vertical tube. Afterwards, the simulated models in the water–air condition were validated against the measurements. Our results show a relatively low relative error between the simulation and experiment indicating the validation of our simulations. Finally, we designed an Artificial Neural Network (ANN) model in order to predict the void fractions in any two-phase flow consisting of petroleum products as the liquid phase in pipelines. In this regard, we simulated a range of various liquid–gas, two-phase flows including crude oil, oil, diesel fuel, gasoline and water using the validated simulation. We developed our ANN model by a multi-layer perceptron (MLP) neural network in MATLAB 9.12.0.188 software. The input parameters of the MLP model were set to the capacitance of the sensor and the liquid phase material, whereas the output parameter was set to the void fraction. The void fraction was predicted with an error of less than 2% for different liquids via our proposed methodology. Using the presented novel metering system, the void fraction of any annular two-phase flow with different liquids can be precisely measured.

The air kerma, which is the amount of energy given off by a radioactive substance, is essential for medical specialists who use radiation to diagnose cancer problems. The amount of energy that a photon has when it hits something can be described as the air kerma (the amount of energy that was deposited in the air when the photon passed through it). Radiation beam intensity is represented by this value. Hospital X-ray equipment has to account for the heel effect, which means that the borders of the picture obtain a lesser radiation dosage than the center, and that air kerma is not symmetrical. The voltage of the X-ray machine can also affect the uniformity of the radiation. This work presents a model-based approach to predict air kerma at various locations inside the radiation field of medical imaging instruments, making use of just a small number of measurements. Group Method of Data Handling (GMDH) neural networks are suggested for this purpose. Firstly, a medical X-ray tube was modeled using Monte Carlo N Particle (MCNP) code simulation algorithm. X-ray tubes and detectors make up medical X-ray CT imaging systems. An X-ray tube’s electron filament, thin wire, and metal target produce a picture of the electrons’ target. A small rectangular electron source modeled electron filaments. An electron source target was a thin, 19,290 kg/m3 tungsten cube in a tubular hoover chamber. The electron source–object axis of the simulation object is 20° from the vertical. For most medical X-ray imaging applications, the kerma of the air was calculated at a variety of discrete locations within the conical X-ray beam, providing an accurate data set for network training. Various locations were taken into account in the aforementioned voltages inside the radiation field as the input of the GMDH network. For diagnostic radiology applications, the trained GMDH model could determine the air kerma at any location in the X-ray field of view and for a wide range of X-ray tube voltages with a Mean Relative Error (MRE) of less than 0.25%. This study yielded the following results: (1) The heel effect is included when calculating air kerma. (2) Computing the air kerma using an artificial neural network trained with minimal data. (3) An artificial neural network quickly and reliably calculated air kerma. (4) Figuring out the air kerma for the operating voltage of medical tubes. The high accuracy of the trained neural network in determining air kerma guarantees the usability of the presented method in operational conditions.

Design and implementation of biological neural networks is a vital research field in the neuromorphic engineering. This paper presents LUT-based modeling of the Adaptive Exponential integrate-and-fire (ADEX) model using Nyquist frequency method. In this approach, a continuous term is converted to a discrete term by sampling factor. This new modeling is called N-LUT-ADEX (Nyquist-Look Up Table-ADEX) and is based on accurate sampling of the original ADEX model. Since in this modeling, the high-accuracy matching is achieved, it can exactly reproduce the spiking patterns, which have the same behaviors of the original neuron model. To confirm the N-LUT-ADEX neuron, the proposed model is realized on Virtex-II Field-Programmable Gate Array (FPGA) board for validating the final hardware. Hardware implementation results show the high degree of similarity between the proposed and original models. Furthermore, low-cost and high-speed attributes of our proposed neuron model will be validated. Indeed, the proposed model is capable of reproducing the spiking patterns in terms of low overhead costs and higher frequencies in comparison with the original one. The properties of the proposed model cause can make it a suitable choice for neuromorphic network implementations with reduced-cost attributes.

Scale formation inside oil and gas pipelines is always one of the main threats to the efficiency of equipment and their depreciation. In this study, an artificial intelligence method method is presented to provide the flow regime and volume percentage of a two-phase flow while considering the presence of scale inside the test pipe. In this non-invasive method, a dual-energy source of barium-133 and cesium-137 isotopes is irradiated, and the photons are absorbed by a detector as they pass through the test pipe on the other side of the pipe. The Monte Carlo N Particle Code (MCNP) simulates the structure and frequency features, such as the amplitudes of the first, second, third, and fourth dominant frequencies, which are extracted from the data recorded by the detector. These features use radial basis function neural network (RBFNN) inputs, where two neural networks are also trained to accurately determine the volume percentage and correctly classify all flow patterns, independent of scale thickness in the pipe. The advantage of the proposed system in this study compared to the conventional systems is that it has a better measuring precision as well as a simpler structure (using one detector instead of two).