This study estimates the equivalent continuous sound pressure level (Leq) during peak daily periods (‘rush hour’) along the New Klang Valley Expressway (NKVE) in Shah Alam, Malaysia, using a land use regression (LUR) model based on machine learning, statistical regression, and geographical information systems (GIS). The research utilises two types of soft computing methods including machine learning (i.e., decision tree, random frost algorithms) and statistical regression (i.e., linear regression, support vector regression algorithms) to determine the best approach to create a prediction Leq map at the NKVE in Shah Alam, Malaysia. The selection of the best algorithm is accomplished by considering correlation, correlation coefficient, mean-absolute-error, mean-square-error, root-mean-square-error, and mean absolute percentage error. Traffic noise level was monitored using three sound level meters (TES 52A), and a traffic tally was done to analyse the traffic flow. Wind speed was gauged... [more]

Deep learning has recently emerged as a successful approach to produce accurate subgrid-scale (SGS) models for Large Eddy Simulations (LES) in combustion. However, the ability of these models to generalize to configurations far from their training distribution is still mainly unexplored, thus impeding their application to practical configurations. In this work, a convolutional neural network (CNN) model for the progress-variable SGS variance field is trained on a canonical premixed turbulent flame and evaluated a priori on a significantly more complex slot burner jet flame. Despite the extensive differences between the two configurations, the CNN generalizes well and outperforms existing algebraic models. Conditions for this successful generalization are discussed, including the effect of the filter size and flame−turbulence interaction parameters. The CNN is then integrated into an analytical reaction rate closure relying on a single-step chemical source term formulation and a presume... [more]

The aim of this research is to determine the influence of electrical energy market regulation reform in Ukraine on the competitive environment, the reproduction processes of financial and innovative support, and the energy efficiency of the national economy. The authors have put forward and verified the hypothesis that, under conditions of institutional maturity of the Ukrainian electrical energy market, its liberalization and separation of the kinds of activity related to generation, transmission, and distribution leads to a decrease in prices, and the level of economic concentration stimulates implementation of innovations and the formation of reports on sustainable development. Over the thirteen-year time interval, a steady trend of decreasing energy intensity of the Ukrainian economy was established, and the appropriateness of energy efficiency management based on strategic targets was substantiated. The electricity market model in Ukraine is defined as a hybrid one, with an emphas... [more]

This study focuses on the numerical investigation of the underlying mechanism of transition from chaotic to periodic dynamics of circular cylinder wake under the action of time-dependent fluidic actuation at the Reynolds number = 2000. The forcing is realized by blowing and suction from the slits located at ±90∘ on the top and bottom surfaces of the cylinder. The inverse period-doubling cascade is the underlying physical mechanism underpinning the wake transition from mild chaos to perfectly periodic dynamics in the spanwise-independent, time-dependent forcing at twice the natural vortex-shedding frequency.

The deployment of low-carbon hydrogen in gas grids comes with strategic benefits in terms of energy system integration and decarbonization. However, hydrogen thermophysical properties substantially differ from natural gas and pose concerns of technical and regulatory nature. The present study investigates the blending of hydrogen into distribution gas networks, focusing on the steady-state fluid dynamic response of the grids and gas quality compliance issues at increasing hydrogen admixture levels. Two blending strategies are analyzed, the first of which involves the supply of NG−H2 blends at the city gate, while the latter addresses the injection of pure hydrogen in internal grid locations. In contrast with traditional case-specific analyses, results are derived from simulations executed over a large number (i.e., one thousand) of synthetic models of gas networks. The responses of the grids are therefore analyzed in a statistical fashion. The results highlight that lower probabilities... [more]

To support farmers and improve the quality of crops production, designing of smart greenhouses is becoming indispensable. In this paper, a novel prototype for remote monitoring of a greenhouse is designed. The prototype allows creating an adequate artificial environment inside the greenhouse (e.g., water irrigation, ventilation, light intensity, and CO2 concentration). Thanks to the Internet of things technique, the parameters controlled (air temperature, relative humidity, capacitive soil moisture, light intensity, and CO2 concentration) were measured and uploaded to a designed webpage using appropriate sensors with a low-cost Wi-Fi module (NodeMCU V3). An Android mobile application was also developed using an A6 GSM module for notifying farmers (e.g., sending a warning message in case of any anomaly) regarding the state of the plants. A low-cost camera was used to collect and send images of the plants via the webpage for possible diseases identification and classification. In this co... [more]

UV lamps are being increasingly used in the treatment of swimming pool water, mainly due to their abilities to disinfect and effectively remove chloramines (combined chlorine). However, the application of UV lamps in a closed loop system, such as that in which swimming pool water is treated, creates conditions under which chlorinated water is then also irradiated with UV. Thus, the advanced oxidation process occurs, which affects the transformation of organic matter and its increased reactivity, and hence the higher usage of chlorine disinfectant. In addition, UV lamps require electrical power and the periodic replacement of filaments. In order to assess whether the application of a low-pressure UV lamp is justified, water quality tests and an analysis of the operating costs (including the energy consumption) of the water treatment system were carried out for two operation variants—those of the low-pressure UV lamp being turned on and off. The experiments were carried out on the real o... [more]

Since 2010, the European Union countries have been implementing the objectives of the Europe 2020 Strategy aimed at smart, sustainable, and inclusive growth. The Strategy formulates nine indicators that are systematically monitored and assessed. Not all the indicators of the Europe 2020 Strategy could be used in the analysis in a direct way. Due to the limited availability and comparability of statistical data, this problem is presented in detail in part 2 of the article. The assessment of the achievement level of the Europe 2020 Strategy targets, both at the level of the entire European Union (the EU-level targets approach) and its individual Member States (the national-level targets approach) is the primary research purpose of the study. The composite index proposed and constructed on the basis of a dynamic relative taxonomy was used in the conducted research to present the diversified distance of the individual European Union countries in relation to the EU-level targets as well as... [more]

In thermal power plants, the internal combustion engines are constantly subjected to stresses, requiring a continuous monitoring system in order to check their operating conditions. However, most of the time, these monitoring systems only indicate if the monitored parameters are in nonconformity close to the occurrence of a catastrophic failure—they do not allow a predictive analysis of the operating conditions of the machine. In this paper, a statistical model, based on the statistical control process and Nelson Rules, is proposed to analyze the operational conditions of the machine based on the supervisory system data. The statistical model is validated through comparisons with entries of the plant logbook. It is demonstrated that the results obtained with the proposed statistical model match perfectly with the entries of the logbook, showing our model to be a promising tool for making decisions concerning maintenance in the plant.

Fracture-driven interactions (FDIs) in unconventional reservoirs significantly affect well production and have thus garnered extensive attention from the scientific community. Furthermore, since the industry transitioned to using large completion designs with closer well spacing and infill drilling, FDIs have occurred more frequently and featured more prominently, which has primarily led to destructive interference. When infill wells (i.e., “child” wells) are fractured, older, adjacent producing wells (i.e., “parent” wells) are put directly at risk of premature changes in production behavior. Some wells may never fully recover following exposure to severe FDIs and, in the worst case scenario, will permanently stop producing. To date, previous investigations into FDIs have focused mainly on diagnosis and detection. As such, their formation mechanism is not well understood. To address this deficiency, a three-dimensional, multi-fracture propagation simulator was constructed based on the... [more]

The study was based on saline soils with surface mineral layers impacted by the waste produced by the soda plant in Poland. The activity of selected enzymes (catalase CAT, alkaline AlP, and acid phosphatase AcP), pH in KCl, content of the clay, total organic carbon (TOC), total nitrogen (TN), total exchangeable bases (TEB), electrical conductivity (ECe), CaCO3, and concentration of available phosphorus AP were investigated in the soil next to the soda plant. Based on the enzyme activity, the following were calculated: enzymatic pH indicator AlP/AcP, the resistance index (RS), resilience index (RL), relative changes (RCh), and the time index (TI). The soil was sampled from the mineral horizon in spring and autumn from eight (S1−S8) soil sampling sites in the area of the soda plant and from the control point (C). Soil is characterized by alkaline reaction. Statistical analysis (ANOVA, η2 effect size) showed significant variation in parameters under the influence of different sites next t... [more]

The estimation of hydraulic and mechanical properties of bedrock is important for the evaluation of energy-related structures, including high-level nuclear waste repositories, hydraulic fracturing wells, and gas-hydrate production wells. The hydraulic conductivity and stress−strain curves of rocks are conventionally measured through laboratory tests on cylindrical samples. Both ASTM standards for hydraulic conductivity and compressive strength involve the use of the planar bases of a cylindrical sample. Hence, an alternative test method is required for the simultaneous measurement of hydraulic conductivity and stress−strain curves. This study proposes a novel electrical resistivity estimation method using two perimeter electrodes for the estimation of hydraulic properties. The theoretical background for the perimeter electrode setup is derived and the COMSOL MultiPhysics® finite element numerical simulation tool is employed to verify the derived theoretical equation. The accuracy of th... [more]

The construction sector has a large impact on the environment and available resources. Natural resources and energy consumption occurs not only during the operation of the facility, but also during its construction. In addition, this situation often occurs when work already completed requires rework. In such cases, not only the reuse of resources and energy occurs but also generation of waste. Many studies support the relationship between communication and project efficiency, which is expressed in the cost of rework. At present there is no available tool to quantify the evaluation of this relationship. This study aims to fill this knowledge gap. The article purpose was to create ANNs (artificial neural networks) for assessing and predicting the impact of communication factors on rework costs in construction projects. During the data collection phase, 12 factors that influence communication were identified and assessed. The level of rework costs in 18 construction projects was also calc... [more]

This paper presents an efficient harmonic detection for real-time generation of the reference current fed to a shunt active power filter using the ADALINE neural network. This proposed method is a single layer with 101 nodes generating the coefficients referred to as weights of the reference current model. It effectively overcomes the drawback of the current technology, which is instantaneous power theory (PQ). The proposed method was implemented on the TMS320F28335 DSP board and tested against MATLAB with Simulink as a hardware-in-loop (HIL) structure. This method gives a good performance by producing a precise reference current in a short period with uncomplicated calculation. It also efficiently can eliminate individual harmonic current. The achieved percentage of total harmonic distortion (%THD) in the current is reduced following the IEEE standard, while the power factor can be maintained to unity.

Buildings account for 40% of the energy consumption and 31% of the CO2 emissions in the United States. Energy retrofits of existing buildings provide an effective means to reduce building consumption and carbon footprints. A key step in retrofit planning is to predict the effect of various potential retrofits on energy consumption. Decision-makers currently look to simulation-based tools for detailed assessments of a large range of retrofit options. However, simulations often require detailed building characteristic inputs, high expertise, and extensive computational power, presenting challenges for considering portfolios of buildings or evaluating large-scale policy proposals. Data-driven methods offer an alternative approach to retrofit analysis that could be more easily applied to portfolio-wide retrofit plans. However, current applications focus heavily on evaluating past retrofits, providing little decision support for future retrofits. This paper uses data from a portfolio of 550... [more]

As long as non-contact digital printing remains an uncommon standard in the corrugated packaging industry, corrugated board crushing remains a real issue that affects the load capacity of boxes. Crushing mainly occurs during the converting of corrugated board (e.g., analog flexographic printing or laminating) and is a process that cannot be avoided. However, as this study shows, it can be controlled. In this work, extended laboratory tests were carried out on the crushing of double-walled corrugated board. The influence of fully controlled crushing (with a precision of ±10 μm) in the range from 10 to 70% on different laboratory measurements was checked. The typical mechanical tests—i.e., edge crush test, four-point bending test, shear stiffness test, torsional stiffness test, etc.—were performed on reference and crushed specimens. The residual thickness reduction of the crushed samples was also controlled. All empirical observations and performed measurements were the basis for buildin... [more]

The purpose of this research is the evaluation of artificial neural network models in the prediction of stresses in a 400 MVA power transformer winding conductor caused by the circulation of fault currents. The models were compared considering the training, validation, and test data errors’ behavior. Different combinations of hyperparameters were analyzed based on the variation of architectures, optimizers, and activation functions. The data for the process was created from finite element simulations performed in the FEMM software. The design of the Artificial Neural Network was performed using the Keras framework. As a result, a model with one hidden layer was the best suited architecture for the problem at hand, with the optimizer Adam and the activation function ReLU. The final Artificial Neural Network model predictions were compared with the Finite Element Method results, showing good agreement but with a much shorter solution time.

Digital Twins (DTs) are receiving considerable attention from multiple disciplines. Much of the literature at this time is dedicated to the conceptualization of digital twins, and associated enabling technologies and challenges. In this paper, we consider these propositions for the specific application of nuclear power. Our review finds that the current DT concepts are amenable to nuclear power systems, but benefit from some modifications and enhancements. Further, some areas of the existing modeling and simulation infrastructure around nuclear power systems are adaptable to DT development, while more recent efforts in advanced modeling and simulation are less suitable at this time. For nuclear power applications, DT development should rely first on mechanistic model-based methods to leverage the extensive experience and understanding of these systems. Model-free techniques can then be adopted to selectively, and correctively, augment limitations in the model-based approaches. Challeng... [more]

This paper presents the use of artificial neural networks in diagnosing the technical condition of drive systems operating under variable conditions. The effects of temperature and load variations on the values of diagnostic parameters were considered. An experiment was conducted on a testing rig where a variable load was introduced corresponding to the load of the main gearbox of the bucket wheel excavator. The signals of vibration acceleration on the gearbox body, rotational speed, and current consumption of the drive motor for different values of oil temperature were measured. Synchronous analysis was performed, and the values of order amplitudes and the corresponding values of current, speed, and temperature were determined. Such datasets were the learning vectors for a set of artificial deep learning neural networks. A new approach proposed in this paper is to train the network using a learning set consisting only of data from the efficient system. The responses of the trained neu... [more]

The aim of this article was to determine the significance of modern marketing communication channels used in the process of shaping the external image of an enterprise as an employer. An analysis of the world literature on marketing, management, marketing communication and human resource management was used to prepare the theoretical part. The results of the analysis indicate a cognitive and research gap regarding the use of modern communication channels for building the external image of an enterprise in the role of an employer. In order to reduce the gap, empirical studies were conducted among young Polish potential employees, in which the survey method was used to gather primary data. The collected data were subjected to statistical analysis, during which the following methods and statistical tests were applied: the analysis of average values, exploratory factor analysis, Kruskal−Wallis test (KW), Pearson chi-square independence test and V-Cramer coefficient analysis. The results of... [more]

The use of high voltage power cables in distribution and transmission networks is still increasing. As a result, the research on the electrical performance of cable lines is still up to date. In the paper, an analytical method of determining the power losses and the temperature distribution in the medium voltage cable was proposed. The main feature of the method is direct including the skin and proximity effects. Then the Joule law is used to express the power losses in the conductor and screen, and the Fourier-Kirchhoff equation is applied to find out the temperature distribution in the cable. The research was focused on a cable with isolated screen and return current in the screen taken into account. The proposed method was tested by using the commercial COMSOL software(5.6/COMSOL AB, Stockholm, Sweden) as well as by carrying out laboratory measurements. Furthermore, the results obtained via the proposed method were compared with those given in literature. The differences between the... [more]

As natural gas reserves are generally decreasing there is a need to successfully characterize potential research objects using geophysical data. Presented is a study of amplitude vs. offset, attribute and artificial neural network analysis on a research area of a small gas field with one well with commercial accumulations and two wells with only gas shows. The purpose of the research is to aid in future well planning and to distinguish the geophysical data in dry well areas with those from an economically viable well. The amplitude vs. offset analysis shows the lack of anomaly in the wells with only gas shows while the anomaly is present in the economically viable well. The artificial neural network analysis did not aid in the process of distinguishing the possible gas accumulation but it can point out the sedimentological and structural elements within the seismic volume.

This paper describes a simple method of approximating hysteresis changes in electrical steel sheets. This method is based on assumptions that flux density or field strength changes are a sum or a difference of functions that describe one curve of the limiting hysteresis loop and a certain ‘transient’ component. Appropriate formulas that present the flux density as functions of the field strength and those that present inverse dependencies are proposed. An application of this approximation requires knowledge of the measured limiting hysteresis loop and a few minor loops. Algorithms for determining changes in the flux density or field strength are proposed and discussed. The correctness of the proposed approximation of hysteresis changes was verified through a comparison of measured hysteresis loops with the loops calculated for several different excitations of the magnetic field occurring in dynamo and transformer steel sheets. Additionally, an example of the application of the proposed... [more]

This work presents a comprehensive analysis of two cubic techniques for Power Flow (PF) studies. In this regard, the families of Weerakoon-like and Darvishi-like techniques are considered. Several theoretical findings are presented and posteriorly confirmed by multiple numerical results. Based on the obtained results, the Weerakoon’s technique is considered more reliable than the Newton-Raphson and Darvishi’s methods. As counterpart, it presents a high computational burden. Regarding this point, the Darvishi’s technique has turned out to be quite efficient and fully competitive with the Newton’s scheme.

The advent of deep-learning technology promises major leaps forward in addressing the ever-enduring problems of wireless resource control and optimization, and improving key network performances, such as energy efficiency, spectral efficiency, transmission latency, etc. Therefore, a common understanding for quantum deep-learning algorithms is that they exploit advantages of quantum hardware, enabling massive optimization speed ups, which cannot be achieved by using classical computer hardware. In this respect, this paper investigates the possibility of resolving the energy efficiency problem in wireless communications by developing a quantum neural network (QNN) algorithm of deep-learning that can be tested on a classical computer setting by using any popular numerical simulation tool, such as Python. The computed results show that our QNN algorithm can be indeed trainable and that it can lead to solution convergence during the training phase. We also show that the proposed QNN algorit... [more]