The input layer, hidden layer, and output layer are three models of the neural processors that make up feedforward neural networks (FNNs). Evolutionary algorithms have been extensively employed in training FNNs, which can correctly actualize any finite training sample set. In this paper, an enhanced marine predators algorithm (MPA) based on the ranking-based mutation operator (EMPA) was presented to train FNNs, and the objective was to attain the minimum classification, prediction, and approximation errors by modifying the connection weight and deviation value. The ranking-based mutation operator not only determines the best search agent and elevates the exploitation ability, but it also delays premature convergence and accelerates the optimization process. The EMPA integrates exploration and exploitation to mitigate search stagnation, and it has sufficient stability and flexibility to acquire the finest solution. To assess the significance and stability of the EMPA, a series of experi... [more]

At present, PE pipelines are widely used in urban gas pipeline networks. As pipeline safety is of great importance to the gas supply, this paper focuses on the safety and reliability of PE pipes during service. First of all, this paper analyzes the aging factors of PE materials and the failure forms and mechanisms of PE pipes. Second, the performance testing methods of PE pipelines are summarized, including pipeline pressure tests, pipeline condition inspections, welded joint inspections, mechanical performance tests, and physical performance tests. In addition, life prediction methods for PE pipelines are introduced and analyzed. The methods and applicability of tensile experiments and DSC experiments based on thermal oxygen aging are both analyzed, and the corresponding experiments are also carried out. On the basis of the above research, the technical system of the integrity detection and evaluation of urban gas pipelines is finally established. The system includes the integrity det... [more]

Vehicle mass is crucial to autonomous vehicles control. Affected by the nonlinearity of vehicle dynamics between vehicle states, it is still a tough issue to estimate vehicle mass precisely and stably. The transformer aided adaptive extended Kalman filter is proposed to further improve the accuracy and stability of estimation. Firstly, the transformer-based estimator is introduced to provide an accurate pre-estimation of vehicle mass, with the nonlinear dynamics among vehicle states being learned. Secondly, on the basis of comparing the real-time input and training data of neural network, the weight adjustment module is designed to present an adaptive law. Finally, the adaptive extended Kalman filter is proposed to meet the demand of accuracy and stability, where the pre-estimation of transformer-based estimator is integrated with the adaptive law. Dataset is collected by conducting heavy-duty vehicle simulation. The mean absolute percentage error, mean absolute error, root mean square... [more]

The use of coal as a source of energy is crucial for the growth of the national economy, but mining poses numerous risks and a potential for significant disasters. Coal mine safety is the prerequisite and guarantee for coal industry to achieve new industrialization and sustainable development. Therefore, it is crucial to predict a safety accident in the coal mine in advance. In order to facilitate the early warning of coal mine safety accidents, this study seeks to present a prediction model based on emergency management of safety accidents, which is a fusion model of principal component analysis (PCA) and long short-term memory neural network. According to the results, the correlation coefficients of risk identification and monitoring (a11), safety inspection and warning (a12), emergency planning and training (a13), material and technical support (a15), and macroenvironmental management (a21) were 0.718, 0.653, 0.628, 0.444, and 0.553, respectively, after the PCA dimensionality reduct... [more]

S-wave velocity (Vs) is a critical petrophysical parameter for reservoir characterization. It is desirable to predict Vs based on conventional logging data, but the logging cost is high. Therefore, a deep hybrid neural network coupling the convolutional neural network (CNN), Stacked gated recurrent unit (SGRU) is proposed to predict the Vs, where the inputs to the model are drill cutting features. In the proposed CNN-SGRU hybrid model, CNN is adopted to capture the spatial features from the input data, and SGRU is used to extract the temporal patterns of variation from both the forward and backward directions. To illustrate the prediction effect, the glutenite reservoir in the Baikouquan Formation of Mahu Sag, Junggar Basin is taken as an example. Mineral and pore information of drill cuttings, including siliciclastic content, clay content, quartz content, and void area ratio is chosen as the input data of the CNN-SGRU hybrid model. Three indices are used to quantitatively evaluate the... [more]

The incorrect labeling, as well as the bioaccumulation of heavy metals in seafood, represent a recurring problem worldwide, not only for natural resources but also for the consumers’ health. Heavy metals can be accumulated through the food chain and transferred to the final human consumer. Despite its toxicology, arsenic does not have a concentration limit on food, unlike other heavy metals like cadmium, mercury, and lead. Tuna species, with a worldwide distribution and high per capita consumption, represent a well-known toxicological issue caused by heavy metals. In this context, 80 samples of canned tuna were analyzed to check if the information contained in the label was correct and complete. Genetic identification was made by sequencing a fragment of 16S rDNA from 80 samples. For the heavy metal quantification, only those samples with the complete FAO fishing area information on the label were analyzed. Only 29 out of 80 samples presented enough information on the labels for the an... [more]

Shale gas plays an important role in supplementing energy demand and reducing carbon footprint. A precise and effective prediction of shale gas production is important for optimizing completion parameters. This paper established a gated recurrent unit and multilayer perceptron combined neural network (GRU-MLP model) to forecast multistage fractured horizontal shale gas well production. A nondominated sorting genetic algorithm II (NSGA II) was introduced into the model to enable its automatic architectural optimization. In addition, embedded discrete fracture models (EDFM) and a reservoir simulator were used to generate training datasets. Meanwhile, a sensitivity analysis was carried out to find the variable’s importance and support the history matching. The results illustrated that the GRU-MLP model can precisely and efficiently predict the productivity of multistage fractured horizontal shale gas in a rapid and effective manner. Additionally, the model fits better at peak values of sh... [more]

Topology optimization results are highly dependent on the given design constraints and boundary conditions. Moreover, small changes in initial design conditions can result in different topological configurations, which makes topology optimization time-consuming in a given design constraint domain and inefficient in structural design. To address this problem, a data-driven real-time topology optimization framework and method coupled with machine learning by using a principal component analysis algorithm combined with a feedforward neural network are developed in this paper. Meanwhile, through the offline training, the mapping relationship between initial design conditions and topology optimization results is obtained. From this mapping, we estimate the optimal topologies for novel loading configurations. Numerical examples display that the online prediction results are consistent with the results of the topology optimization method. Furthermore, the network parameters are calibrated, an... [more]

This article summarizes the arguments surrounding the scientific discussion of the cement production process at a selected company in Slovakia. (1) The main goal of this article is to evaluate the quality of the cement production process with the intention of increasing the performance and quality of the process and the quality of the cement in various assortments. The object of this research was a selected company in Slovakia which focuses on cement production. (2) The methods of research were focused on using statistical, economic, and financial analyses and instruments of quality management, such as the Ishikawa diagram, regression diagram, correlation, and box plot diagram. The relevance of the decision of this scientific research relating to the innovation of the cement production process focused on Industry 4.0 requirements. (3) This paper presents the results of the clinker CaOF content and LS and their quality. These components are used for cement products and are responsible f... [more]

There are many criticisms for the association between the Six Sigma concept and the two statistical metrics associated to 6σ processes: 1.5σ shift for maximum deviation and 3.4 PPM non-conformities for the long-term performance. As a result, the paper aims to carry out an analysis of this problem, and the first result obtained is that a stable process can reach a maximum drift, but its value depends on the volume of the sample. It is also highlighted that, using only the criterion “values outside the control limits” for monitoring stability through the Xbar chart, a minimum value can be calculated for the long-term performance of a process depending on the sample size. The main conclusion resulting from the calculations is that, in the case of a 6σ process, the long-term performance is much better than the established value of 3400 PPB: For small volume samples of two pieces it is below 700 PPB, for three pieces it is below 200 PPB, and for samples with a volume greater than or equal t... [more]

Impact energy, the main performance subject of hydraulic breakers, is required to evaluate value from consumers. This study proposes a neural network algorithm-based model to predict the impact energy of a hydraulic breaker without measuring it. The proposed model was developed using 1451 data points for various parameters as an input to predict the impact energy of hydraulic breakers in a small class to a large class. Different machine learning methods have been studied, including correlation analysis, linear regression, and neural networks. The results revealed that the working pressure, working flow rate, chisel diameter, nitrogen gas pressure, operating frequency, and power significantly influenced impact energy formation. The results obtained provide a reliable model for predicting the impact energy of hydraulic circuit breakers of various sizes.

PBL stiffeners, made of thin-walled steel plates with circular openings and welded to the steel tube of a square concrete-filled steel tubular (CFST) column, can improve the combined effect effectively by co-carrying axial compressive forces and confining the concrete core. A numerical simulation study based on the previous test was conducted to study the ultimate strength of the CFST stub column with PBL stiffeners. Finite element models of CFST with different stiffeners were made and verified by the test results of typical failure modes and load−strain curves of specimens. The parameter study was conducted, including PBL stiffener detailing (i.e., material strength, stiffener thickness, opening diameter, and opening spacing). Finally, based on the study and analysis results, an ultimate bearing capacity prediction formula was proposed, which can reasonably predict the bearing capacity of a square CFST column with longitudinal or diagonal stiffeners, while the methods in ACI, BS5400,... [more]

The World Health Organization promotes risk assessment and risk management through Water Safety Plans (WSPs) as the most effective way to manage drinking-water supplies. Despite proven advantages of this approach in other regions, WSPs are still not widely used across small drinking-water supplies in Sub Saharan Africa. The aim of this research is to identify good practices and related gaps which may assist with formal uptake of WSPs for small drinking-water supplies in Rwanda. Through semi-structured interviews with the key stakeholders involved in small drinking-water supply management across Rwanda, the aim is achieved through the investigation of the following: (i) current drinking-water management challenges; (ii) stakeholder collaboration and data management activities including reporting of information; and (iii) the regulatory and policy environment. The use and awareness of WSPs in Rwanda was confirmed as low. However certain drinking-water management activities which align wi... [more]

Semi-submersible offshore platforms play a vital role in deep-sea energy exploitation. However, the vast waves threaten the platform’s operation, usually leading to severe consequences. It is essential to study the wave-slamming mechanism of offshore platforms under extreme wave conditions. Existing research usually simplifies the offshore platform slamming problem. This paper establishes a model of a semi-submersible platform and a flexible mooring system in a numerical pool by means of the computational fluid dynamics (CFD) method. The distribution and the sensitivity of the slamming load on columns and deck in waves were investigated, and the model was verified through the basin test. Firstly, based on the Reynolds-averaged Navier−Stokes model, this study considers the volume-of-fluid method to track the free liquid level. After the column and floating body grid are locally refined, the slamming load under extreme regular wave impact is measured by measuring points on the column and... [more]

Onions have a high moisture content, which makes them more susceptible to microbial growth. Drying is one of the postharvest preservation methods applied to decrease onion moisture content, thereby increasing its storage life. In this study, onions were peeled, washed, cut into quarters, hot water blanched, and pureed. The puree was further dried using two different drying methods: refractance window drying (RWD) (water temperature: 70 °C) and convective drying (CD) (50 °C). The puree was spread on prefabricated trays at varying thicknesses of 2 mm, 4 mm, and 6 mm. It was observed that, irrespective of the drying method, moisture ratio (MR) decreased and drying time and effective moisture diffusivity increased with respect to the thickness of the puree. In addition, the Lewis model and the Wang and Singh model showed the highest R2 and lowest SEE value for RWD and CD, respectively. Moreover, the MR of onion puree during RWD and CD was predicted using a multi-layer feed-forward (MLF) ar... [more]

A large amount of casing deformation has occurred in shale gas wells during the complex fracturing process, which affects the fracturing construction and single well production. Based on the statistical analysis of casing deformation wells and caliper logging interpretation, the main mechanism of casing deformation of shale gas wells is revealed as formation slip. By comprehensively considering the rotating speed under casing running condition, the cement solidification heat release under cementing condition, and the fracturing fluid temperature drop under fracturing condition, the safe service margin of the casing is large. Moreover, simply increasing the casing steel grade and wall thickness has no obvious effect on casing deformation prevention, so full wellbore casing deformation prevention measures should be considered. By using the method of unconventional oil and gas well casing string simulation test and numerical simulation, the mechanical response of wellbore and the mechanis... [more]

Programmable logic controllers (PLCs) are everywhere today and perform critical tasks in industries. They are considered as a key component for the Industry 4.0. Before they are put into operation, it is necessary to check the accuracy of the PLC programs. This verification operation can be performed using model checkers. This stage is often long and costly and requires a domain expert who can understand the system, as well as the different model checker tools able to verify the code implemented in the controller. Furthermore, this verification often requires a conversion of the PLC code into a language understood by a model checker which can influence the behavior of the observed PLC. Hence, there is a need to propose methods and tools which could be used by technicians and engineers. The aim of this paper is to propose methods that require little work to set up and are robust to program sizes used in Industry 4.0. This paper explores some fresh ideas for human-adapted PLC code verifi... [more]

Brain tumors can cause serious health complications and lead to death if not detected accurately. Therefore, early-stage detection of brain tumors and accurate classification of types of brain tumors play a major role in diagnosis. Recently, deep convolutional neural network (DCNN) based approaches using brain magnetic resonance imaging (MRI) images have shown excellent performance in detection and classification tasks. However, the accuracy of DCNN architectures depends on the training of data samples since it requires more precise data for better output. Thus, we propose a transfer learning-based DCNN framework to classify brain tumors for example meningioma tumors, glioma tumors, and pituitary tumors. We use a pre-trained DCNN architecture VGGNet which is previously trained on huge datasets and used to transfer its learning parameters to the target dataset. Also, we employ transfer learning aspects such as fine-tune the convolutional network and freeze the layers of the convolutiona... [more]

Changes in the pore water pressure of soil are essential factors that affect the movement of structures during and after construction in terms of stability and safety. Soil permeability represents the quantity of water transferred using pore water pressure. However, these changes cannot be easily identified and require considerable time and money. This study predicted and evaluated the soil permeability coefficient using a multiple regression (MR) model, adaptive network-based fuzzy inference system (ANFIS), general deep neural network (DNN) model, and DNN using the dendrite concept (DNN−T, which was proposed in this study). The void ratio, unit weight, and particle size were obtained from 164 undisturbed samples collected from the embankments of reservoirs in South Korea as input variables for the aforementioned models. The data used in this study included seven input variables, and the ratios of the training data to the validation data were randomly extracted, such as 6:4, 7:3, and 8... [more]

Phytochemical investigation on the bark of Nauclea officinalis led to the isolation of a new monoterpenoid indole alkaloid, nauclediol. The structure of the compound was identified through extensive spectroscopic analysis. Nauclediol displayed cholinesterase-inhibitory activities towards AChE and BChE with IC50 values of 15.429 and 8.756 µM, respectively. Statistical analysis revealed that the mode of inhibition of nauclediol was non-competitive inhibitor for both AChE and BChE. Molecular docking revealed that nauclediol interacts with the choline-binding site and the catalytic triad of TcAChE and hBChE. This study also demonstrated the neuroprotective potential of nauclediol against amyloid beta-induced cytotoxicity and LPS-induced neuroinflammation activity in a dose-dependent manner.

Municipal waste biogas plants are an important element of waste treatment and energy policy. In this study, odorant concentrations and emissions were measured together with the air temperature (T) and relative humidity (RH) to confirm the hypothesis that the microclimatic conditions have an important impact on the level of odorant emission at municipal waste biogas plants. A simple correlation analysis was made to evaluate the strength and the direction of the relationship between the odorant concentration and emission and air temperature and relative humidity. The mean volatile organic compound (VOC) and NH3 concentrations vary depending on the stage of the technological line of the analysed municipal waste biogas plants and are in the following ranges, respectively: 0−38.64 ppm and 0−100 ppm. The odorant concentrations and emissions correlated statistically significantly with T primarily influences VOC concentrations and emissions while RH mainly affects NH3 concentrations and emissi... [more]

The high-pressure gaseous hydrogen (HPGH2) storage method is widely used owing to the low density of hydrogen gas at ambient temperature and atmospheric pressure. Therefore, rigorous safety analysis of the filling and discharging of compressed gas in a hydrogen tank is required to achieve reliable operational solutions for the safe storage of hydrogen. In this study, the behavior of compressed hydrogen gas in a hydrogen tank was investigated for its discharge. Numerical models for the adaptation of gas and turbulence models were examined. Gas model effects were examined to account for hydrogen gas behavior at the discharge temperature and pressure conditions. Turbulence model effects were analyzed to consider the accuracy of each model: the assessment of the turbulence models was compared in terms of the turbulence intensity. From the study of gas model effect, the Redlich−Kwong equation was found to be one of the realistic gas models of the discharging gas flow. Among the turbulence m... [more]

With the growing demand for clean and economically feasible renewable energy, solar photovoltaic (PV) system usage has increased. Among many factors, the tilt and azimuth angles are of great importance and influence in determining the photovoltaic panel’s efficiency to generate electricity. Although much research was conducted related to solar PV panels’ performance, this work critically determined the tilt and azimuth angles for PV panels in all countries worldwide. The optimum tilt and azimuth angles are estimated worldwide by the photovoltaic geographic information system (PVGIS). Also, annual and average daily solar irradiation incident on the tilted and oriented plate optimally (AR1 and DR1) are calculated. Besides, annual and average daily solar irradiation incident on plate tilt optimally and oriented because of the south in the northern hemisphere and because of the north in the southern hemisphere (AR2 and DR2) are estimated. PVGIS is also used to calculate the annual and aver... [more]

We experimentally explored the modulation of various forward- and backward-facing topographic steps on the wake and power output of a wind turbine model. The sharp surface changes located in the vicinity of the turbine tower consisted of steps Δz0/dT=−0.64, −0.42, −0.21, 0, 0.21, and 0.42, where Δz0 is the level difference between the upwind and downwind sides of the step and dT is the turbine diameter. Particle image velocimetry was used to obtain the wake statistics in the wake within the streamwise distance x/dT∈[2, 5] and vertical span z/dT∈[−0.7, 0.7], where the origin is set at the rotor hub. Complementary single-point hotwire measurements were obtained in the wake along the rotor axis every Δx/dT=1 within x/dT∈[1, 8]. Mean power output and its fluctuations were obtained for each of the six scenarios. The results indicate strong modulation of the steps in the wake statistics and some effect on the power output. Remarkably, the backward-facing steps induced a larger velocity defic... [more]

An accurate monitoring of power system behavior is a hot-topic for modern grid operation. Low-frequency oscillations (LFO), such as inter-area electromechanical oscillations, are detrimental phenomena impairing the development of the grid itself and also the integration of renewable sources. An interesting countermeasure to prevent the occurrence of such oscillations is to continuously identify their characteristic electromechanical mode parameters, possibly realizing an online monitoring system. In this paper an attempt to develop an online modal parameters identification system is done using machine learning techniques. An approach based on the development of a proper artificial neural network exploiting the frequency measurements coming from actual PMU devices is presented. The specifically developed offline training stage is fully detailed. The output results from the dynamic mode decomposition method are considered as reference in order to validate the machine learning approach. S... [more]