(1) Background: Functional abdominal pain disorders (FAPDs) represent one of the main etiologies of chronic abdominal pain in the pediatric population. A wide spectrum of probiotic or prebiotic mixtures has been evaluated in trials regarding benefits in patients with FAPDs, mainly in the adult population. (2) Methods: This study was interested in evaluating the effect of oral supplementation with a symbiotic mixture on intestinal microbiota in children with functional dyspepsia (FD), irritable bowel syndrome with diarrhea (IBS-D), and irritable bowel syndrome with constipation (IBS-C). A combination of six bacterial strains (Lactobacillus rhamnosus R0011, Lactibacillus casei R0215, Bifidobacterium lactis BI-04, Lactobacillus acidophilus La-14, Bifidobacterium longum BB536, Lactobacillus plantarum R1012) and 210 mg of fructo-oligosaccharides-inulin were administered orally, daily, for 12 weeks and patients were scored for severity of symptoms and fecal microbiota before and after the tr... [more]

Given the urgent pursuit of carbon neutrality and stringent climate policies, the H2 shaft furnace (H2-SF) is starting to gain widespread attention in the steel industry. In this study, the performance of the H2-SF under operation with a dual-row injection top gas recycling system was investigated by a one-dimensional mathematical model. The potential of microwave heating as a means to supply thermal energy in regions of energy deficit was also assessed briefly. The results showed that for scenarios without microwave heating, increasing the upper-row injection rate can improve the furnace performance, and increasing the distance of the upper-row injection level from the furnace top also has a positive effect. A high microwave heating efficiency is expected in regions above the upper-row injection level. For scenarios with microwave heating, a higher microwave power leads to a better furnace performance. Thus, a higher furnace productivity can be achieved by increasing either the upper-... [more]

Purpose: The purpose of this in vivo study was to investigate whether the less invasive approach (reduced thickness of the restoration) will result in a comparable risk of failure and similar aesthetic results, compared to conventional layered full porcelain crowns, and can, therefore, be used as a good alternative. Material and Methods: The tested ceramic was lithium-disilicate ceramic (IPS e.max). Forty-four patients with endodontically treated premolars or molars were randomized into two groups and provided with single crowns. One group received conventional all-ceramic crowns made from a lithium-disilicate core and hand-veneered aesthetic ceramic, while another group received full-contoured lithium-disilicate ceramic crowns with reduced wall thickness than manufactures recommendations. The teeth for conventional crowns were prepared with 1 mm rounded shoulder and 2 mm occlusal reduction, while teeth for monolithic crowns were prepared with 0.6 mm wide rounded shoulder and 1 mm occl... [more]

Hazard and operability (HAZOP) is an important safety analysis method, which is widely used in the safety evaluation of petrochemical industry. The HAZOP analysis report contains a large amount of expert knowledge and experience. In order to realize the effective expression and reuse of knowledge, the knowledge ontology is constructed to store the risk propagation path and realize the standardization of knowledge expression. On this basis, a comprehensive algorithm of ontology semantic similarity based on the ant clony optimization generalized neural network (ACO-GRNN) model is proposed to improve the accuracy of semantic comparison. This method combines the concept name, semantic distance, and improved attribute coincidence calculation method, and ACO-GRNN is used to train the weights of each part, avoiding the influence of manual weighting. The results show that the Pearson coefficient of this method reaches 0.9819, which is 45.83% higher than the traditional method. It could solve t... [more]

Direct interaction with the neuronal cells is a prerequisite to deciphering useful information in understanding the underlying causes of diseases and functional abnormalities in the brain. Precisely fabricated nanoelectrodes provide the capability to interact with the brain in its natural habitat without compromising its functional integrity. Yet, challenges exist in terms of the high cost and complexity of fabrication as well as poor control over the chemical composition and geometries at the nanoscale, all imposed by inherent limitations of current micro/nanofabrication techniques. In this work, we report on electrochemical fabrication and optimization of vertically oriented TiO2 nanotube arrays as nanoelectrodes for neural interface application. The effects of zinc, strontium, and copper doping on the structural, electrochemical, and biocompatibility properties of electrochemically anodized TiO2 nanotube arrays were investigated. It was found that doping can alter the geometric feat... [more]

Considering the helicity of Archimedean spiral coils, this paper proposes accurate expressions of mutual inductance and their numerical calculation methods, which can be applied in the wireless power transmission field, etc. Accurate expressions of mutual inductance are deduced respectively for two coils that are coaxial, laterally misaligned, or non-parallel, and numerical calculations are performed using Gaussian integration as well. In the case of coaxial coils, the calculation results are verified by the 3D finite element method (3D FEM) and compared with the results gained by the traditional method that approximates two spiral coils to two clusters of series-connected circular coils ignoring helicity. The comparison of the three methods shows that results achieved by the proposed expression are close to that of 3D FEM, while there is increasing error with the screw pitches of the coils when using the traditional circular coil approximation method. The influence of relative positio... [more]

To avoid power supply hazards caused by cable failures, this paper presents an approach of incipient cable failure recognition and classification based on variational mode decomposition (VMD) and a convolutional neural network (CNN). By using VMD, the original current signal is decomposed into seven modes with different center frequencies. Then, 42 features are extracted for the seven modes and used to construct a feature vector as input of the CNN to classify incipient cable failure through deep learning. Compared with using the original signals directly as the CNN input, the proposed approach is more efficient and robust. Experiments on different classifiers, namely, the decision tree (DT), K-nearest neighbor (KNN), BP neural network (BP) and support vector machine (SVM), and show that the CNN outperforms the other classifiers in terms of accuracy.

This paper describes a hierarchy of increasingly complex statistical models for wind power generation in Alberta applied to wind power production data that are publicly available. The models are based on combining spatial and temporal correlations. We apply the method of Gaussian random fields to analyze the wind power time series of the 19 existing wind farms in Alberta. Following the work of Gneiting et al., three space-time models are used: Stationary, Separability, and Full Symmetry. We build several spatio-temporal covariance function estimates with increasing complexity: separable, non-separable and symmetric, and non-separable and non-symmetric. We compare the performance of the models using kriging predictions and prediction intervals for both the existing wind farms and a new farm in Alberta. It is shown that the spatial correlation in the models captures the predominantly westerly prevailing wind direction. We use the selected model to forecast the mean and the standard devia... [more]

An abundant volume of literature has been devoted to the analysis of the drivers of renewable electricity capacity additions in general and wind energy in particular. Nevertheless, whereas the direct influence of several explanatory variables has been considered, indirect effects, which refer to impacts of explanatory variables on another explanatory variable which, in turn, influence capacity additions, have been neglected. However, those effects need to be taken into account in order to properly grasp the full influence of the explanatory variables in general, and the policy variable in particular, on capacity additions (whether in wind energy generation or other energy systems). The aim of this paper is to identify the drivers of wind energy capacity additions. Based on data over the 1998−2015 period for Spain, a country with a substantial deployed wind capacity, we estimate a three-stage least squares multiecuational econometric model, which allows the analysis of direct and indire... [more]

Data-driven models are essential tools for the development of surrogate models that can be used for the design, operation, and optimization of industrial processes. One approach of developing surrogate models is through the use of input−output data obtained from a process simulator. To enhance the model robustness, proper sampling techniques are required to cover the entire domain of the process variables uniformly. In the present work, Monte Carlo with pseudo-random samples as well as Latin hypercube samples and quasi-Monte Carlo samples with Hammersley Sequence Sampling (HSS) are generated. The sampled data obtained from the process simulator are fitted to neural networks for generating a surrogate model. An illustrative case study is solved to predict the gas stabilization unit performance. From the developed surrogate models to predict process data, it can be concluded that of the different sampling methods, Latin hypercube sampling and HSS have better performance than the pseudo-r... [more]

Load forecasting is of crucial importance for smart grids and the electricity market in terms of the meeting the demand for and distribution of electrical energy. This research proposes a hybrid algorithm for improving the forecasting accuracy where a non-dominated sorting genetic algorithm II (NSGA II) is employed for selecting the input vector, where its fitness function is a multi-layer perceptron neural network (MLPNN). Thus, the output of the NSGA II is the output of the best-trained MLPNN which has the best combination of inputs. The result of NSGA II is fed to the Adaptive Neuro-Fuzzy Inference System (ANFIS) as its input and the results demonstrate an improved forecasting accuracy of the MLPNN-ANFIS compared to the MLPNN and ANFIS models. In addition, genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), differential evolution (DE), and imperialistic competitive algorithm (ICA) are used for optimized design of the ANFIS. Electricity demand da... [more]

The authors wish to make the following corrections to this paper [...]

In microgrids, forecasting solar power output is crucial for optimizing operation and reducing the impact of uncertainty. To forecast solar power output, it is essential to forecast solar irradiance, which typically requires historical solar irradiance data. These data are often unavailable for residential and commercial microgrids that incorporate solar photovoltaic. In this study, we propose an hourly day-ahead solar irradiance forecasting model that does not depend on the historical solar irradiance data; it uses only widely available weather data, namely, dry-bulb temperature, dew-point temperature, and relative humidity. The model was developed using a deep, long short-term memory recurrent neural network (LSTM-RNN). We compare this approach with a feedforward neural network (FFNN), which is a method with a proven record of accomplishment in solar irradiance forecasting. To provide a comprehensive evaluation of this approach, we performed six experiments using measurement data fro... [more]

It is of great significance for wind power plant to construct an accurate multi-step wind speed prediction model, especially considering its operations and grid integration. By integrating with a data pre-processing measure, a parameter optimization algorithm and error correction strategy, a novel forecasting method for multi-step wind speed in short period is put forward in this article. In the suggested measure, the EEMD (Ensemble Empirical Mode Decomposition) is applied to extract a series of IMFs (intrinsic mode functions) from the initial wind data sequence; the LSTM (Long Short Term Memory) measure is executed as the major forecasting method for each IMF; the GRNN (general regression neural network) is executed as the secondary forecasting method to forecast error sequences for each IMF; and the BSO (Brain Storm Optimization) is employed to optimize the parameter for GRNN during the training process. To verify the validity of the suggested EEMD-LSTM-GRNN-BSO model, eight models w... [more]

Frequency Response Analysis (FRA) is an important tool used for diagnostic measurements of power transformers. Standard test configuration applied in the industry is the end-to-end open test setup; however, an interwinding capacitive configuration is also used. This paper presents a method—Cross Test Comparison (CTC)—for simultaneous analysis of results coming from both the mentioned test setups. Such an approach could offer a more sensitive tool for detecting some faults; moreover, it takes into consideration the influence of both voltage sides of a transformer in a one test result. The authors have used several indices to quantitatively assess the test results and proposed new approach to data interpretation. CTC method was tested using data from measurements performed in three cases: a unit tested in laboratory with introduced controlled deformations; transformers measured under industrial conditions; and a transformer with FRA changes resulting from tap-changer operations. The resu... [more]

Economic load dispatch (ELD) is an important optimization problem for operating and controlling modern power systems, and if ELD is effectively executed, power systems work stably and economically. The main objective of this paper is to develop a novel method to solve the ELD with the purpose of minimizing the total fuel cost of all available generating units while requirements are to satisfy all constraints regarding thermal units, generators, and transmission power networks. The proposed high performance cuckoo search algorithm (HPCSA) is developed from the efficient technique for the second new solution generation of conventional cuckoo search algorithm (CCSA), called adaptive mutation technique. This proposed technique diversifies the local search ability based on a new comparison criterion. The HPCSA is verified on difference systems under special conditions, namely the 10-unit system with multi fuels, 15-unit system considering prohibited operating zones, and three IEEE systems w... [more]

Conventional photoplesthymograph (PPG) measurements for heart rate (HR) determination require direct contact between the patient and the PPG device sensor. When using the conventional method, it is possible for users to suffer undesirable skin irritation, discomfort and soreness. Thus, the development of non-contact PPG has been investigated with various technologies and methods. One of the technologies that able to measure PPG in a non-contact way and at low cost is using digital cameras such as webcams. Various filters have been implemented to do non-contact PPG using digital cameras. This paper proposes a non-contact PPG filter system utilizing singular value decomposition (SVD) and Burg’s algorithm. The main role of SVD is for noise removal and as PPG signal extractor. As for the Burg algorithm, it was utilized for estimating the heart rate value from the filtered PPG signal. In this paper, we show and analyze an experiment for HR measurement using our method and a previous method... [more]

Flow maldistribution represents a problem of particular interest in the engineering field for several thermal systems. In flat plate solar collectors, the thermal efficiency strongly depends on the flow distribution through the riser tubes, where a uniform distribution causes a uniform temperature distribution and therefore a higher efficiency. In this work, a Computational Fluid Dynamics (CFD) numerical analysis has been carried out using the commercial software FLUENT®, in order to determine the flow distribution, pressure drop and hence the thermal efficiency of a solar collector with distribution flow plates inside the manifolds. The obtained numerical solution for this type of thermal systems has been validated with experimental results available in literature for laminar and turbulent flow. Four distribution plate configurations were analyzed. Results show that using two distribution plates in each of both manifolds improves the flow uniformity up to 40% under the same operating... [more]

In this research paper, a hybrid Artificial Neural Network (ANN)-Fuzzy Logic Control (FLC) tuned Flower Pollination Algorithm (FPA) as a Maximum Power Point Tracker (MPPT) is employed to amend root mean square error (RMSE) of photovoltaic (PV) modeling. Moreover, Gaussian membership functions have been considered for fuzzy controller design. This paper interprets the Luo converter occupied brushless DC motor (BLDC)-directed PV water pump application. Experimental responses certify the effectiveness of the suggested motor-pump system supporting diverse operating states. The Luo converter, a newly developed DC-DC converter, has high power density, better voltage gain transfer and superior output waveform and can track optimal power from PV modules. For BLDC speed control there is no extra circuitry, and phase current sensors are enforced for this scheme. The most recent attempt using adaptive neuro-fuzzy inference system (ANFIS)-FPA-operated BLDC directed PV pump with advanced Luo conver... [more]

Recently, the well-industry-production-scheme (WIPS) has attracted more and more attention to improve tight oil recovery. However, multi-well pressure interference (MWPI) induced by well-industry-production-scheme (WIPS) strongly challenges the traditional transient pressure analysis methods, which focus on single multi-fractured horizontal wells (SMFHWs) without MWPI. Therefore, a semi-analytical methodology for multiwell productivity index (MPI) was proposed to study well performance of WIPS scheme in tight reservoir. To facilitate methodology development, the conceptual models of tight formation and WIPS scheme were firstly described. Secondly, seepage models of tight reservoir and hydraulic fractures (HFs) were sequentially established and then dynamically coupled. Numerical simulation was utilized to validate our model. Finally, identification of flow regimes and sensitivity analysis were conducted. Our results showed that there was good agreement between our proposed model and nu... [more]

As grid-connected wind farms become more common in the modern power system, the question of how to maximize wind power generation while limiting downtime has been a common issue for researchers around the world. Due to the complexity of wind turbine systems and the difficulty to predict varying wind speeds, artificial intelligence (AI) and machine learning (ML) algorithms have become key components when developing controllers and control schemes. Although, in recent years, several review papers on these topics have been published, there are no comprehensive review papers that pertain to both AI and ML in wind turbine control systems available in the literature, especially with respect to the most recently published control techniques. To overcome the drawbacks of the existing literature, an in-depth overview of ML and AI in wind turbine systems is presented in this paper. This paper analyzes the following reviews: (i) why optimizing wind farm power generation is important; (ii) the cha... [more]

Malfunctions in relay protection devices are predominantly caused by current transformer (CT) saturation which produces distortion in current measurements and disturbances in power system protection. The development of deep learning in power system protection is on the rise recently because of its robustness. This study presents a CT saturation detection where the secondary current becomes distorted. The proposed scheme offers a wide range of saturation detection and consists of a moving-window technique and stacked denoising autoencoders. Moreover, Bayesian optimization was used to minimize the difficulty of determining neural network structure for the proposed approach. The performance of the algorithm was evaluated for a-g faults on 154 kV and 345 kV overhead transmission line in South Korea. The waveform variation has been generated by PSCAD for different scenarios that heavily influence CT saturation. Moreover, a comparative analysis with other methods demonstrated the superiority... [more]

Since existing energy system models often represent storage behavior in a simplified way, in this work, a tool chain for deriving consistent storage model parameters for optimization models is developed. The aim of our research work is to identify what are non-negligible influences on the the technical characteristics and dynamic behavior of the storage, to quantify the effect of these influences, and represent these effects in the model. This paper describes the developed tool chain and presents its application using an example. The tool chain consists of the steps “parameter screening”, “dynamic simulation”, “regression analysis” and “refining optimization model”. It is investigated which parameters have an influence on the storage system (here pumped hydroelectric energy storage (PHES)), how the storage behavior is modeled, which influencing factors have a measurable effect on the system, and how these findings can be integrated into optimization models. The main finding is that in... [more]

The phase selection and fault location methods of generator stator winding single-phase grounding fault are greatly affected by the transition resistance. A new phase selection and generator stator ground fault location approach based on the BP neural network is proposed in this research from a data-driven angle. This method uses a neural network to calculate the probability of three-phase fault occurrence to identify the fault phase and directly calculate the fault location that takes the amplitude and phase angle characteristics of zero-sequence voltage as input. The simulation results show that the stator ground fault phase selection and location algorithm based on the neural network can achieve correct phase selection and small positioning error, which has verified the effectiveness of the method.

Gas−liquid two-phase countercurrent flow limitation (CCFL) phenomena widely exist in nuclear power plants. In particular, the gas−liquid countercurrent flow limitation phenomena in a pressurized water reactor (PWR) during a loss-of-coolant accident (LOCA) or a small-break loss-of-coolant accident (SBLOCA) play an important role in nuclear reactor safety research. Over several decades, a series of experimental investigations and numerical studies have been carried out to study the CCFL phenomena in a PWR. For the experimental investigations, numerous experiments have been conducted, and different CCFL mechanisms and CCFL characteristics have been obtained in various test facilities simulating different scenarios in a PWR. The CCFL phenomena are affected by many factors, such as geometrical characteristics, liquid flow rates, and fluid properties. For the numerical studies, more and more numerical models were presented and applied to the calculations of two-phase countercurrent flow over... [more]