Weighted regularized extreme learning machine to model the discharge coefficient of side slots

In the current research, a modern learning machine algorithm named “Weighted Regularized Extreme Learning Machine (WRELM)" is implemented for the first time for the simulation of the coefficient of discharge of side slots. For this purpose, an effective variable on the coefficient of discharge of side slots is firstly introduced, then five distinctive WRELM models are produced by it for the estimation of the coefficient. In the next stage, a database is created for verification of WRELM results. it should be mentioned that 70% of the data are utilized for training the WRELM models, while the rest (i.e. 30%) for testing them. After that, the optimal number of hidden layer neurons as well as the best activation function of the WRELM algorithm are chosen. In addition, the best regularization parameter and also the weight function of the WRELM are achieved. By conducting a sensitivity analysis, the most effective variable for the simulation of the coefficient of discharge along with the WRELM superior model is introduced. The WRELM superior model estimates values of the coefficient of discharge with the maximum exactness and the highest correlation. For instance, the estimations of the correlation coefficient and scatter index for this model are computed to be 0.930 and 0.051, respectively. The sensitivity analysis shows that the ratio of the side slot crest height to its length and the Froude number should be considered as the most important input variables. A comparison between the WRELM with the ELM displays that the former works much better. Furthermore, an uncertainty analysis is executed for both models. Eventually, an equation is suggested for the estimation of the coefficient of discharge and a partial derivative sensitivity analysis is performed on it.     

Individual-based multi-objective optimal structured treatment interruption for HIV infection

In this paper, based on the measurable quantities from an individual patient that has infection to human immunodeficiency virus (HIV) and his/her condition is near to acquired immune deficiency syndrome (AIDS), individual-based multi-objective optimal treatments have been proposed. Firstly, the most effective parameters of the patient in computing Long-term non-progressor (LTNP) equilibrium are derived using global sensitivity analysis (GSA). To accomplish GSA effectively, Latin hypercube sampling (LHS) and partial rank correlation coefficients (PRCC) are utilized to rank each of the parameters based on each state of the 5-dimensional model. Then, these results are used by Dempster–Shafer (D–S) evidence theory (DSET) to rank the most effective parameters comprehensively. Now, these effective identified parameters are estimated using extended Kalman filter (EKF), which its covariance matrices are optimized based on particle swarm optimization (PSO) algorithm. Thus, the proposed methodology gives a calibrated model corresponding to the individual patient. Based on this calibrated model, the LTNP equilibrium related to the individual patient is derived. Using the derived individual-based LTNP equilibrium optimal structured treatment interruption (STI) strategies are extracted by defining suitable multi-objective optimization problem and solving it through using non-dominated sorting genetic algorithm-II (NSGA-II). The results demonstrate that the proposed optimal treatments are able to effectively reach LTNP equilibrium with using the minimum and maximum drug usage of 3.6% and 35.1% of full drug usage treatment. Meanwhile, the different optimal treatments give the decision-makers enough flexibility to choose the suitable treatment based on existing facilities and necessities.     

Chromatic processing for feature extraction of PD-induced UHF signals in GIS

Partial discharge (PD) detection is an effective means of discovering insulation faults in gas-insulated switchgear (GIS). One of the most extensively used methods in PD detection has historically been the ultrahigh frequency (UHF) method. This study evaluates the chromatic processing methodology and its key factors for feature extraction of UHF signals in GIS. Three types of artificial defects are installed in the GIS tank at 0°, 90°, and 180°, respectively. The features of the UHF signals are extracted in the chromatic space, and PD discrimination of the defects is achieved. The influences of processors are studied before the feature selections are suggested. The time-stepping method is proposed to determine the rules of UHF signal frequency characteristics that vary with time. Finally, the process and options of the chromatics-inspired methodology are summarized.     

Syngas production via partial oxidation of dimethyl ether over Rh/Ce0.75Zr0.25O2 catalyst and its application for SOFC feeding

Dimethyl ether (DME) partial oxidation (PO) was studied over 1 wt% Rh/Ce_0.75Zr_0.25O₂ catalyst at temperatures 300–700 °C, O₂:C molar ratio of 0.25 and GHSV 10000 h⁻¹. The catalyst was active and stable under reaction conditions. Complete conversion of DME was reached at 500 °C, but equilibrium product distribution was observed only at T ≥ 650 °C. High concentration of CH₄ and low contents of CO and H₂ were observed at 500–625 °C 75 cm³ of composite catalyst 0.24 wt% Rh/Ce_0.75Zr_0.25O₂/Al₂O₃/FeCrAl showed excellent catalytic performance in DME PO at O₂:C molar ratio of 0.29 and inlet temperature 840 °C which corresponded to carbon-free region. 100% DME conversion was reached at GHSV of 45,000 h⁻¹. The produced syngas contained (vol. %): 33.4 H₂, 34.8 N₂, 22.7 CO, 3.6 CO₂ and 1.6 CH₄. Composite catalyst demonstrated the specific syngas productivity (based on CO and H₂) in DME PO of 42.8 m³·L_cat⁻¹·h⁻¹ (STP) and the syngas productivity of more than 3 m³·h⁻¹ (STP) that was sufficient for 3 kW_e SOFC feeding. PO of natural gas and liquified petroleum gas can be carried out over the same catalyst with similar productivity, realizing the concept of multifuel hydrogen generation. The syngas composition obtained via DME PO was shown to be sufficient for YSZ-based SOFC feeding.     

Application of Vis–NIR hyperspectral imaging in classification between fresh and frozen-thawed pork Longissimus Dorsi muscles

Fresh and frozen-thawed (F-T) pork meats were classified by Vis–NIR hyperspectral imaging. Eight optimal wavelengths (624, 673, 460, 588, 583, 448, 552 and 609 nm) were selected by successive projections algorithm (SPA). The first three principal components (PCs) obtained by principal component analysis (PCA) accounted for over 99.98% of variance. Gray-level-gradient co-occurrence matrix (GLGCM) was applied to extract 45 textural features from the PC images. The correct classification rate (CCR) was employed to evaluate the performance of the partial least squares-discriminate analysis (PLS-DA) models, by using (A) the reflected spectra at full wavelengths and (B) those at the optimal wavelengths, (C) the extracted textures based on the PC images, and (D) the fused variables combining spectra at the optimal wavelengths and textures. The results showed that the best CCR of 97.73% was achieved by applying (D), confirming the high potential of textures for fresh and F-T meat discrimination.     

Performance evaluation of noise reduction method during on-line monitoring of MV switchgear for PD measurements by non-intrusive sensors

Partial Discharge (PD) measurement is a globally accepted method for insulation diagnosis of electrical assets. The consequences of insulation breakdown are well known. The trend is to move from conventional offline testing to online monitoring for insulation life prediction, which results in the inclusion of high frequency noise in the captured signals. Therefore de-noising is of paramount importance in online monitoring to obtain useful information from the signal.In this research, a 20 kV switchgear panel has been subjected to PD faults in the laboratory and measurements have been carried out by using different non-intrusive sensors including a novel sensor, the D-dot sensor and recorded by a high frequency oscilloscope. The measured results show the effective applicability of sensors for switchgear. The Discrete Wavelet Transform (DWT) has been used to de-noise PD signals in this paper. Time domain and frequency domain comparison of original and de-noised PD signals reveals the significance of this technique for online monitoring of Medium Voltage (MV) switchgear. Finally, an adaptive online de-noising concept, based on automatic de-noising is also proposed in this paper.     

An inventory model with truncated exponential replenishment intervals and special sale offer

In this study, we develop an inventory model with stochastic replenishment intervals and special sale offer from a supplier. The replenishment interval is assumed to obey a truncated exponential distribution and shortage is partially backordered. Our goal in this research is to maximize the total profit of cost savings due to special sale offer from supplier. A closed-form solution of the model and its convexity condition is developed. A numerical example with real world data is provided to illustrate the theory.     

To outsource or not: The impact of information leakage risk on information security strategy

Emerging studies advocate that firms shall completely outsource their information security for cost and technical advantages. However, the risk of information leakage in outsourcing to managed security service providers (MSSPs) is overlooked and poses a confidentiality threat. We develop analytical models to describe several strategies for firms to consider when they decide to outsource to MSSPs. Based on our results, we suggest partial outsourcing as an alternative strategy when the firm faces information leakage risk. Besides, we suggest that in-house information security strategy is the optimal solution when the risk of being attacked is low regardless of the risk of information leakage. We then extend scenarios to the competitive environment where firms that are in the same market are highly likely to choose the same strategy.     

Obtaining electrical characteristics of a PV module by FPGA based experimental system

Electrical characteristics of photovoltaic (PV) modules are significant for using in PV array and standalone applications. Therefore, when designing a PV system, these characteristics must be considered. Some methods are developed to achieve the electrical characteristics of PV modules and one of them is using a capacitor where it is connected to output of the PV module as a load. This method based on measuring the current and voltage of the capacitor during its charging. Thus, the electrical characteristics of the module can be obtained by using these measurements. In this article, Field Programmable Gate Array (FPGA) based method with using a capacitor is proposed to get electrical characteristics of the PV module. The method firstly simulated in Matlab-Simulink environment and promising results are obtained. Then, the method is performed by the FPGA based experimental system. At the same time, to prove experimental results, the electrical characteristics are derived by using an electronic load device. The experimental results show that the electrical characteristics can be accurately obtained by the proposed method.     

How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research

Partial least squares path modeling (PLS-PM) is an estimator that has found widespread application for causal information systems (IS) research. Recently, the method has been subject to many improvements, such as consistent PLS (PLSc) for latent variable models, a bootstrap-based test for overall model fit, and the heterotrait-to-monotrait ratio of correlations for assessing discriminant validity. Scholars who would like to rigorously apply PLS-PM need updated guidelines for its use. This paper explains how to perform and report empirical analyses using PLS-PM including the latest enhancements, and illustrates its application with a fictive example on business value of social media.