Built-in Parameters Research Articles

A computational sustainable approach for energy storage systems performance evaluation based on spherical-fuzzy MCDM with considering uncertainty

Incorporating energy storage systems (ESSs) can mitigate the intermittency of renewable energy sources. There are a variety of ESSs for renewable energy with vastly different characteristics. The problem of diversity of characteristics in selecting the most appropriate ESS can be approached as a multi-criteria decision-making (MCDM) problem. This research evaluates sustainable ESSs through a case study in Egypt. A sustainable computational approach is presented through which experts can use verbal expressions to express their opinions in determining the priorities of the dimensions that affect the selection of ESSs. Determining the appropriate energy storage system requires consideration of several main dimensions such as the technology dimension, environmental dimension, economic dimension, and social-political dimension and, in addition to the sub-indicators. Hence, this research applies a hybrid MCDM approach that deals with different indicators and characteristics. Also, uncertainty in applying the proposed approach was dealt with by a spherical fuzzy (SF) environment and by using the spherical fuzzy numbers (SFNs). At first, the SF analytical hierarchy process (SF-AHP) method was used to assess the priorities of the four main dimensions and their sub-indicators. Then, the SF mixed aggregation by comprehensive normalization technique (SF-MACONT) was applied to evaluate and rank the ESSs selected for analysis through research. An illustrative case study was presented that included seven ESSs out of the eighteen systems listed in the research to confirm the feasibility of the developed approach. Sensitivity analysis was carried out by changing some parameters like λ, μ, δ, and ϑ based on the SF-MACONT method and changing the weights of some main dimensions. A comparative analysis with some MCDM approaches was conducted to show the advantages of the developed approach through its flexibility and built-in parameters. The findings show that the technology dimension is the most influential in choosing a sustainable ESS, while the economic dimension is the least influential. Also, the results of the evaluation and ranking of the seven selected ESSs indicate that the "Pumped Hydro" system is the most suitable system for energy storage in Egypt.

A weak Galerkin finite element method for nonlinear convection-diffusion equation

In this paper, a weak Galerkin (WG) finite element method for one-dimensional nonlinear convection-diffusion equation with Dirichlet boundary condition is developed. Based on a special variational form featuring two built-in parameters, the semi-discrete and fully discrete WG finite element schemes are proposed. The backward Euler method is utilized for time discretization. The WG finite element method adopts locally piecewise polynomials of degree k for the approximation of the primal variable in the interior of elements, and piecewise polynomials of degree k+1 for the weak derivatives. Theoretically, the optimal error estimates in both discrete H1 and standard L2 norms are derived. Numerical experiments are performed to demonstrate the effectiveness of the WG finite element approach and validate the theoretical findings.

A fast and effective approach for classification medical data sets

This study's objective is to offer a practical computer method for handling classification problems on large datasets. The aim of this study is to offer a practical computer approach for handling classification tasks on big datasets. We show that using Python’s built-in parameters to balance classes can improve the accuracy and the metrics of a classification task. We employ logistic regression, support vector machines, decision trees, and random forest classifier. We use the parameter “class_weight='balanced'” to run each classification model as well as stratified train/test splitting to ensure that relative class frequencies are approximately preserved in each train and set subsets. We use our methods on medical datasets because class imbalance is frequently a problem there. Our research shows that the proposed algorithms can improve the accuracy and classification metrics of the given medical datasets. We propose an effective and easy-to-apply alternative to improve the prediction ability of the presented classification models in medical datasets. We test an easily reproducible set-up where any classification model can be used to model imbalanced classes. The key tuning of the model lies in the stratified train/test split as well as the parameter “class weight='balanced'”. By combination of parameter tuning, better classification performance can be obtained in a quick and simple manner. It is simple and quick to replicate our algorithms to examine various medical datasets and determine which model best fits the data. It can be reproduced in biostatistical laboratories and by medical companies. Because it is simple to comprehend, medical researchers can swiftly review the information and determine the best course of action.

Fuzzy logic controller development for prediction of student performance based on activity parameters

Owing to the COVID-19 pandemic, most of the academic education has suddenly shifted from traditional teaching methods to advanced technological methods on the internet. Many teachers encountered difficulties in successfully evaluating and monitoring their students. We address these challenges and propose a fuzzy logic-based controller that can assists teachers during classes and support allocation of appropriate resources to students. The purpose of the controller is to provide early warning about students who have performed poorly in the initial part of the course assessment. The controller makes predictions based on 5 input parameters which, by applying statistical tools, have been proven to accurately reflect the students' achievements. The model was tested on a group of 50 students and the results indicate 82% prediction accuracy. There is a possibility for additional improvements related to the built-in parameters, both in terms of their selection and in terms of their number.

Investigating the effect of food trays porosity on the drying process

Improving the dynamics of airflow behavior in the drying system is essential for improving its efficiency. In this study, the impact of the porosity of the plum-filled drying trays on the airflow behavior inside the chamber is investigated. Three configurations of constant porosity (30%, 50%, 70%), and 4 additional configurations of porosity varying linearly along trays 1 and 2 as a function of the x-axis are evaluated. To model the trays as a porous medium, the Power-low mathematical model was used, with the built-in parameters C0 and C1 were given experimentally. Both were formed as a subroutine in C++ and integrated into the CFD code, as was done for the integration of unsteady weather parameters. The CFD method used for visualizing and analyzing the airflow behavior in the drying chamber has been previously validated with the experimental result. The obtained results indicate that the average temperature of the drying trays reaches a maximum of 316,91 k at tray 1 for a porosity of 50% and an inlet velocity of 0.1 m/s. For the case of non-uniform trays porosity, it was found that for low air inlet velocity of 0.1 m/s, the velocity difference ΔV is generally low in cases (b) and (d), where it is equal respectively to 0.02 m/s and 0.01 m/s for tray 1, and 0.01 in both cases for tray 2. While for high air inlet velocity of 1 m/s the difference ΔV in both trays reaches 0.17 m/s.

A Potential Screening Index of Corneal Biomechanics in Healthy Subjects, Forme Fruste Keratoconus Patients and Clinical Keratoconus Patients.

Purpose: This study aims to evaluate the validity of corneal elastic modulus (E) calculated from corneal visualization Scheimpflug technology (Corvis ST) in diagnosing keratoconus (KC) and forme fruste keratoconus (FFKC). Methods: Fifty KC patients (50 eyes), 36 FFKC patients (36 eyes, the eyes were without morphological abnormality, while the contralateral eye was diagnosed as clinical keratoconus), and 50 healthy patients (50 eyes) were enrolled and underwent Corvis measurements. We calculated E according to the relation between airpuff force and corneal apical displacement. One-way analysis of variance (ANOVA) and receiver operating characteristic (ROC) curve analysis were used to identify the predictive accuracy of the E and other dynamic corneal response (DCR) parameters. Besides, we used backpropagation (BP) neural network to establish the keratoconus diagnosis model. Results: 1) There was significant difference between KC and healthy subjects in the following DCR parameters: the first/second applanation time (A1T/A2T), velocity at first/second applanation (A1V/A2V), the highest concavity time (HCT), peak distance (PD), deformation amplitude (DA), Ambrosio relational thickness to the horizontal profile (ARTh). 2) A1T and E were smaller in FFKC and KC compared with healthy subjects. 3) ROC analysis showed that E (AUC = 0.746) was more accurate than other DCR parameters in detecting FFKC (AUC of these DCR parameters was not more than 0.719). 4) Keratoconus diagnosis model by BP neural network showed a more accurate diagnostic efficiency of 92.5%. The ROC analysis showed that the predicted value (AUC = 0.877) of BP neural network model was more sensitive in the detection FFKC than the Corvis built-in parameters CBI (AUC = 0.610, p = 0.041) and TBI (AUC = 0.659, p = 0.034). Conclusion: Corneal elastic modulus was found to have improved predictability in detecting FFKC patients from healthy subjects and may be used as an additional parameter for the diagnosis of keratoconus.

Learning Neural Representations and Local Embedding for Nonlinear Dimensionality Reduction Mapping

This work explores neural approximation for nonlinear dimensionality reduction mapping based on internal representations of graph-organized regular data supports. Given training observations are assumed as a sample from a high-dimensional space with an embedding low-dimensional manifold. An approximating function consisting of adaptable built-in parameters is optimized subject to given training observations by the proposed learning process, and verified for transformation of novel testing observations to images in the low-dimensional output space. Optimized internal representations sketch graph-organized supports of distributed data clusters and their representative images in the output space. On the basis, the approximating function is able to operate for testing without reserving original massive training observations. The neural approximating model contains multiple modules. Each activates a non-zero output for mapping in response to an input inside its correspondent local support. Graph-organized data supports have lateral interconnections for representing neighboring relations, inferring the minimal path between centroids of any two data supports, and proposing distance constraints for mapping all centroids to images in the output space. Following the distance-preserving principle, this work proposes Levenberg-Marquardt learning for optimizing images of centroids in the output space subject to given distance constraints, and further develops local embedding constraints for mapping during execution phase. Numerical simulations show the proposed neural approximation effective and reliable for nonlinear dimensionality reduction mapping.

Study on ultra-precision phase synchronization technique employing phase-locked loop

Microwave-to-optical phase synchronization techniques have attracted growing research interests in recent years. Here, we demonstrate tight, real-time phase synchronization of an optical frequency comb to a rubidium atomic clock. A detailed mathematical model of the phase locking system is developed to optimize its built-in parameters. Based on the model, we fabricate a phase locking circuit with high integration. Once synchronized, the fractional frequency instability of the repetition rate agrees to 6.35×10−12 at 1 s and the standard deviation is 1.5 mHz, which indicates the phase synchronization system can implement high-precision stabilization. This integrated stable laser comb should enable a wide range of applications beyond the laboratory.

Response adaptive designs for Phase II trials with binary endpoint based on context-dependent information measures

In many rare disease Phase II clinical trials, two objectives are of interest to an investigator: maximising the statistical power and maximising the number of patients responding to the treatment. These two objectives are competing, therefore, clinical trial designs offering a balance between them are needed. Recently, it was argued that response-adaptive designs such as families of multi-arm bandit (MAB) methods could provide the means for achieving this balance. Furthermore, response-adaptive designs based on a concept of context-dependent (weighted) information criteria were recently proposed with a focus on Shannon’s differential entropy. The information-theoretic designs based on the weighted Renyi, Tsallis and Fisher informations are also proposed. Due to built-in parameters of these novel designs, the balance between the statistical power and the number of patients that respond to the treatment can be tuned explicitly. The asymptotic properties of these measures are studied in order to construct intuitive criteria for arm selection. A comprehensive simulation study shows that using the exact criteria over asymptotic ones or using information measures with more parameters, namely Renyi and Tsallis entropies, brings no sufficient gain in terms of the power or proportion of patients allocated to superior treatments. The proposed designs based on information-theoretical criteria are compared to several alternative approaches. For example, via tuning of the built-in parameter, one can find designs with power comparable to the fixed equal randomisation’s but a greater number of patients responded in the trials.

INFORMATION TECHNOLOGIES IN IMPROVING ENERGY EFFICIENCY OF ELECTRIC MACHINES

The article provides results of research and suggestions for increasing energy efficiency of electric machines based of informational technologies in programming environment “КОМПАС-3D”. The article shows the relevance of resource saving for transport field where material, energetic, financial and human resources are being extensively used. Also, according to the statistical data almost 95% of electricity used by enterprises are being used to power transportation itself, hence power is used solemnly by electric vehicles – traction engines and auxiliary engines. Based on the analysis of research and their publications methods of increasing electric machines’ energy ef-ficiency were discovered. Usage of methodology of designing and manufacturing electric machines based on CAD/CAM systems gives ability to forecast their energy efficiency and solve a problem of resource storage, quality and competitiveness. Development of resource-efficient electric engine is a process of finding optimal parameters or structure. A choice of optimal engine structure lies in choosing design of each individual element, quantity of elements and relations between them, in defining spatial distribution of the elements. Built-in parameters of en-gine make it possible to conduct further development without using of mathematical apparatus, manufacturing engines of different configurations and calculation of technical and economical indicators, which evaluate effi-ciency of given solutions. On the example of the traction motor of the trolleybus the dependences of SEC on its geometrical parameters, properties of magnetic materials, power of the vehicle, and also speed of movement of the vehicle are resulted. The expediency of solving resource saving issues during the whole design process is shown. Keywords: resource saving, information technologies, energy efficiency, electric motor, computer-aided design systems, efficiency coefficient.

A weak Galerkin finite element method for nonlinear conservation laws

<p style='text-indent:20px;'>A weak Galerkin (WG) finite element method is presented for nonlinear conservation laws. There are two built-in parameters in this WG framework. Different choices of the parameters will lead to different approaches for solving hyperbolic conservation laws. The convergence analysis is obtained for the forward Euler time discrete and the third order explicit TVDRK time discrete WG schemes respectively. The theoretical results are verified by numerical experiments.</p>

Indigenization of the median of markers for Down syndrome screening based on statistical analysis of medical big data

ObjectiveTo indigenize the median of Down syndrome (DS) screening markers for first and second trimester, and compare the impact of the indigenized and built-in median data on the efficiency of DS screening. Materials and MethodsData derived from first and Second-trimester screening (FTS and STS) for DS, composed of selected pregnancies deemed to be normal, were examined in a retrospective study. Indigenization regression analysis was calculated by using five models to fit statistical the raw data. Multiple of median (MoM) values estimated by using indigenized medians were compared with those calculated by using built-in. ResultsThis study established a regression equation which is more suitable for the median of each screening marker in the local pregnant women. The changes of median MoM of screening markers were statistically significant after indigenization. For FTS, the detection rate was 100% when the false positive rate was 5%, and the cut-off value was 1/262. On the other hand, for STS, the detection rate of the model with indigenized parameters was 77.42%, which is 16.13% higher than that of built-in parameters. ConclusionFor the individual specific risk of pregnancy, when the indigenized parameters was used to calculate, is more accurately and screening effectiveness has been improved. This is a great reference significance for the current prenatal screening whether indigenized data should be used.

High-Order Low-Dissipation Targeted ENO Schemes for Ideal Magnetohydrodynamics

The recently proposed targeted ENO (TENO) schemes (Fu et al. J Comput Phys 305:333–359, 2016) are demonstrated to feature the controllable low numerical dissipation and sharp shock-capturing property in compressible gas dynamic simulations. However, the application of low-dissipation TENO schemes to ideal magnetohydrodynamics (MHD) is not straightforward. The complex interaction between fluid mechanics and electromagnetism induces extra numerical challenges, including simultaneously preserving the ENO-property, maintaining good numerical robustness and low dissipation as well as controlling divergence errors. In this paper, based on an unstaggered constrained transport framework to control the divergence error, we extend a set of high-order low-dissipation TENO schemes ranging from 5-point to 8-point stencils to solving the ideal MHD equations. A unique set of built-in parameters for each TENO scheme is determined. Different from the TENO schemes in Fu et al. (2016), a modified scale-separation formula is developed. The new formula can achieve stronger scale separation, and it is simpler and more efficient than the previous version as the computation cost of high-order global smoothness measure $${\tau _K}$$ is avoided. The performances of tailored schemes are systematically studied by several benchmark simulations. Numerical experiments demonstrate that the TENO schemes in the constrained transport framework are promising to simulate more complex MHD flows.

Analysis of QoS in Different Application by using Opnet Workbench

Quality of service gauges the overall performance of different services within the network in many applications like voice telephony, video conferencing, audio conferencing etc. This paper involves the analysis of quality of service among different application by using Opnet workbench. With the help of Opnet which includes many built-in parameters thus by using some parameters of QoS within an Opnet, the effect of QoS has been detected. The central idea is to mainly determine the role of QoS on some applications. This paper includes a simple network which does not include quality of service and a complete network with some applications in which authentic parameters regarding the quality of service, are applied. Thus, it has been concluded there is a great impact of QoS in a network. A network without quality is considered to be a poor network so this paper majorly emphasizes the importance of QoS.

Evaluate dry deposition velocity of the nitrogen oxides using Noah-MP physics ensemble simulations for the Dinghushan Forest, Southern China

There has been a rapid growth of reactive nitrogen (Nr) deposition over the world in the past decades. The Pearl River Delta region is one of the areas with high loading of nitrogen deposition. But there are still large uncertainties in the study of dry deposition because of its complex processes of physical chemistry and vegetation physiology. At present, the forest canopy parameterization scheme used in WRF-Chem model is a single-layer “big leaf” model, and the simulation of radiation transmission and energy balance in forest canopy is not detailed and accurate. Noah-MP land surface model (Noah-MP) is based on the Noah land surface model (Noah LSM) and has multiple parametric options to simulate the energy, momentum, and material interactions of the vegetation-soil-atmosphere system. Therefore, to investigate the improvement of the simulation results of WRF-Chem on the nitrogen deposition in forest area after coupled with Noah-MP model and to reduce the influence of meteorological simulation biases on the dry deposition velocity simulation, a dry deposition single-point model coupled by Noah- MP and the WRF-Chem dry deposition module (WDDM) was used to simulate the deposition velocity (Vd). The model was driven by the micro-meteorological observation of the Dinghushan Forest Ecosystem Location Station. And a series of numerical experiments were carried out to identify the key processes influencing the calculation of dry deposition velocity, and the effects of various surface physical and plant physiological processes on dry deposition were discussed. The model captured the observed Vd well, but still underestimated the Vd. The self-defect of Wesely scheme applied by WDDM, and the inaccuracy of built-in parameters in WDDM and input data for Noah-MP (e.g. LAI) were the key factors that cause the underestimation of Vd. Therefore, future work is needed to improve model mechanisms and parameterization.

Demand response implementation in smart households

Home energy management system (HEMS) is essential for residential electricity consumers to participate actively in demand response (DR) programs. Dynamic pricing schemes are not sufficiently effective for end-users without utilizing a HEMS for consumption management. In this paper, an intelligent HEMS algorithm is proposed to schedule the consumption of controllable appliances in a smart household. Electric vehicle (EV) and electric water heater (EWH) are incorporated in the HEMS. They are controllable appliances with storage capability. EVs are flexible energy-intensive loads, which can provide advantages of a dispatchable source. It is expected that the penetration of EVs will grow considerably in future. This algorithm is designed for a smart household with a rooftop photovoltaic (PV) system integrated with an energy storage system (ESS). Simulation results are presented under different pricing and DR programs to demonstrate the application of the HEMS and to verify its’ effectiveness. Case studies are conducted using real measurements. They consider the household load, the rooftop PV generation forecast and the built-in parameters of controllable appliances as inputs. The results exhibit that the daily household energy cost reduces 29.5%–31.5% by using the proposed optimization-based algorithm in the HEMS instead of a simple rule-based algorithm under different pricing schemes.

Locating Structural Centers: A Density-Based Clustering Method for Community Detection.

Uncovering underlying community structures in complex networks has received considerable attention because of its importance in understanding structural attributes and group characteristics of networks. The algorithmic identification of such structures is a significant challenge. Local expanding methods have proven to be efficient and effective in community detection, but most methods are sensitive to initial seeds and built-in parameters. In this paper, we present a local expansion method by density-based clustering, which aims to uncover the intrinsic network communities by locating the structural centers of communities based on a proposed structural centrality. The structural centrality takes into account local density of nodes and relative distance between nodes. The proposed algorithm expands a community from the structural center to the border with a single local search procedure. The local expanding procedure follows a heuristic strategy as allowing it to find complete community structures. Moreover, it can identify different node roles (cores and outliers) in communities by defining a border region. The experiments involve both on real-world and artificial networks, and give a comparison view to evaluate the proposed method. The result of these experiments shows that the proposed method performs more efficiently with a comparative clustering performance than current state of the art methods.

Overlapping Community Detection Based on Structural Centrality in Complex Networks

Community structure is an important mesoscale topological characteristic of complex networks, which is significant for understanding structural features and organizational functions in networks. Local expansion methods have been proved to be efficient and effective for community detection. However, it has been shown that there are inherent drawbacks for these methods to uncover overlapping communities. Most methods are sensitive to initial seeds and built-in parameters, while others are inadequate to reveal the pervasive overlaps. In this paper, we propose a new local expansion method for uncovering overlapping communities based on structural centrality. The key idea of our approach is to locate structural centers of communities with the structural centrality and then expand these structural centers with a weighted strategy and a local search procedure. Experimental results both on artificial and real-world networks demonstrate that our method is effective and promising in term of finding overlapping community structures. We also show that our local expansion strategies are efficient in uncovering cohesive clusters and producing stable clustering results.

Virtual Screening and Molecular Dynamics Study of Potential Negative Allosteric Modulators of mGluR1 from Chinese Herbs.

The metabotropic glutamate subtype 1 (mGluR1), a member of the metabotropic glutamate receptors, is a therapeutic target for neurological disorders. However, due to the lower subtype selectivity of mGluR1 orthosteric compounds, a new targeted strategy, known as allosteric modulators research, is needed for the treatment of mGluR1-related diseases. Recently, the structure of the seven-transmembrane domain (7TMD) of mGluR1 has been solved, which reveals the binding site of allosteric modulators and provides an opportunity for future subtype-selectivity drug design. In this study, a series of computer-aided drug design methods were utilized to discover potential mGluR1 negative allosteric modulators (NAMs). Pharmacophore models were constructed based on three different structure types of mGluR1 NAMs. After validation using the built-in parameters and test set, the optimal pharmacophore model of each structure type was selected and utilized as a query to screen the Traditional Chinese Medicine Database (TCMD). Then, three different hit lists of compounds were obtained. Molecular docking was used based on the latest crystal structure of mGluR1-7TMD to further filter these hits. As a compound with high QFIT and LibDock Score was preferred, a total of 30 compounds were retained. MD simulation was utilized to confirm the stability of potential compounds binding. From the computational results, thesinine-4ʹ-O-β-d-glucoside, nigrolineaxanthone-P and nodakenin might exhibit negative allosteric moderating effects on mGluR1. This paper indicates the applicability of molecular simulation technologies for discovering potential natural mGluR1 NAMs from Chinese herbs.

Annealed Kullback–Leibler divergence minimization for generalized TSP, spot identification and gene sorting

This work explores learning LCGM (lattice-connected Gaussian mixture) models by annealed Kullback–Leibler (KL) divergence minimization for a hybrid of topological and statistical pattern analysis. The KL divergence measures the general criteria of learning an LCGM model that is composed of a lattice of multivariate Gaussian units. A planar lattice emulates topological order of cortex-like neighboring relations and built-in parameters of connected Gaussian units represent statistical features of unsupervised data. Learning an LCGM model involves collateral optimization tasks of resolving mixture combinatorics and extracting geometric features from high-dimensional patterns. Under assumption that mixture combinatorics encoded by Potts variables obey the Boltzmann distribution, approximating their joint probability by the product of individual probabilities is qualified by the KL divergence whose minimization under physical-like deterministic annealing faithfully optimizes involved mixture combinatorics and geometric features. Numerical simulations show the proposed annealed KL divergence minimization is effective and reliable for solving generalized TSP, spot identification, self-organization and visualization and sorting of yeast gene expressions.