The relationship between night-time light and socioeconomic factors in China and India

This paper re-examines the relationships between night-time light (NTL) and gross domestic product (GDP), population, road networks, and carbon emissions in China and India. Two treatments are carried out to those factors and NTL, which include simple summation in each administrative region (total data), and summation normalized by region area (density data). A series of univariate regression and multiple regression experiments are conducted in different countries and at different scales, in order to find the changes in the relationship between NTL and every parameter in different situations. Several statistical metrics, such as R2, Mean Relative Error (MRE), multiple regression weight coefficient, and Pearson’s correlation coefficient are given special attention. We found that GDP, as a comprehensive indicator, is more representative of NTL when the administrative region is relatively comprehensive or highly developed. However, when these regions are unbalanced or undeveloped, the representation of GDP becomes weak and other factors can have a more important influence on the multiple regression. Differences in the relationship between NTL and GDP in China and India can also be reflected in some other factors. In many cases, regression after normalization with the administrative area has a higher R2 value than the total regression. But it is highly influenced by a few highly developed regions like Beijing in China or Chandigarh in India. After the scale of the administrative region becomes fragmented, it is necessary to adjust the model to make the regression more meaningful. The relationship between NTL and carbon emissions shows obvious difference between China and India, and among provinces and counties in China, which may be caused by the different electric power generation and transmission in China and India. From these results, we can know how the NTL is reflected by GDP and other factors in different situations, and then we can make some adjustments.

An adaptive learning algorithm for spectrum sensing based on direction of arrival estimation in cognitive radio systems

<p class="Abstract">In cognitive radio systems, estimating primary user direction of arrival (DOA) measurement is one of the key issues. In order to increase the probability detection multiple sensor antennas are used and they are analysed by using subspace-based technique. In this work, we considered wideband spectrum with sub channels and here each sub channel facilitated with a sensor for the estimation of DOA. In practical spectrum sensing process interference component also encounters in the sensing process. To avoid this interference level at output of receiver, we used an adaptive learning algorithm known as Normalised Least Absolute Mean Deviation (NLAMD) algorithm. Further to achieve better performance a bias compensator function is applied in weight coefficient updating process. Using this hybrid realization, the vacant spectrum can be sensed based on DOA estimation and number of vacant locations in each channel can be identified using maximum likelihood approach. In order to test at the diversified conditions different threshold parameters 0.1, 0.5, 1 are analysed.</p>

Salvia spinosa L. Protects against Diabetes-Induced Nephropathy by Attenuation of Mitochondrial Oxidative Damage in Mice

Mitochondrial oxidative damage is a crucial factor in the pathogenesis of diabetic nephropathy (DN), which is among the most prevalent problems of diabetes, and there hasn’t been an effective treatment for DN yet. This study planned to investigate the effects of Salvia spinosa L. on mitochondrial function along with its protection against streptozotocin-induced nephropathy in diabetic mice. After the injection of streptozotocin (STZ) and verification of the establishment of diabetes, mice (n = 30) were randomly divided into the following groups: control group, diabetic-control, S. spinosa-treated diabetic (50, 100, and 200 mg/kg), and metformin-treated diabetic group (500 mg/kg). After four weeks of treatment, the mice were weighed. Blood and kidney tissues were examined for biochemical and histological evaluation. Hematoxylin and eosin staining was used for evaluating renal pathologic damage. Oxidative damage in the kidney was assessed by the evaluation of lipid peroxidation and glutathione oxidation. Furthermore, differential centrifugation was used to obtain the isolated mitochondria, and mitochondrial toxicity endpoints (mitochondrial function and mitochondrial oxidative markers) were determined in them. S. spinosa remarkably reduced the blood urea and creatinine concentrations, and also normalized kidney weight/body weight coefficient in the diabetic mice. S. spinosa ameliorated the incidence of glomerular and tubular pathological changes in histological analyses. Moreover, the oxidative and mitochondrial damages were notably attenuated in renal tissues of S. spinosa-treated mice. These results indicate that the methanolic extract of S. spinosa modulates the nephropathy in the diabetic mice by the amelioration of oxidatively induced mitochondrial damage and provides a reliable scientific base, suggesting S. spinosa as a promising alternative remedy against DN.

Accelerating the learning process of a neural network by predicting the weight coefficient

The purpose of this study is to analyze and implement the acceleration of the neural network learning process by predicting the weight coefficients. The relevance of accelerating the learning of neural networks is touched upon, as well as the possibility of using prediction models in a wide range of tasks where it is necessary to build fast classifiers. When data is received from the array of sensors of a chemical unit in real time, it is necessary to be able to predict changes and change the operating parameters. After assessment, this should be done as quickly as possible in order to promptly change the current structure and state of the resulting substances.. Work on speeding up classifiers usually focuses on speeding up the applied classifier. The calculation of the predicted values of the weight coefficients is carried out using the calculation of the value using the known prediction models. The possibility of the combined use of prediction models and optimization models was tested to accelerate the learning process of a neural network. The scientific novelty of the study lies in the effectiveness analysis of prediction models use in training neural networks. For the experimental evaluation of the effectiveness of prediction models use, the classification problem was chosen. To solve the experimental problem, the type of neural network “multilayer perceptron” was chosen. The experiment is divided into several stages: initial training of the neural network without a model, and then using prediction models; initial training of a neural network without an optimization method, and then using optimization methods; initial training of the neural network using combinations of prediction models and optimization methods; measuring the relative error of using prediction models, optimization methods and combined use. Models such as “Seasonal Linear Regression”, “Simple Moving Average”, and “Jump” were used in the experiment. The “Jump” model was proposed and developed based on the results of observing the dependence of changes in the values of the weighting coefficient on the epoch. Methods such as “Adagrad”, “Adadelta”, “Adam” were chosen for training neural and subsequent verification of the combined use of prediction models with optimization methods. As a result of the study, the effectiveness of the use of prediction models in predicting the weight coefficients of a neural network has been revealed. The idea is proposed and models are used that can significantly reduce the training time of a neural network. The idea of using prediction models is that the model of the change in the weight coefficient from the epoch is a time series, which in turn tends to a certain value. As a result of the study, it was found that it is possible to combine prediction models and optimization models. Also, prediction models do not interfere with optimization models, since they do not affect the formula of the training itself, as a result of which it is possible to achieve rapid training of the neural network. In the practical part of the work, two known prediction models and the proposed developed model were used. As a result of the experiment, operating conditions were determined using prediction models.

Forecasting Sugarcane Yield of India based on rough set combination approach

This study applied a novel rough set combination approach for forecasting sugarcane production in India. The paper uses autoregressive integrated moving average (ARIMA), double exponential smoothing (DES) and Grey model (GM) to generate the single forecasts. Also, the weight coefficient is evaluated by underlying the rough set approach to combine the single forecasts obtained from different models. To validate our proposed analysis, Sugarcane from 1950 to 2011 was used for the overall empirical analysis and generate out-sample forecasts from 2012 to 2021 for the comparative analysis. Also, ARIMA (2, 1, 1) model is found more appropriate for forecasting Sugarcane production.

β Cyclodextrin Nanocomplexes with Biologically Active Peptides from Hydrolysed Bovine Whey and Colostrum

β Cyclodextrin nanocomplexes with extensive whey and colostrum hydrolysates possessing acceptable flavor properties serve as potential sources of bioactive peptides. In this study, comparative characterization of dairy protein hydrolysates and their complexes with β cyclodextrin is presented. Antioxidant activity of studied samples was estimated by fluorometric method, the formation of clathrates with cyclic oligosaccharide was determined using thermogravimetric analysis. A significant decrease in bitterness of peptides included in cyclic oligosaccharides was established compared with samples of dairy hydrolysates. 2.1/1.3 fold increase in the antioxidant potential of β cyclodextrin clathrates with whey/colostrum hydrolysates was recorded versus unbound peptide fractions. According to toxicological tests on Tetrahymena pyriformis, the samples of whey hydrolysate and the resulting nanocomplex were referred to as non-toxic and slightly hazardous compounds, respectively. The dynamics of body weight gain and the relative weight coefficient of internal organs revealed no differences compared to the control group of Rattus norvegicus. The data on differentiation of blood cells, their death, and cytogenetic disorders demonstrated that a sample of cyclic oligosaccharides with whey peptides is non-toxic at the maximum dosages allowable for administration. β Cyclodextrin complexing with dairy peptides resulted in enhanced radical-reducing activity and improved flavor properties, making the clathrates promising and safe ingredients of special nutrition formulas.

Study of large axial space rebinning algorithm in γ-photon industrial tomography image reconstruction

The accuracy of the existing single slice and Fourier rebinning algorithms depends on the projection angle of the line of response. The increase of such projection angle with the detector size, typical in the large axial space of γ-photon industrial detection, and the loss of some projection data after rebinning, result in the degradation of the image quality. In addition, those algorithms consider the probability of positron annihilation equally distributed along the line of response, which prevents to estimate accurately the positions of the annihilation point, and can originate artifacts and noise in the reconstructed image. In this work, we propose an alternative large axial space rebinning algorithm. In that algorithm, initially the line of response is divided into transverse and axial components. Then, each line of response is uniformly rebinned into all the 2D sinogram data intersecting with it. To improve the accuracy of the estimate of the annihilation point location and suppress the noise effectively, we assign a Gaussian weight coefficient to the projection data, and optimise the rebinning algorithm with it. Finally, we reconstruct the image on the basis of the 2D sinograms with the optimised weights. On the computational side, the algorithm is also accelerated by making use of parallel computing. Both simulation and experimental results show that the proposed method improves the contrast and spatial resolution of 2D reconstructed images. Furthermore, the reconstruction time is not affected by the new method, which is therefore expected to meet the demand of γ-photon industrial inspection imaging.

PARAMETRIC SYNTHESIS OF A MOVING OBJECT STABILIZER

The problem of choosing the variable parameters of a stabilizer of an object which minimize an additive quadratic integral functional reflecting the complex of requirements for a closed stabilization system is considered. To solve the problem a combined method of parametric synthesis of the stabilizer, which is a sequential combination of the Sobol grid method and the Nelder-Mead method, is proposed. At the first stage of the method by applying the Sobolev grid method a working point of the closed system in the pace of its variable parameters is transferred into a neighborhood of the quality functional global minimum point. Then at the second stage the Nelder-Mead method is used to relocated the working point into a small neighborhood of the global minimum. The method proposed comprises a particular algorithm for choosing the weight coefficient of the additive quality functional as well as makes use of the stabilization object state vector main coordinates, which provide the most adequate description of its dynamic features. The properties of a mathematical model of controlled system with discontinuous stabilization process control are studied numerically. The analysis of the plots in the dynamical system state phase space indicates non-spiral approach of the system to its equilibrium state. The synthesized control is realized in the form of a sequence of switchovers.

Evaluation of the Development of Fintech-Served Real Economy Based on Fintech Improvement

The new generation of information technology (IT) promotes the integration of fintech with the real economy. Existing studies emphasize the relationship between fintech and the real economy over the development level of fintech-served real economy (FtRE). To fill up the gap, this paper explores the evaluation of FtRE based on fintech improvement (FtI). Firstly, an evaluation index system (EIS) was established for fintech service efficiency (FtSE), and FtSE was measured through data envelopment analysis (DEA). Then, fuzzy c-means (FCM) clustering was performed to discretize continuous indices. Drawing on matter-element theories, the authors created the classic domain and node domain of FtRE, as well as the evaluation objects of real economy, calculated the correlation between each factor affecting development level and evaluated development level, and computed the weight coefficient of each index. Finally, the influence of FtI-based FtRE development was empirically analyzed through experiments.