Coherence migration in high-dimensional bipartite systems

The conservation law for first-order coherence and mutual correlation of a bipartite qubit state is first proposed by Svozilík et al. [Phys. Rev. Lett. 115, 220501 (2015)], and their theories laid the foundation for the study of coherence migration under unitary transformations. In this paper, we generalize the framework of first-order coherence and mutual correlation to an arbitrary $(m \otimes n)$-dimensional bipartite composite state by introducing an extended Bloch decomposition form of the state. We also generalize two kinds of unitary operators in high-dimensional systems, which can bring about coherence migration and help to obtain the maximum or minimum first-order coherence. Meanwhile, coherence migration in open quantum systems are investigated. We take depolarizing channels as examples and establish that the reduced first-order coherence of the principal system over time is completely transformed into mutual correlation of the $(2 \otimes 4)$-dimensional system-environment bipartite composite state. It is expected that our results may provide a valuable idea or method for controlling the quantum resource such as coherence and quantum correlations.

Oxidative stress and biochemical indicators in blood of patients addicted to alcohol treated for acute ethylene glycol poisoning

Ethylene glycol (EG), in addition to its neurotoxic and nephrotoxic effects, evokes oxidative stress. The aim of this study was to assess the influence of the ethylene glycol on the biochemical indicators and oxidoreductive balance of patients treated for acute poisoning. The total study group consisted of 56 persons including 26 alcoholics who took EG as a substitute for ethyl alcohol in the course of alcohol dependence syndrome and 30 controls. Severity of poisoning, results of acid-base parameters, biochemical, and toxicological tests as well as biomarkers of the oxidative stress in blood were analyzed during the patients’ hospitalization. The key issue was to assess the oxidative stress and biochemical disturbances caused by EG and the type of treatment applied in the course of poisoning. Significant changes in some parameters were found both at time of diagnosis and after treatment initiation (ethanol as an antidote and hemodialysis). The most important differences included the activity of hepatic parameters (aspartate aminotransferase, AST) and oxidative stress markers like catalase (CAT); correlation of the lipid peroxidation products level (TBARS) with urea concentration has been shown. On the last day of the hospitalization, in some cases, the mutual correlation between the evaluated markers were observed, for example, between alanine transaminase (ALT) and glutathione reductase (GR), and urea concentration and glutathione level (GSH/GSSG). The concentration of ions (H+) had a major impact on the oxidoreductive balance, correlating with the elevated GR and GSH/GSSG levels.

Sobornost and Totality in Georges Gurvitch's Social Law Doctrine

Georges Gurvitch (1894-1965), from the 1920s to the end of his life, was solving the problem of combining unity and plurality in the justification of society. He believed that individualism and collectivism represented social processes in a limited way because they were based on the preconception that the binding power of law derives respectively from a private or corporate actor's will. Gurvitch contrasted individual law with the social one, which was intended to overcome the opposition between individualism and collectivism. Social law bases on legal sociology's assumption that social interactions as such are already legal relations. This conclusion allows Gurvitch to consider any social interaction as a source of law and to assert legal pluralism as a way of constructing society. The integrity of the latter is a condition for the mutual correlation of the multiplicity of legal regulations generated by internal social interactions into the unified structure of social law. In a holistic approach to comprehending social interactions, Gurvitch, in his Russian-language works in the migr period, uses the philosophical-legal interpretation of sobornost to describe society's integrity. In French- and English-language works from the 1930s, Gurvitch uses the term "totality," which he learned from Marcel Mauss, to describe social integrity. This article compares sobornost and totality as variants of denoting social integrity in Gurvitch's social law doctrine. The researcher determines that Gurvitch, using the concepts of sobornost and totality, interpreted society's development differently, 1) as anti-hierarchical sobornost equality, and 2) as a hierarchical inordination of totalities. Having analyzed the peculiarities of the interpretation of sobornost and totality in Gurvitch's works, the author concludes that these concepts should be considered multilingual equivalents in denoting communal unity as sources of law, which reflect changes in the interpretation of society in Gurvitch's social law doctrine.

Chemometric characterization of textile waste waters from different processes

The aim of this work is focused on water quality classification of the textile waste water streams and evaluation of pollution. Data from the chemical characterization of the effluents were elaborated to identify a useful separation in potentially treatment for reuse. This was done with the aim of realizing a full scale characterization of effluents. In the two textile companies analyzed, machineries are used to carry out different production processes such as sizing and desizing, weaving, scouring, bleaching, mercerizing, carbonizing, fulling, dying and finishing. Different process effluents from the same machinery were found to be very diverse in pollution level. 25 and 49 samples of textile waste waters from two different textile companies were analysed and physical chemical measurements were performed. The following physicochemical and chemical water quality parameters were controlled: absorbance measured at three different wavelengths, pH, conductivity, turbidity, total suspended solids, volatile suspended solids, chemical oxygen demand, metals content (Ba, Ca, Cu, Mn, K, Sr, Fe, Al, Na) and total nitrogen content. For handling the results, basic statistical methods for the determination of mean and median values, standard deviations, minimal and maximal values of measured parameters and their mutual correlation coefficients, were performed. Different chemometric methods, namely, principal component analysis (PCA), cluster analysis (CA), and linear discriminant analysis (LDA) were used to find hidden information about textile waste water quality.

Chemometrics in analytical chemistry

Chemometrics is a scientific discipline closely connected with statistics and mathematics. It has an important role in analytical chemistry. Modern analytical methods provide opportunity to collect large amounts of data for various samples. For handling analytical results different chemometric methods are employed, such as basic statistical methods for the determination of mean and median values, standard deviations, minimal and maximal values of measured parameters and their mutual correlation coefficients, the principal component analysis (PCA), cluster analysis (CA), and linear discriminant analysis (LDA). The objectives of chemometrics in analytical chemistry are focused on characterization and chemometrical classification of different samples. The quality of environmental samples such as water, sediment, soil, air samples etc. can be determined according to measured physical and chemical parameters, which represent the individual samples. Chemometric methods give information regarding measured parameters about similarity between sampling locations, sources of pollution, seasonal behavior and time trends. Monitoring of general pollution of environmental samples and following measuring parameters which are above permitted level given by legislation can be used for searching of pollution source and for planning prevention measures from pollution. Food samples can also be characterized by chemometrical methods. Chemometrics can be used for fast and efficient determination of food sample categories, such as edible oils, wines, fruits and fruit juices etc. Classification can also be performed according to the origin, source or season. From all these facts it is evident that the aim of chemometrics in analytical chemistry is high and extensive.

A study on resonance characteristics of hydraulic structures and buildings conducted using small-size models

Introduction. The study focuses on resonance characteristics of a hydraulic engineering structure, such as a groin, land retention works on an artificial island, and a school building. Any structure, exposed to dynamic effects of natural or man-induced origin, can be validly simulated as a mechanical resonator. Models are made of materials that have electro-elastic properties allowing to measure responses to induced oscillations, or changes in an alternating electric field, and reevaluate the physical characteristics later. Materials and methods. Small-size physical models of structures are made of electro-elastic materials, such as organic glass and ABS plastic, used for 3D printing. A laboratory test bench, composed of an oscillator and a personal computer, used as an oscillograph, was applied to conduct the testing. Mathematical modeling was performed using SolidWorks software packages. Oscillation modes and structural responses, featuring different response amplitudes, were identified. Models of structures were studied in different contexts, including restraint and free bearing. Results. It has been found that frequencies of the first mode of oscillations in a restrained model correspond to the fourth mode of a model in case of free bearing. Mutual correlation of eigenfrequencies of oscillations in small-size models, made of ABS plastic and organic glass, is demonstrated. Conclusions. The applicability of ABS plastic as the material of small-size physical models of buildings and structures has been proven for the purpose of identifying resonance frequencies of the prototypes. The co-authors have developed an integrated physical and mathematical modeling method that entails the use of SolidWorks software packages. This method allows to identify resonance frequencies, which are most dangerous for structures. Different structure support patterns allow to apply the geometric scale to obtain resonance frequencies when translating small-size model results to natural values and, hence, identify the vulnerability of structures to dynamic effects.

Continuous Multi-DoF Wrist Kinematics Estimation Based on a Human–Machine Interface With Electrical-Impedance-Tomography

This study proposed a multiple degree-of-freedom (DoF) continuous wrist angle estimation approach based on an electrical impedance tomography (EIT) interface. The interface can inspect the spatial information of deep muscles with a soft elastic fabric sensing band, extending the measurement scope of the existing muscle-signal-based sensors. The designed estimation algorithm first extracted the mutual correlation of the EIT regions with a kernel function, and second used a regularization procedure to select the optimal coefficients. We evaluated the method with different features and regression models on 12 healthy subjects when they performed six basic wrist joint motions. The average root-mean-square error of the 3-DoF estimation task was 7.62°, and the average R2 was 0.92. The results are comparable to state-of-the-art with sEMG signals in multi-DoF tasks. Future endeavors will be paid in this new direction to get more promising results.

Forecasting of Market Clearing Volume Using Wavelet Packet-Based Neural Networks with Tracking Signals

In order to analyze the nature of electrical demand series in deregulated electricity markets, various forecasting tools have been used. All these forecasting models have been developed to improve the accuracy of the reliability of the model. Therefore, a Wavelet Packet Decomposition (WPD) was implemented to decompose the demand series into subseries. Each subseries has been forecasted individually with the help of the features of that series, and features were chosen on the basis of mutual correlation among all-time lags using an Auto Correlation Function (ACF). Thus, in this context, a new hybrid WPD-based Linear Neural Network with Tapped Delay (LNNTD) model, with a cyclic one-month moving window for a one-year market clearing volume (MCV) forecasting has been proposed. The proposed model has been effectively implemented in two years (2015–2016) and unconstrained MCV data collected from the Indian Energy Exchange (IEX) for 12 grid regions of India. The results presented by the proposed models are better in terms of accuracy, with a yearly average MAPE of 0.201%, MAE of 9.056 MWh, and coefficient of regression (R2) of 0.9996. Further, forecasts of the proposed model have been validated using tracking signals (TS’s) in which the values of TS’s lie within a balanced limit between −492 to 6.83, and universality of the model has been carried out effectively using multiple steps-ahead forecasting up to the sixth step. It has been found out that hybrid models are powerful forecasting tools for demand forecasting.

Multivariate statistical methods for characterization of waste water quality

The aim of this work is focused on water quality classification of the waste waters and evaluation of pollution by the monitoring measurements during period 2006-2008. Environmental monitoring was performed in the region of Trbovlje, Slovenia, with two sampling sites and 15 chemical and physicochemical water quality parameters (pH, temperature, suspended solids, settling matter, chemical oxygen demand, biochemical oxygen demand, AOX (adsorbable organic halogens), total phosphorus, ammonium, nitrite, sulphate, chloride, fluoride, sulphide and mineral oil content) monitored in monthly periods (total of 60 objects x 15 variables). For handling the results different chemometric methods were employed, such as basic statistical methods for the determination of mean and median values, standard deviations, minimal and maximal values of measured parameters and their mutual correlation coefficients, the principal component analysis (PCA), cluster analysis (CA), and linear discriminant analysis (LDA). Monitoring of general pollution of waste waters and following measuring parameters which are above permitted concentration level can be used for searching of pollution source and for planning prevention measures from pollution, as well. The study allows drawing new information from the data sets such as patterns of similarity between sampling locations, sources of pollution in the environment, seasonal behavior of chemical contents and time trends.

SYSTEM-DYNAMIC MODELING OF THE IMPACT OF SOCIAL CAPITAL ON ECONOMIC GROWTH

This paper presents a qualitative and quantitative system-dynamic modeling of the impact of social capital on economic growth. Social capital is the most problematic of all the concepts that determine progress. On a broad conceptual level, there is agreement about the importance of social capital, which has been used to explain differences in progress among nations with similar natural, human and physical capital. Recent research suggests that it is more important to include an explanation of the interaction of economic actors and their organization when measuring progress than to measure progress without the influence of social capital. The purpose of this paper is to develop a system-dynamic model of the impact of social capital on economic growth that will enable better understanding and management of social capital. In order to build a system dynamics model, the paper will: provide an analysis and overview of social capital and system dynamics; develop a system dynamics structural and mental-verbal model of the impact of social capital on economic growth; and develop a mathematical model of economic growth. This will provide a practical insight into the dynamic behavior of the observed system, i.e., analyzing economic growth and observing the mutual correlation between individual parameters. Keywords: social capital, economic growth, system dynamics, structural model