Stability of Antimicrobial Drug Molecules in Different Gravitational and Radiation Conditions in View of Applications during Outer Space Missions

The evolution of different antimicrobial drugs in terrestrial, microgravity and hypergravity conditions is presented within this review, in connection with their implementation during human space exploration. Drug stability is of utmost importance for applications in outer space. Instabilities may be radiation-induced or micro-/hypergravity produced. The antimicrobial agents used in space may have diminished effects not only due to the microgravity-induced weakened immune response of astronauts, but also due to the gravity and radiation-altered pathogens. In this context, the paper provides schemes and procedures to find reliable ways of fighting multiple drug resistance acquired by microorganisms. It shows that the role of multipurpose medicines modified at the molecular scale by optical methods in long-term space missions should be considered in more detail. Solutions to maintain drug stability, even in extreme environmental conditions, are also discussed, such as those that would be encountered during long-duration space exploratory missions. While the microgravity conditions may not be avoided in space, the suggested approaches deal with the radiation-induced modifications in humans, bacteria and medicines onboard, which may be fought by novel pharmaceutical formulation strategies along with radioprotective packaging and storage.     

Electroanalytical Techniques for the Detection of Selenium as a Biologically and Environmentally Significant Analyte—A Short Review

This short review deals with the properties and significance of the determination of selenium, which is in trace amounts an essential element for animals and humans, but toxic at high concentrations. It may cause oxidative stress in cells, which leads to the chronic disease called selenosis. Several analytical techniques have been developed for its detection, but electroanalytical methods are advantageous due to simple sample preparation, speed of analysis and high sensitivity of measurements, especially in the case of stripping voltammetry very low detection limits even in picomoles per liter can be reached. A variety of working electrodes based on mercury, carbon, silver, platinum and gold materials were applied to the analysis of selenium in various samples. Only selenium in oxidation state + IV is electroactive therefore the most of voltammetric determinations are devoted to it. However, it is possible to detect also other forms of selenium by indirect electrochemistry approach.     

云计算在智慧医院信息服务中的应用

随着医疗服务体系的发展和进步,越来越多的医院开始将云计算技术引入信息服务系统,使医院信息化建设得到了迅速发展。文章分析了云计算技术的特点及其在智能医院信息服务中的应用和影响,以期促进该技术在未来的发展和推广。     

The survival probability of critical and subcritical branching processes in finite state space Markovian environment

Let ${\left(Z_n\right)}_{n\ge 0}$ be a branching process in a random environment defined by a Markov chain ${\left(X_n\right)}_{n\ge 0}$ with values in a finite state space $\mathbb{X}$. Let ${\mathbb{P}}_i$ be the probability law generated by the trajectories of ${\left(X_n\right)}_{n\ge 0}$ starting at $X_0=i\in \mathbb{X}.$ We study the asymptotic behaviour of the joint survival probability ${\mathbb{P}}_i\left(Z_n>0,X_n=j\right)$, $j\in \mathbb{X}$ as $n\to +\infty$ in the critical and strongly, intermediate and weakly subcritical cases.     

Distributed privacy protection strategy for MEC enhanced wireless body area networks

With the rapid development and widespread application of Wireless Body Area Networks (WBANs), the traditional centralized system architecture cannot handle the massive data generated by the edge devices. Meanwhile, in order to ensure the security of physiological privacy data and the identity privacy of patients, this paper presents a privacy protection strategy for Mobile Edge Computing(MEC) enhanced WBANs, which leverages the blockchain-based decentralized MEC paradigm to support efficient transmission of privacy information with low latency, high reliability within a high-demand data security scenario. On this basis, the Merkle tree optimization model is designed to authenticate nodes and to verify the source of physiological data. Furthermore, a hybrid signature algorithm is devised to guarantee the node anonymity with unforgeability, data integrity and reduced delay. The security performance analysis and simulation results show that our proposed strategy not only reduces the delay, but also secures the privacy and transmission of sensitive WBANs data.     

电力市场环境下供电公司的购电风险分析

在打破传统垄断式供电模式,加快向市场化转型的背景形势下,电力市场改革和市场化运作体制的逐渐形成使得供电公司迎来了新的电力市场环境,增加了新的风险类型.为了加强供电公司风险管控能力,保障电力市场稳定有序运作,本文结合电力市场环境,对供电公司购电风险类型及强化风险管控的对策进行了深入探究.     

网络环境下高职院校学生思想政治教育的创新刍议

本文将明确网络环境下高职院校学生思想政治教育进行创新的重要性和必要性，探究网络环境下高职院校学生思想政治教育面临的挑战，并提出一些在网络环境下高职院校学生思想政治教育进行创新的途径。     

Extreme learning machine approach for sensorless wind speed estimation

Precise predictions of wind speed play important role in determining the feasibility of harnessing wind energy. In fact, reliable wind predictions offer secure and minimal economic risk situation to operators and investors. This paper presents a new model based upon extreme learning machine (ELM) for sensor-less estimation of wind speed based on wind turbine parameters. The inputs for estimating the wind speed are wind turbine power coefficient, blade pitch angle, and rotational speed. In order to validate authors compared prediction of ELM model with the predictions with genetic programming (GP), artificial neural network (ANN) and support vector machine with radial basis kernel function (SVM-RBF). This investigation analyzed the reliability of these computational models using the simulation results and three statistical tests. The three statistical tests includes the Pearson correlation coefficient, coefficient of determination and root-mean-square error. Finally, this study compared predicted wind speeds from each method against actual measurement data. Simulation results, clearly demonstrate that ELM can be utilized effectively in applications of sensor-less wind speed predictions. Concisely, the survey results show that the proposed ELM model is suitable and precise for sensor-less wind speed predictions and has much higher performance than the other approaches examined in this study.     

A novel clipping technique for filtering FM signals embedded in intensive noise

A novel adaptive clipping technique for filtering a constant amplitude frequency modulated (FM) signal embedded in non-Gaussian noise is proposed. It is based on the analysis and processing of the estimate of probability density function of a FM signal realization. As a result, modifications of two robust estimators of FM signal amplitude are proposed. It is shown that these estimators can be used for Gaussian and non-Gaussian heavy-tail environments. The proposed clipping technique can exploit one or another obtained robust estimate of the signal amplitude for adaptive setting a threshold. Analysis of signal estimate accuracy for different noise environments is carried out. Comparative analysis of the obtained methods and known approaches based on scanning window nonlinear filtering and optimal robust L-DFT form is performed. It is demonstrated that the usage of clipping-based technique leads to the considerable improvement of the FM signal filtering efficiency in comparison to the aforementioned known approaches for different noise environments and a wide range of input SNR values.