Лечение острых респираторных вирусных инфекций человека поляризованным полихроматическим некогерентным светом

В статье приведены данные литературы и собственных исследований по улучшению лечения и предупреждения возникновения опасных тяжёлых осложнений гриппа и ОРВИ у человека путём использования поляризованного света, направленного на воспалённые участки больного гриппом (ОРВИ).В статті наведено дані літератури та власних досліджень стосовно поліпшення лікування грипу та ГРВІ у людини шляхом використання поляризованого світла, який спрямований на запальні ділянки хворого на грип (ГРВІ) та попередження виникнення небезпечВпервые поступила в редакцию 21.01.2017 г. Рекомендована к печати на заседании редакционной коллегии после рецензирования них важких ускладнень.In the article the literature data and own studies on improving the method of treatment of influenza and SARS in humans by using polarized light, which is aimed at inflammatory sites with influenza virus (SARS) and prevention of hazardous heavy complications

Ab initio molecular dynamics using density based energy functionals: application to ground state geometries of some small clusters

The ground state geometries of some small clusters have been obtained via ab initio molecular dynamical simulations by employing density based energy functionals. The approximate kinetic energy functionals that have been employed are the standard Thomas-Fermi $(T_{TF})$ along with the Weizsacker correction $T_W$ and a combination $F(N_e)T_{TF} + T_W$. It is shown that the functional involving $F(N_e)$ gives superior charge densities and bondlengths over the standard functional. Apart from dimers and trimers of Na, Mg, Al, Li, Si, equilibrium geometries for $Li_nAl, n=1,8$ and $Al_{13}$ clusters have also been reported. For all the clusters investigated, the method yields the ground state geometries with the correct symmetries with bondlengths within 5\% when compared with the corresponding results obtained via full orbital based Kohn-Sham method. The method is fast and a promising one to study the ground state geometries of large clusters.Comment: 15 pages, 3 PS figure

The Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP): Overview and description of models, simulations and climate diagnostics

The Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) consists of a series of time slice experiments targeting the long-term changes in atmospheric composition between 1850 and 2100, with the goal of documenting composition changes and the associated radiative forcing. In this overview paper, we introduce the ACCMIP activity, the various simulations performed (with a requested set of 14) and the associated model output. The 16 ACCMIP models have a wide range of horizontal and vertical resolutions, vertical extent, chemistry schemes and interaction with radiation and clouds. While anthropogenic and biomass burning emissions were specified for all time slices in the ACCMIP protocol, it is found that the natural emissions are responsible for a significant range across models, mostly in the case of ozone precursors. The analysis of selected present-day climate diagnostics (precipitation, temperature, specific humidity and zonal wind) reveals biases consistent with state-of-the-art climate models. The model-to-model comparison of changes in temperature, specific humidity and zonal wind between 1850 and 2000 and between 2000 and 2100 indicates mostly consistent results. However, models that are clear outliers are different enough from the other models to significantly affect their simulation of atmospheric chemistry

Hadamard Walsh space based hybrid technique for image data augmentation

Image data augmentation (IDA) is common when deep learning is used for image classification to address the issue of overfitting. Overfitting occurs when the datasets are small and the deep learning models have a huge capacity. Overfitting models have low training errors but high validation errors and result in poor generalization. Several methods have been researched in this context, but frequency domain-based methods are less explored. In this research, we have explored the Hadamard and Walsh space and developed two hybrid technique for IDA. The proposed techniques use a combination of Hadamard/Walsh transform and geometrical transformations. Empirical study is carried out using the VGG-16 model for image classification on the CIFAR-10 dataset and the results are compared with existing methods. The analysis of the results shows that the proposed techniques improve the evaluation parameters significantly. Further, analysis of training loss vs. validation loss shows that the proposed Hadamard-based hybrid methods have better generalization ability than the proposed Walsh-based hybrid method

Comprehensive Review of State-of-the-Art Applications of Artificial Neural Networks in Predicting Concrete Compressive Strength

Concrete compressive strength prediction is a crucial aspect in civil engineering, with applications ranging from structural design to quality control in construction projects. Traditional methods for predicting concrete compressive strength often rely on empirical formulas or physical testing, which may be limited in accuracy or efficiency. In recent years, Artificial Neural Networks (ANNs) have emerged as powerful tools for predicting concrete compressive strength due to their ability to capture complex nonlinear relationships in data. This paper provides a comprehensive review of the state-of-the-art applications of ANNs in predicting concrete compressive strength. It discusses various architectures, training techniques, input parameters, and datasets used in ANN models, as well as their performance compared to traditional methods. Additionally, challenges and future directions in the field are identified to guide further research efforts

Trusted Net: A Lightweight Cluster-Based Trust Sensing System for IoT Networks

Abstract— The Internet of Things is founded on the premise that objects in the human living environment can be connected to the Internet. Adoption of the Internet of Things cannot be permitted until security issues are addressed. Security solutions for the Internet of Things can be built on unique designs such as partitioning a mobile network into clusters managed by a cluster head, often known as clustering. However, without any security considerations, the clustering process is vulnerable to a variety of security-related internal attacks. A trust management system, which has proven its security efficiency and friability in mobile networks, might be used to defend the IoT against rogue nodes within it. In this research, we provide a trust-based clustering technique for the Internet of Things. Trust Sensing has played an important part in dealing with security concerns. A novel Light Weight Clustered Trust Sensing (LWCTS) Mechanism has been created with the primary goal of reducing The amount of energy that IoT nodes use. The LWCTS first clusters the network and chooses highly resourced Cluster Heads. Additionally, two components are considered in the proposed trust model: the mobility component for trust sensing and the interactive trust factor. The primary purpose of mobility factor inclusion in the network is to reduce the number of false positives. &nbsp

Design of Ultra Low Power Integrated PLL using Ring VCO

The design of an ultra low power Phase Locked Loop (PLL) is presented in this paper. The proposed PLL consists of a phase detector, a charge pump, low pass filter, and a ring oscillator based voltage controlled oscillator (VCO). The performance of Voltage Controlled Oscillator is of great importance for PLL. The circuit is designed using 0.13µm CMOS technology with the supply voltage of 1V and has a power consumption of 254µW. Keywords: Charge Pump, CMOS Technology, Low Pass Filter, Phase Detector, Phase Locked Loop, Voltage Controlled Oscillator