Auto-Scaling Framework for Enhancing the Quality of Service in the Mobile Cloud Environments

On-demand availability and resource elasticity features of Cloud computing have attracted the focus of various research domains.Mobile cloud computing is one of these domains where complex computation tasks are offloaded to the cloud resources to augment mobile devices’cognitive capacity.However,the flexible provisioning of cloud resources is hindered by uncertain offloading workloads and significant setup time of cloud virtual machines(VMs).Furthermore,any delays at the cloud end would further aggravate the miseries of real-time tasks.To resolve these issues,this paper proposes an auto-scaling framework(ACF)that strives to maintain the quality of service(QoS)for the end users as per the service level agreement(SLA)negotiated assurance level for service availability.In addition,it also provides an innovative solution for dealing with the VM startup overheads without truncating the running tasks.Unlike the waiting cost and service cost tradeoff-based systems or threshold-rule-based systems,it does not require strict tuning in the waiting costs or in the threshold rules for enhancing the QoS.We explored the design space of the ACF system with the CloudSim simulator.The extensive sets of experiments demonstrate the effectiveness of the ACF system in terms of good reduction in energy dissipation at the mobile devices and improvement in the QoS.At the same time,the proposed ACF system also reduces the monetary costs of the service providers.

Memory Effect in Chemotaxis Equation

Diffusion-Reaction (DR) equation has been used to model a large number of phenomena in nature. It may be mentioned that a linear diffusion equation does not exhibit any traveling wave solution. But there are a vast number of phenomena in different branches not only of science but also of social sciences where diffusion plays an important role and the underlying dynamical system exhibits traveling wave features. In contrast to the simple diffusion when the reaction kinetics is combined with diffusion, traveling waves of chemical concentration are found to exist. This can affect a biochemical change, very much faster than straight diffusional processes. This kind of coupling results into a nonlinear (NL) DR equation. In recent years, memory effect in DR equation has been found to play an important role in many branches of science. The effect of memory enters into the dynamics of NL DR equation through its influence on the speed of the travelling wavefront. In the present work, chemotaxis equation with source term is studied in the presence of finite memory and its solution is compared with the corresponding chemotaxis equation without finite memory. Also, a comparison is made between Fisher-Burger equation and chemotaxis equation in the presence of finite memory. We have shown that nonlinear diffusion-reaction-convection equation is equivalent to chemotaxis equation.

CFD analysis of performance improvement of the Savonius water turbine by using an impinging jet duct design

The majority of research on water turbines focuses on design improvement of large-scale hydrokinetic turbines for power generation, which may have delayed the utilization of kinetic energy contained in rivers and canals. The aim of this paper is to improve the efficiency of a two bladed Savonius type cross-flow hydrokinetic turbine, which can be used as an energy converter to harness free-stream kinetic energy of water. An impinging jet duct design is presented for improving performance of the Savonius turbine in wind application as seen from literature. The performance of the modified turbine is evaluated using CFD software Fluent, and is compared with that of a simple two bladed Savonius water turbine and some of the prominent literature designs of the Savonius turbine. It is shown that the present design exhibits improved performance compared to the selected designs of the Savonius turbine.Further an insight of the improved performance of the modified turbine is also obtained from flow physics study.

Automatic License Plate Recognition System for Vehicles Using a CNN

Automatic License Plate Recognition(ALPR)systems are important in Intelligent Transportation Services(ITS)as they help ensure effective law enforcement and security.These systems play a significant role in border surveillance,ensuring safeguards,and handling vehicle-related crime.The most effective approach for implementing ALPR systems utilizes deep learning via a convolutional neural network(CNN).A CNN works on an input image by assigning significance to various features of the image and differentiating them from each other.CNNs are popular for license plate character recognition.However,little has been reported on the results of these systems with regard to unusual varieties of license plates or their success at night.We present an efficient ALPR system that uses a CNN for character recognition.A combination of pre-processing and morphological operations was applied to enhance input image quality,which aids system efficiency.The system has various features,such as the ability to recognize multi-line,skewed,and multifont license plates.It also works efficiently in night mode and can be used for different vehicle types.An overall accuracy of 98.13%was achieved using the proposed CNN technique.

Urbanization and Quality of Urban Environment Using Remote Sensing and GIS Techniques in East Delhi-India

An explosive increase of urban population, practically in all major cities and towns, has the consequent strain on the existing system manifested in an environmental chaos. The phenomena of accelerated urbanisation is the main culprit, wherein besides bringing higher standard of living, it has also brought problems, as growth of dense and unplanned residential areas, environmental pollution, non-availability of services and amenities, solid waste etc. Remote sensing satellite data is suitable for urban land use mapping to get detail and up- to-date information for environmental management. Where as GIS helps in developing database system for urban information, which supports decision making process. Development of digital database on all aspects of land use and urban planning is the next crucial task for the future in which remote sensing based informa-tion is going to play a major role. In Delhi, rise in population and growth in economic activity has led to en-vironmental degradation. With this view an attempt has been made to study the quality of urban environment in the East district of Delhi, which is experiencing very high urban growth with 98.75% urban population in 2001. For this study Landsat ASTER (MSS) data of year 2001 (15 m Ground resolution), Guide map of the year 1982 and demographic and environmental data has been used. Eight parameters were selected, which affect the urban environmental quality, namely built-up area, open spaces, household density, occupancy ra-tio, population density, accessibility to roads, noise and smell affected area. The study shows that the quality of environment has been degraded when we compare 1982 and 2003 data. Most of the East district was in a better state of environment in 1982, but in 2003 things have been changed and now 50% area is in very good, fair and desirable condition. The public participation and involvement should be encouraged planning and decisions making for the improvement in better urban environmental quality.

Packet Optimization of Software Defined Network Using Lion Optimization

There has been an explosion of cloud services as organizations take advantage of their continuity,predictability,as well as quality of service and it raises the concern about latency,energy-efficiency,and security.This increase in demand requires new configurations of networks,products,and service operators.For this purpose,the software-defined network is an efficient technology that enables to support the future network functions along with the intelligent applications and packet optimization.This work analyzes the offline cloud scenario in which machines are efficiently deployed and scheduled for user processing requests.Performance is evaluated in terms of reducing bandwidth,task execution times and latencies,and increasing throughput.A minimum execution time algorithm is used to compute the completion time of all the available resources which are allocated to the virtual machine and lion optimization algorithm is applied to packets in a cloud environment.The proposed work is shown to improve the throughput and latency rate.

Gonioscopy: A Review

Gonioscopy allows us to examine the angle of anterior chamber and forms part of complete ophthalmic examination and it is mandatory for the diagnosis and management of glaucoma. Gonioscopy permits the identification of eyes at risk for closure and detects angle abnormalities that could have diagnostic and therapeutic implications. Principle, types and techniques of gonioscopy, various types of gonioscopic lenses used, indications of performing gonioscopy, gonioscopic anatomy and grading are discussed in this article.

Childhood Glaucoma: An Overview

Several types of childhood glaucoma exist, and the terminology is based on the time of onset of disease and its potential cause. Though childhood glaucoma occurs less commonly than adults but can lead to permanent visual damage due to amblyopia, optic neuropathy or refractive error. A detailed evaluation should be done to establish diagnosis. Medical therapy has a limited role and surgery remains main modality for treatment. Childhood glaucoma is a treatable disease, if early diagnosis is established and therapeutic intervention done in time. In children with low vision efforts should be there to maintain residual vision and visual rehabilitation with low vision aids should be done.

Photon Emission from Quark Gluon Plasma at RHIC and LHC

We work on photon production through annihilation and Compton, and annihilation with scattering (AWS) processes through a quark-gluon plasma (QGP) in high energy nuclear collision incorporating the parametrization factors in strong coupling value and thermal dependent quark mass obtained through a coupling value. We find that photon yield increases with the increasing of the temperature and dominated by early times. We also compare the influence of coupling value on the spectrum of thermal photons. The production rate of photons is observed in the range of low and intermediate transverse momentum. The results are compared with other work.

Emission of Thermal Photon in Heavy-Ion Collisions

We study the leading order processes for photon production using a phenomenological model of quark-gluon plasma (QGP) in relativistic heavy-ion collisions incorporating the parametrization factors in thermal dependent quark mass. The measurement of photon emission provides valuable insights into the early conditions of QGP. The production rate is observed in the low and intermediate range of energy and transverse momentum. The photon yield is found to increase marginally with the effect of thermal dependent quark mass. The results are little enhanced and in good agreement with other work.

Dilepton Emission at Temperature Dependent Baryonic Quark-Gluon Plasma

Dilepton emission is calculated from quark-gluon plasma (QGP) with the consideration of temperature dependent chemical potential (TDCP). The model is considered to be quasi-model and hence take the dynamical quark mass as a finite value dependence on temperature. The temperature in quark mass enhances in the growth of dilepton emission rates. The emission rate of dilepton spectrum is shown in the intermediate mass region (IMR) of 1.0 - 4.0 GeV and its rate is observed to be a strong increasing function of the temperature dependent chemical potential for quark and anti-quark annihilation.

Effects of the COVID-19 pandemic on semen quality in male partners of infertile couples: a hospitalbased observational study

The effects of the coronavirus disease 2019(COVID-19)pandemic on male fertility have received considerable attention because human testes contain high levels of angiotensin-converting enzyme-2 receptors,through which severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)can enter.Early studies showed decreases in semen quality during and after recovery from COVID-19.However,no semen quality studies have examined the effects of widespread subclinical and mild disease,as well as changes in lifestyle,psychosocial behavior,intake of dietary supplements,and stress.This cross-sectional study compared semen quality parameters in male partners of infertile couples between men who underwent semen analysis before the COVID-19 pandemic(prepandemic group)and men who underwent semen analysis during the pandemic period(pandemic group);the analysis sought to clarify the overall effects of the pandemic.No participants in the pandemic group had experienced clinically overt disease.Among the 239 participants,mean body weight(P=0.001),mean body mass index(P<0.001),median sperm concentration(P=0.014),total sperm count(P=0.006),and total percentages of motile(P=0.013)and abnormal cells(P<0.001)were significantly greater in the pandemic group(n=137)than those in the prepandemic group(n=102).Among abnormal cells,the percentages of cells with excess residual cytoplasm(P<0.001),head defects(P<0.001),and tail defects(P=0.015)were significantly greater in the pandemic group than those in the prepandemic group.With the exception of morphology,the overall semenogram results were better in the pandemic group than those in the prepandemic group.

Investigation of magnetoelectric effect in lead free K0.5Na0.5NbO3–BaFe12O19 novel composite system

Lead-free magnetoelectric composites(1-x)K0.5Na0.5NbO3–(x)BaFe12O19(x = 30, 40, and 50 wt%) are synthesized using solid state reaction method. X-ray diffraction(XRD) patterns confirm formation of diphase composites. Field emission scanning electron microscopy(FE-SEM) gives information about grain size, connectivity, and microstructure of constituent phases. Dielectric parameters of composite samples are studied as a function of temperature and the transition temperatures corresponding to both the constituent phases are observed in the composite samples. Dielectric constant has been found to decrease with addition of ferrite. Room temperature multiferroic behaviour has been confirmed using P–E and M–H hysteresis loops and magnetoelectric measurement. Polarization is found to decrease;however, magnetization increases with ferrite weight percentage. The highest αME of 4.08 mV/(cm·Oe) is obtained for x = 30 wt% composite and it is realized that ferrite content significantly affects magnetoelectric behaviour.

Novel Z-Scheme ZnIn_(2)S_(4)-based photocatalysts for solar-driven environmental and energy applications: Progress and perspectives

Benefiting from strong redox ability,improved charge transport,and enhanced charge separation,Zscheme heterostructures of ZnIn_(2)S_(4) based photocatalysts have received considerable interest to tackle energy needs and environmental issues.The present review highlights the properties of ZnIn_(2)S_(4),which make it a promising photocatalyst,and a suitable combination with oxidation photocatalyst to form Zscheme,leading to improve their photocatalytic properties dramatically.As the central part of this review,various types of Z-scheme heterojunction developed recently based on ZnIn_(2)S_(4) and their application in pollutant degradation,water splitting,CO_(2) reduction,and toxic metals remediation.Some analytical techniques to detect or trap the active radical and study the charge separation and lifetime of charge carriers in these Z-schemes are highlighted.This review offers its readers a broad optical window for the structural architecture of ZnIn_(2)S_(4)-based Z-schemes,photocatalytic activity,stability,and their technological applications.Finally,we discuss the challenges and opportunities for further development on Z-Scheme ZnIn_(2)S_(4)-based photocatalysts toward energy and environmental applications based on the recent progress.

Enhanced electrical conductivity in Zr-Doped(La_(0.7)Ba_(0.3))(Mn_(0.5)Fe_(0.5))O_(3)solid solutions

The perovskite(La_(0.7)Ba_(0.3))(Mn_(0.5)Fe_(0.5))_(1−x)Zr_(x)O_(3),where x=0.1,0.2 and 0.3,ceramics were synthesized by solid-state reaction method.The introductory structural studies were followed through by X-ray diffraction technique and the results have disclosed that all the samples were crystallized into an isolated phase.The Zr substitution in the resulting solid solutions increases the electrical conductivity and the maximum value of ac conductivity has been found to be~118.8 S.cm^(−1)for x=0.3 at 200°C(at 1 MHz).The frequency dependence of ac conductivity data follows Jonscher’s power law.The variation of the exponent n versus temperature follows the nonoverlapping small polaron tunneling(NSPT)model.The dielectric relaxation has been observed to be of non-Debye nature for all measuring temperatures(50-200°C).The impedance spectroscopy reveals that all the samples exhibit negative temperature coefficient of resistance(NTCR)behavior.The prepared samples(for x>1)are supposed to be suitable for cathode materials in SOFCs.

Artificial neural network (ANNs) and mathematical modelling of hydration of green chickpea

The present study was aimed to model the hydration characteristics of green chickpea(GC)using mathematical modelling and examine predictive ability of artificial neural network(ANN)modelling.Hydration of GC was performed at different temperatures 25,35,45,55 and 65℃.Different mathematical models were tested for the hydration at different temperatures.In ANN modelling,the hydration time and hydration temperature were used as input variables and moisture ratio,moisture content and hydration ratio were taken as output variables.Peleg model best described the hydration behavior at 25℃;while hydration at high-temperature was better described by Page model and Ibarz et al.model.The optimum temperature obtained for hydration was 35℃.Effective mass diffusion coefficient(D_(e))increased from 1.5510^(-11)-1.7910^(-9) m^(2)/s with the increase in the hydration temperature.The low activation energy(39.66 kJ/moL)shows the low-temperature sensitiveness of GC.Low temperature hydration(25℃)required higher time(>200 min)to achieve the equilibrium moisture content(EMC),however high temperature hydration(35–65℃)reduced the EMC time(150 min).ANN was used to predict the hydration behavior and K fold cross validation was performed to check the over fitting of ANN model.Results show that the LOGSIGMOID transfer function showed better performance when used at the hidden layer input node in conjunction to both PURELIN and TANSIGMOID.TANSIGMOID was found suitable for moisture ratio(MR)and hydration ratio(HR)prediction,as opposed to PURELIN for moisture content(MC)data.Satisfactory model prediction was obtained when the number of neurons in the hidden layer for MC,MR and HR was 12,8 and 15,respectively.Mathematical and ANN modelling results are useful to improve/predict the MC,MR and HR during hydration process of GC at different temperature and other similar process.