A Quantum Optimization Model for Dynamic Resource Allocation in Cloud Computing

Quantum Computing and Cloud Computing technologieshave potential capability to change the dynamic of futurecomputing. Similarly, both Complexities, time and Space are the basicconstraints which can determine the efficient cloud service performance.Quantum optimization for the cloud resources in dynamic environmentprovides a way to deal with the present classical cloud computationmodel’s challenges. By combining the fields of quantum computing andcloud computing, will result in evolutionary technology. Virtual resourceallocation is a major challenge facing cloud computing with dynamiccharacteristics, a single aspect for the evaluation of resource allocationstrategy cannot satisfy the real world demands in this case. QuantumOptimization resource allocation mechanism on the cloud computingenvironment based two-way factors, improving user satisfaction and bestuse of resource utilization of cloud computing systems.A dynamic resource allocation mechanism for cloud services, based onnegotiation by keeping the focus on preferences and pricing factor istherefore proposed

IMPLEMENTASI PROGRAM KOTA TANPA KUMUH (KOTAKU)DI KABUPATEN BONE

The notion of this research is to implement of Kotaku Tanpa Kumuh program (KOTAKU) how is the accuracy of the KOTAKU program in achieving the program, so the program can create the livable of residential urban and the slum in the city 0%. This research uses quantitative research with descriptive approach, the type of research used in this research is inductive phenomology research type, data analysis used is, data reduction, data presentation, and conclusion drawing. The data in this research taken from ten informants. Technique of data collecting was deep interview, direct observation, and take documentation. The result of this research showed that the implementation of Kotaku Tanpa Kumuh program (KOTAKU) in District Bone has been accordance with Law No 01th of 2011 as one of the program of the slum areas arrangement in Sub-District Tanete Riattang, but it did not go well, where there were still people who complained about the inequality of infrastructure development in the slums tha caused social jealousy to accur, and the condition of the environment that still did not look healthy

Modeling Emotional Mutation and Evolvement Using Genetic Algorithm in Agency

Human mind has the ability to generate emotions based on internal and external environment. These emotions are based on past experiences and the current situation. Mutation of emotions in human is the change in the intensity of emotion and the more intense the emotion is, it has more chances of existence. In mutative state two emotions are crossover and from the new emotions only the fittest and strongest emotion survive. Emotional mutation and evolvement helps human mind in decision making and in generating response. In agency the phenomenon of emotional modeling can be accomplished by Mutation and Evolvement for generating output. Genetic algorithm is computational model that is inspired by evolution of biological population and by using mutation and crossover of Genetic Algorithm the agency is able to generate output.This paper presents the algorithmic approach for emotional Mutation and Evolvement using Genetic Algorithm for generating output in agency

Learning-Based Routing in Cognitive Networks

Intelligent Routing can influence the overall performance of a communication network’s throughput and efficiency. Routing strategies is required to adapt to changing network loads and different topologies. Learning from the network environment, in order to optimally adapt the network settings, is an essential requirement for providing efficient communication services in such environments. Cognitive networks are capable of learning and reasoning. They can energetically adapt to varying network conditions in order to optimize end-to-end performance and utilize network resources. In this paper we will focus machine learning in routing scheme that includes routing awareness, a routing reconfiguration

Genetic Algorithm & Fuzzy Logic Based PEM Fuel Cells Power Conversion System for AC Integration

In the scientific environment, the leading variables such as voltage, current, power, heat from cooling system, membrane temperature and hydrogen pressure are uses as steady state and transient behaviors of Fuel Cells (FC). In the reproducing process of Fuel Cells (FC) variations, DC-DC converters are connected transversely its terminals, the efficiency, stability and durability are considered as operational problems for steady state. Since the Proton Exchange Fuel Cell is a non-linear process and its parameters change when it is delivering energy to the grid. The conventional controllers can’t content the control objectives. In this paper, an intelligent DC-AC power optimization is proposed for Fuel Cell (FC) control system to produce energy in the grid stations and to improve the power quality when FC is supplying load to grid. Furthermore, a Genetic Algorithm (GA) based reactive power optimization for voltage profile improvement and real power minimization in DC-AC system. A fuzzy logic controller is also used to control active power of PEM fuel cell system. Fuzzy logic controller will modify the hydrogen flow feedback from the terminal load. At the end, we will simulate DC-AC converter for checking its efficiency, stability and durability on the basis of the genetic algorithm and fuzzy logic controller to control power generation

Forecasting of Intellectual Capital by Measuring Innovation Using Adaptive Neuro-Fuzzy Inference System

Purpose – The aim of every organization is to achieve its set goals and objectives as well as secure competitive advantage over its competitors. However, these cannot be achieved or actualized if staff or workers act independently and do not share ideas. Today prominent businesses are becoming more aware that the knowledge of their employees is one of their primary assets. Sometimes organizational decisions cannot be effectively made with information alone; there is need for knowledge application. An effective Knowledge Management System can give a company the competitive edge it needs to be successful, and, for that reason, knowledge Management projects should be high priority. This means that for any organization to be competitive in today’s global world there is need for combination or pooling together of ideas by employees in order to achieve teamwork; this is in support of the saying that ‘two good heads are better than one’. Due to the advent of the knowledge-based economy and the developments in activity nature of the companies at international level, intellectual capital is taken to be one of the fundamental pillars of the companies for achieving efficiency. The aim of this study is to predict the amount and effectiveness of intellectual capital or intangible assets on the basis of innovation ability of the companies using an integrated artificial neural networks fuzzy logic analysis approach in order to cope with future challenges of strategic management. Design/methodology/approach – This paper suggests some guidelines for setting up the development of valuation approach based on application and adaption of selected financial and non-financial indicators by means of artificial neural networks and fuzzy logic. The artificial neural network model is highly accurate in predicting intellectual capital of the companies. This research paper presents the construction and design of Hybrid Application using Neural Network and Fuzzy Logic. This proposed system uses a simplified algorithmic design approach with wide range of input and output membership functions. In this research a hybrid Neuro-Fuzzy systems modelling methodology is developed and applied to an empirical data set in order to determine the hidden fuzzy if-then rules. Furthermore, the proposed methodology is a valuable tool for successful knowledge management. Findings – The findings show the opinion of that the complexity of development has been improved by expansion in the amount of knowledge available to organizations. Future research should contain of high degree of study to analytically examine the successful project knowledge management in different types of plans, companies and commences. Learning comes through creating and applying knowledge, whilst learning increases an individual's and organization's knowledge asset. Both learning and knowledge management feed off the same root: learning, improved capacity to perform work tasks, ability to make effective decisions, predict future parameters on the basis of some certain parameters and positively impact the world around us. Challenges – Identification and evaluation of the significant factors that create and determine enterprise value in industry is based on complex calculations involving many variables. Regardless of this reason, existing business valuation methods for such companies have to be improved with taking into account a numerous qualitative and even additional quantitative factors.Therefore, economic experts and scientists in the field of business valuation are confronted with new challenges in determination of appropriate approaches that should be able to eliminate the disadvantages of existing valuation methods. The environment in which businesses operate is ever changing. The market has become global and the technological advancement has changed the way business is done. The resulting impact of globalization is fierce competition that has altered the business landscape. Firms are increasingly employing various techniques in order to remain relevant and competitive. Since decision making is considered as the management main elements and sometimes equivalent to management itself, it is essential that researchers pay a specific attention to this field because if decisions are made in an optimized and effective form in an organization. This work is motivated by the need for a model that addresses the study of Knowledge in specific environments such as Business and Management, where several situations are very difficult to be analyze in conventional ways and therefore is insufficient in describing the complications of represent a realistic social phenomena and their social actors. Distributed Agency methodology will be used that requires the use of all available computational techniques and interdisciplinary theories as an approach to describe the interactions between agents in the development of social phenomena. Data Mining and Neuro-Fuzzy System are also used as part of the methodology to discover and assign rules on agents that represent real-world companies and employees. Practical implications – Today most organizations have discovered that advanced trainings can be considered as the key asset for modern organizations. This study presents a forecasting model that predicts intangible assets on the basis of innovation performance in organizational training using widely applied innovation criteria. The research focused on criteria, such as organization culture, ability to respond to organizational technology changes, relationship with other organizations in the training process and the use of high technology in education. The adaptive neuro-fuzzy inference systems (ANFIS) approach has been used to verify the proposed model. It is possible to predict innovation performance and it can also adjust allocated resources to organizational training system for its innovation objectives with this method. Originality/value – A great deal of work has been published over the past decade on the application of neural networks in diverse fields. This paper presents a model that measure and forecasts the intangible assets by the development of an Adaptive Neural Network with Fuzzy Inference system (ANFIS), using data that concern human capital, organizational support and innovativeness. The results indicate that fuzzy neural networks could be an efficient system that is easy to apply in order to accurately measure and forecast the intangible assets by measuring innovation capabilities of an organization or firm

Container Performance and Vulnerability Management for Container Security Using Docker Engine

Containers have evolved to support microservice architecture as a low-cost alternative to virtual machines. Containers are increasingly prevalent in the virtualization landscape because of better working; containers can bear considerably less overhead than the conventional hypervisor-based component virtual machines. However, containers directly communicate with the host kernel, and attackers can co-locate containers in the host system quicker than virtual machines. This causes significant security issues in container technology. The security hardening system is currently targeted at implementing universal access management regulations that make it difficult to assess the required procedure for accessing containers. Security mechanisms include an explicit awareness of the purpose and actions of the container and entail manual interaction and configuration. A user-friendly container protection scheme implemented an access policy to comply with its anticipated and legitimate application performance. In this study, container technology constraints have been overcome by proposing a unique Docker-sec mechanism. Docker-sec uses four mechanisms; the original collection has been improved during container runtime by additional rules that constrain the capacity of the container, further representing the applications in practice, file system, processes, network isolation, and vulnerability scanning of Docker images over different workload. Different vulnerabilities have been scanned with a CVE severity level. Results showed that inter-container communication with the system is more secure containers from zero vulnerabilities with an overhead of 3.45%.Qatar University Internal Grant - No. IRCC-2021-010

Vertical Pod Autoscaling in Kubernetes for Elastic Container Collaborative Framework

Kubernetes is an open-source container management tool that automates container deployment, container load balancing, and container(de)scaling, including Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA). HPA enables flawless operation, interactively scaling the number of resource units, or pods, without downtime. Default Resource Metrics, such as CPU and memory use of host machines and pods, are monitored by Kubernetes. Cloud Computing has emerged as a platform for individuals besides the corporate sector. It provides cost-effective infrastructure, platform, and software services in a shared environment. On the other hand, the emergence of industry 4.0 brought new challenges for the adaptability and infusion of cloud computing. As the global work environment is adapting constituents of industry 4.0 in terms of robotics, artificial intelligence, and IoT devices, it is becoming eminent that one emerging challenge is collaborative schematics. Provision of such autonomous mechanism that can develop, manage and operationalize digital resources like CoBots to perform tasks in a distributed and collaborative cloud environment for optimized utilization of resources, ensuring schedule completion. Collaborative schematics are also linked with Bigdata management produced by large-scale industry 4.0 setups. Different use cases and simulation results showed a significant improvement in Pod CPU utilization, latency, and throughput over Kubernetes environment

Genetic Algorithm & Fuzzy Logic Based PEM Fuel Cells Power Conversion System for AC Integration

In the scientific environment, the leading variables such as voltage, current, power, heat from cooling system, membrane temperature and hydrogen pressure are uses as steady state and transient behaviors of Fuel Cells (FC). In the reproducing process of Fuel Cells (FC) variations, DC-DC converters are connected transversely its terminals, the efficiency, stability and durability are considered as operational problems for steady state. Since the Proton Exchange Fuel Cell is a non-linear process and its parameters change when it is delivering energy to the grid. The conventional controllers can’t content the control objectives. In this paper, an intelligent DC-AC power optimization is proposed for Fuel Cell (FC) control system to produce energy in the grid stations and to improve the power quality when FC is supplying load to grid. Furthermore, a Genetic Algorithm (GA) based reactive power optimization for voltage profile improvement and real power minimization in DC-AC system. A fuzzy logic controller is also used to control active power of PEM fuel cell system. Fuzzy logic controller will modify the hydrogen flow feedback from the terminal load. At the end, we will simulate DC-AC converter for checking its efficiency, stability and durability on the basis of the genetic algorithm and fuzzy logic controller to control power generation

Convalescent plasma in patients admitted to hospital with COVID-19 (RECOVERY): a randomised controlled, open-label, platform trial

SummaryBackground Azithromycin has been proposed as a treatment for COVID-19 on the basis of its immunomodulatoryactions. We aimed to evaluate the safety and efficacy of azithromycin in patients admitted to hospital with COVID-19.Methods In this randomised, controlled, open-label, adaptive platform trial (Randomised Evaluation of COVID-19Therapy [RECOVERY]), several possible treatments were compared with usual care in patients admitted to hospitalwith COVID-19 in the UK. The trial is underway at 176 hospitals in the UK. Eligible and consenting patients wererandomly allocated to either usual standard of care alone or usual standard of care plus azithromycin 500 mg once perday by mouth or intravenously for 10 days or until discharge (or allocation to one of the other RECOVERY treatmentgroups). Patients were assigned via web-based simple (unstratified) randomisation with allocation concealment andwere twice as likely to be randomly assigned to usual care than to any of the active treatment groups. Participants andlocal study staff were not masked to the allocated treatment, but all others involved in the trial were masked to theoutcome data during the trial. The primary outcome was 28-day all-cause mortality, assessed in the intention-to-treatpopulation. The trial is registered with ISRCTN, 50189673, and ClinicalTrials.gov, NCT04381936.Findings Between April 7 and Nov 27, 2020, of 16 442 patients enrolled in the RECOVERY trial, 9433 (57%) wereeligible and 7763 were included in the assessment of azithromycin. The mean age of these study participants was65·3 years (SD 15·7) and approximately a third were women (2944 [38%] of 7763). 2582 patients were randomlyallocated to receive azithromycin and 5181 patients were randomly allocated to usual care alone. Overall,561 (22%) patients allocated to azithromycin and 1162 (22%) patients allocated to usual care died within 28 days(rate ratio 0·97, 95% CI 0·87–1·07; p=0·50). No significant difference was seen in duration of hospital stay (median10 days [IQR 5 to >28] vs 11 days [5 to >28]) or the proportion of patients discharged from hospital alive within 28 days(rate ratio 1·04, 95% CI 0·98–1·10; p=0·19). Among those not on invasive mechanical ventilation at baseline, nosignificant difference was seen in the proportion meeting the composite endpoint of invasive mechanical ventilationor death (risk ratio 0·95, 95% CI 0·87–1·03; p=0·24).Interpretation In patients admitted to hospital with COVID-19, azithromycin did not improve survival or otherprespecified clinical outcomes. Azithromycin use in patients admitted to hospital with COVID-19 should be restrictedto patients in whom there is a clear antimicrobial indication