The Quantum Cellular Automata (QCA) is an upcoming and innovative concept which exist in the scope of nanoscale with an estimated range of less than 18nm so as to help the design and implementation of the applications in nano range. Now a days, the reversible logic concept is acquiring wide significance as a rationale configuration style for current nanotechnology and quantum figuring with insignificant warmth age in light of the fact that the current irreversible plans are arriving at their actual cutoff points. Late advances in reversible rationale utilizing and quantum PC calculations take into consideration improved PC engineering and math rationale unit plans. In order to plan and execute a high performing quantum processor that is too in quantum range, it is wanted that the ALU of such processor ought to be actualized with the assistance of reversible rationale entry ways, for example, Fredkin Gate, Peres Gate, Feynman Gate etc. This thus, necessitates that every modules of ALU ought to be actualized with the utilization of best displayed boundaries in order to get the ideal presentation. In this conversation, the author has advanced an imaginative circuit of ALU utilizing the reversible rationale gates for handling of 2-digit information which centers around the point to give the result of this procedure that the QCA has potential in building up the applications, for example, Quantum Computers, given that the fundamental innovation is made attainable. In this discussion, the author has zeroed in on the most prevailing boundaries in order to get the ideal exhibition with a near investigation of leaving work with the proposed one. This correlation has been done regarding size, delay, power utilization, quantum cost. The proposed work prompts an improvement of 19% and 15% regarding entryway tally and quantum cost separately, when contrasted with prior works in reversible ALU plans. The recreation and check of the proposed ALU utilizing the reversible entryways has been completed utilizing QCA Designer Tool not withstanding Cadence and Xilinx apparatuses.

This paper proposes a new variable transformation to solve a linear fractional programming\n(LFP) problem iteratively. Firstly, the objective function is used as a standard decision variable\nand the fractional objective function is converted to a nonlinear constraint. After that the\nnonlinearity of this constraint is eliminated by introducing a new variable. Using this variable\ntransformation, we developed an iterative linear optimization problem to obtain the optimal\nsolution of the LFP problem. The method is illustrated with some numerical examples. Also,\nthe validity of our algorithm is examined by generating random large scale LFP problems.

This research is focused on understanding the interrelation between the different dimensions of the SERVQUAL model in the pharmaceutical area, which have differences in perceptions of service domains depending on the cultures in which they are immersed.\nTo study the proposed relationships, we collect data of 100 pharmacy customers of Mexico and use SEM to test the relationship between SERVQUAL dimensions, purchase frequency, gender, and quality perception.\nOur results show that the SERVQUAL dimensions are interrelated, SERVQUAL dimensions and gender predict the perception of quality but not the purchase frequency. These\nresults provide key information in order to understand in a better way the service quality in\nthe pharmaceutical area.\nWe develop a theoretical framework that is based on interrelationships between the different dimensions of SERVQUAL, assuming that they are not independent of each other.\nThis way of understanding SERVQUAL dimensions can provide relevant information to understand better how individual perceptions affect the service quality in the pharmaceutical\narea

As outdoor activities are necessary for maintaining our health, research interest in the environmental conditions such as the weather, atmosphere, and ultraviolet (UV) radiation is increasing. In particular, UV radiation, which can benefit or harm the human body depending on the degree of exposure, is recognized as an essential environmental factor that needs to be identified. However, unlike the weather and atmospheric conditions that can be identified to some extent by the naked eye, UV radiation corresponds to wavelength bands that humans cannot recognize; hence, the intensity of UV radiation cannot be measured. Recently, although devices and sensors that can measure UV radiation have been launched, it is very difficult for ordinary users to acquire ambient UV radiation information directly because of the cost and inconvenience caused by operating separate devices. Herein, a deep neural network (DNN)-based ultraviolet index (UVI) calculation method is proposed using representative color information of sun object images. First, Mask-region-based convolutional neural networks (R-CNN) is applied to sky images to extract sun object regions and then detect the representative color of the sun object regions. Then, a deep learning model is constructed to calculate the UVI by inputting RGB color values, which are representative colors detected later along with the altitude angle and azimuth of the sun at that time. After selecting each day of spring and autumn, the performance of the proposed method was tested, and it was confirmed that accurate UVI could be calculated within the range of the mean absolute error of 0.3.

Testing is a process of trying to find out every believable fault or weakness in a project. Researches have proved that writing test cases is the most time-consuming and challenging activity among other testing life cycle phases. It is very crucial to sequence and writes optimized test cases to increase the rate of fault identification during the test design phase as early as possible. There are various proven test design techniques available, which focus on optimizing test suites in different test stages. Our key focus in this paper is to identify the optimized test cases minimizing the actual number of test cases with minimal effort using OATS (Orthogonal Array Test Strategy) techniques covering double mode and triple mode test combinations using Artificial Intelligence and fuzzy logic. We have proposed measurements and metrics based on fuzzy logic principles to ensure the quality and effective relevancies of metrics. We have made experiments to check the quality and effectiveness of identifying test combinations covering maximum defects using Artificial Intelligence and fuzzy reasoning. We have tested the proposed measurements of ensuring double and triple mode test combinations using real-time data. Final experimental results show that the proposed AI and Fuzzy logic metrics are efficient in minimizing the number of test cases ensuring double and triple mode test combinations yielding maximum defects in a short span of time.

There exist areas, such as the disease prevention or inclement weather protocols, in which the analysis of the information based on strict protocols require a high level of rigor and security. In this situation, it would be desirable to apply formal methodologies that provide these features. In this scope, recently, it has been proposed a formalism, fuzzy automaton, that captures two relevant aspects for fuzzy information analysis: imprecision and uncertainty. However, the models should be designed by domain experts, who have the required knowledge for the design of the processes, but do not have the necessary technical knowledge. To address this limitation, this paper proposes MODELFY, a novel model-driven solution for designing a decision-making process based on fuzzy automata that allows users to abstract from technical complexities. With this goal in mind, we have developed a framework for fuzzy automaton model design based on a Domain-Specific Modeling Language and a graphical editor. To improve the interoperability and functionality of this framework, it also includes a model-to-text transformation that translates the models designed by using the graphical editor into a format that can be used by a tool for data analysis. The practical value of this proposal is also evaluated through a non-trivial medical protocol for detecting potential heart problems.