A Fuzzy Logic-Based Method for Replica Placement in the Peer to Peer Cloud Using an Optimization Algorithm

The Peer to Peer-Cloud (P2P-Cloud) is a suitable alternative to distributed cloud-based or peer-to-peer (P2P)-based content on a large scale. The P2P-Cloud is used in many applications such as IPTV, Video-On-Demand, and so on. In the P2P-Cloud network, overload is a common problem during overcrowds. If a node receives many requests simultaneously, the node may not be able to respond quickly to user requests, and this access latency in P2P-Cloud networks is a major problem for their users. The replication method in P2P-Cloud environments reduces the time to access and uses network bandwidth by making multiple data copies in diverse locations. The replication improves access to the information and increases the reliability of the system. The data replication's main problem is identifying the best possible placement of replica data nodes based on user requests for data access time and an NP-hard optimization problem. This paper proposes a new replica replacement to improve average access time and replica cost using fuzzy logic and Ant Colony Optimization algorithm. Ants can find the shortest path to discover the optimal node to place the duplicate file with the least access time latency. The fuzzy module evaluates the historical information of each node to analyze the pheromone value per iteration. The fuzzy membership function is also used to determine each node's degree based on the four characteristics. The simulation results showed that the access time and replica cost are improved compared to other replica replacement algorithms.

     

Electrohydrodynamic‐Induced SERS Immunoassay for Extensive Multiplexed Biomarker Sensing

Cancer diagnosis and patient monitoring require sensitive and simultaneous measurement of multiple cancer biomarkers considering that single biomarker analysis present inadequate information on the underlying biological transformations. Thus, development of sensitive and selective assays for multiple biomarker detection might improve clinical diagnosis and expedite the treatment process. Herein, a microfluidic platform for the rapid, sensitive, and parallel detection of multiple cancer‐specific protein biomarkers from complex biological samples is presented. This approach utilizes alternating current electrohydrodynamic‐induced surface shear forces that provide exquisite control over fluid flow thereby enhancing target–sensor interactions and minimizing non‐specific binding. Further, the use of surface‐enhanced Raman scattering‐based spectral encoding with individual barcodes for different targets enables specific and simultaneous detection of captured protein biomarkers. Using this approach, the specific and sensitive detection of clinically relevant biomarkers including human epidermal growth factor receptor 2 (HER2); Mucin 1, cell surface associated (MUC1); epidermal growth factor receptor; and Mucin 16, cell surface associated (MUC16) at concentrations as low as 10 fg mL^?1 in patient serum is demonstrated. Successful target detection from patient samples further demonstrates the potential of this current approach for the clinical diagnosis, which envisages a clinical translation for a rapid and sensitive appraisal of clinical samples in cancer diagnostics.     

Theoretical analysis of shunting effect in resistance spot welding (RSW) of AA2219

Shunting effect is the loss of electrical current via the secondary circuit provided due to existence of previous nugget in a series of welding spots. This phenomenon is important for products containing intermittent spots. In this study, a theoretical model is developed for shunting effect in resistance spot welding for aluminum alloy 2219. Welding distance together with welding current and time is included in the model to analyze the shunting effect on final nugget quality. Thermal and electrical interactions are considered in the model as well as geometrical aspects of the process. Temperature dependence of material properties, integration through the whole volume of the nugget and heat-affected zone, and assuming a simple cooling mechanism are the most important modifications designated in the proposed theoretical model. Predicted results of nugget diameter are compatible with experimental and finite element analysis results.     

Biosynthesis of SeNPs by Mycobacterium bovis and their enhancing effect on the immune response against HBs antigens: an in vivo study

Conventional hepatitis B vaccine contains alum but is less effective to induce Th1 response. Selenium nanoparticles and Bacillus Calmette–Guerin were reported as immune modulators. In this study, SeNPs were extracted from Mycobacterium bovis and characterised. SeNPs were mixed with HBs‐Ag and administered to the mice to investigate he immune response pattern. With an addition of Se ions at a sub‐inhibitory concentration to the Sauton medium broth after 24 h, SeNPs were extracted from M. bovis and characterised by Fourier transform infrared spectroscopy, dynamic light scattering, atomic forcemicroscopy, energy dispersive X‐ray spectrum, transmission electron microscopy, and thermogravimetric analysis. Furthermore, female inbred BALB/c mice were injected subcutaneously on the first, 14th, 28th day with 100 and 200 µg doses of that SeNPs supplemented with HBs‐Ag vaccine. Later, the total antibody, isotypes of Immunoglobulin G1, Interlukin 4, and interferon‐γ were measured by enzye‐linked immunosorbent assay. The size of the SeNPs was <150 nm. Level of total antibody and immunoglobulin G2a increased significantly in the group that received 200 µg/ml nano selenium extracted from M. bovis. SeNPs in dose of 200 µg coated with organic materials of M. bovis could induce an influential immune response in relation to the conventional HBs‐Ag vaccine.Inspec keywords: selenium, nanoparticles, nanofabrication, microorganisms, Fourier transform spectra, infrared spectra, light scattering, atomic force microscopy, X‐ray chemical analysis, transmission electron microscopy, thermal analysis, nanomedicineOther keywords: biosynthesis, selenium nanoparticles, Mycobacterium bovis, enhnced immune response, HBs antigens, Bacillus Calmette‐Guerin, immune modulators, biogenic SeNPs, immune response pattern, Sauton medium broth, Fourier transform infrared spectroscopy, dynamic light scattering, atomic force microscopy, energy dispersive X‐ray spectrum, transmission electron microscopy, thermogravimetric analysis, female inbred BALB/c mice, antibody, IgG1 isotype, IgG2a isotype, IL₄ isotype, interferon‐γ, enzyme‐linked immunosorbent assay, bacterium, time 24 h, time 14 day, time 28 day, Se     

Magnetic soy protein isolate–bovine serum albumin nanoparticles preparation as a carrier for inulinase immobilisation

Magnetic nanoparticles (NPs) were functionalised with soy protein isolate (SPI) and bovine serum albumin (BSA) for inulinase immobilisation. The results revealed the nanomagnetite size of about 50 nm with a polydispersity index (PDI) of 0.242. The average size of the SPI NPs prepared by using acetone was 80–90 nm (PDI, 0.277), and SPI–BSA NPs was 80–90 nm (PDI, 0.233), and their zeta potential was around −34 mV. The mean diameter of fabricated Fe₃ O₄ @SPI–BSA NPs was <120 nm (PDI, 0.187). Inulinase was covalently immobilised successfully through glutaraldehyde on Fe₃ O₄ @SPI–BSA NPs with 80% enzyme loading. Fourier transform infrared spectra, field emission scanning electron microscopy, and transmission electron microscopy images provided sufficient proof for enzyme immobilisation on the NPs. The immobilised inulinase showed maximal activity at 45°C, which was 5°C higher than the optimum temperature of the free enzyme. Also, the optimum pH of the immobilised enzyme was shifted from 6 to 5.5. Thermal stability of the enzyme was considerably increased to about 43% at 75°C, and K _m value was reduced to 25.4% after immobilisation. The half‐life of the enzyme increased about 5.13‐fold at 75°C as compared with the free form. Immobilised inulinase retained over 80% of its activity after ten cycles.Inspec keywords: magnetic particles, nanoparticles, proteins, molecular biophysics, nanofabrication, enzymes, Fourier transform spectra, infrared spectra, scanning electron microscopy, field emission ion microscopy, transmission electron microscopy, pH, biochemistry, nanobiotechnology, biomagnetism, electrokinetic effects, iron compoundsOther keywords: magnetic nanoparticles, soy protein isolate, bovine serum albumin, inulinase immobilisation, nanomagnetite, polydispersity index, SPI‐BSA NP, zeta potential, inulinase, glutaraldehyde, enzyme loading, Fourier transform infrared spectra, field emission scanning electron microscopy, transmission electron microscopy images, enzyme immobilisation, pH, size 80 nm to 90 nm, temperature 45 degC, temperature 75 degC, Fe₃ O₄      

Waveguide filter with improved stopband response using transversal slots

In this article, the design and realization of waveguide filter with improved stop‐band response is presented. By inserting transversal slots at the proper position of cavities, without disturbing the field distribution of the dominant mode, the second longitudinal resonant modes are radiated out. This caused the out‐of‐band frequency response of the filter to be improved. The validity of the proposed technique is confirmed by comparison between measurement values and simulation results from the finite element method through the commercial software (high frequency structure simulator). Also, to assure electromagnetic compatibility the slot surfaces are covered by absorbing material. © 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2011.     

Solving bus terminal location problems using evolutionary algorithms

Bus terminal assignment with the objective of maximizing public transportation service is known as bus terminal location problem (BTLP). We formulate the BTLP, a problem of concern in transportation industry, as a p-uncapacitated facility location problem (p-UFLP) with distance constraint. The p-UFLP being NP-hard (Krarup and Pruzan, 1990), we propose evolutionary algorithms for its solution. According to the No Free Lunch theorem and the good efficiency of the distinctive preserve recombination (DPX) operator, we design a new recombination operator for solving a BTLP by new evolutionary and memetic algorithms namely, genetic local search algorithms (GLS). We also define the potential objective function (POF) for the nodes and design a new mutation operator based on POF. To make the memetic algorithm faster, we estimate the variation of the objective function based on POF in the local search as part of an operator in memetic algorithms. Finally, we explore numerically the performance of nine proposed algorithms on over a thousand randomly generated problems and select the best two algorithms for further testing. The comparative studies show that our new hybrid algorithm composing the evolutionary algorithm with the GLS outperforms the multistart simulated annealing algorithm.     

Adaptive sliding mode control of chaotic dynamical systems with application to synchronization

We address the problem of control and synchronization of a class of uncertain chaotic systems. Our approach follows techniques of sliding mode control and adaptive estimation law. The adaptive algorithm is constructed based on the sliding mode control to ensure perfect tracking and synchronization in presence of system uncertainty and external disturbance. Stability of the closed-loop system is proved using Lyapunov stability theory. Our theoretical findings are supported by simulation results.     

Deriving operating policies for multi-objective reservoir systems: Application of Self-Learning Genetic Algorithm

Optimal multi-reservoir operation is a multi-objective problem in nature and some of its objectives are nonlinear, non-convex and multi-modal functions. There are a few areas of application of mathematical optimization models with a richer or more diverse history than in reservoir systems optimization. However, actual implementations remain limited or have not been sustained.Genetic Algorithms (GAs) are probabilistic search algorithms that are capable of solving a variety of complex multi-objective optimization problems, which may include non-linear, non-convex and multi-modal functions. GA is a population based global search method that can escape from local optima traps and find the global optima. However GAs have some drawbacks such as inaccuracy of the intensification process near the optimal set.In this paper, a new model called Self-Learning Genetic Algorithm (SLGA) is presented, which is an improved version of the SOM-Based Multi-Objective GA (SBMOGA) presented by Hakimi-Asiabar et al. (2009) [45]. The proposed model is used to derive optimal operating policies for a three-objective multi-reservoir system. SLGA is a new hybrid algorithm which uses Self-Organizing Map (SOM) and Variable Neighborhood Search (VNS) algorithms to add a memory to the GA and improve its local search accuracy. SOM is a neural network which is capable of learning and can improve the efficiency of data processing algorithms. The VNS algorithm can enhance the local search efficiency in the Evolutionary Algorithms (EAs).To evaluate the applicability and efficiency of the proposed methodology, it is used for developing optimal operating policies for the Karoon-Dez multi-reservoir system, which includes one-fifth of Iran's surface water resources. The objective functions of the problem are supplying water demands, generating hydropower energy and controlling water quality in downstream river.