A novel MnO2 nanoparticles/chitosan-modified pencil graphite electrode (MnO2 NPs/CS/PGE) was constructed using two different MnO2 polymorphs (g-MnO2 and 3-MnO2 nanoparticles). X-ray single phases of these two polymorphs were obtained by the comproportionation reaction between MnCl2 and KMnO4 (molar ratio of 5 : 1). The temperature of this reaction is the key factor governing the formation of the two polymorphs. Their structures were confirmed by powder X-ray diffraction (XRD), Fourier transform infrared (FTIR) and energy dispersive X-ray (EDX) analysis. Scanning electron microscopy (SEM)was employed to investigate the morphological shape of MnO2 NPs and the surface of the bare and modified electrodes. Moreover, cyclic voltammetry and electrochemical impedance spectroscopy (EIS)were used for surface analysis of the modified electrodes. Compared to bare PGE, MnO2 NPs/CS/PGE shows higher effective surface area and excellent electrocatalytic activity towards the oxidation of the standard K3[Fe(CN)6]. The influence of different suspending solvents on the electrocatalytic activity of MnO2 was studied in detail. It was found that tetrahydrofuran (THF) is the optimum suspending solvent regarding the peak current signal and electrode kinetics. The results reveal that the modified g-MnO2/ CS/PGE is the most sensitive one compared to the other modified electrodes under investigation. The modified g-MnO2/CS/PGE was applied for selective and sensitive determination of FUR. Under the optimized experimental conditions, g-MnO2/CS/PGE provides a linear response over the concentration range of 0.05 to 4.20 mmol L-1 FUR with a low limit of detection, which was found to be 4.44 nmol L-1 (1.47 ng mL-1) for the 1st peak and 3.88 nmol L-1 (1.28 ng mL-1) for the 2nd one. The fabricated sensor exhibits a good reproducibility and selectivity and was applied successfully for the determination of FUR in its dosage forms and in spiked urine samples with good accuracy and precision.

A novel MnO2 nanoparticles/chitosan-modified pencil graphite electrode (MnO2 NPs/CS/PGE) was constructed using two different MnO2 polymorphs (g-MnO2 and 3-MnO2 nanoparticles). X-ray single phases of these two polymorphs were obtained by the comproportionation reaction between MnCl2 and KMnO4 (molar ratio of 5 : 1). The temperature of this reaction is the key factor governing the formation of the two polymorphs. Their structures were confirmed by powder X-ray diffraction (XRD), Fourier transform infrared (FTIR) and energy dispersive X-ray (EDX) analysis. Scanning electron microscopy (SEM)was employed to investigate the morphological shape of MnO2 NPs and the surface of the bare and modified electrodes. Moreover, cyclic voltammetry and electrochemical impedance spectroscopy (EIS)were used for surface analysis of the modified electrodes. Compared to bare PGE, MnO2 NPs/CS/PGE shows higher effective surface area and excellent electrocatalytic activity towards the oxidation of the standard K3[Fe(CN)6]. The influence of different suspending solvents on the electrocatalytic activity of MnO2 was studied in detail. It was found that tetrahydrofuran (THF) is the optimum suspending solvent regarding the peak current signal and electrode kinetics. The results reveal that the modified g-MnO2/ CS/PGE is the most sensitive one compared to the other modified electrodes under investigation. The modified g-MnO2/CS/PGE was applied for selective and sensitive determination of FUR. Under the optimized experimental conditions, g-MnO2/CS/PGE provides a linear response over the concentration range of 0.05 to 4.20 mmol L-1 FUR with a low limit of detection, which was found to be 4.44 nmol L-1 (1.47 ng mL-1) for the 1st peak and 3.88 nmol L-1 (1.28 ng mL-1) for the 2nd one. The fabricated sensor exhibits a good reproducibility and selectivity and was applied successfully for the determination of FUR in its dosage forms and in spiked urine samples with good accuracy and precision.

Abstract : Due to the association of noise pollution and Psychological and physiological diseases, the main aim of this work is to determine the effects of mobile phone radiation on memory of the different ages male rats. In our study male Wistar rats were randomly divided into 4 (control and exposed receiving) groups of 10 rats in each. Memory level was measured in animals using Eight arm Maze and passive avoidance tests. Data were statistically analyzed and compared between groups using ANOVA and T-Test. The results indicate that memory level was decreased in rats exposed to mobile phone radiation compared to control rats.

Abstract : Due to the association of noise pollution and Psychological and physiological diseases, the main aim of this work is to determine the effects of mobile phone radiation on memory of the different ages male rats. In our study male Wistar rats were randomly divided into 4 (control and exposed receiving) groups of 10 rats in each. Memory level was measured in animals using Eight arm Maze and passive avoidance tests. Data were statistically analyzed and compared between groups using ANOVA and T-Test. The results indicate that memory level was decreased in rats exposed to mobile phone radiation compared to control rats.

NULL

Real-life problems are usually time-consuming since they require solving large instances of NP-hard problems. Exact search methods in most of the cases cannot afford practical solutions for such problems. Metaheuristics arise as promising solvers for these problems, by obtaining acceptable solutions in terms of quality and computational cost in a reasonable time bound. Nowadays, energy efficiency is also taken into consideration during the design of new algorithms because of the million times that algorithms run on labs and computation centers. This work presents two novel experiments for investigating the numerical performance and energy efficiency of the sequential and parallel metaheuristics. The main aim of this study is to analyze the energy consumption of three well-known and commonly used metaheuristics (Genetic Algorithm, Variable Neighborhood Search, and Simulated Annealing) and their parallel versions. The discussions reveal the differences/similarities between the different sequential/parallel algorithms, which include trajectory-based and population-based metaheuristics so that this study is useful for the future design of energy-aware algorithms.

Recently, energy efficiency has gained attention from researchers interested in optimizing computing resources. Solving real-world problems using optimization techniques (such as metaheuristics) requires a large number of computing resources and time, consuming an enormous amount of energy. However, only a few and limited research efforts in studying the energy consumption of metaheuristics can be found in the existing literature. In particular, genetic algorithms (GAs) are being used so widely to solve a large range of problems in scientific and real-world problems, but hardly found explained in their internal consumption behavior. In the present article, we analyze the energy consumption behavior of such techniques to offer a useful set of findings to researchers in the mentioned domains. We expand our study to include several algorithms and different problems and target the components of the algorithms so that the results are still more appealing for researchers in arbitrary domains of application. Our experiments on the sequential GAs show the controlling role of the fitness operator on energy consumption and also reveal possible energy hot spots in GAs operations, such as mutation operator. Further, our distributed evaluations besides a statistical analysis of the results demonstrate that the communication scheme could highly affect the energy consumption of the parallel evaluations of the GAs.

Genetic Algorithms (GAs) are being used to solve a wide range of problems in real world problems, and it is important to study their implementations to improve the solution quality and reduce the execution time. Designing parallel (e.g., distributed) GAs is one research line to do so. In distributed GAs, every individual represents a tentative solution. Individuals are split (and sparsely communicated) over many islands, with genetic operators being applied locally in each island. In addition, in order to maintain diversity and reduce the number of the evaluations, a migration operator is used to enhance their behavior. This article presents a basic study on the speed-up of parallel GAs where a common approach is followed to better understand synchronous and asynchronous versions together. We analyze the behavior of GAs over a homogeneous multiprocessor system. We will report results showing linear and even super linear speed-up in both cases of study. The parallel performance of the synchronous and asynchronous versions is very good in a multiprocessor computer, both in terms of time and solution quality. Besides, a statistical analysis of the algorithms clearly proves that both cases have a similar numerical behavior over a homogeneous parallel system.

Because of their effectiveness and flexibility in finding useful solutions, Genetic Algorithms (GAs) are very popular search techniques for solving complex optimization problems in scientific and industrial fields. Parallel GAs (PGAs), and especially distributed ones have been usually presented as the way to overcome the time-consuming shortcoming of sequential GAs. In the case of applying PGAs, we can expect better performance, the reason being the exchange of knowledge during the parallel search process. The resulting distributed search is different compared to what sequential panmictic GAs do, then deserving additional studies. This article presents a performance study of three different PGAs. Moreover, we investigate the effect of synchronizing communications over modern shared-memory multiprocessors. We consider the master-slave model along with synchronous and asynchronous distributed GAs

Optimization arises everywhere in industrial and engineering fields, with complex and time-consuming problems to be solved. Exact search techniques cannot afford practical solutions for most of the real-life problems in reasonable time-bound. Metaheuristics proved to be numerically efficient solvers for such problems in terms of solution quality, however, they could require large time and energy to get the optimal solution. Parallelization (i.e., distributed) is a promising approach for overcoming the overwhelming energy and time consumption values of these methods. Despite recent approaches in running metaheuristics in parallel, the community still lacks for novel studies comparing and benchmarking the canonical optimization techniques while being running in parallel. In this work, we present two extensive studies to the solution quality, energy consumption, and execution time for three different metaheuristics (Genetic Algorithm, Variable Neighborhood Search, and Simulated Annealing) and their distributed counterparts. The main aim of our studies is exploring the efficiency of parallel execution of the metaheuristics while being running in new computing environments. Here, we want to identify the combinatorics between metaheuristics and solving optimization problems while being run in parallel. For our studies, we consider a multicore machine with 32 cores. This choice to a recent and commonly used system shall enrich the existing literature for multicore systems against the enormous existing studies over cluster systems. The analyses and discussions for the results of the different algorithms exhibit the combinatorics between the different metaheuristics and the parallel execution using a different number of cores. The outcome of these studies builds a guide for future designs of efficient and energy-aware optimization techniques.