Nonfactorization in Hadronic Two-body Cabibbo-favored decays of D^0 and D^+

With the inclusion of nonfactorized amplitudes in a scheme with $N_c=3$, we have studied Cabibbo-favored decays of $D^0$ and $D^+$ into two-body hadronic states involving two isospins in the final state. We have shown that it is possible to understand the measured branching ratios and determined the sizes and signs of nonfactorized amplitudes required.Comment: 15 pages, Late

Inelastic Final-State Interactions and Two-body Hadronic B decays into Single-Isospin channels

The role of inelastic final-state interactions in CP asymmetries and branching ratios is investigated in certain chosen single isospin two-body hadronic B decays. Treating final-state interactions through Pomeron and Regge exchanges, we demonstrate that inelastic final state interactions could lead to sizeable effects on the CP asymmetry.Comment: 23 pages, Latex, 1 eps-figur

Integration of a big data emerging on large sparse simulation and its application on green computing platform

The process of analyzing large data and verifying a big data set are a challenge for understanding the fundamental concept behind it. Many big data analysis techniques suffer from the poor scalability, variation inequality, instability, lower convergence, and weak accuracy of the large-scale numerical algorithms. Due to these limitations, a wider opportunity for numerical analysts to develop the efficiency and novel parallel algorithms has emerged. Big data analytics plays an important role in the field of sciences and engineering for extracting patterns, trends, actionable information from large sets of data and improving strategies for making a decision. A large data set consists of a large-scale data collection via sensor network, transformation from signal to digital images, high resolution of a sensing system, industry forecasts, existing customer records to predict trends and prepare for new demand. This paper proposes three types of big data analytics in accordance to the analytics requirement involving a large-scale numerical simulation and mathematical modeling for solving a complex problem. First is a big data analytics for theory and fundamental of nanotechnology numerical simulation. Second, big data analytics for enhancing the digital images in 3D visualization, performance analysis of embedded system based on the large sparse data sets generated by the device. Lastly, extraction of patterns from the electroencephalogram (EEG) data set for detecting the horizontal-vertical eye movements. Thus, the process of examining a big data analytics is to investigate the behavior of hidden patterns, unknown correlations, identify anomalies, and discover structure inside unstructured data and extracting the essence, trend prediction, multi-dimensional visualization and real-time observation using the mathematical model. Parallel algorithms, mesh generation, domain-function decomposition approaches, inter-node communication design, mapping the subdomain, numerical analysis and parallel performance evaluations (PPE) are the processes of the big data analytics implementation. The superior of parallel numerical methods such as AGE, Brian and IADE were proven for solving a large sparse model on green computing by utilizing the obsolete computers, the old generation servers and outdated hardware, a distributed virtual memory and multi-processors. The integration of low-cost communication of message passing software and green computing platform is capable of increasing the PPE up to 60% when compared to the limited memory of a single processor. As a conclusion, large-scale numerical algorithms with great performance in scalability, equality, stability, convergence, and accuracy are important features in analyzing big data simulation

Integration of a big data emerging on large sparse simulation and its application on green computing platform

The process of analyzing large data and verifying a big data set are a challenge for understanding the fundamental concept behind it. Many big data analysis techniques suffer from the poor scalability, variation inequality, instability, lower convergence, and weak accuracy of the large-scale numerical algorithms. Due to these limitations, a wider opportunity for numerical analysts to develop the efficiency and novel parallel algorithms has emerged. Big data analytics plays an important role in the field of sciences and engineering for extracting patterns, trends, actionable information from large sets of data and improving strategies for making a decision. A large data set consists of a large-scale data collection via sensor network, transformation from signal to digital images, high resolution of a sensing system, industry forecasts, existing customer records to predict trends and prepare for new demand. This paper proposes three types of big data analytics in accordance to the analytics requirement involving a large-scale numerical simulation and mathematical modeling for solving a complex problem. First is a big data analytics for theory and fundamental of nanotechnology numerical simulation. Second, big data analytics for enhancing the digital images in 3D visualization, performance analysis of embedded system based on the large sparse data sets generated by the device. Lastly, extraction of patterns from the electroencephalogram (EEG) data set for detecting the horizontal-vertical eye movements. Thus, the process of examining a big data analytics is to investigate the behavior of hidden patterns, unknown correlations, identify anomalies, and discover structure inside unstructured data and extracting the essence, trend prediction, multi-dimensional visualization and real-time observation using the mathematical model. Parallel algorithms, mesh generation, domain-function decomposition approaches, inter-node communication design, mapping the subdomain, numerical analysis and parallel performance evaluations (PPE) are the processes of the big data analytics implementation. The superior of parallel numerical methods such as AGE, Brian and IADE were proven for solving a large sparse model on green computing by utilizing the obsolete computers, the old generation servers and outdated hardware, a distributed virtual memory and multi-processors. The integration of low-cost communication of message passing software and green computing platform is capable of increasing the PPE up to 60% when compared to the limited memory of a single processor. As a conclusion, large-scale numerical algorithms with great performance in scalability, equality, stability, convergence, and accuracy are important features in analyzing big data simulation

Environmental Safety Of Natural And Manufactured Building Materials

Natural radioactivity was estimated in building materials using γ-spectroscopic method. Samples of granite, bricks, concrete and ceramic were collected from different places in Egypt. Samples were prepared for physical and mechanical properties measurements as well as the radioactive content. Gamma spectrometer composed of NaI crystal connected to ORTEC analyser was used for radioactive measurements. Standard sample was prepared with the same geometry factor in NIS using a standard source traceable to NIST. Data of 238U, 232Th and 40K activities were collected, where the effective dose was calculated by the aid of UNSCEAR. Diffusion equation was used to estimate Radon emissions rate from building materials used in proposed model rooms.It was found that the average concentrations of 238U, 232Th and 40K in the studied materials were for granite 63.4, 2.42, 1010.91 Bq/kg, for bricks   20.12, 3.75, 27.25 Bq/kg and for concrete 34.23, 2.36, 506.36Bq/kg. In spite of using materials with permissible activity concentration, the radon emission in model rooms was beyond the safe limits for inhabitants. The maximum dose from Rn concentration was 1.23 mSv/y. This concentration was affected by the space dimension, passing elapsed time and building material radioactivity as well as ventilation. It was also found that the most powerful factor affecting radon concentration is the ventilatio