scholarly journals Data Intensive Scalable Computing (DISC)

2020 ◽  
Author(s):  
Mario A. R. Dantas
Keyword(s):  
Big Data ◽  
Scalable Computing ◽  
Data Intensive ◽  
And Storage

This work presents an introduction to the Data Intensive Scalable Computing (DISC) approach. This paradigm represents a valuable effort to tackle the large amount of data produced by several ordinary applications. Therefore, subjects such as characterization of big data and storage approaches, in addition to brief comparison between HPC and DISC are differentiated highlight.

scholarly journals Infrastructure and Energy Conservation in Big Data Computing: A Survey

2019 ◽  
Vol 2 ◽  
pp. 73-82
Author(s):  
Ewa Niewiadomska-Szynkiewicz ◽  
Michał P. Karpowicz
Keyword(s):  
Big Data ◽  
High Performance ◽  
Physical Sciences ◽  
Communication Management ◽  
Data Intensive ◽  
Efficient Data ◽  
Software Platforms ◽  
Allocation Algorithms ◽  
And Storage ◽  
Big Data Computing

Progress in life, physical sciences and technology depends on efficient data-mining and modern computing technologies. The rapid growth of data-intensive domains requires a continuous development of new solutions for network infrastructure, servers and storage in order to address Big Datarelated problems. Development of software frameworks, include smart calculation, communication management, data decomposition and allocation algorithms is clearly one of the major technological challenges we are faced with. Reduction in energy consumption is another challenge arising in connection with the development of efficient HPC infrastructures. This paper addresses the vital problem of energy-efficient high performance distributed and parallel computing. An overview of recent technologies for Big Data processing is presented. The attention is focused on the most popular middleware and software platforms. Various energy-saving approaches are presented and discussed as well.

Effect of pH and temperature on the infectivity of human coronavirus 229E

1989 ◽  
Vol 35 (10) ◽  
pp. 972-974 ◽  
Author(s):  
Alain Lamarre ◽  
Pierre J. Talbot
Keyword(s):  
Incubation Period ◽  
Storage Conditions ◽  
Viral Infectivity ◽  
Human Coronavirus ◽  
Virus Growth ◽  
The Stability ◽  
And Storage ◽  
Influence Of Ph ◽  
Human Coronavirus 229E

The stability of human coronavirus 229E infectivity was maximum at pH 6.0 when incubated at either 4 or 33 °C. However, the influence of pH was more pronounced at 33 °C. Viral infectivity was completely lost after a 14-day incubation period at 22, 33, or 37 °C but remained relatively constant at 4 °C for the same length of time. Finally, the infectious titer did not show any significant reduction when subjected to 25 cycles of thawing and freezing. These studies will contribute to optimize virus growth and storage conditions, which will facilitate the molecular characterization of this important pathogen.Key words: coronavirus, pH, temperature, infectivity, human coronavirus.

scholarly journals Challenges of Big Data analysis

2014 ◽  
Vol 1 (2) ◽  
pp. 293-314 ◽  
Author(s):  
Jianqing Fan ◽  
Fang Han ◽  
Han Liu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Measurement Errors ◽  
Modern Society ◽  
Big Data Analysis ◽  
Small Scale ◽  
Paradigm Change ◽  
Statistical Inferences ◽  
The One ◽  
And Storage

Abstract Big Data bring new opportunities to modern society and challenges to data scientists. On the one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. On the other hand, the massive sample size and high dimensionality of Big Data introduce unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation, incidental endogeneity and measurement errors. These challenges are distinguished and require new computational and statistical paradigm. This paper gives overviews on the salient features of Big Data and how these features impact on paradigm change on statistical and computational methods as well as computing architectures. We also provide various new perspectives on the Big Data analysis and computation. In particular, we emphasize on the viability of the sparsest solution in high-confidence set and point out that exogenous assumptions in most statistical methods for Big Data cannot be validated due to incidental endogeneity. They can lead to wrong statistical inferences and consequently wrong scientific conclusions.

of storage as short as possible, only; 24 h should not be ex­ ceeded. Table III comprises the most important criteria for valid static and dynamic sampling. It seems that both the guide of Warren Springs, U.K. and the VDI-Guideline might be a useful base to describe commonly accepted sampling procedures aiming at a standardization of sampling which might be a first step for a harmonization of olfactometric measurements in the different laboratories and countri es. REFERENCES (1) BULLEY, N.R. and D. PHILLIPS (1980). Sensory evaluation of agricul­ tural odours: A critical review. Can. Agric. Eng. 22, 107 - 112. (2) HENRY, J.G. and R. GEHR (1980). Odour control: An operator's guide. Journal WPCF 52, 2523 - 2537. (3) ROOS, C., J.A. DON and J. SCHAEFER (1984). Characterization of odour-polluted air. In: Proc.Int.Symp., Soc. Beige de Filtr. (eds.), 25-27 April 1984, Louvain-La-Neuve, Belgium, pp. 3 - 22. (4) BAKER, A.R. and R.C. DOERR (1959). Methods of sampling and storage of air containing vapors and gases. Int.J.Air Poll. 2, 142 - 158. (5) SCHUETTE, F.J. (1967). Plastic bags for collection of gas samples. Atmosph.Environm. 1, 515 - 519. (6) SCHODDER, F. (1977T. Messen von Geruchsstoffkonzentrationen, Erfassen von Geruch. Grundl. Landtechnik 27, 73 - 82. (7) CORMACK, D., T.A. DORLING and B.W7J. LYNCH (1974). Comparison of tech­ niques for organoleptic odour-intensity assessment. Chem.Ind. (Lon­ don) no. 2, 857 - 861. (8) SCHUETZLE, D., T.J. PRATER and S. RUDDELL (1975). Sampling and anal­ ysis of emissions from stationary sources. I. Odour and total hydro­ carbons. APCA Journal 25, 925 - 932. (9) WAUTERS, E., E. WALRAVENS, E. MUYLLE and G. VERDUYN (1983). An evalu­ ation of a fast sampling procedure for the trace analysis of volatile organic compounds in ambient air. Environm.Monitor.Assessm. 3, 151-160. (10) LACHENMAYER, U. and H. KOHLER (1984). Untersuchungen zur Neuentwick-lung eines Olfaktometers. Staub - Reinhalt. Luft 44, 359 - 362. (11) BERNARD, F. (1984). Simplified methods of odour measurement: Indus­ trial application and interest for administrative control. Proc. Int. Symp., Soc. Beige de Filtr. (eds.), 25 - 27 April 1984, Louvain-La-Neuve, Belgium, pp. 139 - 150. (12) GILLARD, F. (1984). Measurement of odours by dynamic olfactometry. Application to the steel and carbonization industries. Proc.Int.Symp., Soc. Beige de Filtr. (eds.), 25 - 27 April 1984, Louvain-La-Neuve, Belgium, pp. 53 - 86. (13) MANNEBECK, H. (1975). Tragbare Olfaktometer. VDI-Bericht 226, 103-105. (14) BEDBOROUGH, D.R. (1980). Sensory measurement of odours. In: Odour Control - a concise guide, F.H.H. Valentin and A.A. North (eds.), Warren Springs Laboratories, Stevenage, Hertfordshire, U.K., pp. 17-30. (15) THIELE, V. (1984). Olfaktometrie an einer Emissionsquelle - Ergebnis-se des VDI-Ringvergleichs. Staub - Reinhalt. Luft 44, 342 - 351. (16) DUFFEE, R.A., J.P. WAHL, W. MARRONE and J.S. NADERT1973). Defining and measuring objectionable odors. Internat. Pollution Eng. Congress, Philadelphia, paper no 25a, pp. 192 - 201.

1986 ◽  
pp. 62-62
Keyword(s):  
Ambient Air ◽  
Sampling Procedure ◽  
Plastic Bags ◽  
Volatile Organic ◽  
Odour Control ◽  
Sampling Procedures ◽  
Dynamic Sampling ◽  
Concise Guide ◽  
And Storage

scholarly journals Development of Materials for Naval, Fluvial and Military Applications

2018 ◽  
Vol 11 (22) ◽  
pp. 63
Author(s):  
Fabio A. Suarez- Bustamante ◽  
Orlando D. Barrios-Revollo ◽  
Anderson Valencia ◽  
Juan P. Hernandez-Ortiz
Keyword(s):  
Energy Dissipation ◽  
High Performance ◽  
Wide Spectrum ◽  
Advanced Characterization ◽  
Mechanical Resistance ◽  
Optimal Processing ◽  
Military Applications ◽  
Temperature Transformation ◽  
And Storage

A platform to design composite materials of a polymeric matrix, that are specifically for military applications on fluvial and naval navigation, has been developed using energy dissipation and storage mechanisms. Our composites are designed to generate synergy between the dissipation capacities of ceramics and high-performance fibers, which are used as the reinforced material in the lightweight laminates. The composite design is combined with processing tools and advanced characterization techniques that result in laminates with reliability, traceability and quality. The platform begins with the identification of energy dissipation mechanisms and the detailed characterization of the polymeric resin. It includes the Time – Temperature – Transformation Diagram (TTT- Diagram) that supplies the optimal processing conditions. Our designs open new paths for military applications including a wide spectrum of protective systems together with geometric versatility, high mechanical resistance and reliability

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