scholarly journals Lightweight Abstraction for Mathematical Computation in Java

2012 ◽  
pp. 47-59
Author(s):  
Pavel Bourdykine ◽  
Stephen M. Watt
Keyword(s):  
Mathematical Computation

The use of the edsac for mathematical computation

1950 ◽  
Vol 1 (1) ◽  
pp. 429-438 ◽  
Author(s):  
M. V. Wilkes
Keyword(s):  
Mathematical Computation

scholarly journals A new look at projecting of visual representation for applied mathematical computation

2018 ◽  
pp. 1-27
Author(s):  
Pavel Vyacheslavovich Kurakin ◽  
Georgii Gennadyevich Malinetskii ◽  
Nikolay Alexeevich Mitin
Keyword(s):  
Visual Representation ◽  
Mathematical Computation ◽  
New Look

A Study of Planning and Mathematics Instruction for Students with Learning Disabilities

1995 ◽  
Vol 76 (3_suppl) ◽  
pp. 1343-1354 ◽  
Author(s):  
Jack A. Naglieri ◽  
Suzanne H. Gottling
Keyword(s):  
Learning Disabilities ◽  
Cognitive Strategies ◽  
Assessment System ◽  
Students With Learning Disabilities ◽  
Mathematics Scores ◽  
Self Reflection ◽  
Mathematics Problems ◽  
Cognitive Education ◽  
And Mathematics ◽  
Mathematical Computation

The purpose of this study was to extend research in training the use of cognitive strategies or planning to mathematical computation for 4 students with specific learning disabilities. A cognitive education method utilized in previous research was duplicated. It was expected that students would find the instruction differentially effective based upon their initial scores on a measure of planning. Using the Planning, Attention, Simultaneous, Successive model as a base, a cognitive instruction which facilitated planning was provided to two students with low scores on planning, obtained using an experimental version of the Das-Naglieri Cognitive Assessment System, and two students with average planning scores. All students completed three sessions of baseline and seven sessions of cognitive instruction in addition and multiplication. During the cognitive instruction phase, 5-min. sessions of self-reflection and verbalization of strategies about the mathematics problems were conducted after each initial 10-min. session of mathematics. Scores on addition problems showed that all students improved. On multiplication, however, 2 students with low planning scores improved considerably but not 2 with higher planning scores. Implications are provided.

ON THE METHODOLOGY APPLICATION OF MODERN COMPUTING TECHNOLOGIES IN STUDYING THE THEORY OF NUMBERS

2020 ◽  
pp. 79-86
Author(s):  
А.П. Горюшкин
Keyword(s):  
Computer Search ◽  
Computing Technologies ◽  
Mathematical Computation ◽  
New Machine ◽  
Theory Of Numbers ◽  
Modern Computing

Обсуждаются проблемы, связанные с компьютерным поиском псевдопростых чисел. Предлагаются новые машинные способы быстрого поиска псевдопростых чисел Кармайкла-Черника с использованием пакета символьных математических вычислений Maple. The problems associated with the computer search for pseudo-simple numbers are discussed. New machine methods are proposed to quickly find Carmichael-Chernick numbers using the Maple symbolic mathematical computation package.

Parallel Hardware for Artificial Neural Networks Using Fixed Floating Point Representation

2011 ◽  
pp. 295-308
Author(s):  
Nadia Nedjah ◽  
Rodrigo Martins da Silva ◽  
Luiza de Macedo Mourelle
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Activation Function ◽  
Floating Point ◽  
Weighted Sum ◽  
Design Environment ◽  
Silicon Area ◽  
Point Representation ◽  
Artificial Neural ◽  
Mathematical Computation

Artificial Neural Networks (ANNs) is a well known bio-inspired model that simulates human brain capabilities such as learning and generalization. ANNs consist of a number of interconnected processing units, wherein each unit performs a weighted sum followed by the evaluation of a given activation function. The involved computation has a tremendous impact on the implementation efficiency. Existing hardware implementations of ANNs attempt to speed up the computational process. However, these implementations require a huge silicon area that makes it almost impossible to fit within the resources available on a state-of-the-art FPGAs. In this chapter, a hardware architecture for ANNs that takes advantage of the dedicated adder blocks, commonly called MACs, to compute both the weighted sum and the activation function is devised. The proposed architecture requires a reduced silicon area considering the fact that the MACs come for free as these are FPGA’s built-in cores. Our system uses integer (fixed point) mathematics and operates with fractions to represent real numbers. Hence, floating point representation is not employed and any mathematical computation of the ANN hardware is based on combinational circuitry (performing only sums and multiplications). The hardware is fast because it is massively parallel. Besides, the proposed architecture can adjust itself on-the-fly to the user-defined configuration of the neural network, i.e., the number of layers and neurons per layer of the ANN can be settled with no extra hardware changes. This is a very nice characteristic in robot-like systems considering the possibility of the same hardware may be exploited in different tasks. The hardware also requires another system (a software) that controls the sequence of the hardware computation and provides inputs, weights and biases for the ANN in hardware. Thus, a co-design environment is necessary.

scholarly journals Potential of Abattoir Waste for Bioenergy as Sustainable Management, Eastern Ethiopia, 2019

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Sina Temsgen Tolera ◽  
Fekade Ketema Alemu
Keyword(s):  
Anaerobic Digestion ◽  
Sustainable Management ◽  
Fossil Fuels ◽  
Environmental Safety ◽  
Cross Sectional Study ◽  
Waste Generation ◽  
Liquefied Petroleum Gas ◽  
Cross Sectional ◽  
Eastern Ethiopia ◽  
Mathematical Computation

Our environment is facing serious problems of high volumes of waste generation and inadequate disposal system in worldwide particularly in developing countries. There is also lack of studies on quantification of abattoir waste and lack of workers awareness towards abattoir waste. Therefore, the purpose of the study was to estimate abattoir waste for bioenergy potential as sustainable management. A cross-sectional study was conducted in four selected abattoirs of Eastern Ethiopia from January 1st, 2018 to December 30th, 2018. The magnitude of abattoir waste composition was computed based on Aniebo mathematical computational from the actual number of slaughtered livestock. The study demonstrated that four selected abattoirs generate 1,606.403 ton of abattoir waste per year and using anaerobic digestion of about 85,139 m3/year of biogas and 111.25 ton/year of biofertilizer can be produced. The biogas or energy from the waste can replace firewood and charcoal and the expensive fossil fuels. Using Banks mathematical computation about 20,054.12 m3/year production of biogas could replace 20.56 ton/year of energy consumed by liquefied petroleum gas, kerosene, charcoal, furnace oil, petrol, and diesel in average. The current estimated biofertilizer (111.25 ton/year) from four abattoir sites can cover about 2,225 hectares/year with its advantage and efficiency of soil. When turned into cost, about $55,645 per year of price could estimate from biogas and biofertilizer. The study concluded that huge amount of biogas and dry biofertilizer yields could produce from abattoir waste through anaerobic digestion. Therefore, installing anaerobic digestion plant is recommended to ensure environmental safety and public health.

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