GENDER PARTICIPATION IN CASSAVA PROCESSING ACTIVITIES IN AYETORO AREA OF OGUN STATE

The research investigated gender participation in cassava processing activities in Ayetoro area of Ogun State. Cassava is a staple food in Nigeria generally processed into “gari”, “lafun” and “fufu”. Male and female processors participate in the various processing activities with the use of different processing techniques. Purposive and random sampling techniques were adopted for the study. Four villages were chosen and 240 respondents comprising of male and female processors in “gari”, “lafun” and “fufu” processing. The data were analyzed using both percentage distribution and analysis of variance. The study reveals that 37.5% of male processors were between 41-50 years while 30.6% of female processor was between 31 – 40 years. Majority (60.4%) of female processors were Christians while (50.0%) of the male processors were Muslim. The household size of most of male processors (50.0%) ranges between 6-10 members while that of 56.9% of female processors range between 1 -5 members. The study further shows that 40.0% of female processors had no formal education compared to only 18.8% of their male counterpart. The evidence from the study concludes that at p =0.05, there are significance difference between gender participation and their ages (F=3.73, p=0.05), religion (F=4.167, p=0.044), household size (F=4.454, p=0.037) and sources of cassava (F=12.17, p=0.001). Also, significant difference exist between the attitude of male and female participating in cassava processing activities about the need for men’s strength (F=9.79, p=0.002), the availability of time on men’s part (F=5.01, p=0.03). However, no significant difference exists between male and female participation based on constraints faced with different processing techniques they are using. Finally, it is recommended that there is the need to motivate male participation in cassava processing activities, and that processing of agricultural products should not be seen as female job alone.Crop Production/Industries,

Parallel Astronomical Data Processing with Python: Recipes for multicore machines

High performance computing has been used in various fields of astrophysical research. But most of it is implemented on massively parallel systems (supercomputers) or graphical processing unit clusters. With the advent of multicore processors in the last decade, many serial software codes have been re-implemented in parallel mode to utilize the full potential of these processors. In this paper, we propose parallel processing recipes for multicore machines for astronomical data processing. The target audience are astronomers who are using Python as their preferred scripting language and who may be using PyRAF/IRAF for data processing. Three problems of varied complexity were benchmarked on three different types of multicore processors to demonstrate the benefits, in terms of execution time, of parallelizing data processing tasks. The native multiprocessing module available in Python makes it a relatively trivial task to implement the parallel code. We have also compared the three multiprocessing approaches - Pool/Map, Process/Queue, and Parallel Python. Our test codes are freely available and can be downloaded from our website.Comment: 15 pages, 7 figures, 1 table, "for associated test code, see http://astro.nuigalway.ie/staff/navtejs", Accepted for publication in Astronomy and Computin

Matchmaking for covariant hierarchies

We describe a model of matchmaking suitable for the implementation of services, rather than their for their discovery and composition. In the model, processing requirements are modelled by client requests and computational resources are software processors that compete for request processing as the covariant implementations of an open service interface. Matchmaking then relies on type analysis to rank processors against requests in support of a wide range of dispatch strategies. We relate the model to the autonomicity of service provision and briefly report on its deployment within a production-level infrastructure for scientic computing

Photonic processing at NASA Ames Research Center

The Photonic Processing group is engaged in applied research on optical processors in support of the Ames vision to lead the development of autonomous intelligent systems. Optical processors, in conjunction with numeric and symbolic processors, are needed to provide the powerful processing capability that is required for many future agency missions. The research program emphasizes application of analog optical processing, where free-space propagation between components allows natural implementations of algorithms requiring a large degree of parallel computation. Special consideration is given in the Ames program to the integration of optical processors into larger, heterogeneous computational systems. Demonstration of the effective integration of optical processors within a broader knowledge-based system is essential to evaluate their potential for dependable operation in an autonomous environment such as space. The Ames Photonics program is currently addressing several areas of interest. One of the efforts is to develop an optical correlator system with two programmable spatial light modulators (SLMs) to perform distortion invariant pattern recognition. Another area of research is optical neural networks, also for use in distortion-invariant pattern recognition

Artificial Neural Networks

Artificial Neural Network is a mathematical model, made in the form of software or hardware, built on the principle of biological neural networks of living cells. The neural network is a system of connected processors interacting with each other. If you compare them with the biological analogue, you will understand that the artificial network and the network of neurons are almost the same things. These processors are usually simple (compared to a CPU used in a PC). Each network processor either receives signals for processing or sent it to other processors. But if they are connected in a huge network with controlled interaction, these «simple» processors in large quantities can complete incredibly complicated tasks

User microprogrammable processors for high data rate telemetry preprocessing

The use of microprogrammable processors for the preprocessing of high data rate satellite telemetry is investigated. The following topics are discussed along with supporting studies: (1) evaluation of commercial microprogrammable minicomputers for telemetry preprocessing tasks; (2) microinstruction sets for telemetry preprocessing; and (3) the use of multiple minicomputers to achieve high data processing. The simulation of small microprogrammed processors is discussed along with examples of microprogrammed processors