A comparative analysis of energy use patterns in small and large scale irrigated rice farming systems: A case study in Ayutthaya Province in the central region of Thailand

Rice is not only the staple food of many countries but it is also a source of earning of millions of people. In Thailand, rice farming is the major source of income of the farmers which is highly dependent on the energy inputs. There is a complex relationship between energy use pattern and rice yield. This study was performed to examine the energy use pattern of the rice growers depending upon the size of the farms and its relationship the crop production in the Ayutthaya province in central region of Thailand. Its subjects are the field practices, technology use and chemical inputs. Information was collected through the field survey. Results shows that mean energy output-input ratio of small farmers is higher with low specific energy and high energy productivity than the large farmers. Energy ratio is 8.20 in small farms, 7.00 in medium farms and 6.02 in large farms.(Résumé d' auteur

About Pyramid Structure in Convolutional Neural Networks

Deep convolutional neural networks (CNN) brought revolution without any doubt to various challenging tasks, mainly in computer vision. However, their model designing still requires attention to reduce number of learnable parameters, with no meaningful reduction in performance. In this paper we investigate to what extend CNN may take advantage of pyramid structure typical of biological neurons. A generalized statement over convolutional layers from input till fully connected layer is introduced that helps further in understanding and designing a successful deep network. It reduces ambiguity, number of parameters, and their size on disk without degrading overall accuracy. Performance are shown on state-of-the-art models for MNIST, Cifar-10, Cifar-100, and ImageNet-12 datasets. Despite more than 80% reduction in parameters for Caffe_LENET, challenging results are obtained. Further, despite 10-20% reduction in training data along with 10-40% reduction in parameters for AlexNet model and its variations, competitive results are achieved when compared to similar well-engineered deeper architectures.Comment: Published in 2016 International Joint Conference on Neural Networks (IJCNN

An Empirical analysis of Open Source Software Defects data through Software Reliability Growth Models

The purpose of this study is to analyze the reliability growth of Open Source Software (OSS) using Software Reliability Growth Models (SRGM). This study uses defects data of twenty five different releases of five OSS projects. For each release of the selected projects two types of datasets have been created; datasets developed with respect to defect creation date (created date DS) and datasets developed with respect to defect updated date (updated date DS). These defects datasets are modelled by eight SRGMs; Musa Okumoto, Inflection S-Shaped, Goel Okumoto, Delayed S-Shaped, Logistic, Gompertz, Yamada Exponential, and Generalized Goel Model. These models are chosen due to their widespread use in the literature. The SRGMs are fitted to both types of defects datasets of each project and the their fitting and prediction capabilities are analysed in order to study the OSS reliability growth with respect to defects creation and defects updating time because defect analysis can be used as a constructive reliability predictor. Results show that SRGMs fitting capabilities and prediction qualities directly increase when defects creation date is used for developing OSS defect datasets to characterize the reliability growth of OSS. Hence OSS reliability growth can be characterized with SRGM in a better way if the defect creation date is taken instead of defects updating (fixing) date while developing OSS defects datasets in their reliability modellin

Dentrification potential of different landuse types in an agricultural watershed, lower Mississippi valley.

Expansion of agricultural land and excessive nitrogen (N) fertilizer use in the Mississippi River watershed has resulted in a three-fold increase in the nitrate load of the river since the early 1950s. One way to reduce this nitrate load is to restore wetlands at suitable locations between croplands and receiving waters to remove run-off nitrate through previous termdenitrification.next term This research investigated previous termdenitrificationnext term potential (DP) of different land uses and its controlling factors in an agricultural watershed in the lower Mississippi valley (previous termLMV)next term to help identify sites with high DP for reducing run-off nitrate. Soil samples collected from seven land-use types of an agricultural watershed during spring, summer, fall and winter were incubated in the laboratory for DP determination. Low-elevation clay soils in wetlands exhibited 6.3 and 2.5 times greater DP compared to high-elevation silt loam and low-elevation clay soils in croplands, respectively. DP of vegetated-ditches was 1.3 and 4.2 times that of un-vegetated ditches and cultivated soils, respectively. Soil carbon and nitrogen availability, bulk density, and soil moisture significantly affected DP. These factors were significantly influenced in turn by landscape position and land-use type of the watershed. It is evident from these results that low-elevation, fine-textured soils under natural wetlands are the best locations for mediating nitrate loss from agricultural watersheds in the previous termLMV.next term Landscape position and land-use types can be used as indices for the assessment/modeling of previous termdenitrificationnext term potential and identification of sites for restoration for nitrate removal in agricultural watersheds