Use Case Point Approach Based Software Effort Estimation using Various Support Vector Regression Kernel Methods

The job of software effort estimation is a critical one in the early stages of the software development life cycle when the details of requirements are usually not clearly identified. Various optimization techniques help in improving the accuracy of effort estimation. The Support Vector Regression (SVR) is one of several different soft-computing techniques that help in getting optimal estimated values. The idea of SVR is based upon the computation of a linear regression function in a high dimensional feature space where the input data are mapped via a nonlinear function. Further, the SVR kernel methods can be applied in transforming the input data and then based on these transformations, an optimal boundary between the possible outputs can be obtained. The main objective of the research work carried out in this paper is to estimate the software effort using use case point approach. The use case point approach relies on the use case diagram to estimate the size and effort of software projects. Then, an attempt has been made to optimize the results obtained from use case point analysis using various SVR kernel methods to achieve better prediction accuracy.Comment: 13 pages, 6 figures, 11 Tables, International Journal of Information Processing (IJIP

Physiological antioxidant system and oxidative stress in stomach cancer patients with normal renal and hepatic function

Role of free radicals has been proposed in the pathogenesis of many diseases. Gastric cancer is a common disease worldwide, and leading cause of cancer death in India. Severe oxidative stress produces reactive oxygen species (ROS) and induces uncontrolled lipid peroxidation. Albumin, uric acid (UA) and Bilirubin are important physiological antioxidants. We aimed to evaluate and assess the role of oxidative stress (OS) and physiological antioxidant system in stomach cancer patients. Lipid peroxidation measured as plasma Thio Barbituric Acid Reactive substances (TBARS), was found to be elevated significantly (p=0.001) in stomach cancer compared to controls along with a decrease in plasma physiological antioxidant system. The documented results were due to increased lipid peroxidation and involvement of physiological antioxidants in scavenging free radicals but not because of impaired hepatic and renal functions

Representation and Bracketing in Repeated Games

In this experimental paper, the author investigates the framing effect of different representations of multiple strategic settings or games on a player’s strategic behavior. Two representations of the same environment are employed, wherein a player engages in two infinitely repeated prisoner’s dilemma games. In the first representation (termed Split), the stage games are shown separately. In contrast, the second representation (termed Linked) displays a combined stage game. The choice bracketing, distinguishing between Narrow and Broad bracketing, is considered a potential cause behind any disparity in behavior between the two representations. The Split representation does not necessitate broad bracketing, whereas the Linked representation compels it. Each type of bracketing has its own equilibrium implications. The author employs both a between-subject design (Study 1), where each subject observes only one representation, and a within-subject design (Study 2), where each subject is shown both the Linked and Split representations. In Study 1, significant differences in average behavior between the two representations are observed for both symmetric and asymmetric payoffs, albeit only after conditioning for session fixed effects. Study 2 reveals a more prominent effect of representation, and a sequence effect is observed, wherein the tendency to defect in both games is higher in the Linked representation if administered after the Split representation. In Study 2, for individuals who cannot be categorized as broad bracketers, the effect of seeing the Linked representation instead of the Split representation is economically significant. It increases the probability of choosing to cooperate in both games by more than 20% and decreases the probability to defect in both games by more than 25%

MECHANISM TO DISCOVER END-TO-END LOSSLESS NETWORK CHARACTERISTIC

Techniques are described herein for a mechanism that is critical for Remote Direct Memory Access (RDMA) over Converged Ethernet version 2 (RoCEv2) adoption. This mechanism may enable discovering end-to-end lossless network characteristics in RoCEv2

Influence of training dataset selection on the performance of a machine learning model

To observe the growth dynamics of the canola flowers during the blooming season and estimate the harvest forecast of the Canola crops, an application called ‘Flower Counter’ has been developed by the researchers of P2IRC located at the University of Saskatchewan. The model has been developed using Deep Learning (DL) based Multi-column Convolutional Neural Network (MCNN) algorithm and TensorFlow framework. This is an object counting model, that counts the Canola flowers from the images based on the learning from a given set of training images, called ‘ground-truths’. This work proposes to compose a good training dataset that would give good accuracy with a robust object detection model by using different training and testing combinations. Various evaluation techniques have been used in this work to check the impact of the training dataset, on the testing results of the model and generalizability. The primary goal of this research work is to define a good training dataset composition having diversity. A good composition also consists of different characteristics present in the dataset, that can impact the testing results and can help in creating a robust object counting model. Different characteristics of the training datasets and testing datasets are used to evaluate the most prominent characteristics and features that impact the test results. The objective is also to evaluate the impact of training dataset selection on testing results produced by the ML model in terms of accuracy. This work would help the researchers and plant scientists gain knowledge about the diversity of characteristics for the composition of a training dataset. This can give insights to reduce the manual effort which is required to create ground truth for training models by identifying the characteristics that impact testing results. Since the entire training of the model depends on the datasets collected during diverse weather conditions, there could be factors that could impact some of the experimental results. The research area for training dataset selection has not been explored much, and this research work will give good insights about model generalization capability and scopes for manual work utilization for getting a robust object counting model

Factors Influencing High School Students’ Intention and Use Of elearning to Study Chemistry in Bangkok, Thailand

Purpose: This research aims to identify factors impacting the behavioral intention and use behavior of eLearning among the students who are studying Chemistry in the final two years (Grade 11 and 12) of international schools in Bangkok, Thailand. The conceptual framework is based on performance expectancy, effort expectancy, social influence, facilitating conditions, habit, behavioral intention and use behavior. Research design, data, and methodology: A quantitative approach of probability and non-probability techniques was used, including judgmental, stratified random and convenience samplings. Constructed on the UTAUT model used for this study, 500 questionnaires were distributed to high school Chemistry studying pupils among international schools in Bangkok. Statistical tool of Structural Equation Modelling (SEM) and Confirmatory Factor Analysis (CFA) of IBM SPSS was adopted to explore the collected data and analyze the model fit, reliability, and validity of the various variables. Results: Results indicate the strongest relationship between the behavioral intention and use behavior of eLearning. Furthermore, performance expectancy, efforts expectancy, facilitating conditions, and habit significantly affect behavioral intention. Facilitating conditions and habit have a significant impact on use behavior. Conclusions: A robust relation has demonstrated a strong association between behavioral intention and the user behavior of eLearning

Selective ring opening of naphthenes present in heavy gas oil derived from Athabasca bitumen

Removal of polynuclear aromatics from diesel fuel has become a focus of intense research due to the stringent environmental legislation associated with clean fuels. In this work, selective ring opening of model compound decalin over the set of catalysts comprising of 1) Ir-Pt supported on mesoporous Zr-MCM-41, large and medium pore zeolites like HY and H-Beta and 2) Ni-Mo/carbide on HY, H-Beta, Al-SBA-15, ¥ã- alumina and silica alumina were studied. All the catalysts were extensively characterized by BET surface area measurement, CO-chemisorption, XRD, FTIR, TPR and TPD of ammonia. Ring opening of decalin was studied on these catalysts in a trickle-bed reactor in a temperature range of 200- 400 ¡ÆC, pressure range of 2-7 MPa and LHSV of 1 to 3 h- 1. 31.7 and 65.0 wt.% of RO yield and selectivity were observed on Ir-Pt/HY catalyst at 220 ¡ÆC, whereas 34.0 and 40.0 wt.% of ring opening yield and selectivity were observed on Ni-Mo carbide/HY catalyst at 240 ¡ÆC. From the model compound studies, Ir-Pt/HY, Ni-Mo carbide/HY and Ni-Mo carbide/H-Beta were selected for study of hydrotreated light gas oil in a trickle bed reactor. Ni-Mo carbide/HY performed better over other catalysts and increased the cetane index of hydrotreated light gas oil by 12 units at 325 ¡ÆC. A first order kinetic model was fitted for the hydrotreated light gas oil study. 89, 111 and 42 KJ/gmol of activation energies was observed for dearomatization, aromatization and naphthenes cracking steps, respectively. The thermodynamic equilibrium calculations reveal that the selectivity of ring opening products of decalin can be maximized by favoring the formation of unsaturated compounds at higher operating temperatures. Energetics of dealkylation and ring opening reactions of naphthenes in gas phase and on the surface of Br©ªnsted acid sites were calculated using quantum chemical simulations. In iv gas phase, ratio of Arrhenius activation energies for forward and reverse reactions of RO and dealkylation reactions are 1.92 and 1.82 respectively. Deakylation on different level clusters revealed that surface reaction is the rate controlling

Design of Visible-light driven catalysts for water oxidation and VOC degradation

The PhD thesis involves one or more articles that are either published,submitted or in the process of manuscript preparation. These all chapters are eloborated in context to the understandings and advancements involved during the PhD period. The whole thesis involves insights about synthesis and characterization of BiVO4 in the form of powder as well as thin films. It also describes the ability of BiVO4 powders and thin films in water splitting and volatile organic compound degradation