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

Superconducting nanowire single-photon detectors for advanced photon-counting applications

The ability to detect infrared photons is increasingly important in many elds of scienti c endeavour, including astronomy, the life sciences and quantum information science. Improvements in detector performance are urgently required. The Superconducting Nanowire Single-Photon Detector (SNSPD/SSPD) is an emerging single-photon detector technology o ering broadband sensitivity, negligible dark counts and picosecond timing resolution. SNSPDs have the potential to outperform conventional semiconductor-based photon-counting technologies, provided the di culties of low temperature operation can be overcome. This thesis describes how these important challenges have been addressed, enabling the SNSPDs to be used in new applications. A multichannel SNSPD system based on a closed-cycle refrigerator has been constructed and tested. E cient optical coupling has been achieved via carefully aligned optical bre. Fibre-coupled SNSPDs based on (i) NbN on MgO substrates and (ii) NbTiN on oxidised Si substrates have been studied. The latter give enhanced performance at telecom wavelengths, exploiting the re ection from the Si=SiO2 interface. Currently, the detector system houses four NbTiN SNSPDs with average detection e ciency >20% at 1310 nm wavelength. We have employed SNSPDs in the characterisation of quantum waveguide circuits, opening the pathway to operating this promising platform for optical quantum computing for the first time at telecom wavelengths

Applications of Functional Analysis to a Class of Elliptic PDES

In this report we discuss about the eigensolutions of an eigenvalue problem for the $p-$Laplace operator by investigating the underlying variational problem. Mostly the discussion is restricted to $p=1$ case however it can be extended to other values of $p$ also. When we have constrained minimizers subject to $L^1-$ norm, it is considered as the eigenvalue problem of $1-$Laplace operator. Several theorems are stated in the report which strongly support the existence of solutions to the variational problem, hence eigensolutions and also the sequence of eigensolutions. Solutions are treated as critical points (for the variational problem) in the sense of weak slope. Finally an additional necessary condition is introduced which is derived using inner variations to filter the non-eigensolutions of the problem. Basic concepts of functional analysis and necessary prerequisites to understand the variational problem and minimizers are also included

Fake News Detection in Social Media Using Machine Learning and Deep Learning

Fake news detection in social media is a process of detecting false information that is intentionally created to mislead readers. The spread of fake news may cause social, economic, and political turmoil if their proliferation is not prevented. However, fake news detection using machine learning faces many challenges. Datasets of fake news are usually unstructured and noisy. Fake news often mimics true news. In this study, a data preprocessing method is proposed for mitigating missing values in the datasets to enhance fake news detection accuracy. The experimental results show that Multi- Layer Perceptron (MLP) classifier combined with the proposed data preprocessing method outperforms the state-of-the-art methods. Furthermore, to improve the early detection of rumors in social media, a time-series model is proposed for fake news detection in social media using Twitter data. With the proposed model, computational complexity has been reduced significantly in terms of machine learning models training and testing times while achieving similar results as state-of-the-art in the literature. Besides, the proposed method has a simplified feature extraction process, because only the temporal features of the Twitter data are used. Moreover, deep learning techniques are also applied to fake news detection. Experimental results demonstrate that deep learning methods outperformed traditional machine learning models. Specifically, the ensemble-based deep learning classification model achieved top performance

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

MODELING AND SIMULATION OF ROTOR FLUX OBSERVER BASED INDIRECT VECTOR CONTROL OF INDUCTION MOTOR DRIVE USING FUZZY LOGIC CONTROL

The indirect vector controlled inductor motor (IM) drive involves decoupling of the stator current into torque and flux producing components. This paper proposes the implementation of fuzzy logic control scheme applied to a two d-q current components model of an induction motor. A Fuzzy logic Controller is developed with the help of knowledge rule base for efficient and robust control. The performance of Fuzzy Logic Controller is compared with that of the PI controller with rotor flux observer in terms of the settling time and dynamic response to sudden load changes. The harmonic pattern of the output current is evaluated for both fixed gain proportional integral controller and the Fuzzy Logic based controller. The performance of the IM drive has been analyzed under steady state and transient conditions. Simulation results of both the controllers are presented for comparison

Electro-Kinetic Remediation Processes -- A Brief Overview and Selected Applications

In this growing world the need for in-situ remediation has grown importance for various reasons. In-situ remediation is the application of remediation in the subsurface – as compared to ex-situ remediation, which applies to media readily accessible above ground and many times involves excavation and disruption of the soil. In-situ remediation may be applied in the unsaturated/vadoze zone or in saturated soils and groundwater. Anthropogenic activities (municipal and industrial) have resulted in contamination in subsurface soil/water environment. There are several in-situ remediation methods for example bio-remediation, thermal desorption, soil vapor extraction, and soil flushing just to name a few. The situation where minimal disruption is required or preferred electro-kinetic remediation processes offer a unique solution for organic as well as inorganic pollutants. Electro-kinetic remediation is defined as a technique which uses electric current (DC) to remove pollutants from a medium. It has been effective in removing organic and inorganic contaminants from the soils

Face Analysis Using Row and Correlation Based Local Directional Pattern

Face analysis, which includes face recognition and facial expression recognition, has been attempted by many researchers and gave ideal solutions. The problem is still active and challenging due to an increase in the complexity of the problem viz. due to poor lighting, face occlusion, low-resolution images, etc. Local pattern descriptor methods introduced to overcome these critical issues and improve the recognition rate. These methods extract the discriminant information from the local features of the face image for recognition. In this paper, the local descriptor based two methods, namely row-based local directional pattern and correlation-based local directional pattern proposed by extending an existing descriptor -- local directional pattern (LDP). Further, the two feature vectors obtained by these methods concatenated to form a hybrid descriptor. Experimentation has carried out on benchmark databases and results infer that the proposed hybrid descriptor outperforms the other descriptors in face analysis