660 research outputs found
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 Laplace operator by investigating the underlying variational problem. Mostly the discussion is restricted to case however it can be extended to other values of also. When we have constrained minimizers subject to norm, it is considered as the eigenvalue problem of 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
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