Interplaying factors of students personal characteristics in online learning modality: evidence in asian context

Mapping the multidimensional impact of learner attributes on behavior demonstrates the importance of models in learning. To this purpose, we examined the correlations between strategies and student characteristics and utilized regression analysis to determine how learner attributes affect strategy selection. A cross-sectional study of 258 students demonstrated widespread strategy use, as well as statistically significant connections within and between the Strategy Inventory for Language Learning and Student Characteristics of Learning measures. Regression analysis found distinctions in the types of learner characteristics associated with strategy adoption, most notably between direct and indirect strategies. Instrumental motivation predicted both direct and indirect Strategy Inventory for Language Learning scores, but self-efficacy affected memory, cognitive, and compensatory strategies, and perseverance predicted reported metacognitive and emotional strategy choice levels. Additionally, a negative route coefficient occurred between persistence and compensation techniques and between competition and memory strategies, implying mediation and a high degree of complexity in the way learner traits impact behavior. The present study's findings have implications for prospective instructor techniques for motivating students to become fully involved in language learning via the online procedure.Campus At

Premature Cell Senescence and T Cell Receptor‐Independent Activation of CD8+ T Cells in Juvenile Idiopathic Arthritis

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/99044/1/art38015.pd

A Flexible Architecture for Semantic Annotation and Automated Multimedia Presentation Generation

Abstract. Multimedia information system design has been recently influenced by state-of-the-art technologies such as those provided by semantic web. This paper introduces an information search and retrieval methodology that employs semantic web technologies for its data representation and reasoning tasks. Creating a meaningful multimedia presentation is an attempt to answer a user’s query using a knowledge representation structure. In this case, the system instead of providing a list of results, as happens in many typical information search and retrieval systems, collects a list of selected items based on the relevancy to the queried topic and also meaningful relationships between data objects. The collected information is used for the construction of an automated presentation to demonstrate the results to the user. The paper concentrates in particular on the content selection, narration structuring, and presentation design and generation processes. The implementation of an automatic presentation generation facility in an integrated system, called MANA, is described through the paper. MANA is designed to generate adaptive and automatic presentations based on users ’ perspectives and preferences on the queried topic

Relation Robustness Evaluation for the Semantic Associations

popularity, and statistical methods to find and prioritise relevant data to a specific query. In recent years, Semantic web has introduced new approaches to specify Web data using machine-interpretable structures. This has led to the establishment of new frameworks for search engines and information systems based on discovering complex and meaningful relationships between information resources. In this paper we discuss a semantic supported information search and retrieval system to answer users ’ information queries. The paper focuses on knowledge discovery aspects of the system and in particular analysis of semantic associations. The information resources are multimedia data, which could be retrieved from heterogeneous resources. The main goal is to provide a hypermedia presentation, which narratively conveys relevant information to the queried term. The structure describes the related entities to the queried topic and a ranking mechanism assigns weights to the entities. The assigned weights express the degree of relevancy of each related entity in the presentation structure

Systemic amyloidosis presenting as mucocutaneous bullous lesions

A 65-year-old male presented with hemorrhagic bullous skin lesions with purpura and ecchymoses. There was increased skin fragility with a strongly positive Nikolsky sign. Histopathology of the skin revealed large amounts of amyloid deposits in the dermis with a positive Congo Red staining around the dermal vessels. Examination and tests in this patient also revealed anemia, hepatomegaly, infiltrative cardiomyopathy, polyneuropathy and immunoglobulin λ deposition, favoring a diagnosis of primary amyloidosis (AL type). The present case is reported in view of the rarity of the bullous variant of primary systemic amyloidosis as well as presence of mucosal lesions and a positive Nikolsky sign

Automatic interactive security monitoring system

Over the years an increasing demand for an automated security system begins to emerge. Many applications that help in protecting life and properties are being developed. Most of them are aimed at improving the work of security personnel and security agencies. However, security is a responsibility of everyone not only the security agencies or security personnel alone. This paper present an interactive security monitoring system based on passive infrared motion detection sensor, which will capture the image of any intruding persons and share it to the entire people that are using the system on both Android platform and in an online portal display. The people on the system can communicate with each other and post information to a commonly accessible board in the online system to discuss any issues or to see if anyone recognizes the felons/intruder on the images. Images of interest can then be transmitted to law enforcement authorities. This could be use in anywhere that needs to be protected against intruder. It will be best use in kindergarten, primary school and or in a neighborhood. That is why we call it neighborhood watch security system (NWSS). Preliminaries evaluation indicated an accurate image captured in a real time with an avoidance of false alarm

Intelligent system for predicting the price of natural gas based on non-oil commodities

We present a preliminary investigation into a novel approach to natural gas prediction. Experimental data were extracted from the Energy Information Administration of the US Department of Energy. The datasets were pre-processed and used to build a feed-forward neural network intelligent system for predicting natural gas prices based on gold, silver, soy and copper. The validation of the intelligent system indicated a Regression (R) = 0.79972 when the reserved datasets were tested on the intelligent system. Natural gas prices can be predicted using non-oil commodities as independent variables. With little additional information, the proposed design can be used to construct intelligent decision support systems to support decision making in the government and private sector

Orthogonal wavelet support vector machine for predicting crude oil prices

Previous studies mainly used radial basis, sigmoid, polynomial, linear, and hyperbolic functions as the kernel function for computation in the neurons of conventional support vector machine (CSVM) whereas orthogonal wavelet requires less number of iterations to converge than these listed kernel functions. We proposed an orthogonal wavelet support vector machine (OSVM) model for predicting the monthly prices of West Texas Intermediate crude oil prices. For evaluation purposes, we compared the performance of our results with that of the CSVM, and multilayer perceptron neural network (MLPNN). It was found to perform better than the CSVM, and the MLPNN. Moreover, the number of iterations, and time computational complexity of the OSVM model is less than that of CSVM, and MLPNN. Experimental results suggest that the OSVM is effective, robust, and can efficiently be used for crude oil price prediction. Our proposal has the potentials of advancing the prediction accuracy of crude oil prices, which makes it suitable for building intelligent decision support systems