Exploring Current Practice of Using Technology to Support Collaborative Argumentation in Science Classrooms

The purpose of this qualitative study was to explore how middle school science teachers enact the practice of using technology to support collaborative argumentation in their science classroom. This study employed qualitative case study and drew on data sources of interviews and observations. This study identified two themes. Six teachers regarded scientific argumentation as an important science practice, but five of them integrated this practice into their science class without formally introducing it. All teachers integrated different forms of technology to engage students in scientific argumentation. In this study, the findings suggested there is a need to provide professional development for teachers to learn about scientific argumentation. The findings can be used as a basis for the design and development of professional development training experiences for in-servic

Exploration of Health Technology Nonuse: The Case of Online Medical Records

Online Medical Records (OMR) platforms remain a key enabler to health management. Yet, how beliefs toward OMR and its subsequent nonuse are related is not understood. Applying the status quo bias (SQB) theory and the privacy paradox paradigm the study examines OMR nonusers and contributes to the health technology use literature. Using the Health Information National Trends Survey (HINTS) iteration 5, Cycle 1 and 3 data, mediation analysis reveals that inertia expressed as preference for speaking directly with healthcare providers predicts perceived need for OMR and partially mediates the relationship between perceived privacy concerns and need; having a chronic disease partially moderates such relationships. Thus, not all nonusers are created equal. Attaining benefits that come with capabilities and functionalities of OMR necessitates meaningful use of OMR by individuals. Healthcare providers or policymakers should intervene to dispel inertia or patient concerns to expand OMR use to facilitate healthcare decision making

Machine Learning and Rule Mining Techniques in the Study of Gene Inactivation and RNA Interference

RNA interference (RNAi) and gene inactivation are extensively used biological terms in biomedical research. Two categories of small ribonucleic acid (RNA) molecules, viz., microRNA (miRNA) and small interfering RNA (siRNA) are central to the RNAi. There are various kinds of algorithms developed related to RNAi and gene silencing. In this book chapter, we provided a comprehensive review of various machine learning and association rule mining algorithms developed to handle different biological problems such as detection of gene signature, biomarker, gene module, potentially disordered protein, differentially methylated region and many more. We also provided a comparative study of different well-known classifiers along with other used methods. In addition, we demonstrated the brief biological information regarding the immense biological challenges for gene activation as well as their advantages, disadvantages and possible therapeutic strategies. Finally, our study helps the bioinformaticians to understand the overall immense idea in different research dimensions including several learning algorithms for the benevolent of the disease discovery

An efficient source localization method in presence of multipath using smart antenna system

In this paper, a MATLAB based smart antenna testbed that efficiently localizes the line-of-sight (LOS) source in the presence of multipath signals is developed. By exploiting the consistent amplitude nature of the LOS signal, a variant of Constant Modulus Algorithm, namely Multitarget-Least Square Constant Modulus Algorithm is employed to adapt and update the weights of the smart antenna for estimation of the direction-of-arrival (DOA) of the of the LOS and multipath interference signals. Performance is compared with the conventional and recently proposed algorithms in the same testbed with alike considerations. Simulation result shows that the proposed method of DOA estimation performs better in terms of probability of resolution and root mean square error

An Efficient Source Localization Method in Presence of Multipath using Smart Antenna System

1069-1073In this paper, a MATLAB based smart antenna testbed that efficiently localizes the line-of-sight (LOS) source in the presence of multipath signals is developed. By exploiting the consistent amplitude nature of the LOS signal, a variant of Constant Modulus Algorithm, namely Multitarget-Least Square Constant Modulus Algorithm is employed to adapt and update the weights of the smart antenna for estimation of the direction-of-arrival (DOA) of the of the LOS and multipath interference signals. Performance is compared with the conventional and recently proposed algorithms in the same testbed with alike considerations. Simulation result shows that the proposed method of DOA estimation performs better in terms of probability of resolution and root mean square error

Using Simulation-based Energy Consumption of NIU Engineering Building to Provide Cost-Saving Solutions

In the current global situation where almost all countries need energy to perform their activities, providing energy is a vital demand for modern society. Furthermore, lack of fossil energy draws attention to the utilization of renewable energy, specifically solar energy. Because no specific published record of considering renewable energy solutions applied to the buildings of Northern Illinois University (NIU) have been found already, in this paper, solar energy as an energy solution for Northern Illinois University (NIU) Engineering Building (EB) has been considered. In this case, building envelope model and HVAC system model have been developed in eQUEST software to perform simulation-based energy consumption of EB. This simulation presents annual energy consumption of boiler, chiller plant, and daylighting in EB. Moreover, economic analysis of using solar energy for lighting has been performed to identify the feasibility and savings associated with solar energy which can potentially reduce costs with a reasonable payback time

Analyzing large gene expression and methylation data profiles using StatBicRM: statistical biclustering-based rule mining.

Microarray and beadchip are two most efficient techniques for measuring gene expression and methylation data in bioinformatics. Biclustering deals with the simultaneous clustering of genes and samples. In this article, we propose a computational rule mining framework, StatBicRM (i.e., statistical biclustering-based rule mining) to identify special type of rules and potential biomarkers using integrated approaches of statistical and binary inclusion-maximal biclustering techniques from the biological datasets. At first, a novel statistical strategy has been utilized to eliminate the insignificant/low-significant/redundant genes in such way that significance level must satisfy the data distribution property (viz., either normal distribution or non-normal distribution). The data is then discretized and post-discretized, consecutively. Thereafter, the biclustering technique is applied to identify maximal frequent closed homogeneous itemsets. Corresponding special type of rules are then extracted from the selected itemsets. Our proposed rule mining method performs better than the other rule mining algorithms as it generates maximal frequent closed homogeneous itemsets instead of frequent itemsets. Thus, it saves elapsed time, and can work on big dataset. Pathway and Gene Ontology analyses are conducted on the genes of the evolved rules using David database. Frequency analysis of the genes appearing in the evolved rules is performed to determine potential biomarkers. Furthermore, we also classify the data to know how much the evolved rules are able to describe accurately the remaining test (unknown) data. Subsequently, we also compare the average classification accuracy, and other related factors with other rule-based classifiers. Statistical significance tests are also performed for verifying the statistical relevance of the comparative results. Here, each of the other rule mining methods or rule-based classifiers is also starting with the same post-discretized data-matrix. Finally, we have also included the integrated analysis of gene expression and methylation for determining epigenetic effect (viz., effect of methylation) on gene expression level

Comparative Study of Endoscopic Temporalis Fascia Versus Endoscopic Cartilage Tympanoplasty

The goal of tympanoplasty is to reconstruct the tympanic membrane and the sound conducting mechanism in a long lasting way. Since its introduction in 1952 by Zoellner and Wullstein, numerous graft materials and methods of placement have been described to reconstruct the tympanic membrane. Temporalis fascia and perichondrium remain the most frequently used material for closure of the drum in tympanoplasty