Short-term and long-term memory analysis of learning using 2D and 3D educational contents

ABSTRACT

The effect of 2D and 3D educational content learning on memory has been studied using electroencephalography (EEG) brain signal. A hypothesis is set that the 3D materials are better than the 2D materials for learning and memory recall. To test the hypothesis, we proposed a classification system that will predict true or false recall for short-term memory (STM) and long-term memory (LTM) after learning by either 2D or 3D educational contents. For this purpose, EEG brain signals are recorded during learning and testing; the signals are then analysed in the time domain using different types of features in various frequency bands. The features are then fed into a support vector machine (SVM)-based classifier. The experimental results indicate that the learning and memory recall using 2D and 3D contents do not have significant differences for both the STM and the LTM.     

A new combination of artificial neural network and <Emphasis Type="Italic">K</Emphasis>-nearest neighbors models to predict blast-induced ground vibration and air-overpressure

Blasting operation is widely used method for rock excavation in mining and civil works. Ground vibration and air-overpressure (AOp) are two of the most detrimental effects induced by blasting. So, evaluation and prediction of ground vibration and AOp are essential. This paper presents a new combination of artificial neural network (ANN) and K-nearest neighbors (KNN) models to predict blast-induced ground vibration and AOp. Here, this combination is abbreviated using ANN-KNN. To indicate performance of the ANN-KNN model in predicting ground vibration and AOp, a pre-developed ANN as well as two empirical equations, presented by United States Bureau of Mines (USBM), were developed. To construct the mentioned models, maximum charge per delay (MC) and distance between blast face and monitoring station (D) were set as input parameters, whereas AOp and peak particle velocity (PPV), as a vibration index, were considered as output parameters. A database consisting of 75 datasets, obtained from the Shur river dam, Iran, was utilized to develop the mentioned models. In terms of using three performance indices, namely coefficient correlation (R ²), root mean square error and variance account for, the superiority of the ANN-KNN model was proved in comparison with the ANN and USBM equations.     

Senti‐CS: Building a lexical resource for sentiment analysis using subjective feature selection and normalized Chi‐Square‐based feature weight generation

Sentiment analysis involves the detection of sentiment content of text using natural language processing. Natural language processing is a very challenging task due to syntactic ambiguities, named entity recognition, use of slangs, jargons, sarcasm, abbreviations and contextual sensitivity. Sentiment analysis can be performed using supervised as well as unsupervised approaches. As the amount of data grows, unsupervised approaches become vital as they cut down on the learning time and the requirements for availability of a labelled dataset. Sentiment lexicons provide an easy application of unsupervised algorithms for text classification. SentiWordNet is a lexical resource widely employed by many researchers for sentiment analysis and polarity classification. However, the reported performance levels need improvement. The proposed research is focused on raising the performance of SentiWordNet3.0 by using it as a labelled corpus to build another sentiment lexicon, named Senti‐CS. The part of speech information, usage based ranks and sentiment scores are used to calculate Chi‐Square‐based feature weight for each unique subjective term/part‐of‐speech pair extracted from SentiWordNet3.0. This weight is then normalized in a range of ?1 to +1 using min–max normalization. Senti‐CS based sentiment analysis framework is presented and applied on a large dataset of 50000 movie reviews. These results are then compared with baseline SentiWordNet, Mutual Information and Information Gain techniques. State of the art comparison is performed for the Cornell movie review dataset. The analyses of results indicate that the proposed approach outperforms state‐of‐the‐art classifiers.     

High throughput blood plasma separation using a passive PMMA microfluidic device

Since plasma is rich in many biomarkers used in clinical diagnostic experiments, microscale blood plasma separation is a primitive step in most of microfluidic analytical chips. In this paper, a passive microfluidic device for on-chip blood plasma separation based on Zweifach–Fung effect and plasma skimming was designed and fabricated by hot embossing of microchannels on a PMMA substrate and thermal bonding process. Human blood was diluted in various times and injected into the device. The main novelty of the proposed microfluidic device is the design of diffuser-shaped daughter channels. Our results demonstrated that this design exerted a considerable positive influence on the separation efficiency of the passive separator device, and the separation efficiency of 66.6 % was achieved. The optimum purity efficiency of 70 % was achieved for 1:100 dilution times.     

Fuzzy based multi-criteria vertical handover decision modeling in heterogeneous wireless networks

Vertical handover gain significant importance due to the enhancements in mobility models by the Fourth Generation (4G) technologies. However, these enhancements are limited to specific scenarios and hence do not provide support for generic mobility. Similarly, various schemes are proposed based on these mobility models but most of them are suffered from the high packet loss, frequent handovers, too early and late handovers, inappropriate network selection, etc. To address these challenges, a generic vertical handover management scheme for heterogeneous wireless networks is proposed in this article. The proposed scheme works in three phases. In the first phase, a handover triggering approach is designed to identify the appropriate place for initiating handover based on the estimated coverage area of a WLAN access point or cellular base station. In the second phase, fuzzy rule based system is designed to eliminate the inappropriate networks before deciding an optimal network for handover. In the third phase, a network selection scheme is developed based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) decision mechanism. Various parameters such as delay, jitter, Bit Error Rate (BER), packet loss, communication cost, response time, and network load are considered for selecting an optimal network. The proposed scheme is tested in a mobility scenario with different speeds of a mobile node ranging from very low to very high. The simulation results are compared with the existing decision models used for network selection and handover triggering approaches. The proposed scheme outperforms these schemes in terms of energy consumption, handover delay and time, packet loss, good put, etc.     

A compact hybrid feature vector for an accurate secondary structure prediction

Amino acid propensity score is one of the earliest successful methods used in protein secondary structure prediction. However, the score performs poorly on small-sized datasets and low-identity protein sequences. Based on current in silico method, secondary structure can be predicted from local folds or local protein structure. In biology, the evolution of secondary structure produces local protein structure with different lengths. To precisely predict secondary structures, we propose a derivative feature vector, DPS that utilizes the optimal length of the local protein structure. DPS is the unification of amino acid propensity score and dihedral angle score. This new feature vector is further normalized to level the edges. Prediction is performed by support vector machines (SVM) over the DPS feature vectors with class labels generated by secondary structure assignment method (SSAM) and secondary structure prediction method (SSPM). All experiments are carried out on RS126 sequences. The results from this proposed method also highlight the overall accuracy of our method compared to other state-of-the-art methods. The performance of our method was acceptable specifically in dealing with low number and low identity sequences.     

GENOTOXICITY IN CHIRONOMUS KIIENSIS (CHIRONOMIDAE: DIPTERA) AFTER EXPOSURE TO POLLUTED SEDIMENTS FROM RIVERS OF NORTH PENINSULAR MALAYSIA: IMPLICATION FOR ECOTOXICOLOGICAL MONITORING

Rapid industrialization and urbanization has led to increasing input of chemical contaminants into the aquatic environment of Malaysia. Despite the threat civilization poses to the biota, there are still very few relevant studies on ecotoxicological testing of river ecosystems. To overcome this knowledge gap, we examined lethal and genotoxic effects of sediments from different rivers of the northern Malaysia against Chironomus kiiensis, a group well represented in the aquatic fauna of this region. We exposed the larvae to sediments from Selama River (SR), Permatang Rawa River (PRR) and Kilang Ubi River (KUR) at various durations (0, 6, 12, 24 and 48 h). The larval mortality was monitored, whereas DNA damage in survivors' cells was determined using the comet assay. Pollution level indexed by the amounts of heavy metals and other organic contaminants in the sediment showed progressive increases from SR to PRR to KUR. Highly polluted sediments (PRR to KUR) were detrimental to C. kiiensis larvae, most of which did not survive following exposure for long periods. DNA analyses revealed greater damages in nuclei derived from larvae maintained on polluted sediments, in particular, those from KUR. The effects on the genomic material of C. kiiensis larvae occurred in a time‐dependent manner, with damage level increasing as exposure time progressed. Our results highlight the genotoxic properties of polluted sediments. More importantly, this study showed that C. kiiensis larvae could respond to different levels of pollution with respect to exposure time. It is concluded that C. kiiensis larvae is a potential candidate for river ecotoxicological monitoring. Copyright © 2012 John Wiley & Sons, Ltd.     

Flexible and Autonomous Integrated System for Characterizing Metal Oxide Gas Sensor Response in Dynamic Environment

Characterization and calibration of gas sensor is a complex problem due to the dynamic behavior of gases and the limitations of current technology. This article reports a flexible, robust, and autonomous integrated system that is able to perform characterization on metal oxide-based gas sensors in dynamic environments. The system controls the concentration and flow of the relevant gases into the gas chamber and simultaneously measuring the sensor response. This feature allows the characterization of the sensor under continuous dynamic flow of gases similar to conditions on a robot or flow pipes. Several experiments have been performed on the system using hydrogen sulfide. The results provide information on the general characteristics of the sensor as well as its sensitivity. The noise levels were studied with different reference voltages. Overall, the results verify that the system is reliable and able to produce repeatable measurements.     

Analysis of Pipe Networks by the Finite Element Method

ABSTRACT

In this paper, the finite element technique used in the analysis and design of water distribution networks will be presented. The method was applied successfully to the solution of three different networks.

In order to demonstrate the feasibility of the approach and to show some merits and demerits of the finite element method, a comparison was made on a digital computer with the standard Hardy Cross method. In all cases the finite element method was shown to provide superior performance.

Moreover, the programmes were run on three different machines [Wang vs 100, Apple IIe, and IBM micro-computer] to show the effect of increasing storage capacity, machine accuracy, and time saving.     

Exploring pre-service teachers' beliefs about using Web 2.0 technologies in K-12 classroom

This qualitative study explored pre-service teachers' behavioral, normative, and control beliefs regarding their intentions to use Web 2.0 technologies in their future classrooms. The Theory of Planned Behavior (TPB) was used as the theoretical framework (Ajzen, 1991) to understand these beliefs and pre-service teachers' intentions for why they want to use Web 2.0 technologies. According to Ajzen's TPB, the behavioral beliefs are based on attitude toward outcomes or consequences of using Web 2.0, the normative beliefs depend on social support and social pressure to use Web 2.0, and the control beliefs lay the foundation of perceived behavioral control over using Web 2.0 in a classroom. Data were collected from open-ended survey questions (n = 190), semi-structured interviews (n = 12) and end of semester reflections (n = 12). Findings suggest that pre-service teachers' intentions to use Web 2.0 technologies are related to their beliefs about the value of these technologies for improving student learning and engagement, its ease of use (behavioral beliefs), its ability to meet the needs/expectations of digital age students (normative beliefs), the participants' high self-efficacy in use, and its potential for affording students anytime/anywhere access to learning and interaction (control beliefs). From these results, we recommend that teacher educators should target these beliefs within teacher development programs to prepare pre-service teachers for successful use of Web 2.0 technologies in their future K-12 classrooms.