Nanoscale iron particles decorated graphene sheets synthesized via sodium borohydride reduction of graphene oxide, showed enhanced magnetic property, surface area and Cr(vi) adsorption capacity compared to bare iron nanoparticles.

Grammaticality judgment tests (GJTs) have been used to elicit data reflecting second language (L2) speakers’ knowledge of L2 grammar. However, the exact constructs measured by GJTs, whether primarily implicit or explicit knowledge, are disputed and have been argued to differ depending on test-related variables (i.e., time pressure and item grammaticality).Using eye-tracking, this study replicates the GJT results in R. Ellis (2005). Twenty native and 40 nonnative English speakers judged sentences with and without time pressure. Analyses revealed that time pressure suppressed regressions (right-to-left eye movements) in nonnative speakers only. Conversely, both groups regressed more on untimed, grammatical items. These findings suggest that timed and untimed GJTs measure different constructs, which could correspond to implicit and explicit knowledge, respectively. In particular, they point to a difference in the levels of automatic and controlled processing involved in responding to the timed and untimed tests. Furthermore, untimed grammatical items may induce GJT-specific task effects.

Although access control based on human face recognition has become popular in consumer applications, it still has several implementation issues before it can realize a stand-alone access control system. Owing to a lack of computational resources, lightweight and computationally efficient face recognition algorithms are required. The conventional access control systems require significant active cooperation from the users despite its non-aggressive nature. The lighting/illumination change is one of the most difficult and challenging problems for human-face-recognition-based access control applications. This paper presents the design and implementation of a user-friendly, stand-alone access control system based on human face recognition at a distance. The local binary pattern (LBP)-AdaBoost framework was employed for face and eyes detection, which is fast and invariant to illumination changes. It can detect faces and eyes of varied sizes at a distance. For fast face recognition with a high accuracy, the Gabor-LBP histogram framework was modified by substituting the Gabor wavelet with Gaussian derivative filters, which reduced the facial feature size by 40% of the Gabor-LBP-based facial features, and was robust to significant illumination changes and complicated backgrounds. The experiments on benchmark datasets produced face recognition accuracies of 97.27% on an E-face dataset and 99.06% on an XM2VTS dataset, respectively. The system achieved a 91.5% true acceptance rate with a 0.28% false acceptance rate and averaged a 5.26 frames/sec processing speed on a newly collected face image and video dataset in an indoor office environment.

Various types of derivative information have been increasing exponentially, based on mobile devices and social networking sites (SNSs), and the information technologies utilizing them have also been developing rapidly. Technologies to classify and analyze such information are as important as data generation. This study concentrates on data clustering through principal component analysis and K-means algorithms to analyze and classify user data efficiently. We propose a technique of changing the cluster choice before cluster processing in the existing K-means practice into a variable cluster choice through principal component analysis, and expanding the scope of data clustering. The technique also applies an artificial neural network learning model for user recommendation and prediction from the clustered data. The proposed processing model for predicted data generated results that improved the existing artificial neural network–based data clustering and learning model by approximately 9.25%.

In the generation and analysis of Big Data following the development of various information devices, the old data processing and management techniques reveal their hardware and software limitations. Their hardware limitations can be overcome by the CPU and GPU advancements, but their software limitations depend on the advancement of hardware. This study thus sets out to address the increasing analysis costs of dense Big Data from a software perspective instead of depending on hardware. An altered [Formula: see text]-means algorithm was proposed with ideal points to address the analysis costs issue of dense Big Data. The proposed algorithm would find an optimal cluster by applying Principal Component Analysis (PCA) in the multi-dimensional structure of dense Big Data and categorize data with the predicted ideal points as the central points of initial clusters. Its clustering validity index and [Formula: see text]-measure results were compared with those of existing algorithms to check its excellence, and it had similar results to them. It was also compared and assessed with some data classification techniques investigated in previous studies and we found that it made a performance improvement of about 3–6% in the analysis costs.

This study sought to propose a big data analysis and prediction model for transmission line tower outliers to assess when something is wrong with transmission line tower big data based on deep reinforcement learning. The model enables choosing automatic cluster K values based on non-labeled sensor big data. It also allows measuring the distance of action between data inside a cluster with the Q-value representing network output in the altered transmission line tower big data clustering algorithm containing transmission line tower outliers and old Deep Q Network. Specifically, this study performed principal component analysis to categorize transmission line tower data and proposed an automatic initial central point approach through standard normal distribution. It also proposed the A-Deep Q-Learning algorithm altered from the deep Q-Learning algorithm to explore policies based on the experiences of clustered data learning. It can be used to perform transmission line tower outlier data learning based on the distance of data within a cluster. The performance evaluation results show that the proposed model recorded an approximately 2.29%~4.19% higher prediction rate and around 0.8% ~ 4.3% higher accuracy rate compared to the old transmission line tower big data analysis model.

No abstract

The purpose of this study was to determine whether processability theory (PT; Pienemann, 1998, 2005) accounts for the emergence of grammatical forms and structures in comprehension. Sixty-one learners of English participated in oral interviews that elicited a variety of structures relevant to PT. Learners were divided into two groups: those who produced these structures productively in speech (high level) and those who did not (low level). These groups then read grammatical and ungrammatical sentences with PT structures in a self-paced reading task. Based on Pienemann (1998), PT predicts that the high-level group should perform similarly to native speakers. However, only the native speaker control group demonstrated sensitivity to ungrammaticalities. There was evidence that learners might have acquired lower-stage structures in an implicational order in comprehension, but it was quite mixed. These results have implications for PT and for models of the L2 linguistic system that include both production and comprehension.