Factors Associated With Academic Achievement In Urban Primary School Children With Special Reference To Iron Status And Blood Lead Concentrations

The overall aim of the study was to examine the relationship between iron status, blood lead concentration and academic achievement of selected urban primary school children. The specific objectives were to assess the iron status and blood lead concentration of subjects and to determine the relationship between these parameters and their academic achievement. In addition, other factors such as socioeconomic status were examined. Study subjects were 108 (47 male and 61 female) aged 132 ± 9 months with good health and nutritional status. Study subjects were urban Malay primary school children attending two schools (Sekolah Kebangsaan lalan Pasar 1 and Sekolah Kebangsaan lalan Pasar 2) in Kuala Lumpur. Five-ml venous blood sample was collected from each subject. The hematological parameters (hemoglobin and hematocrit) were measured by using the Cyanmethemoglobin and M icrohematocrit methods respectively. The biochemical indices of iron status (serum iron, serum ferritin and total iron-binding capacity) were measured by using the methods of colorimetric test with Ferrozin®/ascorbic acid, IMX® Ferritin assay-Micropartical Enzyme Immunoassay (MEIA), an

A Personalized e-Learning Framework

With the advent of web based learning and content management tools, e-learning has become a matured learning paradigm, and changed the trend of instructional design from instructor centric learning paradigm to learner centric approach, and evolved from “one instructional design for many learners” to “one design for one learner” or “many designs for one learner”. Currently, there are mature technologies that can lead to the construction of a personalized e-learning environment, namely: Ontology, Semantic web, learning objects, and content management systems. In this paper, a personalized e-learning framework is proposed, where learning objects are classified according to their suitability for the different types and styles of learning, and where these learning objects are offered to individual learners according to their personal preferences, skills and needs

A review of smart homes in healthcare

The technology of Smart Homes (SH), as an instance of ambient assisted living technologies, is designed to assist the homes’ residents accomplishing their daily-living activities and thus having a better quality of life while preserving their privacy. A SH system is usually equipped with a collection of inter-related software and hardware components to monitor the living space by capturing the behaviour of the resident and understanding his activities. By doing so the system can inform about risky situations and take actions on behalf of the resident to his satisfaction. The present survey will address technologies and analysis methods and bring examples of the state of the art research studies in order to provide background for the research community. In particular, the survey will expose infrastructure technologies such as sensors and communication platforms along with artificial intelligence techniques used for modeling and recognizing activities. A brief overview of approaches used to develop Human–Computer interfaces for SH systems is given. The survey also highlights the challenges and research trends in this area

Modeling Interaction in Multi-Resident Activities

In this paper we investigate the problem of modeling multi-resident activities. Specifically, we explore different approaches based on Hidden Markov Models (HMMs) to deal with parallel activities and cooperative activities. We propose an HMM-based method, called CL-HMM, where activity labels as well as observation labels of different residents are combined to generate the corresponding sequence of activities as well as the corresponding sequence of observations on which a conventional HMM is applied. We also propose a Linked HMM (LHMM) in which activities of all residents are linked at each time step. We compare these two models to baseline models which are Coupled HMM (CHMM) and Parallel HMM (PHMM). The experimental results show that the proposed models outperform CHMM and PHMM when tested on parallel and cooperative activities

Active Learning for Data Streams under Concept Drift and concept evolution.

Data streams classification is an important problem however, poses many challenges. Since the length of the data is theoretically infinite, it is impractical to store and process all the historical data. Data streams also experience change of its underlying dis-tribution (concept drift), thus the classifier must adapt. Another challenge of data stream classification is the possible emergence and disappearance of classes which is known as (concept evolution) problem. On the top of these challenges, acquiring labels with such large data is expensive. In this paper, we propose a stream-based active learning (AL) strategy (SAL) that handles the aforementioned challenges. SAL aims at querying the labels of samples which results in optimizing the expected future error. It handles concept drift and concept evolution by adapting to the change in the stream. Furthermore, as a part of the error reduction process, SAL handles the sampling bias problem and queries the samples that caused the change i.e., drifted samples or samples coming from new classes. To tackle the lack of prior knowledge about the streaming data, non-parametric Bayesian modelling is adopted namely the two representations of Dirichlet process; Dirichlet mixture models and stick breaking process. Empirical results obtained on real-world benchmarks show the high performance of the proposed SAL method compared to the state-of-the-art methods

A non-parametric hierarchical clustering model

© 2015 IEEE. We present a novel non-parametric clustering model using Gaussian mixture model (NHCM). NHCM uses a novel Dirichlet process (DP) prior allowing for more flexible modeling of the data, where the base distribution of DP is itself an infinite mixture of Gaussian conjugate prior. NHCM can be thought of as hierarchical clustering model, in which the low level base prior governs the distribution of the data points forming sub-clusters, and the higher level prior governs the distribution of the sub-clusters forming clusters. Using this hierarchical configuration, we can maintain low complexity of the model and allow for clustering skewed complex data. To perform inference, we propose a Gibbs sampling algorithm. Empirical investigations have been carried out to analyse the efficiency of the proposed clustering model

A Bi-Criteria Active Learning Algorithm for Dynamic Data Streams

Active learning (AL) is a promising way to efficiently building up training sets with minimal supervision. A learner deliberately queries specific instances to tune the classifier’s model using as few labels as possible. The challenge for streaming is that the data distribution may evolve over time and therefore the model must adapt. Another challenge is the sampling bias where the sampled training set does not reflect the underlying data distribution. In presence of concept drift, sampling bias is more likely to occur as the training set needs to represent the whole evolving data. To tackle these challenges, we propose a novel bi-criteria AL approach (BAL) that relies on two selection criteria, namely label uncertainty criterion and density-based cri- terion . While the first criterion selects instances that are the most uncertain in terms of class membership, the latter dynamically curbs the sampling bias by weighting the samples to reflect on the true underlying distribution. To design and implement these two criteria for learning from streams, BAL adopts a Bayesian online learning approach and combines online classification and online clustering through the use of online logistic regression and online growing Gaussian mixture models respectively. Empirical results obtained on standard synthetic and real-world benchmarks show the high performance of the proposed BAL method compared to the state-of-the-art AL method

Performance of Spatial Modulation using Measured Real-World Channels

In this paper, for the first time real-world channel measurements are used to analyse the performance of spatial modulation (SM), where a full analysis of the average bit error rate performance (ABER) of SM using measured urban correlated and uncorrelated Rayleigh fading channels is provided. The channel measurements are taken from an outdoor urban multiple input multiple output (MIMO) measurement campaign. Moreover, ABER performance results using simulated Rayleigh fading channels are provided and compared with a derived analytical bound for the ABER of SM, and the ABER results for SM using the measured urban channels. The ABER results using the measured urban channels validate the derived analytical bound and the ABER results using the simulated channels. Finally, the ABER of SM is compared with the performance of spatial multiplexing (SMX) using the measured urban channels for small and large scale MIMO. It is shown that SM offers nearly the same or a slightly better performance than SMX for small scale MIMO. However, SM offers large reduction in ABER for large scale MIMO.Comment: IEEE Vehicular Technology Conference Fall 2013 (VTC-Fall 2013), Accepte

Clonal Composition of Human Adrenocortical Neoplasms

The mechanisms of tumorigenesis of adrenocortical neoplasms are still not understood. Tumor formation may be the result of spontaneous transformation of adrenocortical cells by somatic mutations. Another factor stimulating adrenocortical cell growth and potentially associated with formation of adrenal adenomas and, less frequently, carcinomas is the chronic elevation of proopiomelanocortin-derived peptides in diseases like ACTH-dependent Cushing's syndrome and congenital adrenal hyperplasia. To further investigate the pathogenesis of adrenocortical neoplasms, we studied the clonal composition of such tumors using X-chromosome inactivation analysis of the highly polymorphic region Xcen-Xp11.4 with the hybridization probe M27ß, which maps to a variable number of tandem repeats on the X-chromsome. In addition, polymerase chain reaction amplification of a phosphoglycerokinase gene polymorphism was performed. After DNA extraction from tumorous adrenal tissue and normal leukocytes in parallel, the active X-chromosome of each sample was digested with the methylation-sensitive restriction enzyme HpaII. A second digestion with an appropriate restriction enzyme revealed the polymorphism of the region Xcen-Xp11.4 and the phosphoglycerokinase locus. Whereas in normal polyclonal tissue both the paternal and maternal alleles are detected, a monoclonal tumor shows only one of the parental alleles. A total of 21 female patients with adrenal lesions were analyzed; 17 turned out to be heterozygous for at least one of the loci. Our results were as follows: diffuse (n = 4) and nodular (n = 1) adrenal hyperplasia in patients with ACTH-dependent Cushing's syndrome, polyclonal pattern; adrenocortical adenomas (n = 8), monoclonal (n = 7), as well as polyclonal (n = 1); adrenal carcinomas (n = 3), monoclonal pattern. One metastasis of an adrenocortical carcinoma showed a pattern most likely due to tumor-associated loss of methylation. In the special case of a patient with bilateral ACTH-independent macronodular hyperplasia, diffuse hyperplastic areas and a small nodule showed a polyclonal pattern, whereas a large nodule was monoclonal. We conclude that most adrenal adenomas and carcinomas are monoclonal, whereas diffuse and nodular adrenal hyperplasias are polyclonal. The clonal composition of ACTH-independent massive macronodular hyperplasia seems to be heterogeneous, consisting of polyclonal and monoclonal areas