The validity and reliability of the Parent Fever Management Scale: A study from Palestine

Background Parental fever phobia and overuse of antipyretics to control fever is increasing. Little is known about childhood fever management among Arab parents. No scales to measure parents’ fever management practices in Palestine are available. Aims The aims of this study were to translate and examine the psychometric properties of the Arabic version of the Parent Fever Management Scale (PFMS). Methods A standard “forward–backward” procedure was used to translate PFMS into Arabic language. It was then validated on a convenience sample of 402 parents between July and October 2012. Descriptive statistics were used, and instrument reliability was assessed for internal consistency using Cronbach's alpha coefficient. Validity was confirmed using convergent and known group validation. Results Applying the recommended scoring method, the median (interquartile range) score of the PFMS was 26 (23-30). Acceptable internal consistency was found (Cronbach’s alpha = 0.733) and the test–retest reliability value was 0.92 (P < 0.001). The chi-squared (χ2) test showed a significant relationship between PFMS groups and frequent daily administration of antipyretic groups (χ2 = 52.86; P < 0.001). The PFMS sensitivity and specificity were 77.67% and 57.75%, respectively. The positive and negative predictive values were 67.89% and 32.11%, respectively. Conclusions The findings of this validation study indicate that the Arabic version of the PFMS is a reliable and valid measure which can be used as a useful tool for health professionals to identify parents’ fever management practices and thus provide targeted education to reduce the unnecessary burden of care they place on themselves when concerned for a febrile child.

Dual-functional gum arabic binder for silicon anodes in lithium ion batteries

Si has attracted enormous research and manufacturing attention as an anode material for lithium ion batteries (LIBs) because of its high specific capacity. The lack of a low cost and effective mechanism to prevent the pulverization of Si electrodes during the lithiation/ delithiation process has been a major barrier in the mass production of Si anodes. Naturally abundant gum arabic (GA), composed of polysaccharides and glycoproteins, is applied as a dualfunction binder to address this dilemma. Firstly, the hydroxyl groups of the polysaccharide in GA are crucial in ensuring strong binding to Si. Secondly, similar to the function of fiber in fiberreinforced concrete (FRC), the long chain glycoproteins provide further mechanical tolerance to dramatic volume expansion by Si nanoparticles. The resultant Si anodes present an outstanding capacity of ca. 2000 mAh/g at a 1 C rate and 1000 mAh/g at 2 C rate, respectively, throughout 500 cycles. Excellent long-term stability is demonstrated by the maintenance of 1000 mAh/g specific capacity at 1 C rate for over 1000 cycles. This low cost, naturally abundant and environmentally benign polymer is a promising binder for LIBs in the future.

The Arab Spring on Twitter: Language Communities in #egypt and #libya

Building on hashtag datasets gathered since January 2011, this paper will compare patterns of Twitter usage during the popular revolution in Egypt and the civil war in Libya. Using custom-made tools for processing ‘big data’ (boyd & Crawford, 2011), we will examine the volume of tweets sent by English-, Arabic-, and mixed-language Twitter users over time, and examine the networks of interaction (variously through @replying, retweeting, or both) between these groups as they developed and shifted over the course of these uprisings. Examining @reply and retweet traffic, we will identify general patterns of information flow between the English- and Arabic-speaking sides of the Twittersphere, and highlight the roles played by key boundary riders connecting both language spheres. Further, we will examine the URLs shared in these hashtags by Twitter participants, to identify the most prominent overall information sources, examine differences in the information diet experienced by English- and Arabic-language users, and investigate whether there are any online sources whose URLs are transcending language boundaries more frequently than others.

Electronic medical record systems in Saudi Arabia: Knowledge and preferences of healthcare professionals

Background The use of Electronic Medical Record (EMR) systems is increasing internationally, though developing countries, such as Saudi Arabia, have tended to lag behind in the adoption and implementation of EMR systems due to several barriers. The literature shows that the main barriers to EMR in Saudi Arabia are lack of knowledge or experience using EMR systems and staff resistance to using the implemented EMR system. Methods A quantitative methodology was used to examine health personnel knowledge and acceptance of and preference for EMR systems in seven Saudi public hospitals in Jeddah, Makkah and Taif cities. Results Both English literacy and education levels were significantly correlated with computer literacy and EMR literacy. Participants whose first language was not Arabic were more likely to prefer using an EMR system compared to those whose first language was Arabic. Conclusion This study suggests that as computer literacy levels increase, so too do staff preferences for using EMR systems. Thus, it would be beneficial for hospitals to assess English language proficiency and computer literacy levels of staff prior to implementing an EMR system. It is recommended that hospitals need to offer training and targeted educational programs to the potential users of the EMR system. This would help to increase English language proficiency and computer literacy levels of staff as well as staff acceptance of the system.

Religion and Science in Abraham Ibn Ezra's Sefer ha-Olam

The thesis examines rabbi Abraham Ibn Ezra's (11096-1064) conceptions of the relationship between religion and science with special focus on his seventh astrological treatise Sefer ha-Olam (The Book of the World). The thesis includes an analysis of medieval arabic astrology and the concepts science and religion in the relevant period. The appendix holds a tentative english translation of the hebrew text.

Word Sense Discovery and Disambiguation

The work is based on the assumption that words with similar syntactic usage have similar meaning, which was proposed by Zellig S. Harris (1954,1968). We study his assumption from two aspects: Firstly, different meanings (word senses) of a word should manifest themselves in different usages (contexts), and secondly, similar usages (contexts) should lead to similar meanings (word senses). If we start with the different meanings of a word, we should be able to find distinct contexts for the meanings in text corpora. We separate the meanings by grouping and labeling contexts in an unsupervised or weakly supervised manner (Publication 1, 2 and 3). We are confronted with the question of how best to represent contexts in order to induce effective classifiers of contexts, because differences in context are the only means we have to separate word senses. If we start with words in similar contexts, we should be able to discover similarities in meaning. We can do this monolingually or multilingually. In the monolingual material, we find synonyms and other related words in an unsupervised way (Publication 4). In the multilingual material, we ?nd translations by supervised learning of transliterations (Publication 5). In both the monolingual and multilingual case, we first discover words with similar contexts, i.e., synonym or translation lists. In the monolingual case we also aim at finding structure in the lists by discovering groups of similar words, e.g., synonym sets. In this introduction to the publications of the thesis, we consider the larger background issues of how meaning arises, how it is quantized into word senses, and how it is modeled. We also consider how to define, collect and represent contexts. We discuss how to evaluate the trained context classi?ers and discovered word sense classifications, and ?nally we present the word sense discovery and disambiguation methods of the publications. This work supports Harris' hypothesis by implementing three new methods modeled on his hypothesis. The methods have practical consequences for creating thesauruses and translation dictionaries, e.g., for information retrieval and machine translation purposes. Keywords: Word senses, Context, Evaluation, Word sense disambiguation, Word sense discovery.

Talking About Time in Russian and Finnish

In this study I look at what people want to express when they talk about time in Russian and Finnish, and why they use the means they use. The material consists of expressions of time: 1087 from Russian and 1141 from Finnish. They have been collected from dictionaries, usage guides, corpora, and the Internet. An expression means here an idiomatic set of words in a preset form, a collocation or construction. They are studied as lexical entities, without a context, and analysed and categorized according to various features. The theoretical background for the study includes two completely different approaches. Functional Syntax is used in order to find out what general meanings the speaker wishes to convey when talking about time and how these meanings are expressed in specific languages. Conceptual metaphor theory is used for explaining why the expressions are as they are, i.e. what kind of conceptual metaphors (transfers from one conceptual domain to another) they include. The study has resulted in a grammatically glossed list of time expressions in Russian and Finnish, a list of 56 general meanings involved in these time expressions and an account of the means (constructions) that these languages have for expressing the general meanings defined. It also includes an analysis of conceptual metaphors behind the expressions. The general meanings involved turned out to revolve around expressing duration, point in time, period of time, frequency, sequence, passing of time, suitable time and the right time, life as time, limitedness of time, and some other notions having less obvious semantic relations to the others. Conceptual metaphor analysis of the material has shown that time is conceptualized in Russian and Finnish according to the metaphors Time Is Space (Time Is Container, Time Has Direction, Time Is Cycle, and the Time Line Metaphor), Time Is Resource (and its submapping Time Is Substance), Time Is Actor; and some characteristics are added to these conceptualizations with the help of the secondary metaphors Time Is Nature and Time Is Life. The limits between different conceptual metaphors and the connections these metaphors have with one another are looked at with the help of the theory of conceptual integration (the blending theory) and its schemas. The results of the study show that although Russian and Finnish are typologically different, they are very similar both in the needs of expression their speakers have concerning time, and in the conceptualizations behind expressing time. This study introduces both theoretical and methodological novelties in the nature of material used, in developing empirical methodology for conceptual metaphor studies, in the exactness of defining the limits of different conceptual metaphors, and in seeking unity among the different facets of time. Keywords: time, metaphor, time expression, idiom, conceptual metaphor theory, functional syntax, blending theory

Design issues and caching strategies for CD-ROM based multimedia storage

CD-ROMs have proliferated as a distribution media for desktop machines for a large variety of multimedia applications (targeted for a single-user environment) like encyclopedias, magazines and games. With CD-ROM capacities up to 3 GB being available in the near future, they will form an integral part of Video on Demand (VoD) servers to store full-length movies and multimedia. In the first section of this paper we look at issues related to the single- user desktop environment. Since these multimedia applications are highly interactive in nature, we take a pragmatic approach, and have made a detailed study of the multimedia application behavior in terms of the I/O request patterns generated to the CD-ROM subsystem by tracing these patterns. We discuss prefetch buffer design and seek time characteristics in the context of the analysis of these traces. We also propose an adaptive main-memory hosted cache that receives caching hints from the application to reduce the latency when the user moves from one node of the hyper graph to another. In the second section we look at the use of CD-ROM in a VoD server and discuss the problem of scheduling multiple request streams and buffer management in this scenario. We adapt the C-SCAN (Circular SCAN) algorithm to suit the CD-ROM drive characteristics and prove that it is optimal in terms of buffer size management. We provide computationally inexpensive relations by which this algorithm can be implemented. We then propose an admission control algorithm which admits new request streams without disrupting the continuity of playback of the previous request streams. The algorithm also supports operations such as fast forward and replay. Finally, we discuss the problem of optimal placement of MPEG streams on CD-ROMs in the third section.

Compressive sensing for music signals: comparison of transforms with coherent dictionaries

Compressive Sensing (CS) is a new sensing paradigm which permits sampling of a signal at its intrinsic information rate which could be much lower than Nyquist rate, while guaranteeing good quality reconstruction for signals sparse in a linear transform domain. We explore the application of CS formulation to music signals. Since music signals comprise of both tonal and transient nature, we examine several transforms such as discrete cosine transform (DCT), discrete wavelet transform (DWT), Fourier basis and also non-orthogonal warped transforms to explore the effectiveness of CS theory and the reconstruction algorithms. We show that for a given sparsity level, DCT, overcomplete, and warped Fourier dictionaries result in better reconstruction, and warped Fourier dictionary gives perceptually better reconstruction. “MUSHRA” test results show that a moderate quality reconstruction is possible with about half the Nyquist sampling.

Grassmannian fusion frames and its use in block sparse recovery

Tight fusion frames which form optimal packings in Grassmannian manifolds are of interest in signal processing and communication applications. In this paper, we study optimal packings and fusion frames having a specific structure for use in block sparse recovery problems. The paper starts with a sufficient condition for a set of subspaces to be an optimal packing. Further, a method of using optimal Grassmannian frames to construct tight fusion frames which form optimal packings is given. Then, we derive a lower bound on the block coherence of dictionaries used in block sparse recovery. From this result, we conclude that the Grassmannian fusion frames considered in this paper are optimal from the block coherence point of view. (C) 2013 Elsevier B.V. All rights reserved.

A Split-and-Merge Dictionary Learning Algorithm for Sparse Representation: Application to Image Denoising

In big data image/video analytics, we encounter the problem of learning an over-complete dictionary for sparse representation from a large training dataset, which cannot be processed at once because of storage and computational constraints. To tackle the problem of dictionary learning in such scenarios, we propose an algorithm that exploits the inherent clustered structure of the training data and make use of a divide-and-conquer approach. The fundamental idea behind the algorithm is to partition the training dataset into smaller clusters, and learn local dictionaries for each cluster. Subsequently, the local dictionaries are merged to form a global dictionary. Merging is done by solving another dictionary learning problem on the atoms of the locally trained dictionaries. This algorithm is referred to as the split-and-merge algorithm. We show that the proposed algorithm is efficient in its usage of memory and computational complexity, and performs on par with the standard learning strategy, which operates on the entire data at a time. As an application, we consider the problem of image denoising. We present a comparative analysis of our algorithm with the standard learning techniques that use the entire database at a time, in terms of training and denoising performance. We observe that the split-and-merge algorithm results in a remarkable reduction of training time, without significantly affecting the denoising performance.

A coupled discriminative dictionary and transformation learning approach with applications to cross domain matching

Cross domain and cross-modal matching has many applications in the field of computer vision and pattern recognition. A few examples are heterogeneous face recognition, cross view action recognition, etc. This is a very challenging task since the data in two domains can differ significantly. In this work, we propose a coupled dictionary and transformation learning approach that models the relationship between the data in both domains. The approach learns a pair of transformation matrices that map the data in the two domains in such a manner that they share common sparse representations with respect to their own dictionaries in the transformed space. The dictionaries for the two domains are learnt in a coupled manner with an additional discriminative term to ensure improved recognition performance. The dictionaries and the transformation matrices are jointly updated in an iterative manner. The applicability of the proposed approach is illustrated by evaluating its performance on different challenging tasks: face recognition across pose, illumination and resolution, heterogeneous face recognition and cross view action recognition. Extensive experiments on five datasets namely, CMU-PIE, Multi-PIE, ChokePoint, HFB and IXMAS datasets and comparisons with several state-of-the-art approaches show the effectiveness of the proposed approach. (C) 2015 Elsevier B.V. All rights reserved.

A coupled discriminative dictionary and transformation learning approach with applications to cross domain matching

Cross domain and cross-modal matching has many applications in the field of computer vision and pattern recognition. A few examples are heterogeneous face recognition, cross view action recognition, etc. This is a very challenging task since the data in two domains can differ significantly. In this work, we propose a coupled dictionary and transformation learning approach that models the relationship between the data in both domains. The approach learns a pair of transformation matrices that map the data in the two domains in such a manner that they share common sparse representations with respect to their own dictionaries in the transformed space. The dictionaries for the two domains are learnt in a coupled manner with an additional discriminative term to ensure improved recognition performance. The dictionaries and the transformation matrices are jointly updated in an iterative manner. The applicability of the proposed approach is illustrated by evaluating its performance on different challenging tasks: face recognition across pose, illumination and resolution, heterogeneous face recognition and cross view action recognition. Extensive experiments on five datasets namely, CMU-PIE, Multi-PIE, ChokePoint, HFB and IXMAS datasets and comparisons with several state-of-the-art approaches show the effectiveness of the proposed approach. (C) 2015 Elsevier B.V. All rights reserved.