Miracle-dz/UniFAR-FTUs huggingface dataset:
Mothers’ experiences of neonatal jaundice: A qualitative study

Background & Aim : The importance of neonatal jaundice is not only for its social, economic, and psychological complications resulted from hospitalization of neonates in the hospital, but also for the permanent neurological handicaps and its consequent high morbidity. Since Iranian mothers have no, in spite of the importance of matter, appropriate approach in coping with their newborn jaundice, this study aimed to explore mother’s experiences of neonatal jaundice. Methods & Materials : This qualitative study was carried out through in-depth semi-structured interviews with 14 mothers whose infants were hospitalized in one of the hospitals affiliated to Shahrood University of Medical Sciences with a diagnosis of neonatal jaundice. The participants were selected purposefully and the data were analyzed using qualitative content analysis. Results : Two main themes and several subthemes emerged from the data. The main themes were as follows: mothers’ experiences of jaundice crises and its management, and mothers’ present experiences after crisis of jaundice. First theme arranged into four subthemes and second theme was grouped into three subthemes. Conclusion : Mothers’ and grandmothers’ cultural and dietary beliefs have an important impact on mothers’ health-seeking behavior. Educational programs about neonatal jaundice and its management through prenatal care consulting and mass media is recommended as an important priority for health system of country.
PixelSNE: Pixel‐Aligned Stochastic Neighbor Embedding for Efficient 2D Visualization with Screen‐Resolution Precision

Embedding and visualizing large‐scale high‐dimensional data in a two‐dimensional space is an important problem, because such visualization can reveal deep insights of complex data. However, most of the existing embedding approaches run on an excessively high precision, even when users want to obtain a brief insight from a visualization of large‐scale datasets, ignoring the fact that in the end, the outputs are embedded onto a fixed‐range pixel‐based screen space. Motivated by this observation and directly considering the properties of screen space in an embedding algorithm, we propose Pixel‐Aligned Stochastic Neighbor Embedding (PixelSNE), a highly efficient screen resolution‐driven 2D embedding method which accelerates Barnes‐Hut tree‐based t‐distributed stochastic neighbor embedding (BH‐SNE), which is known to be a state‐of‐the‐art 2D embedding method. Our experimental results show a significantly faster running time for PixelSNE compared to BH‐SNE for various datasets while maintaining comparable embedding quality.
Chapter 1 A First Encounter

Abstract The first chapter of the book gives a short overview of what network analysis does and why it is considered to be a vital part of complex system science: the network analytic framework allows to represent the interaction structure of a complex system as a complex network. The network’s structure can then be analyzed by the application of several structural measures. However, there are two different branches in network analysis that either use the resulting values to find so-called universal features of complex systems or to allow a contextual, semantic analysis. The latter focuses on the connection between structure and function of a network with respect to the complex system of interest and some specific research question. There is a caveat, though: while, in principle, structural measures can be applied to all kinds of networks, if one is only searching for universal features, their results are not always interpretable with respect to a predefined research question. The term “network analysis literacy” is introduced to describe the knowledge of when to apply which measure to yield an interpretable result with respect to the complex system of interest.
Coverage Analysis for Millimeter Wave Cellular Networks With Imperfect Beam Alignment

Millimeter wave (mmWave) communications is a promising approach to satisfy the increasing high data rate requirement of next generation mobile communications. This paper studies the downlink coverage performance of mmWave cellular networks with imperfect beam alignment. An enhanced antenna model is adopted to model the directional antenna beamforming pattern, in which the mainlobe beamwidth and directivity gain can be expressed as functions of the number of elements in the antenna array. After deriving the probability density function of the distance between mobile station and its serving base station (BS), the directivity gain with imperfect beam alignment is obtained as a discrete random variable. Then, a computationally tractable expression is obtained for the coverage probability of mmWave cellular networks. This generalized expression can be applied in different blockage regimes, e.g., general blockage regime, full-blockage regime, and nonblockage regime with or without beam alignment errors. Numerical results show that small beam alignment errors will not deteriorate the coverage performance significantly, and the antenna array with the less number of elements provides higher robustness against the beam alignment errors. Moreover, when the beam alignment error is small enough, the coverage performance can be improved by increasing the BS intensity and the number of elements in the antenna array.
Delocalized excitons in natural light-harvesting complexes

Natural organisms such as photosynthetic bacteria, algae, and plants employ complex molecular machinery to convert solar energy into biochemical fuel. An important common feature shared by most of these photosynthetic organisms is that they capture photons in the form of excitons typically delocalized over a few to tens of pigment molecules embedded in protein environments of light harvesting complexes (LHCs). Delocalized excitons created in such LHCs remain well protected despite being swayed by environmental fluctuations, and are delivered successfully to their destinations over hundred nanometer length scale distances in about hundred picosecond time scales. Decades of experimental and theoretical investigation have produced a large body of information offering insights into major structural, energetic, and dynamical features contributing to LHCs' extraordinary capability to harness photons using delocalized excitons. The objective of this review is (i) to provide a comprehensive account of major theoretical, computational, and spectroscopic advances that have contributed to this body of knowledge, and (ii) to clarify the issues concerning the role of delocalized excitons in achieving efficient energy transport mechanisms. The focus of this review is on three representative systems, Fenna-Matthews-Olson complex of green sulfur bacteria, light harvesting 2 complex of purple bacteria, and phycobiliproteins of cryptophyte algae. Although we offer more in-depth and detailed description of theoretical and computational aspects, major experimental results and their implications are also assessed in the context of achieving excellent light harvesting functionality. Future theoretical and experimental challenges to be addressed in gaining better understanding and utilization of delocalized excitons are also discussed.
Implementation of Two Light Weight Cryptographic Algorithms

In recent years, one of the primary reasons that intruders can be successful is that most of the information they acquire from a system is in the form they can read and understand. Hence, Cryptography is one of the important features in secure communication . It makes use of key to encrypt the data so that the data sent cannot be accessed by unauthorized users. This paper focuses on Lightweight symmetric cryptography. Lightweight cryptography is used for resource-limited devices such as radio frequency identification tags,smart card etc. American National Security Agency (NSA) proposed a new block cipher family named SIMON and the aim of SIMON design is to fill the gap for secure, flexible, and analyzable and to perform exceptionally well across the full spectrum of lightweight applications. International Data Encryption Algorithm (IDEA) is a block cipher aims at providing high level security not based on keeping the algorithm a secret, but rather upon ignorance of the secret key. In this project comparative study of selected lightweight symmetric block ciphers such as IDEA and SIMON are implemented using Xilinx ISE 14.2 simulator.
Effects of Psychological Strains on Chinese College Students Depression and Suicidal Ideation

Psychological strains have been found more prevalent than mental disorders among Chinese rural young suicides, but the association between psychological strains and mental disorders, especially depression and suicidal ideation, is still unknown. For the present study, we used the data from 1,298 college students for a survey research conducted on a university campus in Beijing China, in 2012. Psychological strains were measured by the 60-item Psychological Strain Scales (PSS). We hypothesized that college students with higher scores of strains would report greater levels of depression and also demonstrate greater suicidal ideation. Findings suggested that aspiration strain and coping strain were positively associated with depression while value strain and coping strain were significantly correlated with suicidal ideation. Reduction of college students’ psychological strains might be an effective way to decrease their depression and suicidal ideation, so as to lower the suicide risks and improve the quality life among college students.
Tree Breadth of the Continued Fractions Root Finding Method

The continued fractions algorithm for isolating the real roots of polynomials with certainty is one of the most efficient known and widely used. It can be viewed as exploring a tree created by a sequence of simple transformations. In this paper we produce new upper bounds for the breadth of the tree that are significantly smaller than the degree of the input polynomial. We also consider the expected breadth under a reasonable distribution and derive a bound, subject to a plausible assumption, that grows logarithmically with the degree and coefficient size.
ICT competency framework for library and information science schools in Nigeria: The need for model curriculum

All professions and disciplines stumble upon change internally and externally, and to move forward successfully in this information age, change and evolution are necessary and inevitable especially in the area of information and communication technology, as well as the digitization of information of all kinds. These Digital age scenarios have set the scene for changes in many spheres and in particular for Nigerian Library and Information Science schools. The influence of Information and Communication Technology (ICT) on every discourse of human knowledge is undisputed and is also considered all pervasive, this indicate the pressing need to educate and train the library and information manpower towards a sustainable ICT competence. The LIS curriculum development in Nigeria has shown considerable strides in infusing ICT competencies as most LIS schools have developed relevant ICT courses and also merged relevant ICT knowledge in traditional curriculum. However, most LIS schools teach these ICT courses theoretically because they have inadequate laboratories of computers and poor Internet access. The problems are to be found in the overall ICT policy and infrastructures both at national and institutional levels, as well as the lack of sustained funding, appropriate equipment, expertise and management in Nigeria and LIS schools in particular. This paper discusses the pros and cons of ICT competency and LIS Education Scenario in the Nigeria and stresses the need for ICT competency framework and model curriculum.
Classification of Latent Fingerprints Using Neural Networks

To classify latent fingerprint images by using papillary patterns, the proposed method applies the Gabor filter, Haar and Daubechies wavelet transformations, and a multilevel neural network. Numerical experiments were performed, and the results of the proposed solution were compared. The results have shown that we can effectively classify latent fingerprints by applying in combination the Gabor filter, Daubechies wavelet transform of the fifth level, and a neural network.
2 2 M ay 2 01 8 The Meniscus on the Outside of a Circular Cylinder from Nanometer to Macroscopic Scales

We systematically study the meniscus on the outside of a small circular cylinder vertically immersed in a liquid bath in a cylindrical container. The cylinder has a radius R much smaller than the capillary length κ and the container with radius L is coaxial with the cylinder. In the limit of L ≪ κ we analytically solve the general Young-Laplace equation governing the meniscus profile and show that the meniscus height, ∆h, scales approximately with R ln(L/R). In the opposite limit L ≫ κ, ∆h becomes independent of L and scales with R ln(κ/R). We implement a numerical scheme to solve the general Young-Laplace equation for an arbitrary L and demonstrate the crossover of the meniscus profile between these two limits. The crossover region has been determined to be roughly 0. 4κ . L . 4κ. An approximate analytical expression has been found, enabling accurate prediction of ∆h at any values of L that ranges from nanometer to macroscopic scales.
Particle-based fluid dynamics: comparison of different BGK models and DSMC for hypersonic flows

The Bhatnagar-Gross-Krook (BGK) model as well as its extensions (ellipsoidal statistical BGK, Shakhov BGK, unified BGK) are used in particle-based fluid dynamics and compared with the Direct Simulation Monte Carlo Method (DSMC). To this end, a method based on the Metropolis-Hastings algorithm is presented that allows efficient sampling of the Shakhov and the unified target distribution functions. As a consequence, particle simulations based on the Shakhov BGK and the unified BGK model are possible in an efficient way. The models are validated with a Couette flow problem at different Knudsen numbers and wall velocities. Furthermore, the models are compared with DSMC results of a hypersonic flow around a 70 blunted cone. It is shown that the unified BGK model is able to reproduce rarefied gas phenomena and the Shakhov model performs better in the reproduction of shock structures compared with the ellipsoidal statistical BGK model. Additionally, a computational time study is done to show the efficiency of BGK-based simulations for low Knudsen number flows compared with DSMC. This is especially interesting for gas flows which cover a wide range of Knudsen numbers including continuum and rarefied gas regions as in nozzle expansion flows. The fact that DSMC and the utilized BGK methods are both cell local Monte-Carlo based particle methods, makes a coupling very attractive.
Fast Iterative algorithms for Airborne Radar Processing

The conventional space-time adaptive processing (STAP) such as the sample matrix inversion (SMI) or the principal components (PC) methods are computationally costly and require the estimation of the clutter covariance matrix from secondary data, which are assumed to be independent and identically distributed. However, in monostatic airborne radar, the data are not stationary. Consequently, to circumvent such a problem, we propose to investigate the performances of adaptive recursive subspacebased algorithms of linear complexity using projection approximation subspace tracking (PAST) and orthonormal PAST(OPAST). In addition, we apply the fast implementation of the power iterations method for subspace tracking (FAPI), based on a less restrictive approximation than the well known projection approximation (API). Performance curves show that PAST, OPAST, API and FAPI algorithms do indeed allow a good detection of slow moving targets even with a low rank covariance matrix. We also show that in the case of Doppler ambiguous environment when combined a pseudo random staggered PRF, these algorithms give better results than the methods based on eigenvalues decomposition. Keywords-Airborne Radar Processing; STAP.
Audiovisual Synchrony Detection with Optimized Audio Features

Audiovisual speech synchrony detection is an important part of talking-face verification systems. Prior work has primarily focused on visual features and joint-space models, while standard mel-frequency cepstral coefficients (MFCCs) have been commonly used to present speech. We focus more closely on audio by studying the impact of context window length for delta feature computation and comparing MFCCs with simpler energy-based features in lip-sync detection. We select state-of-the-art hand-crafted lip-sync visual features, space-time auto-correlation of gradients (STACOG), and canonical correlation analysis (CCA), for joint-space modeling. To enhance joint space modeling, we adopt deep CCA (DCCA), a nonlinear extension of CCA. Our results on the XM2VTS data indicate substantially enhanced audiovisual speech synchrony detection, with an equal error rate (EER) of 3.68%. Further analysis reveals that failed lip region localization and beardedness of the subjects constitutes most of the errors. Thus, the lip motion description is the bottleneck, while the use of novel audio features or joint-modeling techniques is unlikely to boost lip-sync detection accuracy further.
Developing a MOOC on Introductory Programming as Additional Preparation Course for CS Freshmen

At our university, the undergraduate program in Computer Science and Business Informatics (CSBI) faces a high student dropout rate especially during the first year of studies. One reason is the students' heterogeneous pre-university CS education, in particular concerning programming, due to inhomogeneous biographical backgrounds and different types of secondary schools. To tackle this knowledge-gap by levelling up those without or with little programming experience we have developed the massive open online course (MOOC) Programming with Processing. The MOOC comprises various learning activities and materials to study, exercise and self-assess basic programming concepts at individual learning speed using the programming environment Processing. The first course run comprised 6 chapters and has been implemented and evaluated with 577 prospective CSBI students. In this practice-based paper, we present the MOOC course design and the development, testing and implementation processes, the pedagogical background and we discuss our lessons learned so far.
Bioinformatics analysis and characteristics of envelop glycoprotein E epitopes of dengue virus

The major envelope glycoprotein E of dengue (DEN) virus plays a central role in the biology of flaviviruses. It is capable of inducing a protective immune response in vivo and responsible for the viral binding to the cellular receptor. The crystal structures of glycoprotein E ectodomains have already been determined. However, it is still un-clear where the well-defined B-cell epitopes for glycoprotein E which induce the neutralizing an-tibodies locates. Thus, in order to characterize the role of glycoprotein E in the pathogenesis of dengue virus infection, we first used network servers (http://bio.dfci. harvard.edu/Tools/ & http://www. imtech. res. in) to predict and analyze the well defined B-cell and T-cell epitopes of the glycoprotein of the DEN-1 HAWAII strain. Then based on the highly conserved envelop glyco-protein amino acids, the hydrophilicity, antigenic-ity, accessibility and flexibility of envelop glyco-protein E were further predicted by using Biotic softwares (DNASTAR) and network servers (http://bio. dfci.harvard.edu/Tools/), the secondary structure was putatively obtained. In our study, the sequence at 281-295 amino acid (aa) for den-gue virus type 1 HAWAII strain and the sequence at 345-359, 383-397 for dengue virus type 2 NGC strain were predicted as the more prevalent epi-topes by using multiple parameters and different analysis softwares, respectively. Two epitopes of DEN-2 and one of DEN-1 locate on the domain Ш and domainⅡ of the protein E, respectively. Sub-sequently, further studies will be carried out to examine the antigenicity and protection of the synthetic peptides with higher scores in the av-erage antigen index (AI) and better hydrophilic properties determined by our data.
Device-independent randomness generation with sublinear shared quantum resources

In quantum cryptography, device-independent (DI) protocols can be certified secure without requiring assumptions about the inner workings of the devices used to perform the protocol. In order to display nonlocality, which is an essential feature in DI protocols, the device must consist of at least two separate components sharing entanglement. This raises a fundamental question: how much entanglement is needed to run such DI protocols? We present a two-device protocol for DI random number generation (DIRNG) which produces approximatelynbits of randomness starting fromnpairs of arbitrarily weakly entangled qubits. We also consider a variant of the protocol wheremsinglet states are diluted intonpartially entangled states before performing the first protocol, and show that the numbermof singlet states need only scale sublinearly with the numbernof random bits produced. Operationally, this leads to a DIRNG protocol between distant laboratories that requires only a sublinear amount of quantum communication to prepare the devices.
Towards Efficient and Effective Query Variant Generation

Relevance modeling and data fusion are powerful yet simple approaches to improving the effectiveness of Information Retrieval Systems. For many of the classic TREC test collections, these approaches were used in many of the top performing retrieval systems. However, these approaches are often inefficient and are therefore rarely applied in production systems which must adhere to strict performance guarantees. Inspired by our recent work with human derived query variations, we propose a new sampling-based system which provides significantly better efficiency-effectiveness tradeoffs while leveraging both relevance modeling and data fusion. We show that our new end-to-end search system approaches the state-of-the-art in effectiveness while still being efficient in practice. Orthogonally, we also show how to leverage query expansion and data fusion to achieve significantly better risk-reward trade-offs than plain relevance modeling approaches.
The Role of Supplier, Top Management and Government in Halal Practices Integrity of Malaysian Food Business

In the last few years, halal food industry has grown substantively and globally. It become crucial to the Muslim consumers due to its safety, hygiene and quality assurance of what is consumed and used. Muslim consumers follow the Islamic dietary law as explained clearly in Quran and Sunnah, which is called as halalan-thoyyiban, i.e. lawful or permissible, wholesome, authentic and safety. In Malaysia, the organization has to comply with MS1500:2009 in the production of halal food and to obtain halal certification. Suppliers, top management of the companies and government should play their role in ensuring the integrity of halal food. Any improper description and inappropriate preparation of halal food provide a huge impact to the demand for halal food as well as to retain the buyers? trust in consuming the halal food. Given the significant role of supplier, top management and government in the halal production over the Muslim consumers? life, this study aims to examine the role of supplier credibility, top management capability and government support in halal practices integrity of food business in Malaysia. The questionnaires distributed to the halal food manufacturers during the Malaysian halal food events and festivals. Structural Equation Modeling with Partial Least Square was used to analyze the collected data. The findings show the important role those parties in the halal food production. The results also provide a better understanding of their role in sustaining the integrity of halal food.