Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk of Suboptimal Iodine Intake in Pregnant Norwegian Women

Pregnant women and infants are exceptionally vulnerable to iodine deficiency. The aims of the present study were to estimate iodine intake, to investigate sources of iodine, to identify predictors of low or suboptimal iodine intake (defined as intakes below 100 μg/day and 150 μg/day) in a large population of pregnant Norwegian women and to evaluate iodine status in a sub-population. Iodine intake was calculated based on a validated Food Frequency Questionnaire in the Norwegian Mother and Child Cohort. The median iodine intake was 141 μg/day from food and 166 μg/day from food and supplements. Use of iodine-containing supplements was reported by 31.6%. The main source of iodine from food was dairy products, contributing 67% and 43% in non-supplement and iodine-supplement users, respectively. Of 61,904 women, 16.1% had iodine intake below 100 μg/day, 42.0% had iodine intake below 150 μg/day and only 21.7% reached the WHO/UNICEF/ICCIDD recommendation of 250 μg/day. Dietary behaviors associated with increased risk of low and suboptimal iodine intake were: no use of iodine-containing supplements and low intake of milk/yogurt, seafood and eggs. The median urinary iodine concentration measured in 119 participants (69 μg/L) confirmed insufficient iodine intake. Public health strategies are needed to improve and secure the iodine status of pregnant women in Norway.
A 2.4GHz 2Mb/s digital PLL-based transmitter for 802.15.4 in 130nm CMOS

A fully integrated 2.4GHz transmitter for 802.15. 4 based on a digital ΣΔ fractional-N PLL is presented. A self-calibrated two-point modulation scheme enables modulation rates much larger than the loop bandwidth. An oversampled 1-bit quantizer is used as a phase detector, reducing spurs and nonlinearity associated with some TDC-based digital PLLs. The prototype achieves an MSK modulation rate of 2Mb/s, delivers −2dBm of output power, and is free of in-band fractional spurs. The transmitter, implemented in 130nm CMOS, consumes 17mW from a 1.2V supply and occupies an active area of 0.6mm2.
Scaling Neural Network Performance through Customized Hardware Architectures on Reconfigurable Logic

Convolutional Neural Networks have dramatically improved in recent years, surpassing human accuracy on certain problems and performance exceeding that of traditional computer vision algorithms. While the compute pattern in itself is relatively simple, significant compute and memory challenges remain as CNNs may contain millions of floating-point parameters and require billions of floating-point operations to process a single image. These computational requirements, combined with storage footprints that exceed typical cache sizes, pose a significant performance and power challenge for modern compute architectures. One of the promising opportunities to scale performance and power efficiency is leveraging reduced precision representations for all activations and weights as this allows to scale compute capabilities, reduce weight and feature map buffering requirements as well as energy consumption. While a small reduction in accuracy is encountered, these Quantized Neural Networks have been shown to achieve state-of-the-art accuracy on standard benchmark datasets, such as MNIST, CIFAR-10, SVHN and even ImageNet, and thus provide highly attractive design trade-offs. Current research has focused mainly on the implementation of extreme variants with full binarization of weights and or activations, as well typically smaller input images. Within this paper, we investigate the scalability of dataflow architectures with respect to supporting various precisions for both weights and activations, larger image dimensions, and increasing numbers of feature map channels. Key contributions are a formalized approach to understanding the scalability of the existing hardware architecture with cost models and a performance prediction as a function of the target device size. We provide validating experimental results for an ImageNet classification on a server class platform, namely the AWS F1 node.
Dengue NS1 Detection using Chemically Modified Silicon Micropillars

Dengue fever is a mosquito born viral disease that is reaching epidemic proportions. Despite of various control means, it is still flourishing and is becoming a major health concern around the world. In this thesis, a novel diagnosis method for the detection of Dengue virus has been developed. Micro-spotintegrated pillars chip (MSIP) has been used to increase the surface area for the immunoreaction and hence the probability of the Dengue virus detection. The MSIP chip used for the diagnosis of Dengue NS1 was chemically modified to immobilize the capture antibodies. Each step of the surface modification and biofunctionalization process was characterized by using different characterization tools. An automated and unsupervised algorithm was developed to quantify the fluorescent signal. The result showed that the method developed was five fold more sensitive than the immunoassay performed on chemically modified at silicon substrate and hence, meet the objective of the thesis.
Simplicial Isosurface Compression

In this work, we introduce a new algorithm for direct and progressive encoding of isosurfaces extracted from volumetric data. A binary multi-triangulation is used to represent and adapt the 3D scalar grid. This simplicial scheme provides geometrical and topological control on the decoded isosurface. The resulting algorithm is an efficient and flexible isosurface compression scheme.
Mesenchymal Stem Cells for Cardiac Regenerative Therapy: Optimization of Cell Differentiation Strategy

With the high mortality rate, coronary heart disease (CHD) has currently become a major life-threatening disease. The main pathological change of myocardial infarction (MI) is the induction of myocardial necrosis in infarction area which finally causes heart failure. Conventional treatments cannot regenerate the functional cell efficiently. Recent researches suggest that mesenchymal stem cells (MSCs) are able to differentiate into multiple lineages, including cardiomyocyte-like cells in vitro and in vivo, and they have been used for the treatment of MI to repair the injured myocardium and improve cardiac function. In this review, we will focus on the recent progress on MSCs derived cardiomyocytes for cardiac regeneration after MI.
A class of well-posed approximations for constrained second order hyperbolic equations

The purpose of this paper is to present a new family of numerical methods for the approximation of second order hyperbolic partial diﬀerential equations submitted to a convex constraint on the solution. The principle is a singular modiﬁcation of the mass matrix obtained by the mean of diﬀerent discretizations of the solution and of its time derivative. The major interest of these methods is that the semi-discretized problem is well-posed and energy conserving. Numerical experiments show that this is a crucial property to build stable numerical schemes.
Behavioral Performance of Rats Following Transient Forebrain Ischemia

Rats subjected to transient forebrain ischemic injury by the method of four vessel occlusion (4-VO) develop irreversible injury to select populations of vulnerable neurons which include pyramidal cells in the CA-1 region of the hippocampus. This brain area is thought to be crucial for learning and memory. Rats subjected to 30 minutes of 4-VO, and then cerebral reperfusion were tested on a radial 8-arm maze task after they had recovered. The data shows that both 4-VO and control animals improve their performance over trials, but that 4-VO rats are impaired on "working" and "reference" tasks. The data suggest that 4- VO rats' impaired "working" performance is permanent, compared to their transient "reference" impair- ment. Alterations in sensorimotor activity could not account for these performance deficits since control and 4-VO rats demonstrated equivalent choice time per maze arm. Performance deficits in rats following forebrain ischemic injury may be similar to some of the cognitive deficits found In humans survivors of cerebral hypoxia-ischemia. Stroke Vol 15, No 3, 1984
Small-Scale Energy Harvesting from Environment by Triboelectric Nanogenerators

The increasing needs to power trillions of sensors and devices for the Internet of Things require effective technology to harvest small-scale energy from renewable natural resources. As a new energy technology, triboelectric nanogenerators (TENGs) can harvest ambient mechanical energy and convert it into electricity for powering small electronic devices continuously. In this chapter, the fundamental working mechanism and fundamental modes of a TENG will be presented. It can harvest all kinds of mechanical energy, especially at low frequencies, such as human motion, walking, vibration, mechanical triggering, rotating tire, wind, moving automobile, flowing water, rain drops, ocean waves, and so on. Such variety of energy harvesting methods promises TENG as a new approach for small-scale energy harvesting.
Diffraction-Compensating Coded Aperture for Inspection in Manufacturing

In this paper, we adopt the coded aperture technique to the alignment process for industrial machinery. As a special setting for assembly and inspection, such machinery uses an illumination of narrow wavelength range for imaging with less aberration. This leads to significant influence of light diffraction on the image restoration. Although the diffraction was treated as negligible in most previous studies of coded aperture since they were carried out for natural images, the aperture patterns of them do not achieve enough accuracy for the alignment. We optimize the aperture pattern by performing a simulation of light diffraction, and we experimentally show that it achieves better performance in the alignment.
Right-weight kernels: an off-the-shelf alternative to custom light-weight kernels

DOE has put forth a considerable effort into light-weight kernels for high performance computing, yet there has been a lack of acceptance and use, perhaps due to the limited support for hardware and software. The arguments for light-weight kernels have been based on the problem of interference, i.e. changes in application performance that occur when the operating system pre-empts the application. Nevertheless, using existing, well supported operating systems for HPC systems has been quite successful. The problems with the standard operating systems remain, however, although their impact on applications is still not quantified. At LANL, we have undertaken a research program to determine whether Linux and/or Plan 9 can be used to realize the benefits of light-weight kernels while maintaining the benefits of a full-featured operating system. Specifically, we are evaluating measures that quantify what is "good" in a Light-Weight Kernel. We are using this knowledge to modify Linux and Plan 9 to make them competitive with custom light weight operating systems, in essence, a Right-Weight Kernel. This paper represents both a summary of early results as well as a description of work in progress.
Tuberculosis transmission in the 1990s.

Two studies reported in this issue of the Journal1,2 use new techniques of molecular epidemiology to extend our understanding of the transmission and control of an ancient disease -- tuberculosis. The conventional wisdom has been that 90 percent of active cases arose from foci of infection first acquired years or decades ago. In the United States alone, the pool of persons with latent tuberculous infection is estimated at 10 to 15 million3. But where do today's active cases come from? The reports in this issue suggest that at least in San Francisco and New York City, a third .
Wide-spectrum-range power-efficient compact thermo-optic switch based on coupled photonic crystal microcavities

We demonstrate a thermo-optic switch comprising a 3. 78μm-long coupled photonic crystal resonators coupled to a photonic crystal waveguide. The device has 6nm optical bandwidth, 20dB optical extinction ratio, 18.2mW switching power, and 14. 8μsec rise time.
A Fusion Approach of RSSI and LQI for Indoor Localization System Using Adaptive Smoothers

Due to the ease of development and inexpensiveness, indoor localization systems are getting a significant attention but, with recent advancement in context and location aware technologies, the solutions for indoor tracking and localization had become more critical. Ranging methods play a basic role in the localization system, in which received signal strength indicator- (RSSI-) based ranging technique gets the most attraction. To predict the position of an unknown node, RSSI measurement is an easy and reliable method for distance estimation. In indoor environments, the accuracy of the RSSI-based localization method is affected by strong variation, specially often containing substantial amounts of metal and other such reflective materials that affect the propagation of radio-frequency signals in nontrivial ways, causing multipath effects, dead spots, noise, and interference. This paper proposes an adaptive smoother based location and tracking algorithm for indoor positioning by making fusion of RSSI and link quality indicator (LQI), which is particularly well suited to support context aware computing. The experimental results showed that the proposed mathematical method can reduce the average error around 25%, and it is always better than the other existing interference avoidance algorithms.
Applying bit-vector projection approach for efficient mining of N-most interesting frequent itemsets

Real world datasets are sparse, dirty and contain hundreds of items. In such situations, discovering interesting rules (results) using traditional frequent itemset mining approach by specifying a user defined input support threshold is not appropriate. Since without any domain knowledge, setting support threshold small or large can output nothing or a large number of redundant uninteresting results. Recently a novel approach of mining N-most interesting itemsets is proposed, which discovers only top N interesting results without specifying any user defined support threshold. However, mining N-most interesting itemsets are more costly in terms of itemset search space exploration and processing cost. Thereby, the efficiency of mining process highly depends upon the itemset frequency (support) counting, implementation techniques and projection of relevant transactions to lower level nodes of search space. In this paper, we present a novel N-most interesting itemset mining algorithm (N-MostMiner) using the bit-vector representation approach which is very efficient in terms of itemset frequency counting and transactions projection. Several efficient implementation techniques of N-MostMiner are also present which we experienced in our implementation. Our different experimental results on benchmark datasets suggest that the N-MostMiner is very efficient in terms of processing time as compared to currently best algorithm BOMO.
The durability of white Portland cement to chemical attack

The durability of white Portland cement to chemical attack In terms of volume, the severest conditions that reinforced concrete structures are subjected to usually involve some form of attack by chlorides. These include marine environments, roads and bridges exposed to de-icing salt, etc. The time it takes for the chlorides to reach the reinforcement is the main parameter used to predict the service life of the concrete structures.
Current-Driven Insulator-To-Metal Transition in Strongly Correlated 
VO2

Electric current has been experimentally demonstrated to be able to drive the insulator-to-metal transition (IMT) in VO$_2$. The main mechanisms involved are believed to be the Joule heating effect and the strong electron-correlation effect. These effects are often entangled with each other in experiments, which complicates the understanding of the essential nature of the observations. We formulate a phase-field model to investigate theoretically in mesoscale the pure correlation effect brought by the current on the IMT in VO$_2$, i.e., the isothermal process under the current. We find that a current with a large density ($\sim 10^1$ nA/nm$^2$) induces a few-nanosecond ultrafast switch in VO$_2$, in agreement with the experiment. The temperature-current phase diagram is further calculated, which reveals that the current may induce the M2 phase at low temperatures. The current is also shown capable of driving domain walls to move. Our work may assist related experiments and provide guidance to the engineering of VO$_2$-based electric switching devices.
Parallel programming patterns with granular computing

Parallel programming contributes many methods and techniques to solve problems that require great computational speed and large memory than a single computer. In this paper, we proposed the notion of the parallel programming granulation. We construct a parallel programming pattern with granular computing, and we illustrate how to perform parallel programming and computing with granules. A sample of parallel programming granulation is also given to interpret this notion.
OCAT: object capture-based automated testing

Testing object-oriented (OO) software is critical because OO languages are commonly used in developing modern software systems. In testing OO software, one important and yet challenging problem is to generate desirable object instances for receivers and arguments to achieve high code coverage, such as branch coverage, or find bugs. Our initial empirical findings show that coverage of nearly half of the difficult-to-cover branches that a state-of-the-art test-generation tool cannot cover requires desirable object instances that the tool fails to generate. Generating desirable object instances has been a significant challenge for automated test-generation tools, partly because the search space for such desirable object instances is huge, no matter whether these tools compose method sequences to produce object instances or directly construct object instances. To address this significant challenge, we propose a novel approach called Object Capture based Automated Testing (OCAT). OCAT captures object instances dynamically from program executions (e.g., ones from system testing or real use). These captured objects assist an existing automated test-generation tool, such as a random testing tool, to achieve higher code coverage. Afterwards, OCAT mutates collected instances, based on observed not-covered branches. We evaluated OCAT on three open source projects, and our empirical results show that OCAT helps a state-of-the-art random testing tool, Randoop, to achieve high branch coverage: on average 68.5%, with 25.5% improved from only 43.0% achieved by Randoop alone.
Modulation of endothelial cell hemostatic properties by tumor necrosis factor

Tumor necrosis factor/cachectin (TNF) is a mediator of the septic state, which involves diffuse abnormalities of coagulation throughout the vasculature. Since previous studies have shown that endothelial cells can play an active role in coagulation, we wished to determine whether TNF could modulate endothelial cell hemostatic properties. Incubation of purified recombinant TNF with cultured endothelial cells resulted in a time- and dose-dependent acquisition of tissue factor procoagulant activity. Concomitant with enhanced procoagulant activity, TNF also suppressed endothelial cell cofactor activity for the anticoagulant protein C pathway; both thrombin-mediated protein C activation and formation of functional activated protein C-protein S complex on the cell surface were considerably attenuated. Comparable concentrations of TNF (half-maximal affect at approximately 50 pM) and incubation times (half-maximal affect by 4 h after addition to cultures) were required for each of these changes in endothelial cell coagulant properties. This unidirectional shift in cell surface hemostatic properties favoring promotion of clot formation indicates that, in addition to leukocyte procoagulants, endothelium can potentially be instrumental in the pathogenesis of the thrombotic state associated with inflammatory and malignant disorders.
Robust speaker identification against computer aided voice impersonation

Speaker Identification (SID) systems offer good performance in the case of noise free speech and most of the on-going research aims at improving their reliability in noisy environments. In ideal operating conditions very low identification error rates can be achieved. The low error rates suggest that SID systems can be used in real-life applications as an extra layer of security along with existing secure layers. They can, for instance, be used alongside a Personal Identification Number (PIN) or passwords. SID systems can also be used by law enforcements agencies as a detection system to track wanted people over voice communications networks. In this thesis, the performance of the existing SID systems against impersonation attacks is analysed and strategies to counteract them are discussed. A voice impersonation system is developed using Gaussian Mixture Modelling (GMM) utilizing Line Spectral Frequencies (LSF) as the features representing the spectral parameters of the source-target pair. Voice conversion systems based on probabilistic approaches suffer from the problem of over smoothing of the converted spectrum. A hybrid scheme using Linear Multivariate Regression and GMM, together with posterior probability smoothing is proposed to reduce over smoothing and alleviate the discontinuities in the converted speech. The converted voices are used to intrude a closed-set SID system in the scenarios of identity disguise and targeted speaker impersonation. The results of the intrusion suggest that in their present form the SID systems are vulnerable to deliberate voice conversion attacks. For impostors to transform their voices, a large volume of speech data is required, which may not be easily accessible. In the context of improving the performance of SID against deliberate impersonation attacks, the use of multiple classifiers is explored. Linear Prediction (LP) residual of the speech signal is also analysed for speaker-specific excitation information. A speaker identification system based on multiple classifier system, using features to describe the vocal tract and the LP residual is targeted by the impersonation system. The identification results provide an improvement in rejecting impostor claims when presented with converted voices. It is hoped that the findings in this thesis, can lead to the development of speaker identification systems which are better equipped to deal with the problem with deliberate voice impersonation.
Nanofibers and Electrospinning Method

As a result of their peculiar features like extremely high surface area to weight ratio, low density, as well as high pore volume and controllable pore size, which may not be present in other structures, nanofibers have taken centre stage in nanotechnology. The indicated properties, consequently, make non-woven nanofibers as appropriate materials for wide-spread applications. Electrospinning, because of its high productivity, simplicity, low cost, reproducibility and its potentialities of being utilised at the industrial level is regarded as one of the most potential processes in nanotechnology. This method implies the application of high voltage electric field aiming to extract very thin fibres from a polymeric fluid stream (solution or melt) potentially deliverable through a millimetre-scale needle. Electrospinning, as a technique, is reliant on various processing standards like solution properties and processing parameters. Consequently, altering these parameters could exert a considerable degree of influence on the nanofiber size, shape and morphology. Thus, by controlling those parameters well, specific fibres can be produced to benefit various applications.