Miracle-dz/UniFAR-FTUs huggingface dataset:
A balanced flow-through viscosity sensor based on a torsionally resonating pipe

In this paper we present a new balanced flowthrough viscosity sensor working with a torsionally resonating pipe. The whole system is machined in aluminum and is designed to be placed in the pipes of a chemical production plant. The quality factor Q and the resonance frequency fr are used as measures for the viscosity and density. We describe the sensor setup, its working principle for different resonant modes, the actuation and the readout of the sensor and present first measurement results for liquids featuring different viscosities.
Bridging the TB data gap: in silico extraction of rifampicin-resistant tuberculosis diagnostic test results from whole genome sequence data

Background Mycobacterium tuberculosis rapid diagnostic tests (RDTs) are widely employed in routine laboratories and national surveys for detection of rifampicin-resistant (RR)-TB. However, as next generation sequencing technologies have become more commonplace in research and surveillance programs, RDTs are being increasingly complemented by whole genome sequencing (WGS). While comparison between RDTs is difficult, all RDT results can be derived from WGS data. This can facilitate continuous analysis of RR-TB burden regardless of the data generation technology employed. By converting WGS to RDT results, we enable comparison of data with different formats and sources particularly for low and middle income high TB burden countries that employ different diagnostic algorithms for drug resistance surveys. This allows national TB control programs (NTPs) and epidemiologists to utilize all available data in the setting for improved RR-TB surveillance. Methods We developed the Python-based MTB Genome to Test (MTBGT) tool that transforms WGS-derived data into laboratory-validated results of the primary RDTs – Xpert MTB/RIF, XpertMTB/RIF Ultra, GenoType MDRTBplus v2.0, and GenoscholarNTM+MDRTB II. The tool was validated through RDT results of RR-TB strains with diverse resistance patterns and geographic origins and applied on routine-derived WGS data. Results The MTBGT tool correctly transformed the SNP data into the RDT results and generated tabulated frequencies of the RDT probes as well as rifampicin susceptible cases. The tool supplemented the RDT probe reactions output with the RR-conferring mutation based on identified SNPs. The MTBGT tool facilitated continuous analysis of RR-TB and Xpert probe reactions from different platforms and collection periods in Rwanda. Conclusion Overall, the MTBGT tool allows low and middle income countries to make sense of the increasingly generated WGS in light of the readily available RDT results, and assess whether currently implemented RDTs adequately detect RR-TB in their setting. With its feature to transform WGS to RDT results and facilitate continuous RR-TB data analysis, the MTBGT tool may bridge the gap between and among data from periodic surveys, continuous surveillance, research, and routine tests, and may be integrated within the existing national connectivity platform for use by the NTP and epidemiologists to improve setting-specific RR-TB control. The MTBGT source code and accompanying documentation is available at https://github.com/KamelaNg/MTBGT.
Conduction mechanism in electronic polymers: Effect of morphology

In this paper we report the mechanism of charge transport in conducting polymers. The basic concept of defect states in conjugated polymers (solitons, polarons and bipolarons in conjugated polymers) are briefly discussed. This paper addresses sources of disorder in conducting polymers depend on inhomogeneous doping and also the synthesis routes. We synthesized the conjugated polymers by different synthesis routes and characterized by scanning: electron microscope (SEM) for study of their various morphologies. The effect of morphology and crystallinity of polymers discussed for conductivity in view of their electronic applications.
A Statistical Analysis of Atmospheric CO 2 Levels at Mauna Loa

This honors capstone project was an analysis of atmospheric carbon dioxide levels measured at the Mauna Loa Observatory in Hawaii. The Scripps CO2 Program through the Scripps Institution of Oceanography collects data constantly and makes the data available to the public in the form of daily, weekly, and monthly observations. Due to the seasonal variation in the monthly observations, traditional modeling methods learned at the undergraduate mathematics and statistics level, like least-squares regression, are not appropriate. An exponential smoother, like the HoltWinters model, can be used to fit these types of data as well as be used to forecast future observations. A brief discussion about atmospheric carbon dioxide levels and the potential impact on the climate follows the analysis.
Robust control of singular systems with time delay. Part I: Continuous time case

In this paper for the class of continuous time nonlinear uncertain singular time delay dynamical systems we present robust stability analyze results. Next, we consider a control problem for nonlinear continuous time uncertain singular time delay dynamical systems involving a notion of optimality with respect to an auxiliary cost which guarantees a bound on the worst-case value of a nonlinear-nonquadratic cost criterion over a prescribed uncertainty set. Further we specialize result to affine uncertain systems to obtain controllers predicated on an inverse optimal control problem. In particular, to avoid the complexity in solving the steady-state Hamilton-Jacobi-Bellman equation we parameterize a family of stabilizing controllers that minimize some derived cost functional that provides flexibility in specifying the control law. The performance integrand is shown to explicitly depend on the continuous time nonlinear singular time delay system dynamics, the Lyapunov function of the closed-loop system, and the stabilizing feedback control law wherein the coupling is introduced via the Hamilton-Jacobi-Bellman equation. By varying the parameters in the Lyapunov function and the performance integrand, the proposed framework can be used to characterize a class of globally stabilizing controllers that can meet the closed-loop system response constraints. Obtained results for nonlinear case are further specialized to continuous time linear singular time delay dynamical systems.
Successive Interference Cancellation for Hierarchical Parallel Optical Wireless Communication Systems

A parallel optical wireless communication system using two-dimensional LED array (2D LED array) and two-dimensional image sensor (2D image sensor) was proposed for the visible light communication systems. In this system, each LED of the 2D LED array is individually modulated, and the 2D image sensor recognizes each LED modulated data. However, the received data pattern is degraded due to the reduction of pixel size and/or defocusing of the LED data pattern. This phenomenon corresponds to the degradation of the high spatial frequency components of the received data pattern. To overcome this, we employ a hierarchical transmission scheme at a transmitter and a successive interference cancellation at a receiver. By allocating high priority data to low frequency components and low priority data to high frequency components, the reception of high priority data can be guaranteed. We also attempt to apply interference cancellation to overcome the degradation of high spatial frequency components of the received data pattern. By evaluating the bit error rate, we clarify the effect of the proposed system
Chromosomal Aberrations Induced in Root Tips of Allium cepa by Squeezed Garri Extracts

The potential genotoxic effects of squeezed extracts from toasted cassava granules popularly known as garri, a popular Nigerian cassava meal, obtained from different fermentation days were investigated using the Allium cepa assay. The squeezed extracts were prepared with potable water as practiced conventionally by soaking 1-day fermented, 2-day fermented, 3-day fermented and 4-day fermented garri and squeezing out the liquid. A series of 5 onion bulbs were exposed to 1, 2.5, 5 and 10% (effluents, v/v) concentrations of each of the extracts for macroscopic and microscopic analyses. There was fermentation and concentration-dependent and statistically significant (P 2-day > 3-day > 4-day fermented garri extracts. The results further go to confirm findings from other studies that proper fermentation aids in the reduction of toxic cyanogenic components present in poorly processed cassava products including garri. (Daniel I. Olorunfemi and Emmanuel O. Ehwre. Chromosomal Aberrations Induced in Root Tips of Allium cepa by Squeezed Garri Extracts. Report and Opinion 2010;2(12):166-171). (ISSN: 1553-9873). http://www.sciencepub. net.
ContlensNet: Robust Iris Contact Lens Detection Using Deep Convolutional Neural Networks

Contact lens detection in the eye is a significant task to improve the reliability of iris recognition systems. A contact lens overlays the iris region and prevents the iris sensor from capturing the normal iris region. In this paper, we present a novel scheme for detection to detecting a contact lens using Deep Convolutional Neural Network (CNN). The proposed CNN architecture ContlensNet is structured to have fifteen layers and configured for the three-class detection problem with the following classes: images with textured (or colored) contact lens, soft (or transparent) contact lens, and no contact lens. The proposed ContlensNet is trained using numerous iris image patches and the problem of overfitting the network is addressed by using the dropout regularization method. Extensive experiments are carried out on two publicly available large-scale databases, namely: IIIT-Delhi Contact lens iris database (IIITD) and Notre Dame cosmetic contact lens database 2013 (ND) that are comprised of contact lens iris samples captured using four different sensors. The obtained results have demonstrated the improved performance of the proposed scheme with an average performance improvement of more than 10% in Correct Classification Rate (CCR%) when compared with eight different state-of-the-art contact lens detection systems.
PRODUCTIVITY, TECHNICAL EQUIPMENT AND SALES. EMPIRICAL EVIDENCE FROM THE ROMANIAN COMPANIES

The relationship between turnover and organizational performance represented the field of interest for various authors in literature, some of the studies identifying even a strong association between the organizational performance and the management of human resources itself. Turnover thus represents an indicator of results which has a fundamental role in characterizing the efficiency of the activity carried out by a company. The final results are conditioned by the volume of resources, which are however, limited, an efficient strategy of management being necessary for the correct accomplishment of the previous established objectives and capitalization of its real potential. Findings regarding the size of a business or a company and its variation in time are necessary in attracting and securing the resources which are indispensable for achieving the objectives and goals proposed. To fully understand a company's activity, any analysis of an indicator must include a description of its dynamics. Thus, the analysis of the turnover in time can be achieved using conventional statistical models. In concrete terms, it is part of the economic and financial results indicators, helping to diagnose and to evaluate the company, estimating efficient management practices. The present paper aims at illustrating the relevance of a factorial analysis model for a sample of enterprises selected, the results highlighting the connection between the sales and labor productivity, but also between sales and the degree of technical equipment. The study revealed the following aspects: a positive and direct connection between labor productivity and sales, so we can state that higher levels of productivity produce higher sales, but also a negative connection between the degree of technical equipment and the level of sales, relation which is very important since we want to know how well a business is employing its assets to generate sales.
Genea cephalonicae sp. nov. (Ascomycota, Pezizales), a new hypogeous species from Greece

A new species of Genea Vittad. from Greek forests of Abies cephalonica is proposed based on morphological and genetic studies. Macroscopic images of fresh specimens, as well as observations made under optical microscopes, drawings, a 28S rDNA phylogenetic tree and a small key are provided
Sperm is epigenetically programmed to regulate gene transcription in embryos

For a long time, it has been assumed that the only role of sperm at fertilization is to introduce the male genome into the egg. Recently, ideas have emerged that the epigenetic state of the sperm nucleus could influence transcription in the embryo. However, conflicting reports have challenged the existence of epigenetic marks on sperm genes, and there are no functional tests supporting the role of sperm epigenetic marking on embryonic gene expression. Here, we show that sperm is epigenetically programmed to regulate embryonic gene expression. By comparing the development of sperm- and spermatid-derived frog embryos, we show that the programming of sperm for successful development relates to its ability to regulate transcription of a set of developmentally important genes. During spermatid maturation into sperm, these genes lose H3K4me2/3 and retain H3K27me3 marks. Experimental removal of these epigenetic marks at fertilization de-regulates gene expression in the resulting embryos in a paternal chromatin-dependent manner. This demonstrates that epigenetic instructions delivered by the sperm at fertilization are required for correct regulation of gene expression in the future embryos. The epigenetic mechanisms of developmental programming revealed here are likely to relate to the mechanisms involved in transgenerational transmission of acquired traits. Understanding how parental experience can influence development of the progeny has broad potential for improving human health.
Fuzzy function approximation with ellipsoidal rules

A fuzzy rule can have the shape of an ellipsoid in the input-output state spare of a system. Then an additive fuzzy system approximates a function by covering its graph with ellipsoidal rule patches. It averages rule patches that overlap. The best fuzzy rules cover the extrema or bumps in the function. Neural or statistical clustering systems can approximate the unknown fuzzy rules from training data. Neural systems can then both tune these rules and add rules to improve the function approximation. We use a hybrid neural system that combines unsupervised and supervised learning to find and tune the rules in the form of ellipsoids. Unsupervised competitive learning finds the first-order and second-order statistics of clusters in the training data. The covariance matrix of each cluster gives an ellipsoid centered at the vector or centroid of the data cluster. The supervised neural system learns with gradient descent. It locally minimizes the mean-squared error of the fuzzy function approximation. In the hybrid system unsupervised learning initializes the gradient descent. The hybrid system tends to give a more accurate function approximation than does the lone unsupervised or supervised system. We found a closed-form model for the optimal rules when only the centroids of the ellipsoids change. We used numerical techniques to find the optimal rules in the general case.
Effective neural network pruning using cross-validation

This paper addresses the problem of finding neural networks with optimal topology such that their generalization capability is maximized. Our approach is to combine the use of a penalty function during network training and a subset of the training samples for cross-validation. The penalty is added to the error function so that the weights of network connections that are not useful have small magnitude. Such network connections can be pruned if the resulting accuracy of the network does not change beyond a preset level. Training samples in the cross-validation set are used to indicate when network pruning is terminated. Our results on 32 publicly available data sets show that the proposed method outperforms existing neural network and decision tree methods for classification.
New Approaches to Prophylactic Human Papillomavirus Vaccines for Cervical Cancer Prevention

The currently licensed human papillomavirus (HPV) vaccines are safe and highly effective at preventing HPV infection for a select number of papillomavirus types, thus decreasing the incidence of precursors to cervical cancer. It is expected that vaccination will also ultimately reduce the incidence of this cancer. The licensed HPV vaccines are, however, type restricted and expensive, and also require refrigeration, multiple doses and intramuscular injection. Second-generation vaccines are currently being developed to address these shortcomings. New expression systems, viral and bacterial vectors for HPV L1 capsid protein delivery, and use of the HPV L2 capsid protein will hopefully aid in decreasing cost and increasing ease of use and breadth of protection. These second-generation vaccines could also allow affordable immunization of women in developing countries, where the incidence of cervical cancer is high.
Visual aesthetic analysis using deep neural network: model and techniques to increase accuracy without transfer learning

We train a deep Convolutional Neural Network (CNN) from scratch for visual aesthetic analysis in images and discuss techniques we adopt to improve the accuracy. We avoid the prevalent best transfer learning approaches of using pretrained weights to perform the task and train a model from scratch to get accuracy of 78.7% on AVA2 Dataset close to the best models available (85.6%). We further show that accuracy increases to 81.48% on increasing the training set by incremental 10 percentile of entire AVA dataset showing our algorithm gets better with more data.
Practice of Epidemiology Bayesian Posterior Distributions Without Markov Chains

Bayesian posterior parameter distributions are often simulated using Markov chain Monte Carlo (MCMC)methods. However, MCMCmethods are not always necessary and do not help the uninitiated understand Bayesian inference. As a bridge to understanding Bayesian inference, the authors illustrate a transparent rejection sampling method. In example 1, they illustrate rejection sampling using 36 cases and 198 controls from a case-control study (1976–1983) assessing the relation between residential exposure to magnetic fields and the development of childhood cancer. Results from rejection sampling (odds ratio (OR) 1⁄4 1.69, 95% posterior interval (PI): 0.57, 5.00) were similar to MCMC results (OR 1⁄4 1.69, 95% PI: 0.58, 4.95) and approximations from data-augmentation priors (OR 1⁄4 1.74, 95% PI: 0.60, 5.06). In example 2, the authors apply rejection sampling to a cohort study of 315 human immunodeficiency virus seroconverters (1984–1998) to assess the relation between viral load after infection and 5-year incidence of acquired immunodeficiency syndrome, adjusting for (continuous) age at seroconversion and race. In this more complex example, rejection sampling required a notably longer run time than MCMC sampling but remained feasible and again yielded similar results. The transparency of the proposed approach comes at a price of being less broadly applicable than MCMC.
Understanding microstructure of the brain by comparison of neurite orientation dispersion and density imaging (NODDI) with transparent mouse brain

Background Neurite orientation dispersion and density imaging (NODDI) is a diffusion magnetic resonance imaging (MRI) technique with the potential to visualize the microstructure of the brain. Revolutionary histological methods to render the mouse brain transparent have recently been developed, but verification of NODDI by these methods has not been reported. Purpose To confirm the concordance of NODDI with histology in terms of density and orientation dispersion of neurites of the brain. Material and Methods Whole brain diffusion MRI of a thy-1 yellow fluorescent protein mouse was acquired with a 7-T MRI scanner, after which transparent brain sections were created from the same mouse. NODDI parameters calculated from the MR images, including the intracellular volume fraction (Vic) and the orientation dispersion index (ODI), were compared with histological findings. Neurite density, Vic, and ODI were compared between areas of the anterior commissure and the hippocampus containing crossing fibers (crossing areas) and parallel fibers (parallel areas), and the correlation between fiber density and Vic was assessed. Results The ODI was significantly higher in the crossing area compared to the parallel area in both the anterior commissure and the hippocampus (P = 0.0247, P = 0.00022, respectively). Neurite density showed a similar tendency, but was significantly different only in the hippocampus (P = 7.91E−07). There was no significant correlation between neurite density and Vic. Conclusion NODDI was verified by histology for quantification of the orientation dispersion of neurites. These results indicate that the ODI is a suitable index for understanding the microstructure of the brain in vivo.
The Causes of Fiscal Stalemate

Why do lawmakers in some states routinely pass late budgets, while their counterparts elsewhere rarely do? We argue that a late budget imposes a number of political and private costs on lawmakers, the magnitudes of which are shaped principally by institutions and features of the political environment. When the private and public costs of delay are high we expect the budget to be adopted on time, but when these costs are low the probability and length of delay should increase. We test our expectations using an original dataset of the timing of budget adoption for all states over a forty-six year period. As expected, we find that variables which shape the costs of delay - legislative session length, the reversion point in the absence of an on-time budget, and divided government - are key determinants of the probability of stalemate. The length of the stalemate, however, is best predicted by the complexity of the budget. Insights from our analysis can be applied to budgeting at the national level. In particular, they help explain variation in the frequency and length of late federal appropriations bills and suggest reasons why federal budgets experience more stalemate than those adopted by states.
Klinefelter's Syndrome and Psychopathology: A Case Study of the Combined Effects of Nature and Nurture

A patient with Klinefelter's syndrome and genotype 47 XXY was abused and ostracized by his family during childhood. Later, this pattern was repeated by a series of homosexual lovers. The separate and interactive effects between genetic and psychosocial risk factors may account for this patient's subsequent psychosexual and personality disorders and reminds scholars of psychopathology of the importance of early detection and intervention. This single case study is presented for heuristic value, illustrative purposes, and serves as a link between mental health clinicians and sexologists.