Miracle-dz/UniFAR-FTUs huggingface dataset:
Water Repellence and Oxygen and Water Vapor Barrier of PVOH-Coated Substrates before and after Surface Esterification

This study investigates chemical grafting with fatty acid chlorides as a method for the surface modification of hydrophilic web materials. The resulting changes in the water repellence and barrier properties were studied. For this purpose, different grades of polyvinyl alcohol (PVOH) were coated on regenerated cellulose films (“cellophane”) and paper and then grafted with fatty acid chlorides. The PVOH grades varied in their degree of hydrolysis and average molecular weight. The surface was esterified with two fatty acid chlorides, palmitoyl (C16) and stearoyl chloride (C18), by chemical grafting. The chemical grafting resulted in water-repellent surfaces and reduced water vapor transmission rates by a factor of almost 19. The impact of the surface modification was greater for a higher degree of hydrolysis of the polyvinyl alcohol and for shorter fatty acid chains. Although the water vapor barrier for palmitoyl-grafted PVOH was higher than for stearoyl-grafted PVOH, the contact angle with water was lower. Additionally, it was shown that a higher degree of hydrolysis led to higher water vapor barrier improvement factors after grafting. Furthermore, the oxygen permeability decreased after grafting significantly, due to the fact that the grafting protects the PVOH against humidity when the humidity is applied on the grafted side. It can be concluded that the carbon chain length of the fatty acid chlorides is the limiting factor for water vapor adsorption, but the grafting density is the bottleneck for water diffusing in the polymer.
False vacuum bubble nucleation due to a nonminimally coupled scalar field

We study the possibility of forming the false vacuum bubble nucleated within the true vacuum background via the true-to-false vacuum phase transition in curved spacetime. We consider a semiclassical Euclidean bubble in the Einstein theory of gravity with a nonminimally coupled scalar field. In this paper we present the numerical computations as well as the approximate analytical computations. We mention the evolution of the false vacuum bubble after nucleation.
The First Extracellular Domain Plays an Important Role in Unitary Channel Conductance of Cx50 Gap Junction Channels

Gap junction (GJ) channels provide direct passage for ions and small molecules to be exchanged between neighbouring cells and are crucial for many physiological processes. GJ channels can be gated by transjunctional voltage (known as Vj-gating) and display a wide range of unitary channel conductance (γj), yet the domains responsible for Vj-gating and γj are not fully clear. The first extracellular domain (E1) of several connexins has been shown to line part of their GJ channel pore and play important roles in Vj-gating properties and/or ion permeation selectivity. To test roles of the E1 of Cx50 GJ channels, we generated a chimera, Cx50Cx36E1, where the E1 domain of Cx50 was replaced with that of Cx36, a connexin showing quite distinct Vj-gating and γj from those of Cx50. Detailed characterizations of the chimera and three point mutants in E1 revealed that, although the E1 domain is important in determining γj, the E1 domain of Cx36 is able to effectively function within the context of the Cx50 channel with minor changes in Vj-gating properties, indicating that sequence differences between the E1 domains in Cx36 and Cx50 cannot account for their drastic differences in Vj-gating and γj. Our homology models of the chimera and the E1 mutants revealed that electrostatic properties of the pore-lining residues and their contribution to the electric field in the pore are important factors for the rate of ion permeation of Cx50 and possibly other GJ channels.
A Temporal Auditory Model with Adaptation for Automatic Speech Recognition

Rapid and short-term adaptation are dynamic mechanisms of human auditory system. An auditory model based on zero-crossings with peak amplitudes (ZCPA) was used as a front-end for automatic speech recognition (ASR) with the perceptual property of adaptation as determined by psychoacoustic observations. The model performance was evaluated on the isolated digits (TIDIGITS) database using continuous density HMM recognizer in additive noise environment. Experimental results indicate that the ASR performance of the ZCPA may be improved with adaptation over the static baseline performance in white Gaussian and factory noise. The perceptual front-end was also evaluated with dynamic (delta and delta-delta) features added to the adaptation. It was observed that adaptation with dynamic features performed better in factory, babble and car noise over a wide range of SNR values.
Supporting ad-hoc ranking aggregates

This paper presents a principled framework for efficient processing of ad-hoc top-k (ranking) aggregate queries, which provide the k groups with the highest aggregates as results. Essential support of such queries is lacking in current systems, which process the queries in a naïve materialize-group-sort scheme that can be prohibitively inefficient. Our framework is based on three fundamental principles. The Upper-Bound Principle dictates the requirements of early pruning, and the Group-Ranking and Tuple-Ranking Principles dictate group-ordering and tuple-ordering requirements. They together guide the query processor toward a provably optimal tuple schedule for aggregate query processing. We propose a new execution framework to apply the principles and requirements. We address the challenges in realizing the framework and implementing new query operators, enabling efficient group-aware and rank-aware query plans. The experimental study validates our framework by demonstrating orders of magnitude performance improvement in the new query plans, compared with the traditional plans.
Using Association Rule Mining for Phenotype Extraction from Electronic Health Records

The increasing adoption of electronic health records (EHRs) due to Meaningful Use is providing unprecedented opportunities to enable secondary use of EHR data. Significant emphasis is being given to the development of algorithms and methods for phenotype extraction from EHRs to facilitate population-based studies for clinical and translational research. While preliminary work has shown demonstrable progress, it is becoming increasingly clear that developing, implementing and testing phenotyping algorithms is a time- and resource-intensive process. To this end, in this manuscript we propose an efficient machine learning technique—distributional associational rule mining (ARM)—for semi-automatic modeling of phenotyping algorithms. ARM provides a highly efficient and robust framework for discovering the most predictive set of phenotype definition criteria and rules from large datasets, and compared to other machine learning techniques, such as logistic regression and support vector machines, our preliminary results indicate not only significantly improved performance, but also generation of rule patterns that are amenable to human interpretation .
Effect of Electrochemically Deposited MgO Coating on Printable Perovskite Solar Cell Performance

Herein, we studied the effect of MgO coating thickness on the performance of printable perovskite solar cells (PSCs) by varying the electrodeposition time of Mg(OH)2 on the fluorine-doped tin oxide (FTO)/TiO2 electrode. Electrodeposited Mg(OH)2 in the electrode was confirmed by energy dispersive X-ray (EDX) analysis and scanning electron microscopic (SEM) images. The performance of printable PSC structures on different deposition times of Mg(OH)2 was evaluated on the basis of their photocurrent density-voltage characteristics. The overall results confirmed that the insulating MgO coating has an adverse effect on the photovoltaic performance of the solid state printable PSCs. However, a marginal improvement in the device efficiency was obtained for the device made with the 30 s electrodeposited TiO2 electrode. We believe that this undesirable effect on the photovoltaic performance of the printable PSCs is due to the higher coverage of TiO2 by the insulating MgO layer attained by the electrodeposition technique.
Learning Strategies and Force Precue in Force-Feedback controlled Learning Situations

Learning strategies have been researched especially respect to program theories. WulfS Krakauer, Ghilardi & Ghez 1999) a force-feedback device (Impulse-Stick by Immersion) combined with my personally developed software-tool has been used to control movement situations. Six groups with 20 students of each participated in this experiment. None had prior experience with the task. The task required participants to move the stick of the forcefeedback device on a linear trajectory without visual control. Dependent on three learning strategies (blocked condition, variability condition and differential condition) the movement had been disturbed by a special sequence of six different force fields. The forces in each force field acted perpendicular to the required movement and were generated by a sine function of the general form: force.x = a*sin(position.y*b) + a*sin(position.y*c) where force.x denotes the applied force and position.y denotes the position along the required trajectorie. There were two precue conditions (no precue and force precue presented 3 sec before acting on a monitor). The participants performed three sessions within a week each of 2 x 50 trials. At the beginning and at the end of the experiment they performed an interpolation test.
A Novel Approach for an Early Test Case Generation using Genetic Algorithm and Dominance Concept based on Use cases

This paper presents an integrated approach for generating test cases using Genetic Algorithm and dominance relation with fitness function. We investigated early generation of test cases using use cases and scenarios. In the process of generating test cases first we constructed control flow graph based on that dominance tree is generated. Test cases are generated by applying Genetic algorithm on dominance tree with concepts of crossover and mutation. Keywords—Genetic Algorithms, Dominance, Usecase, Scenarios, Testing;
Formal Models for Learning of User Preferences , a Preliminary Report

We present a preliminary report about formal models for the task of learning user preferences. Learning user preferences by examples is described under content-based learning models, such as the Mistake-bound Model and the Probably Approximately Correct (PAC) model of Computational Learning Theory. Then we describe the learning of user preferences under a collaborative-based learning schema inspired by Collaborative Information Filtering and Re-commender Systems. Finally we formalize the problem as a joint learning problem under the Co-training model [Blum and Mitchell 1998], in order to combine content and social information for a more accurate model of human preferences. As result, different concepts and methods of learning user preferences can be expressed under a unified formal framework.
Improved Adaptive–Reinforcement Learning Control for Morphing Unmanned Air Vehicles

This paper presents an improved adaptive-reinforcement learning control methodology for the problem of unmanned air vehicle morphing control. The reinforcement learning morphing control function that learns the optimal shape change policy is integrated with an adaptive dynamic inversion control trajectory tracking function. An episodic unsupervised learning simulation using the Q-learning method is developed to replace an earlier and less accurate actor-critic algorithm. Sequential function approximation, a Galerkin-based scattered data approximation scheme, replaces a K-nearest neighbors (KNN) method and is used to generalize the learning from previously experienced quantized states and actions to the continuous state-action space, all of which may not have been experienced before. The improved method showed smaller errors and improved learning of the optimal shape compared to the KNN.
Evaluation of the delivery fee exemption policy in ghana: population estimates of changes in delivery service utilization in two regions.

SUMMARY OBJECTIVES To determine changes in the proportion of deliveries at health facilities and the proportion attended by health professionals after delivery fee exemption implementation. DESIGN Pre and post intervention implementation cluster-sampled household survey. SETTING Central and Volta regions of Ghana. PARTICIPANTS Women who had delivered in these regions during the fee exemption policy and an equivalent period of time prior to it. MAIN OUTCOME MEASURES Place of delivery and person attending. RESULTS After fee exemption implementation the likelihood of delivering in a health facility increased significantly in Central (OR 1.83, p<0.001) and Volta (OR 1.34, p<0.05) regions when accounting for the mothers' education and poverty levels and the clustered data. Results from Central Region showed increases in facility deliveries mainly occurred in health centres (from 13.7% to 22.3% of deliveries), and were attended by midwives (from 49.0% to 59.7%). There was evidence that after implementation some inequalities in the uptake of facility deliveries decreased. The greatest increase in the proportion of deliveries taking place in facilities occurred among women with the lowest levels of education (Central Region) and wealth (Volta Region). These changes reduced the differentials observed. CONCLUSIONS After the implementation of fee exemption the proportion of deliveries in health facilities increased in both regions. Although changes cannot be directly attributed to delivery fee exemption, results demonstrating that the greatest increases in facility-based deliveries occurred among the poorest and least educated women are consistent with the expectation that the policy would particularly benefit women with the greatest financial barrier to health care and at the greatest risk of maternal mortality.
Essays On Aging-Related Disease In The U.s. Population

I research three topics in adult morbidity in the United States, focusing on two increasingly prevalent chronic diseases, diabetes and dementia. In the first essay, I investigate changes in age-specific diabetes prevalence across cohorts born in the 20th century and use the cross-cohort comparisons to generate model age patterns of diabetes prevalence and incidence. I show that most of the increase in diabetes prevalence over time is attributable to increases in age-specific prevalence from one cohort to the next. Because the risk of diabetes is embodied in cohorts, diabetes prevalence is likely to increase in the future even if the prevalence of risk factors such as obesity plateau. In the second essay, I use multiple-decrement life tables to estimate age-specific lifetime risks of dementia for a dementia-free person. I estimate that about a quarter of dementia-free 70-year-old males and a third of females will develop dementia in their lifetimes. Although interventions that delay dementia onset could substantially reduce dementia risk, the results indicate a widespread need to prepare for a life stage with dementia. In the third essay, I use recent advances in propensity-score matching techniques to estimate the association between incidence diabetes and subsequent accumulation of mobility limitations. Among observationally similar pairs of individuals, those who developed diabetes reported an average of 25% more mobility limitations at study exit than those who did not develop diabetes. My estimates of this association are smaller than those found in most of the existing literature, but they are likely less biased. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Demography First Advisor Irma T. Elo Second Advisor Samuel H. Preston
ombining detection algorithms for robust tracking of honeybees

References [1] Thakur, R. K. , Bienefeld, K. ; Keller, R. : Varroa defence behavior in Apis mellifera carnica. American Bee Journal 137, pp. 143-148, 1997. [2] Viola, P., Jones, M. : Rapid Object Detection using a Boosted Cascade of Simple Features. CVPR 2001. [3] Knauer, U. et al.: Application of an Adaptive Background Model for Monitoring Honeybees. VIIP 2005 . [4] Knauer, U. et al.: Evaluation based combining of classifiers. WACV 2009. Methods TPR FPR Rank 1 Template Matching 93,01% 4,98% 3 2 Haarlike Features 91,26% 4,01% 5 3 Contour Detection 95,97% 1,74% 1 4 Interest Point Density 95,87% 4% 2 5 Hough Transform 92,59% 5,23% 4 Methods Precision Recall Rank 1 Template Matching 69,40% 82,50% 5 2 Haarlike Features 91,17% 90,50% 2 3 Contour Detection 94,66% 91,20% 1 4 Interest Point Density 86,70% 88% 3 5 Hough Transform 79,41% 84,37% 4
Sky segmentation with ultraviolet images can be used for navigation

Inspired by ant navigation, we explore a method for sky segmentation using ultraviolet (UV) light. A standard camera is adapted to allow collection of outdoor images containing light in the visible range, in UV only and in green only. Automatic segmentation of the sky region using UV only is significantly more accurate and far more consistent than visible wavelengths over a wide range of locations, times and weather conditions, and can be accomplished with a very low complexity algorithm. We apply this method to obtain compact binary (sky vs non-sky) images from panoramic UV images taken along a 2km route in an urban environment. Using either sequence SLAM or a visual compass on these images produces reliable localisation and orientation on a subsequent traversal of the route under different weather conditions.
Adaptive Generation of Unique IDs for Digital Chips through Analog Excitation

Globalization of the integrated circuit design and manufacturing flow has successfully ameliorated design complexity and fabrication cost challenges, and helped deliver cost-effective products while meeting stringent time-to-market requirements. On the flip side, it has resulted in various forms of security vulnerabilities in the supply chain that involves designers, fabs, test facilities, and distributors until the end-product reaches customers. One of the biggest threats to semiconductor industry today is the entry of aged, reject, or cloned parts, that is, counterfeit chips, into the supply chain, leading to annual revenue losses in the order of billions of dollars. While traceability of chips between trusted parties can help monitor the supply chain at various points in the flow, existing solutions are in the form of integrating costly hardware units on chip, or utilizing easy-to-circumvent inspection-based detection techniques. In this article, we propose a technique for adaptive unique ID generation that leverages process variations, enabling chip traceability. The proposed method stimulates digital chips with an analog signal from the supply lines, which serve as primary inputs to each gate in the signal path. Using a sinusoidal signal that exercises the transistors as gain components, we create a chip-specific response that can be post-processed into a digital ID. The proposed technique enables quick and cost-effective authenticity validation that requires no on-chip hardware support. Our simulation and experimentation on actual chips show that the proposed technique is capable of generating unique IDs even in the presence of environmental noise.
Noise versus Facial Expression on 3D Face Recognition

This paper presents a new method for 3D face recognition. The method combines a Simulated Annealing-based approach for image registration using the surface interpenetration measure (SIM) to perform a precise matching between two face images. The recognition score is obtained by combining the SIM scores of four different face regions after their alignment. Experiments were conducted on two databases with a variety official expressions. The images from the databases were classified according to noise level and facial expression, allowing the analysis of each particular effect on 3D face recognition. The method allows a verification rate of 99.9%, at a false acceptance rate (FAR) of 0%, for the FRGC ver 2.0 database when only noiseless, neutral expression face images are used. Also, the results using face images with expressions and noise demonstrate that subjects still can be recognized with 87.5% of verification rate, at a FAR of 0%.
Stanniocalcin-1 Regulates Re-Epithelialization in Human Keratinocytes

Stanniocalcin-1 (STC1), a glycoprotein hormone, is believed to be involved in various biological processes such as inflammation, oxidative responses and cell migration. Riding on these emerging evidences, we hypothesized that STC1 may participate in the re-epithelialization during wound healing. Re-epithelialization is a critical step that involves keratinocyte lamellipodia (e-lam) formation, followed by cell migration. In this study, staurosporine (STS) treatment induced human keratinocyte (HaCaT) e-lam formation on fibronectin matrix and migration via the activation of focal adhesion kinase (FAK), the surge of intracellular calcium level [Ca2+]i and the inactivation of Akt. In accompanied with these migratory features, a time- and dose-dependent increase in STC1 expression was detected. STC1 gene expression was found not the downstream target of FAK-signaling as illustrated by FAK inhibition using PF573228. The reduction of [Ca2+]i by BAPTA/AM blocked the STS-mediated keratinocyte migration and STC1 gene expression. Alternatively the increase of [Ca2+]i by ionomycin exerted promotional effect on STS-induced STC1 gene expression. The inhibition of Akt by SH6 and GSK3β by lithium chloride (LiCl) could respectively induce and inhibit the STS-mediated e-lam formation, cell migration and STC1 gene expression. The STS-mediated e-lam formation and cell migration were notably hindered or induced respectively by STC1 knockdown or overexpression. This notion was further supported by the scratched wound assay. Collectively the findings provide the first evidence that STC1 promotes re-epithelialization in wound healing.
Rumour Detection in Information Warfare: Understanding Publishing Behaviours as a Prerequisite

In the context of information warfare, rumour detection has become a central issue. From classical media-related campaign, to propaganda and indoctrination that lie at the core of terrorism, rumour is a mean widely used and thus a threat that must be identified as soon as possible, and in the best-case scenario, anticipated and curbed. The emergence of a new informational environment due to the adoption of the Internet as a massive information diffusion medium has led to a situation suitable to the creation and propagation of rumours. Indeed, the Web gives everyone not only the possibility to observe information flows but also the opportunity to influence and create them. In order to tackle the issue of rumour detection, one has to understand the mechanisms underlying their propagation. In this perspective, we believe that it is essential to identify and understand the publishing behaviours of the sources. Therefore, we focus in this paper on the identification of groups of sources with similar publishing characteristics. We propose to tackle this problematic by using clustering methods on data extracted from Web sources. The four resulting clusters obtained from the clustering are then interpreted as groups of Websites behaving similarly and used to characterize publishing behaviours.