Miracle-dz/UniFAR-FTUs huggingface dataset:
An evaluation study of vital parameter determination with RGB cameras in the field of ambient assisted living

Abstract The remote determination of vital parameters can contribute to the field of ambient assisted living (AAL) to face the challenges of a steadily ageing society. In this study, we examined the possibility to remotely measure the four vital parameters heart rate, respiration rate, oxygen saturation and blood pressure in AAL environments. For this, we evaluated the state of the art, implementations and concepts. While, the determination of heart and respiration rate is ready for usage, further attention has to be paid to the determination of oxygen saturation and blood pressure.
Efficient techniques for inductance extraction of complex 3-D geometries

The combination of a mesh analysis equation formulation technique with a preconditioned GMRES matrix solution algorithm to accelerate the determination of inductances of complex three-dimensional structures is described. Addition of the preconditioner to GMRES can reduce the cost of solution to m/sup 2/ operations compared to m/sup 2/ for direct inversion. Results from FASTHENRY, a 3-D inductance extraction program, demonstrate that the iterative approach can accelerate solution times by more than an order of magnitude. <<ETX>>
Augmenting Europeana content with linked data resources

Annotations allow end users to augment digital items with information, which can then be exploited for search and retrieval. We are currently extending Europeana, a platform which links to millions of digital items in European institutions, with an annotation mechanism that exposes annotations as linked data and enriches newly created annotations with links to contextually relevant resources on the Web. In two demos we showcase how we integrated that kind of content augmentation into two clients that allow users to annotate videos and historic maps.
Parameter passing for the Java virtual machine

The portability and runtime safety of programs which are executed on the Java Virtual Machine (JVM) makes the JVM an attractive target for compilers of languages other than Java. Unfortunately, the JVM was designed with language Java in mind, and lacks many of the primitives required for a straightforward implementation of other languages. Most fundamental of these obstacles is the limited range of parameter passing modes offered by the JVM. We discuss possible ways of using the JVM to provide parameter passing modes with alternative semantics, and explore one particular architecture in practice. The interaction between these mechanisms and the Java "byte code verifier" is also considered. The open source Gardens Point Component Pascal compiler compiles the entire Component Pascal language, a dialect of Oberon-2, to JVM bytecodes. This compiler achieves runtime efficiencies which are comparable to native-code implementations of procedural languages.
Expansion of the First Hindi-Nepali Word-Net based Bi- Lingual Dictionary and the advancement of the Human- Machine Interface

Language Processing is introducing a new era in the field of Computer Science and Machine translation. Human- Machine interaction is to play a very important role in the coming centuries as the dependency of human over the machine is increasing variably. Word-Net was first introduced by Miller and Fellbaum in 1985. WordNet is a Lexical database for the Human Languages. It groups the Human Language into sets of synonyms called "synsets" which provides short, general definitions, and records the various semantic relations between these synonym sets. Word-Net based Bi-Lingual Dictionary (Started in January, 2009) is a part of the Nepali WordNet Project under Indo- WordNet Project. It is an approach towards the construction and application of Multilingual Indo-WordNet, which is a pioneering effort on Nepali to Hindi and vice versa translation. The making of the Hindi-Nepali Word-Net was a challenge to us as the machine has to translate the sense of each word to produce the expected result. One of the major reasons behind this is the non- availability of rich lexical re-sources in Hindi and Nepali. The Hindi-Nepali WordNet based dictionary is thus an endeavor to prepare a rich lexical resource for the Hindi and Nepali Languages for effective machine translation and to facilitate the development of Information and Communication Technologies in Hindi-Nepali. The endeavor is inspired by the famous English WordNet, Hindi Word-Net and the Nepali Word-Net (Nepali Word-Net is also, created by us as a research and project work at Assam University, Silchar as a part of the Indo-Word-Net Project). In this paper we have discussed about the expansion approach of first Hindi-Nepali WordNet based Bi-Lingual Dictionary, the linguistic challenges involved, Word-Net creation tool interface and the synsets" storage structure.
A survey of research literature on Vehicular Networking

This document is a brief survey of research literature on vehicular networking. Broadly speaking, research publications on vehicular networking can be grouped into two categories: (i) WAVE (IEEE 802.11p and IEEE 1609.1-1609.4) papers, and (ii) VANET papers. Papers on WAVE mostly assume single-hop V2V, V2I and I2V communications, while the VANET papers focus on multi-hop V2V networks. The sections are organized as follows: tutorials, simulation platforms and tools, simulation studies, experimental studies, testbeds/trials, VANET routing protocols, transport protocols, MAC protocols, and security. This document is prepared as an addendum to the Vehicular Networking Tutorial that will presented by the author at IEEE ICC, Kyoto, Japan, in June 2011.
Multicenter Pivotal Clinical Trial of Urine Malaria Test for Rapid Diagnosis of Plasmodium falciparum Malaria

ABSTRACT The need to expand malaria diagnosis capabilities alongside policy requirements for mandatory testing before treatment motivates exploration of noninvasive rapid diagnostic tests (RDTs). We report the outcome of the first cross-sectional, single-blind clinical performance evaluation of a urine malaria test (UMT) for diagnosis of Plasmodium falciparum malaria in febrile patients. Matched urine and finger-prick blood samples from participants ≥2 years of age with fever (axillary temperature of ≥37.5°C) or with a history of fever in the preceding 48 h were tested with UMT and microscopy (as the gold standard). BinaxNOW (Pf and Pan versions) blood RDTs were done to assess relative performance. Urinalysis and rheumatoid factor (RF) tests were conducted to evaluate possible interference. Diagnostic performance characteristics were computed at 95% confidence intervals (CIs). Of 1,800 participants screened, 1,691 were enrolled; of these 566 (34%) were febrile, and 1,125 (66%) were afebrile. Among enrolled participants, 341 (20%) tested positive by microscopy, 419 (25%) were positive by UMT, 676 (40%) were positive by BinaxNOW Pf, and 368 (22%) were positive by BinaxNow Pan. UMT sensitivity among febrile patients (for whom the test was indicated) was 85%, and specificity was 84%. Among febrile children ≤5 years of age, UMT sensitivity was 93%, and specificity was 83%. The area under the receiver-operator characteristic curve (AUC) of UMT (0.84) was not significantly different from that of BinaxNOW Pf (0.86) or of BinaxNOW Pan (0.87), indicating that the tests do not differ in overall performance. Gender, seasons, and RF did not impact UMT performance. Leukocytes, hematuria, and urobilinogen concentrations in urine were associated with lower UMT specificities. UMT performance was comparable to that of the BinaxNOW Pf/Pan tests, making UMT a promising tool to expand malaria testing in public and private health care settings where there are challenges to blood-based malaria diagnosis testing.
Chemical Approaches for Functionally Probing the Proteome*

With the availability of complete genome sequences, emphasis has shifted toward the understanding of protein function. We have developed a functional proteomic methodology that makes use of chemically reactive fluorescent probes to profile and identify enzymes in complex mixtures by virtue of their catalytic activity. This methodology allows a comparison of changes in activity of multiple enzymes under a variety of conditions using a single two-dimensional separation. The probes can also be used to localize active enzymes in intact cells using fluorescence microscopy. Furthermore, the probes enable screens for selective small molecule inhibitors of each enzyme family member within crude lysates or intact cells. Ultimately, this technology allows the rapid identification of potential drug targets and small molecule lead compounds targeted to them.
A multivariate analysis of the relative impact of performance and relationship attributes on customer satisfaction

This paper discusses an approach able to simultaneously compare the importance of performance and relationship marketing variables in building satisfaction among frequent customers. Multivariate statistical techniques are used - factor analysis and multiple regression analysis - to model the satisfaction of frequent customers of a group of hotels in the north of Portugal. The conclusion is that the relationship marketing policies are not strong determinants as regards the overall satisfaction of this kind of customers.
Energy Demand and Temperature: A Dynamic Panel Analysis

This paper is a first attempt to investigate the effect of climate on the demand for different energy vectors from different final users. The ultimate motivation for this is to arrive to a consistent evaluation of the impact of climate change on key consumption goods and primary factors such as energy vectors. This paper addresses these issues by means of a dynamic panel analysis of the demand for coal, gas, electricity, oil and oil products by residential, commercial and industrial users in OECD and (a few) non-OECD countries. It turns out that temperature has a very different influence on the demand of energy vectors as consumption goods and on their demand as primary factors. In general, residential demand responds negatively to temperature increases, while industrial demand is insensitive to temperature increases. As to the service sector, only electricity demand displays a mildly significant negative elasticity to temperature changes.
Rates of Gyrase Supercoiling and Transcription Elongation Control Supercoil Density in a Bacterial Chromosome

Gyrase catalyzes negative supercoiling of DNA in an ATP-dependent reaction that helps condense bacterial chromosomes into a compact interwound “nucleoid. ” The supercoil density (σ) of prokaryotic DNA occurs in two forms. Diffusible supercoil density (σD) moves freely around the chromosome in 10 kb domains, and constrained supercoil density (σC) results from binding abundant proteins that bend, loop, or unwind DNA at many sites. Diffusible and constrained supercoils contribute roughly equally to the total in vivo negative supercoil density of WT cells, so σ = σC+σD. Unexpectedly, Escherichia coli chromosomes have a 15% higher level of σ compared to Salmonella enterica. To decipher critical mechanisms that can change diffusible supercoil density of chromosomes, we analyzed strains of Salmonella using a 9 kb “supercoil sensor” inserted at ten positions around the genome. The sensor contains a complete Lac operon flanked by directly repeated resolvase binding sites, and the sensor can monitor both supercoil density and transcription elongation rates in WT and mutant strains. RNA transcription caused (−) supercoiling to increase upstream and decrease downstream of highly expressed genes. Excess upstream supercoiling was relaxed by Topo I, and gyrase replenished downstream supercoil losses to maintain an equilibrium state. Strains with TS gyrase mutations growing at permissive temperature exhibited significant supercoil losses varying from 30% of WT levels to a total loss of σD at most chromosome locations. Supercoil losses were influenced by transcription because addition of rifampicin (Rif) caused supercoil density to rebound throughout the chromosome. Gyrase mutants that caused dramatic supercoil losses also reduced the transcription elongation rates throughout the genome. The observed link between RNA polymerase elongation speed and gyrase turnover suggests that bacteria with fast growth rates may generate higher supercoil densities than slow growing species.
How to exploit prior information in low-complexity models

Compressed Sensing refers to extracting a low-dimensional structured signal of interest from its incomplete random linear observations. A line of recent work has studied that, with the extra prior information about the signal, one can recover the signal with much fewer observations. For this purpose, the general approach is to solve weighted convex function minimization problem. In such settings, the convex function is chosen to promote the low-dimensional structure and the optimal weights are so chosen to reduce the number of measurements required for the optimization problem. In this paper, we consider a generalized non-uniform model in which the structured signal falls into some partitions, with entries of each partition having a definite probability to be an element of the structure support. Given these probabilities and regarding the recent developments in conic integral geometry, we provide a method to choose the unique optimal weights for any general low-dimensional signal model. This class of low-dimensional signal model includes many popular examples such as $\ell_1$ analysis (entry-wise sparsity in an arbitrary redundant dictionary), $\ell_{1,2}$ norm (block sparsity) and total variation semi-norm (for piece-wise constant signals). We show through precise analysis and simulations that the weighted convex optimization problem significantly improves the regular convex optimization problem as we choose the unique optimal weights.
Learning Arbiter and Combiner Trees from Partitioned Data for Scaling Machine Learning

Knowledge discovery in databases has become an increasingly important research topic with the advent of wide area network computing. One of the crucial problems we study in this paper is how to scale machine learning algorithms, that typically are designed to deal with main memory based datasets, to efficiently learn from large distributed databases. We have explored an approach called meta-learning that is related to the traditional approaches of data reduction commonly employed in distributed query processing systems. Here we seek efficient means to learn how to combine a number of base classifiers, which are learned from subsets of the data, so that we scale efficiently to larger learning problems, and boost the accuracy of the constituent classifiers if possible. In this paper we compare the arbiter tree strategy to a new but related approach called the combiner tree strategy.
A Novel Approach to Forecast Electricity Price for PJM Using Neural Network and Similar Days Method

Price forecasting in competitive electricity markets is critical for consumers and producers in planning their operations and managing their price risk, and it also plays a key role in the economic optimization of the electric energy industry. This paper explores a technique of artificial neural network (ANN) model based on similar days (SD) method in order to forecast day-ahead electricity price in the PJM market. To demonstrate the superiority of the proposed model, publicly available data acquired from the PJM Interconnection were used for training and testing the ANN. The factors impacting the electricity price forecasting, including time factors, load factors, and historical price factors, are discussed. Comparison of forecasting performance of the proposed ANN model with that of forecasts obtained from similar days method is presented. Daily and weekly mean absolute percentage error (MAPE) of reasonably small value and forecast mean square error (FMSE) of less than 7$/MWh were obtained for the PJM data, which has correlation coefficient of determination of 0.6744 between load and electricity price. Simulation results show that the proposed ANN model based on similar days method is capable of forecasting locational marginal price (LMP) in the PJM market efficiently and accurately.
The burden and determinants of HIV and sexually transmitted infections in a population-based sample of female sex workers in Goa, India

Background: Interventions targeting sex workers are central to the National AIDS Control programme of India’s third 5-year plan. Understanding the way in which societal and individual factors interact to shape sex workers’ vulnerability would better inform interventions. Methods: 326 female sex workers, recruited throughout Goa using respondent-driven sampling, completed interviewer-administered questionnaires. Biological samples were tested for Trichomonas vaginalis, Neisseria gonorrhoea, Chlamydia trachomatis and antibodies to herpes simplex virus type 2 (HSV-2) and HIV. Multivariate analysis was used to define the determinants of HIV infection and any bacterial sexually transmitted infection (STI). Results: Infections were common, with 25.7% prevalence of HIV and 22.5% prevalence of bacterial STI; chlamydia 7.3%, gonorrhoea 8.9% and trichomonas 9.4%. Antibodies to HSV-2 were detected in 57.2% of women. STI were independently associated with factors reflecting gender disadvantage and disempowerment, namely young age, lack of schooling, no financial autonomy, deliberate self-harm, sexual abuse and sex work-related factors, such as having regular customers and working on the streets. Other factors associated with STI were Goan ethnicity, not having an intimate partner and being asymptomatic. Having knowledge about HIV and access to free STI services were associated with a lower likelihood of STI. HIV was independently associated with being Hindu, recent migration to Goa, lodge or brothel-based sex work and dysuria. Conclusion: Sex workers working in medium prevalence states of India are highly vulnerable to HIV and STI and need to be rapidly incorporated into existing interventions. Structural and gender-based determinants of HIV and STI are integral to HIV prevention strategies.
Leveraging Cross-Domain Social Media Analytics to Understand TV Topics Popularity

The way we watch television is changing with the introduction of attractive Web activities that move users away from TV to other media. The social multimedia and user-generated contents are dramatically changing all phases of the value chain of contents (production, distribution and consumption). We propose a concept-level integration framework in which users' activities on different social media are collectively represented, and possibly enriched with external knowledge, such as information extracted from the Electronic Program Guides, or available ontological domain knowledge. The integration framework has a knowledge graph as its core data model. It keeps track of active users, the television events they talk about, the concepts they mention in their activities, as well as different relationships existing among them. Temporal relationships are also captured to enable temporal analysis of the observed activity. The data model allows different types of analysis and the definition of global metrics in which the activity on different media concurs with the measure of success.
Statistical thermal modeling and optimization considering leakage power variations

Unaddressed thermal issues can seriously hinder the development of reliable and low power systems. In this paper, we propose a statistical approach for analyzing thermal behavior under leakage power variations stemming from the manufacturing process. Based on the proposed models, we develop floorplanning techniques targeting thermal optimization. The experimental results show that peak temperature is reduced by up to 8.8°C, while thermal-induced leakage power and maximum thermal variance are reduced by 13% and 17%, respectively, with no additional area overhead compared with best performance-driven optimized design.
Down-Regulation of the Carcinogen-Metabolizing Enzyme Cytochrome P450 1a1 by Vanadium

Vanadium (V5+), a heavy metal contaminant with important toxicological consequences, has received considerable attention as an anticancer agent, although the mechanisms remain unknown. As a first step to investigate these mechanisms, we examined the effect of V5+ (as ammonium metavanadate, NH4VO3) on the expression of the aryl hydrocarbon receptor (AhR)-regulated gene: cytochrome P450 1a1 (Cyp1a1) at each step of the AhR signal transduction pathway, using Hepa 1c1c7 cells. Our results showed a significant reduction in 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD)-mediated induction of Cyp1a1 mRNA, protein and activity levels after V5+ treatments in a dose-dependent manner. Investigation of the effect of coexposure to V5+ and TCDD at transcriptional levels revealed that V5+ significantly inhibited TCDD-mediated induction of AhR-dependent luciferase reporter gene expression. Furthermore, despite not affecting the direct activation of the cytosolic AhR by TCDD and subsequently transforming it to a DNA-binding form, V5+ inhibited the nuclear accumulation of liganded AhR and subsequent formation of the AhR/aryl hydrocarbon nuclear translocator (Arnt)/xenobiotic responsive element (XRE) complex. Importantly, the V5+-mediated inhibition of AhR/Arnt/XRE complex formation coincided with a significant decrease in ecto-ATPase activity. Looking at the post-transcriptional and post-translational effects of V5+ on existing Cyp1a1 mRNA and protein levels, we showed that V5+ did not affect Cyp1a1 mRNA or protein stability, thus eliminating possible role of V5+ in modifying Cyp1a1 gene expression through these mechanisms. This study provides the first evidence that V5+ down-regulates the expression of Cyp1a1 at the transcriptional level through an ATP-dependent mechanism.
Nanostructured Nickel Film Deposition on Carbon Fibers for Improving Reinforcement-matrix Interface in Metal Matrix Composites

The issues in dispersing any form of carbon in metal matrix is the major problem in the field of metal matrix composites with carbon reinforcement (MMCcr). The low wettability of carbon in molten metals and the difference in density are some of the difficulties to obtain a good dispersion of carbon fibers in the matrix and, as a consequence, an improvement of some critical properties for metals in a wide range of application (mechanical properties, electrical properties, optical properties). For this reason, the aim of this work is to obtain a metallic coated carbon fiber to enhance the interaction between the reinforcement and the matrix. Moreover, also the density of carbon fibers could be adjusted depending on the thickness of the coating. Electroless Nickel-Phosphorus Plating (ENP) is one of the candidate to be a coating technique to improve the interaction between the carbon fibers and the metal matrix. Despite of its versatility in terms of complex geometry of the substrate and homogeneity and adhesion of the coating, the presence of the phosphorus in the alloy could create some problems with the metal matrix such as the formation of metal-phosphorus products that can drastically decrease the mechanical properties of the composite. For this reason, in this work, is presented a new way of Electroless Pure Nickel Plating (EPP) without any introduction of phosphorus in the nickel coating. The dependence of the coating thickness and the density of the coated fibers were studied under different plating parameters (temperature of the plating solution, deposition rate and plating solution composition). All the samples were characterized with SEM and XRD and the thickness, density and homogeneity were compared for all the samples obtained.
N-[(E)-4-Bromobenzylidene]-2,3-dimethylaniline

The title compound, C15H14BrN, has an E conformation about the C=N bond and the dihedral angle between the benzene rings is 50.7 (2)°. In the crystal, molecules are linked via C—H⋯π interactions, forming columns propagating along [010].