Miracle-dz/UniFAR-FTUs huggingface dataset:
Driver Drowsiness Monitoring Based On Eye Map And Mouth Contour

Number of accidents occurs now a days due to the drowsiness of driver. Therefore to assist the driver with the problem of drowsiness, the system must be design to carefully developed to provide an interface and interaction the make sense for the driver. This article introduces a new approach towards detection of drives' drowsiness based on yawning detection. The main aim is to reduce the number of accidents occurred due to drivers fatigue and hence increases the road safety. The special gestures of body and face are used as the sign of driver fatigue, including yawning, eye tiredness and mouth movement, and it indicate that the driver is no more in a proper driving condition. This involves several steps including the real time detection and tracking of driver's face detection, tracking of the mouth contour, eye and the detection of yawning based on measuring both the rate and the amount of changes in the mouth contour area, Eye detection using Eye Map.
Comparative Study of MPPT Methods for Solar PV Driven Induction Motor Load

tracking (MPPT) such as Fuzzy logic controller (FLC) and artificial neural network (ANN) are investigated, and compared with traditional constant voltage (CVC), Perturb & observed (P&O) and Incremental conductance (IC) MPPT methods to achieve maximum power point (MPP) through PV system, in this paper. For transient analysis of MPPT assisted photovoltaic (PV) system powered to the induction motor (IM) in various applications. The system performance is investigated for different irradiation conditions in terms of voltage, current, power and rotor speed response. The detailed study is carried on advantages of advance MPPT techniques over the traditional MPPT techniques. The modeling of solar PV system and considered MPPT techniques have been done in MATLAB/ Simulink environment.
Oral estramustine and cyclophosphamide in patients with metastatic hormone refractory prostate carcinoma

Nearly all cases of metastatic prostate carcinoma progress, after hormonal ablation, to a hormone refractory status. To the authors' knowledge no standard chemotherapy for patients with hormone refractory prostate carcinoma (HRPC) exists. In a prospective study, the efficacy and toxicity of an oral combination of estramustine and cyclophosphamide were evaluated.
On the gasification of wet biomass in supercritical water : over de vergassing van natte biomassa in superkritiek water

Supercritical water gasification (SCWG) is a challenging thermo-chemical conversion route for wet biomass and waste streams into hydrogen and/or methane. At temperatures and pressures above the critical point the physical properties of water differ strongly from liquid water or steam. Because of the physical properties of supercritical water, SCWG is considered to be a promising technology for the thermo chemical conversion of wet biomass. Anticipated applications for supercritical gasification of wet biomass are for example: • The on-site production of a fuel gas in industry, vehicles, buildings. • The production of pure hydrogen for the process industry. • The production of a hydrogen or methane rich gas from manure or sewage sludge. • The production of syngas, mainly consisting of CO and H2, at high pressure. Although the results on a laboratory scale show that SCWG is a very promising technique, the process is still in an early stage of development. A lot of work remains to be done to get a full understanding of the complex process and to bridge the gap between small-scale testing in laboratories to demonstration on full-scale. For this purpose, adequate design rules for SCWG need to be developed. The primary objective of this thesis is to study the mechanisms and key param- eters of SCWG and to develop design rules for adequate reactor and process design. The following approach is chosen to meet these objectives: 1. Investigation of the influence of key process parameters on the thermal efficiency of supercritical gasification of wet biomass. 2. The development of heat transfer models for water at supercritical pressures. 3. The development of a new high throughput batch reactor for conversion rate measurements of supercritical gasification of wet biomass. 4. The evaluation and experimental validation of sorption enhanced supercritical gasification of wet biomass.
Glomerular changes in the KK‐Ay/Ta mouse: A possible model for human type 2 diabetic nephropathy

Background: In type 2 diabetic nephropathy, there is no animal model which has been completely matched with humans. Advanced glycation end products (AGE) and transforming growth factor‐beta (TGF‐β) are closely related to hyperglycaemia and their pathobiochemistry could explain diabetic nephropathy. The objective of the present study was to evaluate the KK‐Ay/Ta mouse as a suitable model for type 2 diabetic nephropathy including pathological changes and immunohistochemical analyses of AGE and TGF‐β, compared with the non‐diabetic BALB/cA mouse.
TANGO: a generic tool for high-throughput 3D image analysis for studying nuclear organization

Motivation: The cell nucleus is a highly organized cellular organelle that contains the genetic material. The study of nuclear architecture has become an important field of cellular biology. Extracting quantitative data from 3D fluorescence imaging helps understand the functions of different nuclear compartments. However, such approaches are limited by the requirement for processing and analyzing large sets of images. Results: Here, we describe Tools for Analysis of Nuclear Genome Organization (TANGO), an image analysis tool dedicated to the study of nuclear architecture. TANGO is a coherent framework allowing biologists to perform the complete analysis process of 3D fluorescence images by combining two environments: ImageJ (http://imagej.nih.gov/ij/) for image processing and quantitative analysis and R (http://cran.r-project.org) for statistical processing of measurement results. It includes an intuitive user interface providing the means to precisely build a segmentation procedure and set-up analyses, without possessing programming skills. TANGO is a versatile tool able to process large sets of images, allowing quantitative study of nuclear organization. Availability: TANGO is composed of two programs: (i) an ImageJ plug-in and (ii) a package (rtango) for R. They are both free and open source, available (http://biophysique.mnhn.fr/tango) for Linux, Microsoft Windows and Macintosh OSX. Distribution is under the GPL v.2 licence. Contact: thomas.boudier@snv.jussieu. fr Supplementary information: Supplementary data are available at Bioinformatics online.
A novel Arg H52/Tyr H33 conservative motif in antibodies: A correlation between sequence of antibodies and antigen binding

Antibodies are the family of proteins, which are responsible for antigen recognition. The computational modeling of interaction between an antigen and an antibody is very important when crystallographic structure is unavailable. In this research, we have discovered the correlation between the amino acid sequence of antibody and its specific binding characteristics on the example of the novel conservative binding motif, which consists of four residues: Arg H52, Tyr H33, Thr H59, and Glu H61. These residues are specifically oriented in the binding site and interact with each other in a specific manner. The residues of the binding motif are involved in interaction strictly with negatively charged groups of antigens, and form a binding complex. Mechanism of interaction and characteristics of the complex were also discovered. The results of this research can be used to increase the accuracy of computational antibody-antigen interaction modeling and for post-modeling quality control of the modeled structures.
XSLT transformation from UML models to LQN performance models

A graph grammar-based transformation of a UML design model into a Layered Queueing Network (LQN) performance model was previously proposed by the authors of this paper. The actual transformation was implemented in two ways: first by using an existing graph-rewriting tool, and secondly through an ad-hoc graph transformation implemented in Java.This paper extends the previous work of the authors by proposing a third approach to implement the UML to LQN transformation by using XSLT. Recommended by the World Wide Web Consortium (W3C) the Extensible Stylesheet Language for Transformations (XSLT) is a flexible language for transforming XML documents into various formats including HTML, XML, text, PDF, etc. The input to our XSLT transformation is an XML file that contains the UML model in XML format according to the standard XML Metadata Interchange (XMI). The output is the corresponding LQN model description file, which can be read directly by existing LQN solvers. The paper compares the relative advantages and disadvantages of the XSLT transformation with the previous approaches proposed by the authors, describes the principles of the XSLT transformation and applies it to a case study.
High Mechanical Strength Double‐Network Hydrogel with Bacterial Cellulose

Double‐network (DN) hydrogels with high mechanical strength have been synthesized using the natural polymers bacterial cellulose (BC) and gelatin. As‐prepared BC contains 90 % water that can easily be squeezed out, with no more recovery in its swelling property. Gelatin gel is brittle and is easily broken into fragments under a modest compression. In contrast, the fracture strength and elastic modulus of a BC–gelatin DN gel under compressive stress are on the order of megapascals, which are several orders of magnitude higher than those of gelatin gel, and almost equivalent to those of articular cartilage. A similar enhancement in the mechanical strength was also observed for the combination of BC with polysaccharides, such as sodium alginate, gellan gum, and ι‐carrageenan.
Experiments on Term Extraction using Noun Phrase Subclassifications

In this paper we describe and compare three approaches for the automatic extraction of medical terms using noun phrases (NPs) previously recognized on medical text corpus in Spanish. In the first approach, as baseline, we extracted all NPs, while for the second and third ones the extraction process is directed to “specific NPs” that are determined on the basis of the syntactic and positional criteria, among others. As contributions (i) we showed that it is possible to extract medical terms using “specific NPs”, (ii) new terms were added in the software dictionary, and (iii) terms that were not in the reference lists were extracted. For the third contribution, we used the SNOMED CT R terms lists, aiming at improving the IULA reference lists.
Relaxation Effect in RRAM Arrays: Demonstration and Characteristics

In this letter, two distinct retention degradation regions, a rapid resistance relaxation effect followed by a slow resistance loss process, were observed from high-resistance state (HRS) of HfOx-based resistive random access memory (RRAM) array retention tests. The influence of resistance, thickness of HfOx, baking temperature, and RESET pulsewidth on the relaxation effect for HRS were studied in detail. Random telegraph noise (RTN) characterization suggests that this early loss of data for HRS is due to new oxygen vacancies generation which could shorten the gap between the filament and the TaOx layer. Possible causes were provided to describe the relaxation effect and to understand the RTN characteristics correlated with relaxation effect in RRAM array.
Attention Problems and Executive Functions in Children With Spina Bifida and Hydrocephalus

This study addressed the incidence of Attention-Deficit/Hyperactivity Disorder (ADHD) subtypes in children with spina bifida meningomyelocele and shunted hydrocephalus (SBH) as well as differences in executive functions among these subtypes. Parent rating scales revealed that 31% of the group with SBH could be identified with AD/HD, mostly the Inattentive type (23%). The group with SBH differed from normal controls on cognitive measures of executive functions, but subtype differences were not significant. Multivariate tests showed that children with SBH were rated with greater difficulties on the Behavior Rating Inventory of Executive Function (BRIEF) compared to controls; those with SBH and any subtype of ADHD differed from those with SBH and no ADHD; and those with ADHD (Combined Type) differed significantly from those with ADHD (Predominantly Inattentive Type). Subtype differences on univariate tests in the latter comparison were significant on the BRIEF Inhibit scale, showing more disinhibition in those with SBH and ADHD (Combined Type), but no significant differences were apparent on the BRIEF Sustain, Shift, and Initiate scales. The results show that the incidence of ADHD in children with SBH exceeds the population rate, is represented by problems with inattention rather than with impulsivity and hyperactivity; and that as with non-brain injured individuals, subtype differences in cognitive function remain to more clearly delineated.
Distributed Clone Detection in Static Wireless Sensor Networks: Random Walk with Network Division

Wireless Sensor Networks (WSNs) are vulnerable to clone attacks or node replication attacks as they are deployed in hostile and unattended environments where they are deprived of physical protection, lacking physical tamper-resistance of sensor nodes. As a result, an adversary can easily capture and compromise sensor nodes and after replicating them, he inserts arbitrary number of clones/replicas into the network. If these clones are not efficiently detected, an adversary can be further capable to mount a wide variety of internal attacks which can emasculate the various protocols and sensor applications. Several solutions have been proposed in the literature to address the crucial problem of clone detection, which are not satisfactory as they suffer from some serious drawbacks. In this paper we propose a novel distributed solution called Random Walk with Network Division (RWND) for the detection of node replication attack in static WSNs which is based on claimer-reporter-witness framework and combines a simple random walk with network division. RWND detects clone(s) by following a claimer-reporter-witness framework and a random walk is employed within each area for the selection of witness nodes. Splitting the network into levels and areas makes clone detection more efficient and the high security of witness nodes is ensured with moderate communication and memory overheads. Our simulation results show that RWND outperforms the existing witness node based strategies with moderate communication and memory overheads.
RX model based fault detection and diagnosis for chillers using upport vector machines

Efficient and robust fault detection and diagnosis (FDD) can potentially play an important role in developing building management systems (BMS) for high performance buildings. Our research indicates that, in comparison to traditional model-based or data-driven methods, the combination of time series modeling and machine learning techniques produces higher accuracy and lower false alarm rates in FDD for chillers. In this paper, we study a hybrid method incorporating auto-regressive model with exogenous variables (ARX) and support vector machines (SVM). A high dimensional parameter space is constructed eywords: ault detection and diagnosis VAC inear regression RX model upport vector machines by the ARX model and SVM sub-divides the parameter space with hyper-planes, enabling fault classification. Experimental results demonstrate the superiority of our method over conventional approaches with higher prediction accuracy and lower false alarm rates. © 2014 Elsevier B.V. All rights reserved.
Systematic reviews of treatment for inflammatory demyelinating neuropathy *

This review describes the progress made in preparing Cochrane systematic reviews of randomized controlled trials for Guillain–Barré syndrome (GBS), chronic inflammatory demyelinating polyradiculoneuropathy (CIDP), multifocal motor neuropathy (MMN) and the demyelinating neuropathies associated with paraproteins. The discovery of antibodies against myelin and axolemmal glycolipids and proteins has not yet replaced the clinicopathological classification on which treatment trials have been based. Systematic reviews have endorsed the equivalence of plasma exchange (PE) and intravenous immunoglobulin (IVIg) and the lack of efficacy of steroids in GBS. Systematic reviews have also endorsed the value of steroids, PE and IVIg in CIDP but randomized controlled trials have only shown benefit from IVIg in MMN. There is a paucity of evidence concerning the efficacy of treatments in paraproteinaemic demyelinating neuropathy apartment from small trials showing short‐term benefit from PE or IVIg. There is a lack of good quality controlled trials of immunosuppressive agents in any of these conditions. As the number of treatment trials increases, Cochrane systematic reviews will be an increasingly valuable resource for summarizing the evidence from randomised controlled trials on which to base clinical practice. They already demonstrate major deficiencies in the existing evidence base.
Fabrication of Nanostructured Hydrophobic Surfaces with Laser Interference Lithography

The fabrication of large area nanoscale periodic structures on material surfaces for hydrophobicity engineering has been difficult due to the complex processes. Here we propose a two-step fabrication method for periodic nanostructures by combining laser interference lithography (LIL) and reactive ion etching (RIE). Sub-micron periodic nanotip patterns are fabricated in the photoresist by LIL, and then transferred into the silicon substrate using RIE. By measuring the contact angle (CA) of a water droplet on the substrate surface, the wettability of the surface with nanotip structures of various periods is studied. Our experiments show that the nanotip structures fabricated by the combined LIL and RIE process deliver satisfactory hydrophobic tendencies when the periods fall into the submicron scale. When the period of the structure is small enough, the hydrophilicity of the surface can be altered into hydrophobicity. The hydrophobicity achieved by this method is reusable and sustainable with low cost and no composition alteration comparing to chemical methods. The process developed in this work provides potential applications in biosensingand digital fluidics.
Towards pertinent evaluation methodologies for word-space models

This paper discusses evaluation methodologies for a particular kind of meaning models known as word-space models, which use distributional information to assemble geometric representations of meaning similarities. Word-space models have received considerable attention in recent years, and have begun to see employment outside the walls of computational linguistics laboratories. However, the evaluation methodologies of such models remain infantile, and lack efforts at standardization. Very few studies have critically assessed the methodologies used to evaluate word spaces. This paper attempts to fill some of this void. It is the central goal of this paper to answer the question how can we determine whether a given word space is a good word space? 
Learning to rank bag-of-word histograms for large-scale object retrieval

Most state-of-the-art object retrieval systems rely on ad-hoc similarities between histograms of quantised local descriptors to find, in their databases, all the images relevant to an image query. In this work, our goal is to replace those similarities with ones that are specifically trained to maximize the retrieval accuracy. We propose to use a simple and very general linear model whose weights directly represent the similarity values. We devise a variant of rank-SVM to learn those weights automatically from training data with fast convergence and we propose techniques to limit the number of parameters of the model and prevent overfitting. Importantly, the flexibility of our model allows us to seamlessly incorporate well-known image retrieval schemes such as burstiness, negative evidence and idf weighting, and still exploit inverted files for efficiency in the large-scale setting. In our experiments, we show that our approach consistently and significantly outperforms the similarities used in several state-of-the-art systems on 4 standard benchmark datasets. In particular, on the Oxford105k dataset, our method outperforms the closest competitor by 6%.
Foaming of Microcellular PP-MWCNT Nanocomposite in a Sub-Critical CO2 Process

The present work covers the processing route and investigation of PP-MWCNT nanocomposite foams. CO2 is used as a blowing agent in a batch foaming process with a saturation pressure range of 3-7 MPa. Carbon nanotubes act as nucleating agents during the foaming process, which is supported by SEM image analysis and DSC study. Relative density measurements have demonstrated that even though MWCNT promote nucleation of a large amount of cells, the densities of PP-MWCNT nanocomposite foams do not differ significantly from pure PP samples.
Scene Character Detection and Recognition with Cooperative Multiple-Hypothesis Framework

To handle the variety of scene characters, we propose a cooperative multiple-hypothesis framework which consist s of an image operator set module, an Optical Character Recognition (OCR) m odule and an integration module. Multiple image operators activated by multiple parameters probe suspected character regions. The OCR module is t h n applied to each suspected region and returns multiple candidates wi h weight values for future integration. Without the aid of the heuristic rules which impose constraints on segmentation area, aspect ratio, color consistency, text line orientations, etc., the integration module automatic ally prunes the redundant detection /recognition and pads the missing detection /recognition. The proposed framework bridges the gap between scene charac ter detection and recognition, in the sense that a practical OCR engin is effectively leveraged for result refinement. In addition, the proposed m thod achieves the detection and recognition at the character level, which enables dealing with special scenarios such as single character, text along arbitrary orientations or text along curves. We perform experiments on the b enchmark ICDAR 2011 Robust Reading Competition dataset which includ es a text localization task and a word recognition task. The quantita tive results demonstrate that multiple hypotheses outperform a single h ypot esis, and be comparable with state-of-the-art methods in terms of rec all, precision, F-measure, character recognition rate, total edit distanc e d word recognition rate. Moreover, two additional experiments are conduc ted to confirm the simplicity of parameter setting in this proposal. key words: cooperative multiple-hypothesis framework, scene character, OCR, integration, voting