Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulated Reality in Automotive Design

In this paper we introduce Simulated Reality (SR) as a new concept for the interplay between simulation, optimization and interactive visualization. We see SR as a new metaphor for the interactive visual exploration of simulation and optimization results. The vision of Simulated Reality implies interactive behavior of simulations. Fact is today that simulations might still need hours of computation time, especially in crash worthiness. This paper shows approaches to come closer to the vision of SR. Combining design of experiments methods, metamodeling, new interpolation schemes and innovative graphics methods, we enable to user to interact with simulation parameters, optimization criteria and come to a new interpolated crash result within seconds. The approaches have been successfully applied for solution of real life car design optimization problems.
A Method for Deforming-Driven Exaggerated Facial Animation Generation

This paper proposes a method for automatically generating facial animation with exaggerated features from a facial image. According to facial diversity and identity, an exaggerated face can be determined by the neutral face and the exaggeration effect difference that is represented with the deforming parameters. The proposed method utilizes the central point and the feature vector to represent a facial component and then transforms these feature vectors under the control of deforming parameters to generate the exaggerated facial features. The exaggerated facial animation can be driven to generate by the sequence of the deforming parameters. Experimental results prove that the proposed method has the advantages of simplicity, flexibility and directness, and the generated facial animations are expressive.
Iterative development life cycle (IDLC): a management process for large scale intelligent system development

The iterative development life cycle (IDLC) provides a management process for the successful development of large-scale intelligent system applications. The iterative process minimizes both risks and costs by combining both the well structured management techniques of the waterfall process and the early validation techniques of the evolutionary model. This process is more adaptable to the full range of software project situations and provides the flexibility necessary to accommodate a high dynamic range of technical alternatives. <<ETX>>
Factors Affecting Survival of HIV Positive Children Taking AntiretroviralTherapy at Adama Referral Hospital and Medical College, Ethiopia

Background: The aim of this study is to explore factors affecting survival of children living with HIV/AIDS after initiation of ART. In which it highlights the need for local evidence to promote interventions that optimize survival among HIV-infected children on ART in Ethiopia. Methods: Institution based retrospective cohort study was employed on 560 children enrolled on ART from January, 2006-December, 2010. Information on relevant variables was collected from patients` medical cards and registries. Univariate analysis was used to describe the baseline characteristics of the patients`. Life table was used to estimate survival after initiation of ART. Log rank test was used to compare survival between different categories of independent variables. Multivariable Cox proportional model was fitted to identify factors affecting survival after initiation of ART. Results: Children on ART were followed for a median follow up period of 47 months (IQR=29, 62). At the end of follow up, 364 (65%) were alive and 43 (7.6%) were reported dead. More than three fourth of the deaths occurred within the first sixth months of starting ART. The estimated cumulative survival probabilities were 0.939, 0.928, 0.926, 0.923, 0.920, and 0.916 at 6, 12, 18, 36, 48, and 60 months, respectively. Anemia (hemoglobin level<10 gm/dl) (AHR=2.60, 95% CI=1.41, 4.84), absolute CD4 cell count below the threshold for severe immunodeficiency (AHR=3.55, 95% CI 1.48, 8.46), advanced WHO staging (stage IV) (AHR=3.08, 95% CI=1.27, 7.47), and underweight (AHR=2.49, 95% CI 1.27, 4.88) have found to be predictors of mortality after ART initiation. Conclusions: Mortality was high especially during the first sixth months following ART initiation. Therefore, close follow up of HIV exposed children to make early diagnosis and treatment initiation before the development of severe immune deficiency and advanced clinical stage is important.
Assessment of Body Composition Change in a Community-Based Weight Management Program

Objective: The purpose of this study was to validate the use of the leg-to-leg bioelectrical impedance analysis (BIA) system in assessing change in body composition over 32 weeks in overweight and obese women participating in a community weight management program. Design: Intervention, with subjects prescribed an energy-restriction diet and exercise program for 32 weeks and body composition measured pre-study and after 12 and 32 weeks. Subjects and Setting: Overweight and obese premenopausal women (n=201) with no overt disease were recruited at six sites into community-based weight loss programs. One hundred and twenty-four women completed all aspects of the study. Intervention: Energy intake was set at 0.8 × resting metabolic rate (RMR) for weeks 1 through 12, 1.0 × RMR for weeks 13 through 20 and 1.2 × RMR for weeks 21 through 32. Energy intake was based on a food exchange table, with the number of food exchanges adjusted to encourage a percent distribution of 55% carbohydrate, 30% fat and 15% protein. Subjects increased their daily walking distance by 3.2 km above pre-study levels. Measures of Outcome: Underwater weighing, seven skinfolds, and leg-to-leg BIA tests were used to assess body composition. Results: A 3 × 3 repeated measures ANOVA revealed no significant difference in detecting change in FFM at 12 and 32 weeks among underwater weighing, BIA and skinfold, (F(4,492)=1.73, p=0.141) (decrease in FFM of 1.0±3.3 kg, 1.7±2.2 kg, and 1.4±3.3 kg respectively, 32 weeks). Conclusions: The leg-to-leg BIA system provides a valid measure of body composition change in overweight premenopausal women during a 32-week community-based weight loss program.
Autoantibodies in systemic lupus erythematosus: comparison of historical and current assessment of seropositivity

Systemic lupus erythematosus (SLE) is characterized by multiple autoantibodies and complement activation. Recent studies have suggested that anti-nuclear antibody (ANA) positivity may disappear over time in some SLE patients. Anti-double-stranded DNA (dsDNA) antibody titers and complement levels may vary with time and immunosuppressive treatment, while the behavior of anti-extractable nuclear antigen (ENA) over time is less well understood. This study sought to determine the correlation between historical autoantibody tests and current testing in patients with SLE. Three hundred and two SLE patients from the ACR Reclassification of SLE (AROSE) database with both historical and current laboratory data were selected for analysis. The historical laboratory data were compared with the current autoantibody tests done at the reference laboratory and tested for agreement using percent agreement and Kappa statistic. Serologic tests included ANA, anti-dsDNA, anti-Smith, anti-ribonucleoprotein (RNP), anti-Ro, anti-La, rheumatoid factor (RF), C3 and C4. Among those historically negative for immunologic markers, a current assessment of the markers by the reference laboratory generally yielded a low percentage of additional positives (3–13%). However, 6/11 (55%) of those historically negative for ANA were positive by the reference laboratory, and the reference laboratory test also identified 20% more patients with anti-RNP and 18% more with RF. Among those historically positive for immunologic markers, the reference laboratory results were generally positive on the same laboratory test (range 57% to 97%). However, among those with a history of low C3 or C4, the current reference laboratory results indicated low C3 or C4 a low percentage of the time (18% and 39%, respectively). ANA positivity remained positive over time, in contrast to previous studies. Anti-Ro, La, RNP, Smith and anti-dsDNA antibodies had substantial agreement over time, while complement had less agreement. This variation could partially be explained by variability of the historical assays, which were done by local laboratories over varying periods of time. Variation in the results for complement, however, is more likely to be explained by response to treatment. These findings deserve consideration in the context of diagnosis and enrolment in clinical trials.
Skeleton-Based Anime Hair Modeling and Visualization

This paper describes a new hair representation technique for drawing 3D CG characters. In our approach hair models are represented by skeleton-based implicit surfaces. Implicit modeling allows representing union and division of wisps of hair by using a blobby model. Our method gets hair skeletons as input data. The skeleton is interpolated using a spline function and then an implicit surface of hair shape is generated by the skeleton. Finally, non-photo realistic rendering is applied to generate resulting image as output.
A Boolean approach to construct neural networks for non-Boolean problems

A neural network construction method for problems specified for data sets with input and/or output values in the continuous or discrete domain is described and evaluated. This approach is based on a Boolean approximation of the data set and is generic for various neural network architectures. The construction method takes advantage of a construction method for Boolean problems without increasing the dimensions of the input or output vectors, which is an advantage over approaches which work on a binarized version of the data set with an increased number of input and output elements. Further, the networks are pruned in a second phase in order to obtain very small networks.
Wavelet-based signal approximation with multilevel learning algorithms using genetic neuron selection

Neural networks based on wavelets are constructed to study the function learning problems. Two types of learning algorithms, the overall multilevel learning (OML) and the pyramidal multilevel learning (PML) with genetic neuron selection are comparatively studied for the convergence rate and accuracy using data samples of a piecewise defined signal. Moreover, the two algorithms are examined using orthogonal and non orthogonal bases. Experimental studies exhibit that the string representation of genetic algorithms (GA) is a key issue in determining the suitable network structures and the performances of function approximation for the two learning algorithms.
Salmonellae and Shigellae

in spite of painstaking antilarval and antiadult measures against this malaria vector, the five-year, multimillion dollar campaign was biologically unsuccessful-A. labranchiae remains entrenched in Sardinia-but as a public health and bonification project it was supremely successful. Malaria disappeared completely and considerable areas of new farm land were made available as a result of ditching and drainage. The essential story of the campaign is told in Chapter II, "The Campaign," and a forthright evaluation of various influences and circumstances concerned is given in Chapter X, "Critical Review. " Special aspects of the undertaking-historical, procedural, entomologic, technologic, epidemiologic, administrative, quarantine, antihousefly, taxonomic, ecologic, fiscal, personnel, and final recommendations to the Italian government-are treated in more or less detail in the eight other chapters and seven appendixes. The text is bountifully supplemented by excellent photographs, figures, and tables. Perhaps the most remarkable feature of this scientific report is the resolute determination of the authors to examine and appraise impersonally their plans, activities, and results in order to expose their faults and shortcomings. Thus, this record reveals serious deficits in knowledge and technics which should stimulate investigation and development. The volume will be of compelling interest to the fundamental biologist, the malariologist, the medical entomologist, the insect-control engineer, the health administrator, and many others. JUSTIN M. ANDREWS
Image classification and retrieval based on wavelet-SOM

The paper describes a new method to extract and cluster image features for effective still image databases. The feature vectors concerning color and texture are extracted using the multiresolution wavelet. In contrast to traditional image databases where feature vectors extracted from stored images are stored and used to match the feature vector of the input image for similarity retrieval, we use the self-organizing map neural network for clustering stored images. No feature vectors are stored in the databases, which saves storage space. A prototype image database is developed and some experiments are performed using it. The paper reports on the architecture and experimental results.
Diferenças no crescimento e na composição corporal entre escolares de origem germânica e brasileira

OBJETIVO: Analisar possiveis diferencas no crescimento e na composicao corporal de criancas de cinco a dez anos de idade de origem etnica germânica e brasileira dos municipios de Marechal Cândido Rondon e de Foz do Iguacu, Parana. METODOS: Estudo transversal, realizado nos anos de 2002 e 2003. Foram avaliadas 2.673 criancas, das quais 1.352 eram meninas, em 16 escolas publicas e particulares. Avaliou-se peso, estatura e as dobras cutâneas tricipital e subescapular. Obteve-se a medida derivada do indice de massa corporea (IMC) e da soma das dobras tricipital e subescapular (TR+SE). Utilizou-se estatistica descritiva e ANOVA para a comparacao entre genero e idade. O nivel de significância foi p<0,05. RESULTADOS: Os escolares apresentaram valores de peso corporal e estatura proximos ao percentil 50, comparados com estudos de referencia nacional e internacional. Quando comparados com os dados do Centers for Disease Control and Prevention (CDC), divulgados em 2000, eram obesos 10,9% dos escolares de etnia brasileira, 8,8% dos meninos germânicos e 6,6% das escolares germânicas. Nas dobras cutâneas, as meninas das duas etnias apresentaram valores superiores em relacao aos meninos. Os valores de escore Z entre escola publica e particular apresentaram diferencas no peso corporal, estatura e IMC para as criancas germânicas. No entanto, quando analisadas criancas brasileiras e germânicas em escolas particulares, estas diferencas ocorreram apenas para o IMC. CONCLUSOES: Nas escolas publicas, as criancas germânicas sao maiores quando comparadas as brasileiras; no entanto, nas escolas particulares, esta diferenca desaparece, podendo sugerir que o ambiente exerce maior influencia do que a origem etnica.
A novel design and analysis of a 2-DOF compliant parallel micromanipulator for nanomanipulation

A new 2-degrees of freedom compliant parallel micromanipulator (CPM) utilizing flexure joints is proposed for two-dimensional nanomanipulation in this paper. By a proper selection of actuators, flexure hinges, and materials, this system is constructed and analyzed by a pseudorigid-body model, architectural optimization, and finite-element analysis. Both the position and velocity kinematic modelings are established, and afterwards, statics analysis is performed. In view of the physical constraints imposed by pizeo-actuators and flexure hinges, the CPM's workspace area is determined. And in order to achieve a maximum workspace subjected to the given dexterity indices, kinematic optimization of the design parameters is carried out, which results in a manipulator satisfying the operational requirements. Furthermore, the finite-element analysis has been undertaken to validate the analytical modeling, and the influence of architectural parameters on CPM performance has been evaluated as well. Note to Practitioners-This paper is motivated by the problem of designing a nanomanipulator for two-dimensional (2-D) assembly of nanoscale objects via nanomanipulation. A novel planar parallel mechanism incorporating compliant mechanisms is designed for such a purpose. Since the application of the manipulator depends significantly on the kinematic mathematical models, the designed compliant parallel micromanipulator (CPM) is analyzed by the established pseudorigid-body (PRB) model. The architectural optimization leads to a CPM satisfying the workspace and resolution requirements of this work. Moreover, finite-element analysis is performed to verify the accuracy of the developed PRB model, and simulation results illustrate the efficiency of the PRB model in designing and analyzing the CPM. Since the designed CPM is composed solely of flexural elements which are known to be competent in high precise applications, it is reasonable to expect that the CPM could find its way into 2-D manipulation of nanoscale components.
A Simple and Effective Solution for Script Identification in the Wild

We present an approach for automatically identifying the script of the text localized in the scene images. Our approach is inspired by the advancements in mid-level features. We represent the text images using mid-level features which are pooled from densely computed local features. Once text images are represented using the proposed mid-level feature representation, we use an off-the-shelf classifier to identify the script of the text image. Our approach is efficient and requires very less labeled data. We evaluate the performance of our method on a recently introduced CVSI dataset, demonstrating that the proposed approach can correctly identify script of 96.70% of the text images. In addition, we also introduce and benchmark a more challenging Indian Language Scene Text (ILST) dataset for evaluating the performance of our method.
MalAware: Effective and Efficient Run-Time Mobile Malware Detector

Effective detection of malware is of paramount importance for securing the next generation of smart devices. Static detection, the preferred technique used so far, is not sufficiently powerful to defeat state-of-the-art malware, and will be even less effective in the near future. Dynamic malware detection guarantees better protection since it operates at run-time and can identify also unknown malware, however, the computational resources required are usually not affordable for battery operated devices. We propose MalAware, an effective, fast, and lightweight dynamic detection method. We identify malware by applying linear complexity classification algorithms to seven discriminating features and we improve the reliability of our detection using an efficient sliding windows mechanism. Our results, based on testing of about 2000 Android applications, demonstrate the timeliness and the effectiveness of detection in our approach. In fact, malware is detected within the first three minutes of execution with an F-measure of 0.85.
Biomedicine. Rare cancer successes spawn 'exceptional' research efforts.

Most experimental cancer drugs that reach clinical trials fail to help enough patients, and the drugs are shelved. But in many trials, there are the exceptions: a rare patient with advanced cancer whose tumors shrink or even disappear for many months or years. This month, the U.S. National Cancer Institute (NCI) announced that it is casting a wide net for such "exceptional responders" in hopes of ferreting out genetic or other molecular peculiarities that may explain why a drug sometimes has dramatic beneficial effects in certain patients. NCI also intends to launch a new kind of clinical trial that will genotype a patient9s tumor and match it to a potentially effective drug, including experimental drugs that haven9t been approved.
Fast text classification: a training-corpus pruning based approach

With the rapid growth of on-line information available, text classification is becoming more and more important. kNN is a widely used text classification method of high performance. However, this method is inefficient because it requires a large amount of computation for evaluating the similarity between a test document and each training document. In this paper, we propose a fast kNN text classification approach based on pruning the training corpus. By using this approach, the size of training corpus can be condensed sharply so that time-consuming on kNN searching can be cut off significantly, and consequently classification efficiency can be improved substantially while classification performance is preserved comparable to that of without pruning. Effective, algorithm for text corpus pruning is designed. Experiments over the Reuters corpus are carried out, which validate the practicability of the proposed approach. Our approach is especially suitable for on-line text classification applications.
Battery-supercapacitor hybrid system for high-rate pulsed load applications

Modern batteries (e.g., Li-ion batteries) provide high discharge efficiency, but the rate capacity effect in these batteries drastically decreases the discharge efficiency as the load current increases. Electric double layer capacitors, or simply supercapacitors, have extremely low internal resistance, and a battery-supercapacitor hybrid may mitigate the rate capacity effect for high pulsed discharging current. However, a hybrid architecture comprising a simple parallel connection does not perform well when the supercapacitor capacity is small, which is a typical situation because of the low energy density and high cost of supercapacitors. This paper presents a new battery-supercapacitor hybrid system that employs a constant-current charger. The constant-current charger isolates the battery from supercapacitor to improve the end-to-end efficiency for energy from the battery to the load while accounting for the rate capacity effect of Li-ion batteries and the conversion efficiencies of the converters.
Time-Domain Blind Separation of Audio Sources on the Basis of a Complete ICA Decomposition of an Observation Space

Time-domain algorithms for blind separation of audio sources can be classified as being based either on a partial or complete decomposition of an observation space. The decomposition, especially the complete one, is mostly done under a constraint to reduce the computational burden. However, this constraint potentially restricts the performance. The authors propose a novel time-domain algorithm that is based on a complete unconstrained decomposition of the observation space. The observation space may be defined in a general way, which allows application of long separating filters, although its dimension is low. The decomposition is done by an appropriate independent component analysis (ICA) algorithm giving independent components that are grouped into clusters corresponding to the original sources. Components of the clusters are combined by a reconstruction procedure after estimating microphone responses of the original sources. The authors demonstrate by experiments that the method works effectively with short data, compared to other methods.
Implementing Scientific Evidence-Based Guidelines: Case Study of Severe Traumatic Brain Injuries

Abstract Research has proven that all brain damage does not occur at the moment of trauma, but rather evolves over the ensuing hours and days after the initial injury, due to brain swelling. In most cases, this secondary damage can be controlled using scientific, evidence-based treatment guidelines. The US based Brain Trauma Foundation developed those guidelines in 1995. Trauma is the most frequent cause of death between the 1st and the 40th year of life in Austria. The program targets an issue of quality of care improvement in the hospital setting. The program started with six regional ones. Consequently, the program will be extended to more trauma centers later on. Representatives of all participating centers were trained at the spot (within the hospitals) in details of documentation (hardware and software). The Internet based database is used to monitor the compliance with the Guidelines and the outcomes of treatment. The data collected will be analyzed and will provide basic epidemiological and quality of care information.