Miracle-dz/UniFAR-FTUs huggingface dataset:
Semi-Markov Availability Modeling of a Redundant System with Partial and Full Rejuvenation Actions

Software rejuvenation is a preventive maintenance technique to prevent failures in continuously running systems that experience software aging. In this paper, rejuvenation is modeled in a redundant computer system via a semi-Markov process in order to counteract software aging. The system can be switched into the standby unit automatically or manually in the case of a failure at the switching mechanism. Depending on the degradation of the system resources, the running node is either rejuvenated or continues serving and systempsilas steady-state availability is studied along with the optimal time to perform rejuvenation, which increases systempsilas availability. Moreover, an alternative rejuvenation modeling is introduced, in which two different actions; partial and full rejuvenation are performed according to the degradation level. The optimal rejuvenation policies for this case are also derived, leading the system to higher levels of availability.
Improving Readability in an Explicit Genre-based Approach: The Case of an EFL Workplace Context

The present study investigates how learners of English as a foreign language (EFL) improve the readability of their texts in an explicit genre-based approach that is utilized in an oil and gas exploration workplace in Angola. By drawing on the English for Specific Purposes and Systemic Functional Linguistics genre traditions, the study engages 18 intermediate learners (14 males and 4 females) in an explicit instruction of an email making a request and assesses how learners’ written products improve in terms of genre and linguistics choices. The study also explores how learners notice the features that render their texts readable. The results shed light on the features that should require attention in helping learners with readability, and inform on the way an explicit genre-based approach should be utilized to maximize learners’ ability to notice readability features.
Quantitative analysis of cerebral white matter anatomy from diffusion MRI

In this thesis we develop algorithms for quantitative analysis of white matter fiber tracts from diffusion MRI. The presented methods enable us to look at the variation of a diffusion measure along a fiber tract in a single subject or a population, which allows important clinical studies toward understanding the relation between the changes in the diffusion measures and brain diseases, development, and aging. The proposed quantitative analysis is performed on a group of fiber trajectories extracted from diffusion MRI by a process called tractography. To enable the quantitative analysis we first need to cluster similar trajectories into groups that correspond to anatomical bundles and to establish the point correspondence between these variable-length trajectories. We propose a computationally-efficient approach to find the point correspondence and the distance between each trajectory to the prototype center of each bundle. Based on the computed distances we also develop a novel model-based clustering of trajectories into anatomically-known fiber bundles. In order to cluster the trajectories, we formulate an expectation maximization algorithm to infer the parameters of the gamma-mixture model that we built on the distances between trajectories and cluster centers. We also extend the proposed clustering algorithm to incorporate spatial anatomical information at different levels through hierarchical Bayesian modeling. We demonstrate the effectiveness of the proposed methods in several clinical applications. In particular, we present our findings in identifying localized group differences in fiber tracts between normal and schizophrenic populations.
Diffusion-limited reactions in crowded environments: a local density approximation

In the real world, diffusion-limited reactions in chemistry and biology mostly occur in crowded environments, such as macromolecular complex formation in the cell. Despite the paramount importance of such phenomena, theoretical approaches still mainly rely on the Smoluchowski theory, only valid in the infinite dilution limit. In this paper we introduce a novel theoretical framework to describe the encounter rate and the stationary density profiles for encounters between an immobilized target and a fluid of interacting spherical particles, valid in the local density approximation. A comparison with numerical simulations performed for a fluid of hard spheres and square well attractive hard spheres confirms the accuracy of our treatment.
Predicting Hospital-Acquired Infections by Scoring System with Simple Parameters

Background Hospital-acquired infections (HAI) are associated with increased attributable morbidity, mortality, prolonged hospitalization, and economic costs. A simple, reliable prediction model for HAI has great clinical relevance. The objective of this study is to develop a scoring system to predict HAI that was derived from Logistic Regression (LR) and validated by Artificial Neural Networks (ANN) simultaneously. Methodology/Principal Findings A total of 476 patients from all the 806 HAI inpatients were included for the study between 2004 and 2005. A sample of 1,376 non-HAI inpatients was randomly drawn from all the admitted patients in the same period of time as the control group. External validation of 2,500 patients was abstracted from another academic teaching center. Sixteen variables were extracted from the Electronic Health Records (EHR) and fed into ANN and LR models. With stepwise selection, the following seven variables were identified by LR models as statistically significant: Foley catheterization, central venous catheterization, arterial line, nasogastric tube, hemodialysis, stress ulcer prophylaxes and systemic glucocorticosteroids. Both ANN and LR models displayed excellent discrimination (area under the receiver operating characteristic curve [AUC]: 0.964 versus 0.969, p = 0.507) to identify infection in internal validation. During external validation, high AUC was obtained from both models (AUC: 0.850 versus 0.870, p = 0.447). The scoring system also performed extremely well in the internal (AUC: 0.965) and external (AUC: 0.871) validations. Conclusions We developed a scoring system to predict HAI with simple parameters validated with ANN and LR models. Armed with this scoring system, infectious disease specialists can more efficiently identify patients at high risk for HAI during hospitalization. Further, using parameters either by observation of medical devices used or data obtained from EHR also provided good prediction outcome that can be utilized in different clinical settings.
Fair and Latency Aware Uplink Scheduler In IEEE 802.16 Using Customized Deficit Round Robin

In the promising high-speed differentiated-services (diffserv) wireless broadband networks specifically 802.16 standard, bandwidth scheduling algorithms play important role in providing the Quality-of-Service (QoS) guarantees required by many applications. Most of the research is focused on packet scheduling, but it is not clear yet which scheduling algorithms will work best with 802.16. Our scheme is a refined version of the modified Deficit Round Robin (MDRR) technique proposed by Fattah and Leung. We modify the redistribution strategy of MDRR to achieve better performance in terms of delay, throughput and fairness, and analyze the performance of our scheme via exhaustive packet-level simulations, under realistic wireless scenarios. In this paper, a new scheduling discipline called Customized Deficit Round Robin (CDRR) is presented, while preserving the simplicity available in the original DRR design which is fair, efficient and in addition has a low latency bound. The performance of our scheme is compared with MDRR using the OPNET simulator. The comparison is carried out, by investigating the latency and bandwidth sharing properties of the algorithms.
Do studies reporting ‘U’-shaped serum 25-hydroxyvitamin D–health outcome relationships reflect adverse effects?

ABSTRACT Several reports describe U-shaped 25-hydroxyvitamin D [25(OH)D] concentration–health outcomes, including musculo-skeletal disorders such as falls and fractures, several cancers, cardiovascular disease (CVD), cognitive function, all-cause mortality rates, birth outcomes, allergic reactions, frailty, and some other disorders. This paper reviews reports of U-shaped outcome associations with vitamin D status for evidence of underlying pathophysiological processes, or of confounding, finding that some U-shaped associations appear to be biologically meaningful, but that many could well reflect confounding by factors such as lifestyle, or hypovitaminosis D-related disease onset being masked by self-supplementation that was begun too late to correct developing health problems but before baseline vitamin D status assessment. However, the various U-shaped associations for allergic reactions may be due to vitamin D modulation of the phenotype of the immune response, shifting the Th1-Th2 balance toward Th2 formation. For prostate cancer, there seems to be little effect of 25(OH)D concentration on incidence; however, there is an inverse correlation between 25(OH)D concentration and mortality rates. Future observational studies, and randomized controlled trial data analyses, should include adjustment for data collected on prior long-term vitamin D supplementation and solar UVB exposure, as well as other potential confounders.
Toward programming of assembly tasks by demonstration in virtual environments

Service robots require simple programming techniques allowing users with little or no technical expertise to integrate new tasks in a robotic platform. A promising solution for automatic acquisition of robot behaviours is the programming by demonstration (PbD) paradigm. Its aim is to let robot systems learn new behaviours from a human operator demonstration. This paper describes a PbD system able to deal with assembly operation in a 3D block world. The main objective of the research is to investigate the benefits of a virtual demonstration environment. Overcoming some difficulties of real world demonstrations, a virtual environment can improve the effectiveness of the instruction phase. Moreover, the user can also supervise and validate the learned task by means of a simulation module, thereby reducing errors in the generation process. Some experiments involving the whole set of system components demonstrate the viability and effectiveness of the approach.
Automatic Image Annotation Using Semantic with Fuzzy KNN

Large no of information's are available in internet in terms of text, imagesdios, vidieos.Retriving accurate of these information is very essential. For retrieving images different techniques are used automatic image annotation techniques are mostly used for image retrieval. Content-based image retrieval, the problem of searching large image repositories according to their content, has been the subject of a significant amount of computer vision research in the recent past. While early retrieval architectures were based on the query-by-example paradigm, which formulates image retrieval as the search for the best database match to a user-provided query image, it was quickly realized that the design of fully functional retrieval systems would require support for semantic queries. These are systems where the databases of images are annotated with semantic keywords, enabling the user to specify the query through a natural language description of the visual concepts of interest. This realization, combined with the cost of manual image labeling, generated significant interest in the problem of automatically extracting semantic descriptors from images.
Directionally constrained minimization of power algorithm for speech signals

A novel algorithm of adaptive beamforming with a microphone array is presented. Beamforming is one of the simplest methods for discriminating between different signals based on the physical location of sources. The directionally constrained minimization of power (DCMP) algorithm is well-known as a reliable approach to extracting some signals incoming from specific directions. In the DCMP algorithm, the output power of the sensor array is minimized under the constraint of a constant response to the direction of arrival (DOA) of the target signal. Although the conventional DCMP is applied only to narrowband signals, our approach extends this algorithm so that the DCMP criterion is applicable to the broadband signals such as speech. The proposed method can be achieved by a relatively small hardware amount of the system.
Perceptions of E-Cigarettes among Black Youth in California

Research suggests that Black youth are less likely to use e-cigarettes than their white counterparts, yet little is known as to why. We examined perceptions of e-cigarettes among Black young adults (ages 18–25) to explore the meanings these youth ascribe to e-cigarettes and the role that identity plays in how these devices are viewed. Analysis of in-depth interviews with 36 Black smokers and non-smokers in the San Francisco Bay Area suggests that Black youth perceive e-cigarettes as serving distinct, yet overlapping roles: a utilitarian function, in that they are recognized as legitimate smoking cessation tools, and a social function, insofar as they serve to mark social identity, specifically a social identity from which our participants disassociated. Participants described e-cigarette users in highly racialized and classed terms and generally expressed disinterest in using e-cigarettes, due in part perhaps to the fact that use of these devices would signal alignment with a middle class, hipster identity. This analysis is discussed within a highly charged political and public health debate about the benefits and harms associated with e-cigarette use.
A Method for model-reduction of nonlinear building thermal dynamics

We propose a method for model-reduction of a class of non-linear models that are relevant to modeling thermal dynamics of multi-zone buildings. These models can have large state-space dimension even for a moderate number of zones. Reduced order models of building thermal dynamics can be useful to model-based control for improving energy efficiency, especially to computationally intensive ones such as Model Predictive Control (MPC). Although there are a number of well-developed techniques for model reduction of LTI systems, the same cannot be said about non-linear systems. The method we propose exploits the linear portion of the model to compute a transformation (by using balanced realization) and a specific sparsity pattern of the non-linear portion to obtain the reduced order model. Simulations are presented with a four zone building model, which show that the prediction of the zone temperatures and humidity ratios by the reduced model is quite close to that from the full-scale model, even when substantial reduction of model order is specified.
2 Ju n 20 02 Supermatrix models for M-theory based on osp ( 1 | 32 , IR )

Taking seriously the hypothesis that the full symmetry algebra of M-theory is osp(1|32, R), we derive the supersymmetry transformations for all fields that appear in 11and 12dimensional realizations and give the associated SUSY algebras. We study the backgroundindependent osp(1|32, R) cubic matrix model action expressed in terms of representations of the Lorentz groups SO(10, 2) and SO(10, 1). We explore further the 11-dimensional case and compute an effective action for the BFSS-like degrees of freedom. We find the usual BFSS action with additional terms incorporating couplings to transverse 5-branes, as well as a mass-term and an infinite tower of higher-order interactions. NEIP–02-001 LPTENS-02/10 hep–th/0201183 January 2002 e-mail: Maxime.Bagnoud@, Luca.Carlevaro@, Adel.Bilal@unine.ch Work supported in part by the Swiss Office for Education and Science, the Swiss National Science Foundation and the European Community’s Human Potential Programme under contract HPRN-CT-2000-00131 Quantum Spacetime.
Threshold resummed and approximate next to next to leading order results for W+W- pair production at the LHC

The next-to-leading order (NLO) QCD radiative corrections to W+W- production at hadron colliders are well understood. We combine NLO perturbative QCD calculations with soft-gluon resummation of threshold logarithms to find a next-to-next-to leading logarithmic (NNLL) prediction for the total cross section and the invariant mass distribution at the LHC. We also obtain approximate next-to-next-to-leading order (NNLO) results for the total W+W- cross section at the LHC which includes all contributions from the scale dependent leading singular terms. Our result for the approximate NNLO total cross section is the most precise theoretical prediction available. Uncertainties due to scale variation are shown to be small when the threshold logarithms are included. NNLL threshold resummation increases the W+W- invariant mass distribution by ~ 3-4% in the peak region for both \sqrt{S}=8 and 14 TeV. The NNLL threshold resummed and approximate NNLO cross sections increase the NLO cross section by 0.5-3% for \sqrt{S}=7, 8, 13, and 14 TeV.
The method for defocusing selfie taken by mobile frontal camera using burst shot

In this paper, we present a novel human segmentation technique that automatically detects upper-body region in the selfie. To detect and segment upper-body without user interactions, we develop an initial tri-map by combining face detection results and upper-body shape prior in the selfie. Moreover, we employ motion vectors between two images captured in a short time interval to deal with various human poses and cluttered backgrounds. From motion vectors, we estimate the implicit depth layers without auxiliary hardware or time-consuming algorithms. By integrating information from the face detector, shape prior, and motion vectors, we detect and segment the human upper-body accurately. We also implement the proposed algorithm on the mobile phone. In the extensive experiments on selfie dataset, the proposed method shows competitive results in terms of accuracy/recall and outperforms the previous methods.
Shape optimization of a body located in incompressible flow using adjoint method and partial control algorithm

The purpose of this study is to obtain an optimal shape of a body located in an incompressible viscous flow. The optimal shape of the body is defined so as to minimize the fluid forces acting on it by determining the surface coordinates based on the finite element method and the optimal control theory. The performance function, which is used to judge the optimality of a shape, is defined as the square sum of the drag and lift forces. The minimization problem is solved using an adjoint equation method. The gradient in the adjoint equation is affected by the finite element configuration. The use of a finite element mesh whose shape is appropriate for the procedure is important in shape optimization. If the finite element mesh used is not suitable for computations, the exact gradient is not calculated. Therefore, a structured mesh is used for the adjacent area of the body and all finite element meshes are refined using the Delaunay triangulation at each iteration computation. The weighted gradient method is applied as the minimization technique.
The opportunities and challenges of diversity: Explaining its impact on individuals and groups

Demographic trends reveal that modern societies have become increasingly diverse. Within the social sciences, these changes have been reflected in concerns about the implications of social diversity. Although early research noted that diversity may have negative consequences for societies and individuals, more recent scholarship has indicated that diversity is not always translated into negative outcomes. These inconsistent findings initiated a scholarly debate concerning the impact of many different forms of diversity for a host of social outcomes. It is now clear that the boundary conditions of these effects are yet to be fully understood. This Special Issue offers a collection of research advances identifying mediating and moderating variables addressing when and why diversity impacts intergroup relational outcomes. By focusing on different levels of diversity (i.e., in the society and in groups), this research also sheds light on the effectiveness of ideologies and policies for managing diversity.
A new approach to dealing with missing values in data-driven fuzzy modeling

Real word data sets often contain many missing elements. Most algorithms that automatically develop a rule-based model are not well suited to deal with incomplete data. The usual technique is to disregard the missing values or substitute them by a best guess estimate, which can bias the results. In this paper we propose a new method for estimating the parameters of a Takagi-Sugeno fuzzy model in the presence of incomplete data. We also propose an inference mechanism that can deal with the incomplete data. The presented method has the added advantage that it does not require imputation or iterative guess-estimate of the missing values. This methodology is applied to fuzzy modeling of a classification and regression problem. The performance of the obtained models are comparable with the results obtained when using a complete data set.
Impact of Glycosylation on Effector Functions of Therapeutic IgG †

Human IgG has only one conserved glycosylation site located in the Cγ2 domain of the Fc region that accounts for the presence of two sugar moieties per IgG. These IgG sugar cores play a critical role in a number of IgG effector functions. In the present review, we describe the main characteristics of IgG Fc glycosylation and some abnormalities of serum IgG glycosylation. We also discuss how glycosylation impacts on monoclonal antibodies (mAbs) and IVIg effector functions and how these molecules can be engineered. Several therapeutic antibodies have now been engineered to be no- or low-fucose antibodies and are currently tested in clinical trials. They exhibit an increased binding to activating FcγRIIIA and trigger a strong antibody-dependent cell cytotoxicity (ADCC) as compared to their highly-fucosylated counterparts. They represent a new generation of therapeutic antibodies that are likely to show a better clinical efficacy in patients, notably in cancer patients where cytotoxic antibodies are needed.
A grid dynamic analyzing resource model

Grid computing architecture was defined to be a complete physical layer. The functions of information systems based on grid architecture are resources sharing, collaborative processing, etc. However, each resource of collaborated grid nodes changes dynamically, how to make these resources optimize plan and allocation is an important issue. In this paper, we proposed a dynamic analyzing resources model based on grid computing architecture. This model can make the plans and allocations of the resources of collaborated nodes optimize. Via the experiment of this model, we can have that the resource allocations are more efficient.