Miracle-dz/UniFAR-FTUs huggingface dataset:
iBig Hybrid Architecture for Energy IoT: When the Power of Indexing Meets Big Data Processing!

Nowadays, IoT data come from multiple sources from a large number of devices. To manage them, IoT frameworks rely on Big Data ecosystems hosted in the cloud to provide scalable storage systems and to achieve scalable processing. Although these ecosystems scale well to process large sizes of data, in many cases this is done naively. Many datasets, such as IoT Energy measurement data, consist, even partially, of attributes that can be indexed to avoid unnecessary and costly data scan at these scales. In this work, we propose the iBig architecture thatprovides secondary indexing to Big Data processing for energy IoT datasets. Indexes are considered as metadata stored in a separate component integrated in the ecosystem. Subsequently, MapReduce-based and MPP (massively parallel processing)-based processing leverage indexing to handle only relevant data in a given dataset. Our experimental evaluation on the Grid5000 cloud testbed demonstrates that performance gains can exceed 98% for the MapReduce-based Spark and 81% for the MPP-based Drill for Energy IoT Data. Furthermore, we provide comparison insights about the Spark and Drill frameworks when processing the whole dataset or only with relevant data.
Probabilistic ranking of constraints

This paper discusses aspects of ordering, more specifically probabilistic ordering. An algorithm is presented for adaptive learning an optimal ordering based on data, such that the resulting ordering follows the statistical properties in the input data. The properties to be ordered can be phonological rewrite rules, pronunciation rules inferred from observed phonetic data, grammar rules, etc. The novelty in the proposed model is the combination of well-established methods from three different disciplines: parsing, voting theory, and statistics. The set-up of this paper is mainly theoretical, and we follow a quite formal approach. There is a close link with techniques that are currently being developed in the Optimality Theory, one of the mainstream approaches in phonology. The resulting techniques, however, can be applied in a more general domain.
Digestion and absorption of protein along ovine gastrointestinal tract.

Digestion and absorption of protein were determined in ovine gastrointestinal tract with cerium-141 as an unabsorbed reference substance. Nitrogen flows changed little in rumen and reticulum, but in the proximal small intestine flows increased because of secretion of . 9 g nitrogen per day per kg body weight. This secretion included trypsin, chymotrypsin, elastase, and carboxypeptidases A and B; maximal activity was in proximal segments of the small intestine and decreased with distance from the pylorus. Activity of chymotrypsin decreased more rapidly than that of trypsin. Amino acid flows reflected the influx of protein in the duodenum; absorption was approximately 55% in the terminal ileum. No major changes of proportions of individual amino acids were observed. Overall nitrogen absorption was 72.6% of which 6% was in the large intestine. The major soluble protein fraction in the gastrointestinal tract consisted of peptides with molecular weight 7,000 to 14,000 daltons. Soluble high molecular weight protein was observed only in rumen and duodenum. Low molecular weight peptides and amino acids accumulated only in the proximal small intestine. Solubilization of protein and breakdown of peptides of 7,000 to 14,000 molecular weight appear to be rate limiting for protein absorption in sheep.
Improving ethnocultural competence of hospital staff by training: experiences from the European Migrant-friendly Hospitals project

Cultural competence training for staff in healthcare seems one of the most widespread measures to deal with ethnocultural diversity, especially in North America. Most of the studies and experiences of this training originate from the United States; European contributions, especially from continental Europe, are rare. This paper contributes to the cultural competence discourse by presenting experiences from hospitals in eight states in the EuropeanUnion. These experiences were collected as part of the ‘Migrantfriendly Hospitals’ project, which aimed to improve the impact of hospitals on the health and health literacy ofmigrants and ethnic minorities in 12member states. In the part of the project reported here, cultural competence training was provided for all types of hospital staff, primarily with the intention of providing support for staff. The evaluation criteria were feasibility/acceptability, quality, effectiveness, cost-effectiveness and sustainability. Data were collected through a staff questionnaire in a ‘before and after’ design, documentation sheets, telephone interviews with project co-ordinators and group discussions at project meetings. Key findings showed that seven of the eight pilot hospitals managed to implement cultural competence training. Acceptance of the training among staff, measured in terms of participation, varied considerably.Variationswere also identified in the quality of the training as measured by concordance with an agreed implementation pathway. The training had a positive impact on staff perceptions of their knowledge, skills and comfort levels in transcultural situations. The training was also considered to be cost-effective with regard to external costs, and sustainable in that it was accepted as part of continuous professional development in hospitals. The most critical factors for implementing cultural competence training were: (1) support by management is crucial; (2) time and energy are needed to convince staff of the relevance of the training; (3) training oriented at solving the real specific problems of everyday practice is more likely to be accepted; (4) a skills-oriented design including experiential learning is useful but difficult to integrate with long working hours and changing shifts; (5) recruiting competent trainers is crucial but the profile required for an integrated, skills-oriented training is difficult to match; (6) thus, splitting the integrated training model into a short generic introduction combined with the inclusion of cultural diversity issues into the normal quality improvement routines of departments should be tested.
Abusive drinking after liver transplantation is associated with allograft loss and advanced allograft fibrosis

In patients who undergo liver transplantation for alcoholic liver disease (ALD), alcohol relapse is common. A return to abusive or excessive drinking likely decreases overall survival; however, the effects of alcohol use on allograft outcomes and histopathology are less well defined. We reviewed all cases of liver transplantation with ALD as an indication between January 1, 1995 and December 31, 2007. Allograft outcomes and histopathological results were compared for patients who relapsed into alcohol use and patients who maintained abstinence. Three hundred patients who underwent transplantation for ALD during this period survived at least 1 year, and 48 (16.0%) relapsed into alcohol use that came to clinical attention. The pattern of relapse was a single event for 10 patients (20.8%), intermittent relapses for 22 patients (45.8%), and continuous heavy drinking for 16 patients (33.3%). Continuous heavy drinking was associated with allograft loss in a univariate Cox proportional hazards analysis [hazard ratio (HR) = 2.43, 95% confidence interval (CI) = 1.26‐4.68, P = 0.008] and in a multivariate Cox proportional hazards regression (HR = 2.57, 95% CI = 1.32‐5.00, P = 0.006). A matched‐pair analysis that controlled for the hepatitis C virus status and the time to biopsy compared the results of allograft histopathology for patients who relapsed into alcohol use and patients who maintained abstinence. Significant steatosis [odds ratio (OR) = 3.46, 95% CI = 1.29‐9.31, P = 0.01], steatohepatitis (OR = 6.2, 95% CI = 1.70‐22.71, P = 0.006), and advanced (stage 3 or higher) fibrosis (OR = 23.18, 95% CI = 3.01‐177.30, P = 0.003) were associated with alcohol relapse. In conclusion, alcohol relapse after liver transplantation (particularly heavy drinking) is associated with decreased graft survival and advanced allograft fibrosis. Liver Transpl 19:1377‐1386, 2013. © 2013 AASLD.
Delayed dopaminergic neuron differentiation in Lrp6 mutant mice

Wnts are known to bind and activate multiple membrane receptors/coreceptors and to regulate dopaminergic (DA) neuron development and ventral midbrain (VM) morphogenesis. The low density lipoprotein receptor–related protein (Lrp6) is a Wnt co‐receptor, yet it remains unclear whether Lrp6 is required for DA neuron development or VM morphogenesis. Lrp6 is expressed ubiquitously in the developing VM. In this study, we show that Lrp6−/− mice exhibit normal patterning, proliferation and cell death in the VM, but display a delay in the onset of DA precursor differentiation. A transient 50% reduction in tyrosine hydroxylase–positive DA neurons and in the expression of DA markers such as Nurr1 and Pitx3, as well as a defect in midbrain morphogenesis was detected in the mutant embryos at embryonic day 11.5. Our results, therefore, suggest a role for Lrp6 in the onset of DA neuron development in the VM as well as a role in midbrain morphogenesis. Developmental Dynamics 239:211–221, 2010. © 2009 Wiley‐Liss, Inc.
Automatic Arabic Text Summarization for Large Scale Multiple Documents Using Genetic Algorithm and MapReduce

Multi document summarization focuses on extracting the most significant information from a collection of textual documents. Most summarization techniques require the data to be centralized, which may not be feasible in many cases due to computational and storage limitations. The huge increase of data emerging by the progress of technology and the various sources makes automatic text summarization of large scale of data a challenging task. We propose an approach for automatic text summarization of large scale Arabic multiple documents using Genetic algorithm and MapReduce parallel programming model. The approach insures scalability, speed and accuracy in summary generation. It eliminates sentence redundancy and increases readability and cohesion factors between the sentences of summaries. The experiments resulted in acceptable precision and recall scores. This indicates that the system successfully identifies the most important sentences. In Addition to all to that, the approach provided up to 10x speedup score, which is faster than on a single machine. Therefore, it can deal with large-scale datasets successfully. Finally, the efficiency score of the proposed approach indicates that the large data set utilizes the available resources up to 62%.
Investigation of dynamical characteristics of blood pressure and heart rate variation in different blood pressure categories

We continued investigation of variation of blood pressure and heart rate characteristics of patients groups from different blood pressure categories in the present research. We analyze dynamical features of considered data sets by means of power spectrum regression, detrended fluctuation analysis (DFA) and multifractal detrended fluctuation analysis (MF-DFA). We aim to compare dynamical characteristics of analysed data in different blood pressure categories. Special interest is the comparison of these characteristics in normal and high normal blood pressure categories. Based on the results of our analysis we show that scaling features of considered data sets are different in different blood pressure categories. The most interesting is the fact that by their dynamical features normal and high normal categories are different for all considered physiological characteristics. The obtained results point to the fact that important quantitative and qualitative changes occur in these looking close guidelines categories, which finally lead to transition of dynamical system to pathological condition typical for grade I hypertension category.
Gastroduodenal cytomegalovirus infection is common in kidney transplantation patients

Objective Cytomegalovirus (CMV) infection is known to cause ulcerations, erosion and mucosal haemorrhage in the gastrointestinal tract. The aim of this study was to report the CMV findings in the gastroduodenal mucosa of kidney transplantation patients and immunocompetent controls. Material and methods Forty-six kidney transplant patients with upper gastrointestinal symptoms and 43 immunocompetent, dyspeptic patients (controls) prospectively underwent oesophagogastroduodenoscopies (OEGDs), with biopsies from the duodenum and stomach. CMV was demonstrated by immunohistochemistry, both in frozen sections using a monoclonal antibody against CMV-specific antigens (pp65 matrix protein) and in paraffin sections by means of a monoclonal antibody against the delayed early protein (p52). Results CMV was detected in the gastric mucosa in 30% of the kidney transplant patients and in 9% of the controls (p<0.05) and in the duodenal mucosa in 70% and 35%, respectively (p<0.01). The total frequency of CMV findings was similar in patients who underwent OEGDs <1 year and >1 year after transplantation. CMV inclusions were found only in transplantation patients <1 year after transplantation (n=9). CMV findings, especially inclusions, in the gastric biopsies were associated with nausea and upper gastric pain. Histopathological findings in CMV-positive samples were non-specific, focal inflammation in haematoxylin-eosin-stained preparations, while CMV p52 staining showed inclusions in either the epithelial or endothelial cells. Conclusions CMV could be detected in the gastroduodenal mucosa in 74% of kidney transplantation patients and in 40% of immunocompetent controls (p<0.01). CMV diagnostics are always recommended when gastroduodenal biopsies of kidney transplantation patients are performed.
A New MEMS Stochastic Model Order Reduction Method: Research and Application

Modeling and simulation of MEMS devices is a very complex tasks which involve the electrical, mechanical, fluidic, and thermal domains, and there are still some uncertainties that need to be accounted for during the robust design of MEMS actuators caused by uncertain material and/or geometric parameters. According to these problems, we put forward stochastic model order reduction method under random input conditions to facilitate fast time and frequency domain analyses; the method makes use of polynomial chaos expansions in terms of the random input variables for the matrices of a finite element model of the system and then uses its transformation matrix to reduce the model; the method is independent of the MOR algorithm, so it is seamlessly compatible with MOR method used in popular finite element solvers. The simulation results verify the method is effective in large scale MEMS design process.
Color demosaicking via nonlocal tensor representation

A single sensor camera can capture scenes by means of color filter array. Each pixel samples only one of the three primary colors. Color demosaicking (CDM) is a process of reconstruction a full color image from this sensor data. In this paper, we propose a novel CDM scheme based on learned simultaneous sparse coding over nonlocal tensor representation. First, similar 2D patches are grouped to form a three-order tensor, that is, 3D array. Then, three sub-dictionaries, which characterize the coherent structures that appear in each dimension of the grouped tensor, are learned jointly by using Tucker decomposition. The consequent coefficient tensor is imposed by the grouped-block-sparsity constraint, which forces the similar patches to share the same atoms of the dictionaries in their sparse decomposition. Experimental results demonstrate the effectiveness both in the average CPSNR and visual quality.
A mobile-agent based distributed dynamic /spl mu/Firewall architecture

With the development of enterprise Intranet and cluster servers, many emerging security challenges could not be solved by conventional firewall due to its inner deficiency. To address these security problems, we present a mobile-agent based distributed dynamic /spl mu/Firewall architecture. In this architecture, special mobile agents implement a dynamic security policy reconfiguration and enhance the scalability. Each /spl mu/Firewall is built with a packet filter and DTE-enhanced evaluator to provide dual fine-grain protection at the individual host level. A distributed intrusion detection and response (DIDR) system provides a fast response to both external and internal attacks, and allows an adaptive change in the security policy in the protected network. The DIDR system provides the infrastructure to support hierarchical intrusion responses and dynamic security capabilities. The distributed security architecture is scalable, topology independent, and intrusion-tolerant.
Morphological associative memories

The theory of artificial neural networks has been successfully applied to a wide variety of pattern recognition problems. In this theory, the first step in computing the next state of a neuron or in performing the next layer neural network computation involves the linear operation of multiplying neural values by their synaptic strengths and adding the results. A nonlinear activation function usually follows the linear operation in order to provide for nonlinearity of the network and set the next state of the neuron. In this paper we introduce a novel class of artificial neural networks, called morphological neural networks, in which the operations of multiplication and addition are replaced by addition and maximum (or minimum), respectively. By taking the maximum (or minimum) of sums instead of the sum of products, morphological network computation is nonlinear before possible application of a nonlinear activation function. As a consequence, the properties of morphological neural networks are drastically different than those of traditional neural network models. The main emphasis of the research presented here is on morphological associative memories. We examine the computing and storage capabilities of morphological associative memories and discuss differences between morphological models and traditional semilinear models such as the Hopfield net.
WHEN DOES EVOLUTION BY NATURAL SELECTION PREVENT EXTINCTION?

Understanding population responses to novel environments is a central concern of both evolutionary biology and ecology (Maynard Smith 1989; Bradshaw 1991; Hoffman and Parsons 1991; Peters and Lovejoy 1992; Kareiva et al. 1993). Populations subject to substantial environmental stress, such as occurs during colonization attempts, human-mediated introductions or reintroductions, or global climatic change, may face a risk of extinction. There are many examples of such extinctions, but many examples are known in which populations have evolved sufficiently to persist in changed environments, such as cases of evolved resistance to pesticides or heavy metal toxins (e.g., Bradshaw 1991). An important problem for evolutionary biologists is thus to characterize those combinations of genetic and demographic conditions likely to result in persistence versus those expected to lead to extinction in a changed environment. Theoretical work has characterized circumstances in which populations have sufficient genetic capacity to avoid extinction by adapting with sufficient speed to a continuously changing environment (Pease et al. 1989; Lynch et al. 1991; Lynch and Lande 1993; Burger and Lynch 1994). Here we use simple models to highlight an additional risk faced even by populations genetically capable of evolving sufficiently to persist in environments that remain constant following a single, initial abrupt change. Namely, as a population adapts to a novel environment, its de'nsity may fall below a critically low level for a period of time, during which the population is highly vulnerable to extinction by demographic stochasticity. If this occurs, the population is likely to vanish before it can be rescued by evolution. To examine this problem, we coupled models of population dynamics and of evolution by natural selection to identify conditions for which evolution succeeds-or fails-to rescue a closed population from extinction following abrupt environmental change. The models considered here cover extremes in both genetics (one-locus and polygenic models) and population growth (discrete and continuous-time models). These models lead to a similar conclusion: even populations with the genetic wherewithal to potentially persist in a novel environment may often fail to do so. Moreover, our analyses help to quantitatively characterize situations in which evolution by natural selection can effectively rescue a population from impending extinction. We suspect the qualitative properties of our results may be general features of evolutionary dynamics in novel environments.
CONCURRENCY AND COMPUTATION : PRACTICE AND EXPERIENCE Concurrency Computat

Service-oriented computing and cloud computing are playing critical roles in supporting business collaboration over the Internet. Thanks to the latest development in computing technologies, various large-scale, evolving, and rapidly growing service ecosystems emerge. However, service failures greatly hamper the usability and reputation of service ecosystems. In the previous work, service failure is not adequately studied from an ecosystem’s perspective. To address this gap, we propose a service failure analysis framework based on a complex network model of service ecosystem. This framework comprises a feature model of failed services and several service failure impact indicators. By applying the framework, empirical analysis of failed service features and failure impact assessment can be implemented more easily and precisely. Moreover, to provide failure tolerance strategies for service ecosystems, a novel compositionbased service substitution method is designed to replace the failed services with functional similar ones, such that the service systems are more robust when a failure occurs. As the new substitution method requires fewer structural data of services, it is more convenient to be applied in present RESTful Representational State Transfer (REST) service environment. Both the framework and the service substitution method are tested on real-world data set, and their usability and efficiency are demonstrated. Copyright © 2014 John Wiley & Sons, Ltd.
Long-term treatment in pediatric asthma: an update on chemical pharmacotherapy

ABSTRACT Introduction: Asthma is the most common chronic disease in childhood, affecting approximately 10% of all children, and is the leading cause of hospitalization in developed countries. In this paper we aimed to review the evidence on chemical pharmacotherapy for long-term treatment of pediatric asthma, according to the latest updates. Area covered: Long-term treatment, essential for controlling symptoms and reducing future risks including exacerbations and decline in lung function, includes control agents such as inhaled corticosteroids, long-acting beta2-adrenergic agonists, and leukotriene modifiers. More recent strategies based on the use of a biological drug such as omalizumab, which is a monoclonal antibody directed against immunoglobulin E (IgE), can be considered in selected patients with severe asthma. Expert opinion: In the near future, the challenge of childhood asthma treatment will be to improve the chemical drugs that already exist as well as to carefully characterize the several different asthma subtypes, with special regard to children with severe disease. A better definition of patient features, made possible by the current advanced knowledge of the pathobiology of severe asthma, can ultimately allow the identification of specific phenotypes and endotypes of severe asthma, aimed to personalize pharmacological treatment.