Miracle-dz/UniFAR-FTUs huggingface dataset:
From the Selectedworks of Nader Ale Ebrahim Innovation Process Is Facilitated in Virtual Environment of R&d Teams Contact Author Start Your Own Selectedworks Notify Me of New Work Innovation Process Is Facilitated in Virtual Environment of R&d Teams

Innovation is becoming the most important key issue for company's success in the 21st century. In the competitive environment it is necessary for the enterprises to put together different capabilities and services with the goal. It is widely accepted that innovation can be better achieved by working in team particularly in the virtual environments. The employed web services technology, although very popular nowadays but it is still not mature enough, so dealing with it can bring new findings. Virtual teams base on information technology are formed to facilitate transnational innovation processes and it should be noted that innovation has a positive impact on corporate performance if it is well guided. This has the pronounced effect when it comes to the learning issues as the virtual preconditions persist. In this environment the possibility of getting closer to the interest of learner from the new environment raise as the barriers being imposed by the service provider is reduced and the freedom of what is require to learn is improved. Information and communication technology has brought about significant changes in organizations and produced important benefits, including in the areas of innovation which is recognized as a prime source of national competitive advantage. This contribution proposes a conceptual model for understanding and analyzing the process of virtual R&D team as an innovation and technology assimilation facilitator when it comes to the issue of conveying innovative message for learning by individuals. The context of the knowledge-based economy introduces a major shift from serial to simultaneous R&D in the way of idea conception to technology creation is conceived. This paper briefly reviews the existing perspectives on virtual teams and their effect on innovation and technology regarding the learning capability which is being altered. It also discusses the main characteristics of virtual teams and clarifies the different aspects of virtual team application in the topic. To support the theoretical analysis, this paper provides a comprehensive review based on authentic and reputed publications. We argue that scanty research has been conducted to facilitate understanding the problem of systematically governing creative innovation toward a technology through virtual R&D teams in the atmosphere of educating individuals based on what they interest them to learn.
Probiotic Bacteria Regulate Intestinal Epithelial Permeability in Experimental Ileitis by a TNF-Dependent Mechanism

Background We previously showed that the probiotic mixture, VSL#3, prevents the onset of ileitis in SAMP/YitFc (SAMP) mice, and this effect was associated with stimulation of epithelial-derived TNF. The aim of this study was to determine the mechanism(s) of VSL#3-mediated protection on epithelial barrier function and to further investigate the “paradoxical” effects of TNF in preventing SAMP ileitis. Methods Permeability was evaluated in SAMP mice prior to the onset of inflammation and during established disease by measuring transepithelial electrical resistance (TEER) on ex vivo-cultured ilea following exposure to VSL#3 conditioned media (CM), TNF or VSL#3-CM + anti-TNF. Tight junction (TJ) proteins were assessed by qRT-PCR, Western blot, and confocal microscopy, and TNFRI/TNFRII expression measured in freshly isolated intestinal epithelial cells (IEC) from SAMP and control AKR mice. Results Culture with either VSL#3-CM or TNF resulted in decreased ileal paracellular permeability in pre-inflamed SAMP, but not SAMP with established disease, while addition of anti-TNF abrogated these effects. Modulation of the TJ proteins, claudin-2 and occludin, occurred with a significant decrease in claudin-2 and increase in occludin following stimulation with VSL#3-CM or TNF. TNF protein levels increased in supernatants of SAMP ilea incubated with VSL#3-CM compared to vehicle, while IEC-derived TNFR mRNA expression decreased in young, and was elevated in inflamed, SAMP versus AKR mice. Conclusions Our data demonstrate that the previously established efficacy of VSL#3 in preventing SAMP ileitis is due to direct innate and homeostatic effects of TNF on the gut epithelium, modulation of the TJ proteins, claudin-2 and occludin, and overall improvement of intestinal permeability.
An SVM based Classification Method for Cancer Data using Minimum Microarray Gene Expressions

— This paper gives a novel method for improving classification performance for cancer classification with very few microarray Gene expression data. The method employs classification with individual gene ranking and gene subset ranking. For selection and classification, the proposed method uses the same classifier. The method is applied to three publicly available cancer gene expression datasets from Lymphoma, Liver and Leukaemia datasets. Three different classifiers namely Support vector machines-one against all (SVM-OAA), K nearest neighbour (KNN) and Linear Discriminant analysis (LDA) were tested and the results indicate the improvement in performance of SVM-OAA classifier with satisfactory results on all the three datasets when compared with the other two classifiers.
Acoustic Direction of Arrival Estimation Based on Spatial Circular Prediction

Acoustic source localization is one of the most important applied fields of microphone arrays. Microphone array source localization is to estimate location parameters of the acoustic source following from some microphone array configuration. An acoustic direction of arrival (DOA) estimation algorithm based on parameterized spatial circular prediction is deduced in this paper. Associated with eigen-decomposition of array signals, the spatial covariance matrix is modified to compute broadband spatial spectrum aimed at estimating the acoustic DOA. The spatial circular prediction spectrum is compared in this paper with several broadband spectra, such as multichannel cross correlation coefficient (MCCC) and steered response power (SRP). The simulation results demonstrate that the proposed algorithm is superior to the other several algorithms at low signal-to-noise ratio.
Processing in the hepatitis C virus E2-NS2 region: identification of p7 and two distinct E2-specific products with different C termini

The hepatitis C virus (HCV) H strain polyprotein is cleaved to produce at least nine distinct products: NH2-C-E1-E2-NS2-NS3-NS4A-NS4B-NS5A-NS5B-CO OH. In this report, a series of C-terminal truncations and fusion with a human c-myc epitope tag allowed identification of a tenth HCV-encoded cleavage product, p7, which is located between the E2 and NS2 proteins. As determined by N-terminal sequence analysis, p7 begins with position 747 of the HCV H strain polyprotein. p7 is preceded by a hydrophobic sequence at the C terminus of E2 which may direct its translocation into the endoplasmic reticulum, allowing cleavage at the E2/p7 site by host signal peptidase. This hypothesis is supported by the observation that cleavage at the E2/p7 and p7/NS2 sites in cell-free translation studies was dependent upon the addition of microsomal membranes. However, unlike typical cotranslational signal peptidase cleavages, pulse-chase experiments indicate that cleavage at the E2/p7 site is incomplete, leading to the production of two E2-specific species, E2 and E2-p7. Possible roles of p7 and E2-p7 in the HCV life cycle are discussed.
A disappearing left-sided neck mass

The patient was a 48-year-old man who presented to the emergency department with complaints of a left-sided painful neck mass, which changed in size relative to ingestion of meals. He denied voice change, fever, chills, weight loss, dysphagia and hoarseness. Physical examination was unremarkable. CT scan demonstrated a 3.9 mm calculus of the submandibular gland duct. Therapeutic sialendocopy was successfully performed.
ScriptEase: generative design patterns for computer role-playing games

Recently, some researchers have argued that generative design patterns (GDPs) can leverage the obvious design re-use that characterizes traditional design patterns into code re-use. This work provides additional evidence that GDPs are both useful and productive. Specifically, the current state-of-the-art in the domain of computer games is to script individual game objects to provide the desired interactions for each game adventure. We use BioWare Corp.'s popular Neverwinter Nights computer role-playing game to show how GDPs can be used to generate game scripts. This is a particularly good domain for GDPs, since game designers often have little or no programming skills. We demonstrate our approach using a new GDP tool called ScriptEase.
A Modified Multi-HLR Architecture for Implicit De-registration Strategies

This study proposes a modification in the de-registration policy of Multi-HLR architecture. This architecture has made an attempt to overcome the drawbacks of Single centralized HLR architecture by introducing multiple HLRs in PCS networks in performing location managements of mobile units using explicit de-registration policy. This paper presents a modification in the de-registration policy of Multi-HLR architecture by applying the concept of implicit deregistration strategies and gives a performance analysis of this modified architecture to show how these implicit deregistration policies outperform the explicit de-registration strategy.
Tunable Spontaneous Emission From Layered Graphene/Dielectric Tunnel Junctions

There has been a rapidly growing interest in optoelectronic properties of graphene and associated structures. Despite the general belief on absence of spontaneous emission in graphene, which is normally attributed to its unique ultrafast carrier momentum relaxation mechanisms, there exist a few recent evidences of strong optical gain and spontaneous light emission from monolayer graphene, supported by observations of dominant role of out-of-plane excitons in polycyclic aromatic hydrocarbons. In this paper, we develop a novel concept of light emission and optical gain from simple vertical graphene/dielectric tunnel junctions. It is theoretically shown that the possible optical gain or emission spectrum will be easily tunable by the applied voltage. We present details of quantum mechanical calculations and perform an exact analysis of field-assisted tunneling using transfer matrices combined with expansion on Airy's functions.
A message-passing decoding algorithm for q-ary LDPC codes with low-complexity

This paper presents a novel low-complexity iterative reliability-based decoding algorithm for LDPC codes over q-ary finite fields. This proposed algorithm has low complexity and hence provides an effective trade-off between error performance and decoding complexity compared to q-ary sum product algorithm. This decoding algorithm is devised based on simple orthogonal concept of one-step majority-logic decoding for q-ary linear block codes. It requires only integer and finite field operations and converges very fast in decoding. It is particularly effective for decoding LDPC codes constructed based on finite geometries and finite fields.
Molecular signatures and phylogenomic analysis of the genus Burkholderia: proposal for division of this genus into the emended genus Burkholderia containing pathogenic organisms and a new genus Paraburkholderia gen. nov. harboring environmental species

The genus Burkholderia contains large number of diverse species which include many clinically important organisms, phytopathogens, as well as environmental species. However, currently, there is a paucity of biochemical or molecular characteristics which can reliably distinguish different groups of Burkholderia species. We report here the results of detailed phylogenetic and comparative genomic analyses of 45 sequenced species of the genus Burkholderia. In phylogenetic trees based upon concatenated sequences for 21 conserved proteins as well as 16S rRNA gene sequence based trees, members of the genus Burkholderia grouped into two major clades. Within these main clades a number of smaller clades including those corresponding to the clinically important Burkholderia cepacia complex (BCC) and the Burkholderia pseudomallei groups were also clearly distinguished. Our comparative analysis of protein sequences from Burkholderia spp. has identified 42 highly specific molecular markers in the form of conserved sequence indels (CSIs) that are uniquely found in a number of well-defined groups of Burkholderia spp. Six of these CSIs are specific for a group of Burkholderia spp. (referred to as Clade I in this work) which contains all clinically relevant members of the genus (viz. the BCC and the B. pseudomallei group) as well as the phytopathogenic Burkholderia spp. The second main clade (Clade II), which is composed of environmental Burkholderia species, is also distinguished by 2 identified CSIs that are specific for this group. Additionally, our work has also identified multiple CSIs that serve to clearly demarcate a number of smaller groups of Burkholderia spp. including 3 CSIs that are specific for the B. cepacia complex, 4 CSIs that are uniquely found in the B. pseudomallei group, 5 CSIs that are specific for the phytopathogenic Burkholderia spp. and 22 other CSI that distinguish two groups within Clade II. The described molecular markers provide highly specific means for the demarcation of different groups of Burkholderia spp. and they also offer novel and useful targets for the development of diagnostic assays for the clinically important members of the BCC or the pseudomallei groups. Based upon the results of phylogenetic analyses, the identified CSIs and the pathogenicity profile of Burkholderia species, we are proposing a division of the genus Burkholderia into two genera. In this new proposal, the emended genus Burkholderia will correspond to the Clade I and it will contain only the clinically relevant and phytopathogenic Burkholderia species. All other Burkholderia spp. , which are primarily environmental, will be transferred to a new genus Paraburkholderia gen. nov.
Constraint Agents for the Information Age

We propose constraints as the appropriate computational constructs for the design of agents with the task of selecting, merging and managing electronic information coming from such services as Internet access, digital libraries. E-mail, or on-line information repositories. Specifically, we introduce the framework of Constraint-Based Knowledge Brokers, which are concurrent agents that use so-called signed feature constraints to represent partially specified information and can flexibly cooperate in the management of distributed knowledge. We illustrate our approach by several examples, and we define application scenarios based on related technology such as Telescript and workflow management systems.
Detection of Gaussian signals via hexagonal sensor networks

This paper considers a special case of the problem of identifying a static scalar signal, depending on the location, using a planar network of sensors in a distributed fashion. Motivated by the application to monitoring wild fire spreading and pollutants dispersion, we assume the signal to be Gaussian in space. Using a network of sensors positioned to form a regular hexagonal tessellation, we prove that each node can estimate the parameters of the Gaussian from local measurements. Moreover, we study the sensitivity of these estimates to additive errors affecting the measurements. Finally, we show how a consensus algorithm can be designed to fuse the local estimates into a shared global estimate, effectively compensating the measurement errors.
The Evaluation of More Lymph Nodes in Colon Cancer Is Associated with Improved Survival in Patients of All Ages

Background Improvement in survival of patients with colon cancer is reduced in elderly patients compared to younger patients. The aim of this study was to investigate whether the removal of ≥ 12 lymph nodes can explain differences in survival rates between elderly and younger patients diagnosed with colon cancer. Methods In a population-based cohort study, all patients (N = 41,074) diagnosed with colon cancer stage I to III from 2003 through 2010 from the Netherlands Cancer Registry were included. Age groups were defined as < 66, 66–75 and > 75 years of age. Main outcome measures were overall and relative survival, the latter as a proxy for disease specific survival. Results Over an eight years time period there was a 41.2% increase in patients with ≥ 12 lymph nodes removed, whereas the percentage of patients with the presence of lymph node metastases remained stable (35.7% to 37.5%). After adjustment for patient and tumour characteristics and adjuvant chemotherapy, it was found that for patients in which ≥ 12 lymph nodes were removed compared to patients with < 12 lymph nodes removed, there was a statistically significant higher overall survival (< 66: HR: 0.858 (95% CI, 0.789–0.933); 66–75: HR: 0.763 (95% CI, 0.714–0.814); > 75: HR: 0.734 (95% CI, 0.700–0.771)) and relative survival (< 66: RER: 0.783 (95% CI, 0.708–0.865); 66–75: RER: 0.672 (95% CI, 0.611–0.739); > 75: RER: 0.621 (95% CI, 0.567–0.681)) in all three age groups. Conclusions The removal of ≥ 12 lymph nodes is associated with an improvement in both overall and relative survival in all patients. This association was stronger in the elderly patient. The biology of this association needs further clarification.
Efficient minimal-surface regularization of perspective depth maps in variational stereo

We propose a method for dense three-dimensional surface reconstruction that leverages the strengths of shape-based approaches, by imposing regularization that respects the geometry of the surface, and the strength of depth-map-based stereo, by avoiding costly computation of surface topology. The result is a near real-time variational reconstruction algorithm free of the staircasing artifacts that affect depth-map and plane-sweeping approaches. This is made possible by exploiting the gauge ambiguity to design a novel representation of the regularizer that is linear in the parameters and hence amenable to be optimized with state-of-the-art primal-dual numerical schemes.
Activity Recognition Using Hybrid Generative/Discriminative Models on Home Environments Using Binary Sensors

Activities of daily living are good indicators of elderly health status, and activity recognition in smart environments is a well-known problem that has been previously addressed by several studies. In this paper, we describe the use of two powerful machine learning schemes, ANN (Artificial Neural Network) and SVM (Support Vector Machines), within the framework of HMM (Hidden Markov Model) in order to tackle the task of activity recognition in a home setting. The output scores of the discriminative models, after processing, are used as observation probabilities of the hybrid approach. We evaluate our approach by comparing these hybrid models with other classical activity recognition methods using five real datasets. We show how the hybrid models achieve significantly better recognition performance, with significance level p < 0.05, proving that the hybrid approach is better suited for the addressed domain.
Binary Sequences with Optimal Correlations and Large Linear Span

A family of binary sequences is presented and proved to have optimal correlations and large linear span. It includes the small set of Kasami sequences, No sequence set and TN sequence set as special cases. A lower bound on the linear span of the family is provided. With suitable choices of parameters, it is proved that the family has exponentially larger linear spans than both No sequences and TN sequences.
Mycobacterium Avium Subspecies Paratuberculosis-An Environmental Trigger of Type 1 Diabetes Mellitus

Type 1 diabetes mellitus (T1DM) is an autoimmune disease. The etiology of T1DM is incompletely understood but environmental agent(s) are thought to trigger T1DM in the genetically at-risk. Humans are widely exposed to Mycobacterium avium subspecies paratuberculosis (MAP), a proven multi-host chronic enteric pathogen that is mostly studied in ruminant animals and causes the inflammatory disease paratuberculosis or Johne’s disease. In humans, MAP is the putative cause of Crohn’s disease and has been linked to sarcoidosis, autoimmune thyroiditis, multiple sclerosis and autoimmune diabetes. The role of MAP as a trigger for T1DM was first postulated in 2005; subsequent studies suggest a link. This article discusses MAP, human exposure to MAP, genetic susceptibility to MAP and MAP in human disease including T1DM.
Minimum sensitivity control for planning with parametric and hybrid uncertainty

This paper introduces a method to minimize norms on nonlinear trajectory sensitivities during open-loop trajectory optimization. Specifically, we derive new parametric sensitivity terms that measure the variation in nonlinear (continuous-time) trajectories due to variations in model parameters, and hybrid sensitivities, which account for variations in trajectory caused by sudden transitions from nominal dynamics to alternative dynamic modes. We adapt continuous trajectory optimization to minimize these sensitivities while only minimally changing a nominal trajectory. We provide appended states, cost, and linearizations, required so that existing open-loop optimization methods can generate minimally sensitive feedforward trajectories. Although there are several applications for sensitivity optimization, this paper focuses on robot motion planning, where popular sample-based planners rely on local trajectory generation to expand tree/graph structures. While such planners often use stochastic uncertainty propagation to model and reduce uncertainty, this paper shows that trajectory uncertainty can be reduced by minimizing first-order sensitivities. Simulated vehicle examples show parametric sensitivity optimization generates trajectories optimally insensitive to parametric model uncertainty. Similarly, minimizing hybrid sensitivities reduces uncertainty in crossing mobility hazards (e.g. rough terrain, sand, ice). Examples demonstrate the process yields a planner that uses approximate hazard models to automatically and optimally choose when to avoid hazardous terrain and when controls can be adjusted to traverse hazards with reduced uncertainty. Sensitivity optimization offers a simple alternative to stochastic simulation and complicated uncertainty modeling for nonlinear systems.