Miracle-dz/UniFAR-FTUs huggingface dataset:
Fault Diagnosis by Using Selective Ensemble Learning Based on Mutual Information

Fault diagnosis on diesel engine is a difficult problem due to the complex structure of the engines and the presence of multi-excite sources. There have been previous attempts to solve this problem by using artificial neural networks and others methods. In this paper, a novel algorithm named MISEN (Mutual Information based Selective Ensemble) is proposed to improve diagnosis accuracy and efficiency. MISEN is compared with the general case of bagging and GASEN, a baseline method, namely Genetic Algorithm Based Selective ENsemble, on UCI data sets. Then, MISEN is used to diagnose the diesel engine. Computational results show that MISEN obtains higher accuracy than other several methods like bagging of neural networks and GASEN.
Chapter 7 | Intravital Imaging Reveals Extracellular Vesicle Transfer between Tumor Cells and Its Effect on Metastasis Ev-communication between Tumor Cells in Vivo Ev-communication between Tumor Cells in Vivo Ev-communication between Tumor Cells in Vivo Concluding Remarks Ev-communication between Tu

| Most cancer cells release heterogeneous populations of extracellular vesicles (EVs) containing proteins, lipids and nucleic acids. In vitro experiments showed that EV uptake can lead to transfer of functional mRNA and altered cellular behavior. However, similar in vivo experiments remain challenging, since cells that take up EVs cannot be discriminated from non-EV-receiving cells. Here we used the Cre-LoxP system to directly identify and study tumor cells that take up EVs in vivo. We show that EVs released by malignant tumor cells are taken up by more benign tumor cells located within the same and within distant tumors, and that these EVs carry mRNAs involved in migration and metastasis. By intravital imaging we show that the benign tumor cells that take up EVs displayed enhanced migratory behavior and metastatic capacity. We postulate that tumor cells locally and systemically share molecules carried by EVs in vivo, and that this affects cellular behavior. Introduction The heterogeneous nature of tumors dramatically complicates the successful treatment of cancer (Meacham and Morrison, 2013). Tumors display intratumoral heterogeneity as a consequence of genetic differences between individual cells. Together with the diverging microenvironment surrounding tumor cells, this leads to intercellular differences in epigenetic profiles and subsequently to differential cellular behavior (Bissell and Hines, 2011; Postovit et al., 2006). The tumor microenvironment consists of cells, soluble factors such as growth factors, and non-soluble factors including the extracellular matrix (Joyce and Pollard, 2009). A growing number of studies suggest that extracellular vesicles (EVs) may also be an important microenvironmental factor (Raposo and Stoorvogel, 2013) that could potentially affect tumor heterogeneity. Many different cell types have been shown to transfer biomolecules including proteins, lipids and nucleic acids through the release and uptake of EVs (Raposo and Stoorvogel, 2013). In vitro assays showed that EV-cargo, such as mRNA, is functional in cells that take up EVs (Valadi et al., 2007) leading to behavioral changes of recipient cells (Ratajczak et al. The cargo of EVs isolated from tumor cell culture media can also be bioactive, since injection of concentrated tumor cell line-derived EVs educates tumor-supporting cells, such as bone marrow-derived cells, that subsequently prepare sentinel lymph nodes and lungs for metastasis (Hood et al., 2011; Peinado et al., 2012). Vice versa, EVs from stromal cells, for example fibroblasts and activated T cells, have been shown to alter the protrusive and migratory behavior of tumor cells (Cai et al., 2012; Luga et al., …
Towards Life Cycle Sustainability Assessment of Alternative Passenger Vehicles

Sustainable transportation and mobility are key components and central to sustainable development. This research aims to reveal the macro-level social, economic, and environmental impacts of alternative vehicle technologies in the U.S. The studied vehicle technologies are conventional gasoline, hybrid, plug-in hybrid with four different all-electric ranges, and full battery electric vehicles (BEV). In total, 19 macro level sustainability indicators are quantified for a scenario in which electric vehicles are charged through the existing U.S. power grid with no additional infrastructure, and an extreme scenario in which electric vehicles are fully charged with solar charging stations. The analysis covers all life cycle phases from the material extraction, processing, manufacturing, and operation phases to the end-of-life phases of vehicles and batteries. Results of this analysis revealed that the manufacturing phase is the most influential phase in terms of socio-economic impacts compared to other life cycle phases, whereas operation phase is the most dominant phase in the terms of environmental impacts and some of the socio-economic impacts such as human health and economic cost of emissions. Electric vehicles have less air pollution cost and human health impacts compared to conventional gasoline vehicles. The economic cost of emissions and human health impact reduction potential can be up to 45% and 35%, respectively, if electric vehicles are charged through solar charging stations. Electric vehicles have potential to generate income for low and medium skilled workers in the U.S. In addition to quantified sustainability indicators, some sustainability metrics were developed to compare relative sustainability performance alternative passenger vehicles. BEV has the lowest greenhouse gas emissions and ecological land footprint per $ of its contribution to the U.S. GDP, and has the lowest ecological footprint per unit of its energy consumption. The only sustainability metrics that does not favor the BEV is the water-energy ratio, where the conventional gasoline vehicle performed best.
Mobile manipulation with a kinematically redundant manipulator for a pick-and-place scenario

Mobile robots and robotic manipulators have traditionally been used separately performing different types of tasks. For example, industrial robots have typically been programmed to follow trajectories using position sensors. If combining the two types of robots and adding sensors new possibilities emerge. This enables new applications, but it also raises the question of how to combine the sensors and the added kinematic complexity. An omni-directional mobile robot together with a new type of kinematically redundant manipulator for future use as a service robot for grocery stores is proposed. The scenario is that of distributing groceries on refilling shelves, and a constraint-based task specification methodology to incorporate sensors and geometric uncertainties into the task is employed. Sensor fusion is used to estimate the pose of the mobile base online. Force sensors are utilized to resolve remaining uncertainties. The approach is verified with experiments.
Risk Factors of Coxiella burnetii (Q Fever) Seropositivity in Veterinary Medicine Students

Background Q fever is an occupational risk for veterinarians, however little is known about the risk for veterinary medicine students. This study aimed to assess the seroprevalence of Coxiella burnetii among veterinary medicine students and to identify associated risk factors. Methods A cross-sectional study with questionnaire and blood sample collection was performed among all veterinary medicine students studying in the Netherlands in 2006. Serum samples (n = 674), representative of all study years and study directions, were analyzed for C. burnetii IgG and IgM phase I and II antibodies with an immunofluorescence assay (IFA). Seropositivity was defined as IgG phase I and/or II titer of 1∶32 and above. Results Of the veterinary medicine students 126 (18.7%) had IgG antibodies against C. burnetii. Seropositivity associated risk factors identified were the study direction ‘farm animals’ (Odds Ratio (OR) 3.27 [95% CI 2.14–5.02]), advanced year of study (OR year 6: 2.31 [1.22–4.39] OR year 3–5 1.83 [1.07–3.10]) having had a zoonosis during the study (OR 1.74 [1.07–2.82]) and ever lived on a ruminant farm (OR 2.73 [1.59–4.67]). Stratified analysis revealed study direction ‘farm animals’ to be a study-related risk factor apart from ever living on a farm. In addition we identified a clear dose-response relation for the number of years lived on a farm with C. burnetii seropositivity. Conclusions C. burnetii seroprevalence is considerable among veterinary medicine students and study related risk factors were identified. This indicates Q fever as an occupational risk for veterinary medicine students.
Smoking Exposure and Survival of Patients with Esophagus Cancer: A Systematic Review and Meta-Analysis

Smoking is a well-known major risk factor in development of esophageal cancer, but few studies have reported the association between smoking status and prognosis of these patients. We conduct the present study to summarize current evidence. A computerized search of the PubMed and EMBASE was performed up to April 30, 2015. Eight studies, containing 4,286 patients, were analyzed. In the grouping analysis, among esophageal squamous-cell carcinoma patients, current and former smokers, compared to those who have never smoked, seemed to have a poorer prognosis (HR = 1.41, 95% CI 1.22–1.64, and HR = 1.35, 95% CI 0.92–1.97, resp.). In the subgroup analysis, adverse effects on current smoker compared with never smoker were also observed in China and the other countries (HR = 1.5, 95% CI 1.18–1.92, and HR = 1.36, 95% CI 1.12–1.65, resp.). In the group that ever smoked, we could not get a similar result. No significantly increased risk was found in esophageal adenocarcinoma patients compared to the squamous-cell histology ones. In the smoking intensity analysis, heavy smoking was associated with poor survival in esophageal squamous-cell carcinoma. Our pooled results supported the existence of harmful effects of smoking on survival after esophagus cancer diagnosis.
Application of the MLP neural networks for analyzing non Gaussian signal

In this investigation we applied the Multi Layer Perceptron (MLP) neural networks for modeling and predicting a real non Gaussian process. The obtained results show that an agreement between predicted and measured values. The statistical error analysis used to evaluate the performance of the correlations, between measured and predicted values provides satisfactory results. The developed model is tested and compared with an other model based on Volterra system. The obtained result demonstrates the efficiency of the developed model.
GIS for Achieving Sustainable Development of Gas Industry

The main goal of gas industry development strategy is to find and justify the optimum balance between gas production and consumption, ensure harmonious and synchronous advancement of all system developing sectors. This task imposes certain requirements on methods of storing and visualizing information in gas industry, its past, current status and future development. For this purpose, Gazprom VNIIGAZ-NIIGazeconomica LLC has developed its own ver- sion of geo-information system, which allows to efficiently structure, analyze, revise and visualize all information on the long-term development programs of gas industry by means of synthesizing tabulated and cartographic data.
Globalisation strategies and business organisation of a network of logistics service providers

This paper deals with the dynamics of internationalisation and globalisation of the freight‐forwarding and logistics service providers. A case study, based on E.ON, Stinnes, Schenker, BTL and other European and non‐European firms, is presented in order to illustrate the process. The organisation and strategies developed by the firms under study with the aim to expand their activities on European, transnational and global levels are presented. The information was gathered using the Internet as a research tool. Focus is placed on the organisational routes used by the firms in order to expand their activities outside of their home base, and on their business strategies.
E-mail Mining: Emerging Techniques for E-Mail Management

Email has met tremendous popularity over the past few years. People are sending and receiving many messages per day, communicating with partners and friends, or exchanging files and information. Unfortunately, the phenomenon of email overload has grown over the past years becoming a personal headache for users and a financial issue for companies. In this chapter, we will discuss how disciplines like Machine Learning and Data Mining can contribute to the solution of the problem by constructing intelligent techniques which automate email managing tasks and what advantages they hold over other conventional solutions. We will also discuss the particularity of email data and what special treatment it requires. Some interesting email mining applications like mail categorization, summarization, automatic answering and spam filtering will be also presented.
Security Related Data Mining

Data mining is the process that extracts, classifies and analyzes valid and useful information from large volumes of data provided by multiple sources. The data mining has been widely applied into various areas, one of which is to investigate potential security threats. In the literature, various data mining techniques such as classification and clustering have been proposed to detect intrusions, DoS attacks, and malware. This paper surveys different data mining techniques applied to detect security threats and analyzes their advantages and disadvantages. Through comparison, we discuss open research issues about security-related data mining and propose future research focus.
The influence of phospho-tau on dendritic spines of cortical pyramidal neurons in patients with Alzheimer’s disease

The dendritic spines on pyramidal cells represent the main postsynaptic elements of cortical excitatory synapses and they are fundamental structures in memory, learning and cognition. In the present study, we used intracellular injections of Lucifer yellow in fixed tissue to analyse over 19 500 dendritic spines that were completely reconstructed in three dimensions along the length of the basal dendrites of pyramidal neurons in the parahippocampal cortex and CA1 of patients with Alzheimer’s disease. Following intracellular injection, sections were immunostained for anti-Lucifer yellow and with tau monoclonal antibodies AT8 and PHF-1, which recognize tau phosphorylated at Ser202/Thr205 and at Ser396/404, respectively. We observed that the diffuse accumulation of phospho-tau in a putative pre-tangle state did not induce changes in the dendrites of pyramidal neurons, whereas the presence of tau aggregates forming intraneuronal neurofibrillary tangles was associated with progressive alteration of dendritic spines (loss of dendritic spines and changes in their morphology) and dendrite atrophy, depending on the degree of tangle development. Thus, the presence of phospho-tau in neurons does not necessarily mean that they suffer severe and irreversible effects as thought previously but rather, the characteristic cognitive impairment in Alzheimer’s disease is likely to depend on the relative number of neurons that have well developed tangles.
Multi-Document Summarization Using Clustering Algorithm

This paper treat the query sentence as common sentence segmented from multi-documents set for multi-document summarization, and mixed it into sentences set, then this paper create efficient cluster algorithm to cluster all the sentences, The clusters which contain the query sentence will be merged to one cluster, and modified MMR will be used to extract summary sentences. Experimental result shows that the proposed cluster algorithm is efficient.
Plane Surface Detection and Reconstruction using Induced Stereo Symmetry

We propose an algorithm for the detection and reconstruction of plane surfaces using a new stereo approach dubbed SymStereo . SymStereo relies in symmetry analysis for re-covering the 3D curve where a virtual cut plane intersects the scene structure. The result is a proﬁle cut that resembles the one that would be obtained by a Laser Range Finder (LRF). The article shows that the framework is particularly well suited for piecewise-planar reconstruction using only a pair of calibrated views. Since the intersection of two planes is always a line, the 3D space is sampled by a discrete set of virtual planes and the line segments in the proﬁle cuts are extracted. The plane surfaces are determined by grouping co-planar lines using a straightforward RANSAC procedure in the dual Plücker space. We test the algorithm in estimating the relative pose of the stereo rig with respect to planes with different textures. The results are highly accurate and, more importantly, the approach succeeds in situations where current stereo methods fail due to low and/or repetitive texture. We also report experiments in wide-baseline stereo images of complex scenes with multiple planes partially occluded by non-planar objects.
DefDroid: Securing Android with Fine-Grained Security Policy

Android occupies the absolute dominant position in mobile operating system and has the largest market share. Meanwhile, Android faces the risk of malicious insiders leaking sensitive information. In this paper, we present DefDroid, a repackaging tool for enforcing security policies by modifying Android applications without root privilege. The main advantages of DefDroid are that it provides a user-friendly interface to configure fine-grained policies and it supplies multiple deploy- ment methods. We have implemented policies aimed at three types of services of Android system, i.e., content provider, file system, and network. We choose 74 arbitrary applications from Android market and the experimental results show that the successful rate of repackaging applications is about 94.6% which effectively improve the privacy security of Android system while the increased overhead can be tolerated. Keywords—Android; permission restriction; repackage; bytecode instrumentation
oint invariant face recognition using endent component analysis and at tractor networks

We have explored two approaches to recognizing faces across changes in pose. First, we developed a representation of face images based on independent component analysis (ICA) and compared it to a principal component analysis (PCA) representation for face recognition. The ICA basis vectors for this data set were more spatially local than the PCA basis vectors and the ICA representation had greater invariance to changes in pose. Second, we present a model for the development of viewpoint invariant responses to faces from visual experience in a biological system. The temporal continuity of natural visual experience was incorporated into an attractor network model by Hebbian learning following a lowpass temporal filter on unit activities. When combined with the temporal filter, a basic Hebbian update rule became a generalization of Griniasty et al. (1993), which associates temporally proximal input patterns into basins of attraction. The system acquired representations of faces that were largely independent of pose. 1 Independent component representations of faces Important advances in face recognition have employed forms of principal component analysis, which considers only second-order moments of the input (Cottrell & Metcalfe, 1991; Turk & Pentland 1991). Independent component analysis (ICA) is a generalization of principal component analysis (PCA), which decorrelates the higher-order moments of the input (Comon, 1994). In a task such as face recognition, much of the important information is contained in the high-order statistics of the images. A representational basis in which the high-order statistics are decorrelated may be more powerful for face recognition than one in which only the second order statistics are decorrelated, as in PCA representations. We compared an ICAbased representation to a PCA-based representation for recognizing faces across changes in pose. Figure 1: Examples from image set (Beymer, 1994). The image set contained 200 images of faces, consisting of 40 subjects at each of five poses (Figure 1). The images were converted to vectors and comprised the rows of a 200 x 3600 data matrix, X. We consider the face images in X to be a linear mixture of an unknown set of statistically independent source images S, where A is an unknown mixing matrix (Figure 2). The sources are recovered by a matrix of learned filters, W, which produce statistically independent outputs, U .
Designing standard service offerings for reducing IT service management cost

A significant portion of a typical Enterprise IT services budget is spent on steady state management operations known as ”operational expenses”. Recently ”Standard Services Offerings” based approaches are proposed to reduce such cost where only a few set of versions and configurations are used to build an Enterprise IT services platform. A key challenge in these approaches is the choice of appropriate base set of configurations that can be used as seed configurations. Our paper addresses the above problem by studying several methods towards choosing the right set of application stack with configurations that minimizes the cost of building the target services environment from this seed set of offerings. We present four different methods and use model driven simulation studies to compare the performance of these approaches. We validate our key findings by analyzing our approaches for a large Enterprise customer IT environment.
The Coupling of Solute Fluxes in Membranes

Our previous description of solute drag on a synthetic membrane has been extended to include the solutes mannitol, sucrose, raffinose, inulin, and dextran. Labeled and nonlabeled forms of these solutes were used in pairs to quantitate solute flux interaction. Three membranes with pore sizes of 350, 80, and 20 A, respectively, have been utilized. It is shown that solute flux interaction occurs with all the solutes and that the extent of interaction is related directly to solute permeability, concentration, and molecular size. The magnitude of solute interaction is reciprocally related to the radii of the membrane pores, greater interaction occurring with small pored membranes. Solute drag is seen as an increased flux of tracer solute in the direction of the diffusion gradient of a second solute as well as a decreased tracer flux into the diffusion gradient. Values are given for self-diffusion and interaction coefficients as well as for a new coefficient, the "effectiveness coefficient. "