Miracle-dz/UniFAR-FTUs huggingface dataset:
Topology Control in Wireless Sensor Networks: What Blocks the Breakthrough?

Graph-based topology control adapts wireless topologies to achieve certain target graph structures. Wireless sensor networks seem well-suited for the expectations (in particular those on provided energy savings) raised by topology control. Nevertheless, topology control has never made the breakthrough in real-world deployments. This work explores the reasons for this, identifying five practical obstacles of today's topology control: (i) unrealistic assumptions, (ii) unsuitable graph structures, (iii) application agnosticism, (iv) unclear role in the stack, and (v) insufficient framework support. To address the latter obstacle, we provide a re-usable framework for the implementation and evaluation of topology control. Based on this framework, we conduct a testbed-based evaluation for two application scenarios and three topology control algorithms including a novel application-specific algorithm. Indeed, the identified obstacles hinder topology control from boosting the application. However, the achieved graph structures show the practical feasibility of topology control in principle.
Diagnosis of a Class of Non Linear Hybrid Systems by On-line Instantiation of Parameterized Analytical Redundancy Relations (cid:63)

: Embedded controlled systems often undergo non-linear behaviors due in particular to actuator saturations. A way to tackle these non linearities is to model them by Piece-Wise-Afﬁne (PWA) functions and to deﬁne an operating mode for each region of linear behavior. However, the number of components that could introduce saturations leads to an explosion of the number of operating modes, especially when the system under consideration is already a multimode system. Performing mode tracking and diagnosis of this type of hybrid systems requires to build a set of consistency tests issued from Analytical Redundancy Relations (ARRs) for each nominal and faulty mode, which means that the number of ARRs to be stored may be prohibitive. This paper addresses the diagnosis of hybrid systems switching between operating modes that undergo saturation behaviors. It proposes to generate non-linear parameterized ARRs, and to account for all regions of linear behavior, i.e. nominal, faulty and saturation regions, with automata representing the transitions between them at the level of each component. The parameters appearing in the ARRs are then provided by the states of the synchronized component automata and the ARRs needed to track the system mode are instantiated and checked on the ﬂy, then reducing dramatically the required memory space and computation time. The proposed mode tracking algorithm has been tested on the Attitude Control System (ACS) of an industrial telecommunication satellite.
A hybrid searching scheme in unstructured P2P networks

The existing searching schemes in peer-to-peer (P2P) networks are either forwarding-based or non-forwarding based. In forwarding-based schemes, queries are forwarded from the querying source to the query destination nodes. These schemes offer low state maintenance. However, querying sources do not entirely have control over query processing. In non-forwarding based methods, queries are not forwarded, and the querying source directly probes its neighbors for the desired files. Non-forwarding searching provides querying sources flexible control over the searching process at the cost of high state maintenance. In this paper, we seek to combine the powers of both forwarding and non-forwarding searching schemes. We propose an approach where the querying source directly probes its own extended neighbors, forwards the query to a subset of its extended neighbors, and guides these neighbors to probe their own extended neighbors on its behalf. Our approach can adapt query processing to the popularity of the sought files without having to maintain a large set of neighbors because its neighbors' neighbors are also in the searching scope due to the 1-hop forwarding inherent in our approach. It achieves higher query efficiency than the forwarding scheme and a better success rate than the non-forwarding approach. To the best of our knowledge, the work in this paper is the first one to combine forwarding and non-forwarding P2P searching schemes. Experimental results demonstrate the effectiveness of our approach.
Graphical models for dependencies and queries in uncertain data

Dependency and query are two fundamental concepts in databases. Specifically, hypergraph representations of join dependencies and conjunctive queries have been widely investigated for conventional relational databases. However, we still lack a systematic study of such graphical representations for uncertain and probabilistic databases. In this paper we initiate a comprehensive study of the role of graphical models in representing uncertainty and evaluating queries.
The Examination of the Secondary School Students’ Levels of Psychological Well-Being in Terms of Some Variables

ARTICLE INFO ABSTRACT Article History: Received 03.06. 2015 Received in revised form 17.08. 2015 Accepted 12.08. 2015 Available online 12.09. 2015 The purpose of this study is to investigate the secondary school students’ psychological well-being in terms of some variables. The quantitative survey method was used in this study. Study group in this research consists of 381 students who are studying in private and state schools in during the 2014-2015 academic year. The data was collected by using “Personal Information Forms”, developed by the researcher. Also to determine the psychological well-beingof the students, the 42-items version of "Psychological Well-Being Scales" (PİOÖ) which was developed by Ryff (1989), revised by Ryff and Keyes (1995), and adapted by Akın et al. (2012) was used. To analyze the data obtained from this study, various inferential analyses tests such as T test, (Kruskal Wallis), LSD, (Scheffe), U test and ANOVA were used (p<0.05). According to the results gathered from the research, in terms of gender, only in the autonomysubdimension there is a significant difference in favor of males; in terms of socioeconomic variable, there is a significant difference against those who has low socioeconomic levels; in terms of the variants of attitudes of family, there is a significant difference in favor of those whose parents withdemocratic attitude; in terms of academic achievement, there is a significant difference in favor of high academic achievement for those whose psychological well-being is high; in terms of health variables, there is a significant difference in favor of thosewho reported health was important to them; in terms of groups’ doing sports, exercise and etc. variables, there is a significant difference in favor of those who do sports and exercise. © 2015 IOJES. All rights reserved
A Vehicle Type Dependent Car-following Model Based on Naturalistic Driving Study

In this paper, a car-following model considering the preceding vehicle type is proposed to describe the longitudinal driving behavior closer to reality. Based on the naturalistic driving data sampled in real traffic for more than half a year, the relation between ego vehicle velocity and relative distance was analyzed by a multi-variable Gaussian Mixture model, from which it is found that the driver following behavior is influenced by the type of leading vehicle. Then a Hidden Markov model was designed to identify the vehicle type. This car-following model was trained and tested by using the naturalistic driving data. It can identify the leading vehicle type, i.e., passenger car, bus, and truck, and predict the ego vehicle velocity and relative distance based on a series of limited historical data in real time. The experimental validation results show that the identification accuracy of vehicle type under the static and dynamical conditions are 96.6% and 83.1%, respectively. Furthermore, comparing the results with the well-known collision avoidance model and intelligent driver model show that this new model is more accurate and can be used to design advanced driver assist systems for better adaptability to traffic conditions.
A Survey of Nosql Database For Analyzing Large Volume Of Data In Big Data Platform

The massive improvement of data in the present era is drastically increased, where millions of data is emerging from variety of applications. Due to this massive flow of data the importance of data becomes a key factor in all applications. In old technology the space occupied by data is very less, but in the present scenario the value of every piece of data plays a vital role. The organization needs some new technology to process large amount of data in an effective way. For that purpose data exploration and visualization systems play a vital role in the Big Data era. It is a complex task for the companies to explore and visualize very large datasets. Every company should follow some protocol to have accurate insight from analysis of large volume of data. This strategy helps organizations to enhance their business functionalities and it also helps to identify the way to improve the quality of their products. The big data [25-29] contains some unique databases to handle massive volume of data. In this survey, we explain the characteristics of database and challenges. It also describes about the different techniques and tools currently used for handling large sets of data and their capabilities to support massive volume of data from variety of data sources.
The animal welfare battle: the production of affected ignorance in the Swedish meat industry debate

ABSTRACT The Swedish animal welfare debate has for years focused on the meat industry, which sees animals not as sentient creatures but as production factors and commodities to be economically exploited. Although animal rights organizations have tried to change the meat industry and consumer behaviour, meat consumption is increasing. This could be explained as ‘affected ignorance’ generated by what one already knows but does not want to hear about. This paper discusses how various actors, such as meat industry companies, food retailers, and social movement organizations, frame animal welfare in the media debate with the use of discourses, which are important for producing or discouraging affected ignorance. The paper examines a discursive battle in which actors draw on various discourses over time but also hijack opponents’ discourses. This use of discourses seems to blur the debate and confuse people and they will continue to eat meat from factory-farmed animals.
Clinical applications of self-hypnosis: A systematic review and meta-analysis of randomized controlled trials.

We conducted a systematic review of randomized control trials examining the efficacy of self- hypnosis as a clinical treatment. Searching for ‘self-hypnosis’, ‘self-hypnotic’, ‘autosuggestion’, and ‘autohypnosis’ returned 576 studies, of which 22 met the definition of being an RCT. Self-hypnosis has been reported to be efficacious in studies of pain, childbirth, paediatric applications, stress and anxiety. Methodological differences among studies are discussed. Self-hypnosis is most likely to be effective when taught as an independent self-directed skill and when it involves at least three practice sessions before participation in the trial. Experience of hetero-hypnosis does not seem to be essential in producing an effect for self-hypnosis. Studies reporting no effect typically involved participants listening to audio recordings of hetero-hypnosis only. Meta- analysis revealed a medium-to-large effect size for self-hypnosis in clinical treatment.
An integer-valued bilinear type model

A integer-valued bilinear type model is proposed. It can take positive as well as negative values. The existence of the process is established in Lm. In fact, this process is the unique causal solution to an equation that is similar to a classical bilinear type model equation. For the estimation of the parameters, we suggest a quasi-maximum likelihood approach. The estimator is strongly consistent and asymptotically normal.
Correlation controlled bilateral filtering of fMRI data

Functional magnetic resonance imaging (tMRI) is a method for detecting brain regions that are activated when a certain task is carried out. The method is useful in planning of neurosurgical procedures, where knowledge of the exact locations of important functions is needed to avoid damage to these regions. It is also an important tool in neurological research, where it is used to investigate the function of the human brain.To find the activated regions, a sequence of images of the brain is collected while a patient or subject alters between resting and performing the task. The variations in image intensity over time is then compared to a model of the variations expected to be found in active parts of the brain. Locations where the intensity variations are similar to the model are considered to be activated by the task.Since the images are very noisy, filtering is needed before the detection of activation. If adaptive filtering is used, i.e. if the filter at each location is adapted to the local neighborhood, very good detection performance can be obtained. This thesis presents two methods for adaptive filtering of fMRI data. One of these is based on canonical correlation analysis (CCA), and is an extension of a previously proposed CCA-based method. As in the old method, CCA is used in each neighborhood to find a spatial fi lter that maximizes the correlation to the model of the intensity variation. A novel feature of the presented method is that it is rotationally invariant, i.e. that it is equally sensitivelo activated regions in different orientations.The other method is based on bilateral filtering. This method creates spatial filters which averages pixels with similar intensity variation. Since these filters are not optimized to maximize the similarity to the model of activated signals, the risk of declaring inactive pixels as active is lower compared to CCA-based methods.
TRANSPLANTATION CD 4 1 invariant natural killer T cells protect from murine GVHD lethality through expansion of donor CD 4 1 CD 25 1 FoxP 3 1 regulatory T cells

Dysregulated activation and proliferation of donor T cells following allogeneic hematopoietic cell transplantation (HCT) leads to immune-mediated destruction of host tissues resulting in graft-versus-host disease (GVHD). Most established therapeutic approaches involving immunosuppressive drugs to prevent or treat GVHD lead to a global suppression of T-cell function, have significant toxicities, and lead to increased risk of opportunistic infections. Adoptive transfer of donor CD4CD25FoxP3 regulatory T cells (Tregs) has been studied in murine animal models, and promising results have been reported in haploidentical and umbilical cord blood HCTs. A deeper understanding of immune regulatory mechanisms holds promise for controlling dysregulated immune responses and improving outcomes after allogeneic HCT and for the treatment of other conditions, including severe autoimmune disorders, as well as for the induction of tolerance to transplanted organs. Despite their rarity in humans andmice, invariant natural killer T (iNKT) cells harbor potent immunomodulatory functions. They are characterized by rapid effector function upon stimulation of the semi-invariant T-cell receptor (TCRaVa24-Ja18 in humans; Va14-Ja18 in mice) with glycolipids. Host iNKT cells have an important tolerogenic impact on GVHD after reduced-intensity conditioning with total lymphoid irradiation and anti-thymocyte globulin (TLI/ATG). In this study, we investigated the impact of purified and adoptively transferred donor CD4 iNKT cells on GVHD and graft-versus-tumor (GVT) effects in a murine model of allogeneic HCT.
Sensitivity of Near-Surface Shear-Wave Velocity Determination From Rayleigh And Love Waves

Rayleigh and Love waves recorded on seismic-shot gathers can be used to determine the thickness and shear-wave velocity of shallow subsurface layers. After the data are transformed into the k-f domain, the dispersion curve for each of the phases can be picked from maxima on the contour plot. This dispersion curve is then inverted for the velocities and depths. Different frequencies in the dispersion curve yield information about different depths. The fundamental mode has proven to be of greater use than higher modes. Both Rayleigh and Love waves are easily inverted. However, the Love waves seem to yield information in a lower portion of the spectrum than the Rayleigh modes. Three examples are given from field experiments conducted near Canton, Texas.
Applying ontology in architecture-based self-management applications

Context-awareness is the premise of self-management applications. In this paper, a Middleware for Adaptive Coordination (MAC) architecture is proposed. It supports the cycle from modeling and reasoning about the context knowledge to planned architecture-based self-management actions. One of the key elements of our approach is the explicit modeling of context by Web Ontology Language based on which the logic reasoning is performed. Besides, an internal object-oriented architectural self-management mechanism is proposed. A prototype of MAC is also given followed by a case study with quantitative evaluations.
Experimental study for automatic colony counting system based on image processing

Colony number in many colony calculating experiments is counted by manual method at present, therefore it is difficult to get the result quickly and accurately when the number of experimental sample is large. A new automatic colony counting system was developed, which makes use of image-processing technology to feasibly count white bacterial colonies in clear plates according to the RGB color theory. A way of obtaining optimal threshold of segment image was obtained based upon a lot of experiments on the distribution of the colony's chromatic value. It has been proved that the method greatly improves the accuracy and efficiency of the colony counting and the counting result is not affected by inoculation method, shape or size of the colony. It is revealed that automatic detection of colony quantity using image-processing technology could be an effective way.
High-Performance Solvers for Large-Scale Dense Eigenproblems

We introduce a new collection of solvers – subsequently called EleMRRR – for large-scale dense eigenproblems. EleMRRR solves various types of problems: Hermitian-definite and symmetricdefinite generalized eigenproblems; Hermitian and symmetric standard eigenproblems; symmetric tridiagonal eigenproblems. Among these, the latter is of particular importance as it is a solver on its own right, as well as the computational kernel for the other types; we present a fast and scalable tridiagonal solver based on the Algorithm of Multiple Relatively Robust Representations – referred to as PMRRR. Like the other EleMRRR solvers, PMRRR is part of the freely available Elemental library, and is designed to fully support both message-passing (MPI) and multithreading parallelism (SMP). As a result, the solvers can equally be used in pure MPI or in hybrid MPI-SMP fashion. A thorough performance study of EleMRRR and ScaLAPACK’s solvers is conducted on two supercomputers. Such a study, performed with up to 4096 cores, provides precise guidelines to assemble the fastest solver within the ScaLAPACK framework; it also indicates that the EleMRRR outperforms even the fastest solvers built from ScaLAPACK’s components.
Investigation of high frequency effects on soft-switched synchronous rectifier buck converter

This paper discusses about the effects of switching frequency where reduction in losses is significant to the converter's performance. The proposed zero-voltage-switching synchronous buck converter circuit is compared with the conventional synchronous buck converter by applying different frequency ranging from 250 kHz to 1.25 MHz. PSpice simulation is carried out using fixed delay control scheme. It is found that the proposed circuit improves the switching speed by at least 50 % in both turn-off and turn-on transitions. In addition, more than 57 % in switching losses have been reduced. Having a low body diode conduction time, the proposed converter can maintain low loss with minimized dead time delay, indicating suitability in operating at high switching frequency.
Acute in vivo regulation of 11beta-hydroxysteroid dehydrogenase type 1 activity by insulin and intralipid infusions in humans.

CONTEXT Extraadrenal regeneration of cortisol by 11beta-hydroxysteroid dehydrogenase type 1 (11HSD1) is increased after a mixed meal. It is unknown which tissue is responsible and whether this reflects the complex transcriptional control of 11HSD1 or posttranscriptional control exerted by supply of reduced nicotinamide adenine dinucleotide phosphate from hexose-6-phosphate dehydrogenase. OBJECTIVE The objective of this study was to test whether hyperinsulinemia and/or increased serum free fatty acids increase whole-body and intraadipose 11HSD1, and whether adipose 11HSD1 switches from dehydrogenase to reductase activity. METHODS In nine healthy men, we measured whole-body cortisol regeneration (by iv infusion of 9,11,12,12-[2H]4 -cortisol) and intra-adipose interconversion of cortisol and cortisone (by sc microdialysis infusion of [3H]4 -cortisol and [3H]2 -cortisone in separate cannulae) during: 1) a hyperinsulinemic euglycemic clamp; 2) iv lipid infusion (Intralipid 20% fat emulsion); and 3) saline infusion, each for 3.5 h. RESULTS Hyperinsulinemia increased rate of appearance of 9,12,12-[2H]3 -cortisol (19.3 +/- 0.8 vs. 16.7 +/- 1.1 nmol/min with saline, P < 0.001), indicating increased whole-body 11HSD1. Within adipose, the predominant reaction was reductase conversion of cortisone to cortisol (after 3.5 h of saline infusion, reaching 11.0 +/- 2.7% per hour reductase vs. 5.2 +/- 1.3 dehydrogenase, P < 0.02); insulin increased reductase (reaching 15.8 +/- 3.0, P < 0.05) and tended to increase dehydrogenase activity. Intralipid infusion had no effects on whole-body deuterated cortisol metabolism, but increased both dehydrogenase and reductase (reaching 16.7 +/- 1.8, P < 0.01) activities in adipose. CONCLUSIONS Hyperinsulinemia and increased free fatty acids induce acute increases in 11HSD1 activity in adipose tissue that are not attributable to a switch from dehydrogenase to reductase. Hyperinsulinemia also increases systemic cortisol regeneration. These effects may enhance intracellular cortisol concentrations after a meal.
Fuzzy-Rule based Load Pattern Classifier for Short-Tern Electrical Load Forecasting

Based on the knowledge of historical data sets, a fuzzy rule-based classifier for electrical load pattern classification is set up. Considering with the accuracy and interpretation of fuzzy rules, multi-objective genetic algorithm are applied to choose the Pareto optimum rules that are used to classify electrical load. In the computation experiments, the generated fuzzy rule-based classifier is used to load forecasting, the computation results show that it leads to high classification performance, and it can supply more sufficient and effective historical data for load forecasting, better performance of load forecasting is gained accordingly