Miracle-dz/UniFAR-FTUs huggingface dataset:
Regulator of G protein signaling 9-2 ( RGS 9-2 ) mRNA is up regulated during neuronal differentiation of mouse embryonic stem cells

In this study we demonstrate up-regulation of mRNA for Regulator of G protein Signaling (RGS) 6, 7, 9 and 11, R7 family RGS binding protein (R7BP) and RGS9 anchor protein (R9AP) during neuronal differentiation of mouse embryonic stem cells (mESCs). This expression pattern was most robust for RGS9 whose transcript level was low in undifferentiated mESCs but increased over 125 fold when differentiating mESCs began to exhibit a neuronal precursor cell (NPC) phenotype. In addition, we demonstrate that RGS9 mRNA is expressed in neuronal stem cells isolated from embryonic mouse cortex. The expression of RGS9 in two distinct populations of NPCs suggests that RGS9 and its accessory proteins may play an important role in neuron development.
Statistical challenges in genetic association analyses: Populationbased
versus family-based studies

Genetic association tests provide clues on chromosomal locations of putative genes underlying complex traits, both qualitative (such as disease status) and quantitative (such as measurable precursors of clinical outcomes). One can adopt either a population-based or a family-based study design to generate genotype and phenotype data necessary to carry out the tests of association. In this article, we provide an overview of the two contrasting study designs, the statistical issues pertaining to each of these study designs as well as their relative advantages and disadvantages. We also outline the current statistical challenges in interpreting association findings in the presence of population stratification and in carrying out appropriate comparisons of the powers of the association tests based on the two study designs.
The 7 th NICT IVS-TDC Symposium

We have estimated atmospheric slant delays using the KAshima RAytracing Tools (KARAT) through the JMA 10 km MANAL data. The comparisons between KARAT-based slant delays and empirical mapping functions indicate large biases ranging between 18 and 90 mm for summer season, which are considered to be caused by a significant variability of water vapor. We also compared PPP processed position solution using KARAT with that using the latest mapping function for the two week GEONET data sets. The KARAT solution were almost identical to the solution using GMF with linear gradient model, but some cases were slightly worse under the extreme atmospheric condition. Though we need further investigations to evaluate the capability of KARAT to reduce atmospheric path delays under the various topographic and meteorological regimes, the KARAT will be the powerful tool to reduce atmospheric path delay with the numerical weather model improvement.
An Assisting , Constrained 3 D Navigation Technique for Multiscale Virtual 3 D City Models

Virtual 3D city models serve as integration platforms for complex geospatial and georeferenced information and as medium for effective communication of spatial information. In order to explore these information spaces, navigation techniques for controlling the virtual camera are required to facilitate wayfinding and movement. However, navigation is not a trivial task and many available navigation techniques do not support users effectively and efficiently with their respective skills and tasks. In this article, we present an assisting, constrained navigation technique for multiscale virtual 3D city models that is based on three basic principles: users point to navigate, users are lead by suggestions, and the exploitation of semantic, multiscale, hierarchical structurings of city models. The technique particularly supports users with low navigation and virtual camera control skills but is also valuable for experienced users. It supports exploration, search, inspection, and presentation tasks, is easy to learn and use, supports orientation, is efficient, and yields effective view properties. In particular, the technique is suitable for interactive kiosks and mobile devices with a touch display and low computing resources and for use in mobile situations where users only have restricted resources for operating the application. We demonstrate the validity of the proposed navigation technique by presenting an implementation and evaluation results. The implementation is based on service-oriented architectures, standards, and image-based representations and allows exploring massive virtual 3D city models particularly on mobile devices with limited computing resources. Results of a user study comparing the proposed navigation technique with standard techniques suggest that D. Hildebrandt, R. Timm Hasso-Plattner-Institut at University of Potsdam, Prof.-Dr.-Helmert-Str. 2-3, 14482 Potsdam, Germany Tel. : +49-331-5509-3911 E-mail: {dieter.hildebrandt|robert.timm}@hpi.uni-potsdam.de 2 Dieter Hildebrandt, Robert Timm the proposed technique provides the targeted properties, and that it is more advantageous to novice than to expert users.
Teacher development in online teaching : A negotiated intervention strategy

This paper reports a case study of a particular strategy for teacher development in asynchronous, online teaching in a tertiary institution in New Zealand. We propose a particular strategy and process for assisting online teachers in promoting changes and designing and executing those changes in their online classes. A negotiated intervention strategy was used to assist an experienced, face-to-face, tertiary teacher re-develop and teach his existing masters paper as an asynchronous, online course. Reflective conversations with the teacher reveal the complexities of working with a teacher to develop his online pedagogical practice, and also highlights the potential of the negotiated intervention strategy as a promising tool for online teacher development.
Using Deep CNN with Data Permutation Scheme for Classification of Alzheimer's Disease in Structural Magnetic Resonance Imaging (sMRI)

In this paper, we propose a novel framework for structural magnetic resonance image (sMRI) classification of Alzheimer’s disease (AD) with data combination, outlier removal, and entropy-based data selection using AlexNet. In order to overcome problems of conventional classical machine learning methods, the AlexNet classifier, with a deep learning architecture, was employed for training and classification. A data permutation scheme including slice integration, outlier removal, and entropy-based sMRI slice selection is proposed to utilize the benefits of AlexNet. Experimental results show that the proposed framework can effectively utilize the AlexNet with the proposed data permutation scheme by significantly improving overall classification accuracies for AD classification. The proposed method achieves 95.35% and 98.74% classification accuracies on the OASIS and ADNI datasets, respectively, for the binary classification of AD and Normal Control (NC), and also achieves 98.06% accuracy for the ternary classification of AD, NC, and Mild Cognitive Impairment (MCI) on the ADNI dataset. The proposed method can attain significantly improved accuracy of up to 18.15%, compared to previously developed methods. key words: structural magnetic resonance imaging (sMRI), grey matter (GM), white matter (WM), Alzheimer’s disease (AD), normal controls (NC)
PHY and MAC layer performance optimization of the IEEE 802.16 system

Martikainen, Henrik PHY and MAC Layer Performance Optimization of the IEEE 802.16 System Jyvaskyla: University of Jyvaskyla, 2012, 80 p.(+included articles) (Jyvaskyla Studies in Computing ISSN 1456-5390; 154) ISBN 978-951-39-4829-0 (nid.) ISBN 978-951-39-4830-6 (PDF) Finnish summary Diss. This thesis concentrates on how to improve the performance of the IEEE 802.16 system in three main areas. The first problem area is system throughput optimization by selecting the optimal MAC PDU size when ARQ is used. A method for selecting the optimal size is proposed and evaluated. In the second part, various ARQ features are evaluated and also the performance of the ARQ and HARQ error correction methods are evaluated. Finally, the two different frame optimization problems are investigated. First the three duplexing modes of IEEE 802.16 are compared, namely Time Division Duplexing, Full Frequency Division Duplexing and Half Frequency Division Duplexing. A novel group division algorithm is proposed for H-FDD. Second, the relay mechanism of IEEE 802.16 is presented and its performance evaluated. The performance evaluation of all presented problems is done by simulations with theWINSE extension which is build on top of the NS-2 network simulator.
The Linear Switching State Space: A New Modeling Paradigm for Task Scheduling Problems

Task Scheduling (TS) poses a challenging problem in distributed systems for multiple realms such as multiprocessor systems, flow-shop scheduling and project management problems in which there are multiple tasks and processors (resources), and the problem is to efficiently assign tasks to processors. The importance of this problem can be considered from multiple perspectives such as heterogeneity of processors, computational complexity of reaching a solution as well as theoretical performance analysis. Also, we propose a new modeling paradigm based on system engineering. The proposed switching state space approach opens a possibility of using the extensive theoretical developments that have taken place in this field within the past several decades. In its general form, TS is inherently nonlinear because of its many nonlinear constraints. In this paper, we demonstrate that standard TS can be mapped via nonlinear state space and, through theoretical analysis, show stability of the resulting system for static task scheduling problems. The proposed static scheduling schedules dependent tasks on a heterogeneous multiprocessor system. A suitable transformation is then devised to convert this model to linear switching state space with nonlinear constraints. To illustrate the utility of the model, two scheduling approaches are then presented based on Height Sorted (HS) and Ready Tasks (RT). These two methods are examples that show how this model can be used to reach the stability criteria. Finally, the proposed methods are compared against a conventionally accepted scheduling scheme on several random experiments showing comparative performance.
Comparison of Academic Procrastination and Academic Enthusiasm of Rural and Urban Students in Bojnourd City

Purpose : The school is one of the most important social and academic institutions and the most important element of education, which is established and administered in accordance with the goals approved by the academic curriculum and the standards and guidelines of the Ministry of Education. Therefore, the purpose of this study was to compare the degree of academic procrastination and the students’ academic Engagement. Methodology : The research was descriptive in terms of purpose, and applied in terms of methodology. The statistical population consisted of all female students of the sixth grade of rural and urban elementary schools of Bojnourd City; 1164 sixth-grade girls in urban schools and 669 girls in rural elementary schools, according to a report prepared by the Ministry of Education of Bojnourd in the academic year 2017-2018. The stratified sampling method was used to determine the sample size. A sample of 320 urban students and 268 rural students were selected based on Cochran formula at the error level 5%. The instruments for data collection were Solomon & Roth Blum Academic Procrastination Questionnaire (1984), and Academic Engagement Questionnaire of Frederic et al. (2004), which had formal and content validity, and reliability was obtained 0.84 and 0.87, respectively, according to Cronbach's alpha coefficient. Data were analyzed by SPSS-21 software using t-test at the significance level of 0.05. Findings : The results showed that there was a significant difference between the academic procrastination scores of rural and urban students (p <0.05). The mean of urban students' procrastination was greater than that of rural students. There was also a difference between academic Engagement of rural and urban students (p <0.05). Conclusion : About 47 percent of rural students and about 53 percent of urban students had academic procrastination. Also, about 57% of rural students and about 63% of urban students had a high degree of academic Engagement.
CENTERIS-International Conference on ENTERprise Information Systems / ProjMAN-International Conference on Project MANagement / HCist-International Conference on Health and Social Care Information Systems and Technologies , CENTERIS / ProjMAN / HCist 2018 Collecting Information about Air Quality usin

Nowadays, one of the major public health concerns is the quality of the air that surrounds us and knowing how polluted it might be. To compute the Air Quality Index for a specific geographical site, five pollutants can be considered: particulate matter (PM2.5 and PM10), ground-level Ozone (O3), Nitrogen dioxide (NO2) and Sulphur dioxide (SO2). If we consider the data from both atmospheric conditions and the existence of catastrophic events, we will be able to cross it with existing data from sensor nodes (gathering air pollutants' data) in order to accomplish a more detailed understanding of the current air quality in a specific area. Our solution introduces a differentiating factor: the concept of turning every smartphone into a dynamic sensor node that can be used to actively contribute to the centralized platform that gathers all the data from the sensor nodes. This feature enables citizens to play a significant and empowering role regarding healthcare conditions. The proposed system integrates several modules including a Custom Delivery Solution (CDS), and, as an alternative, an out-of-the-box solution named ThingSpeak. Both solutions offer a service delivery system to the mobile app that presents the latest data to the users and allows them to submit their own data. © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of the CENTERIS International Conference on ENTERprise Information Systems / ProjMAN International Conference on Project MANagement / HCist International Conference on Health and Social Care Information Systems and Technologies.
Hand Shape Recognition based on Hyper Column Model

In this paper, we propose a recognition method of hand shapes using Hyper-Column Model (HCM). HCM is a model to recognize images, and consists of Hierarchical Self-Organizing Maps (HSOM) and Neocognitron (NC). HCM complements disadvantages of HSOM and NC, and inherits advantages from them. There is a problem, however, that HCM does not suit general image recognition since its learning method is an unsupervised one with competitive learning which is used by Self-Organizing Map (SOM). Therefore, we extended HCM to a supervised learnable model with an associative memory of SOM. We have found that an ability of HCM with supervised learning is superior to the one with unsupervised learning.
Remote sensing images classifications based on decision fusion

With developing of remote sensing technology and creatation of new various imaging sensors it has been possible to access to various images from one scene. Because of some reasons, such as limited number of training samples, the results of classical classification methods used for these images are not satisfactory. A multiclassifier system is used for classification of these images. In this system, decision fusion strategy is utilized which is the highest level of data fusion approach. By a proper mechanism proposed in this paper, using of one multiclassifier system for classification of extracted data from one source has been available. For this purpose, initially, existed bands are categorized based on minimum correlation criterion and before primary classification in multiclassifier system and data of each group are used for primary classification careless to imaging sensors. In final classification stage, the results of source’s primary classification are combined according to different statistical and neural algorithms and final decision for one pixel’s class is made. Morever, the effect of using multi classifier in primary classification stage is investigated. This procedure is implemented on real data and the results are analyzed qualifiedly and quantitively using various criterion such as accuracy degree and classification provided map. The results show that this classification approach and specially using various classifiers in primary classification stage, can compensate limited amount of training samples in remote sensing data.
Prediction of Outcome of Sepsis at Surgical ICU: A Prospective Multi-Center Prospective Study

Arginine vasopressin (AVP) is a 9-amino acid peptide synthesized by magnocellular neurons of the hypothalamus [1]. The released VAP is then stored in posterior pituitary gland [2] to be released following different stimuli especially change in plasma osmolality and hypovolemia [3] and various stressors such as hypoxia, acidosis, severe infections and different types of shock [4]. Reliable measurement of AVP is hindered by several factors; its short half-life in serum, its instability in withdrawn blood samples and over 90% of AVP is tightly bound to platelets [5]. Copeptin (Cp), a 39-aminoacid glycopeptide, is the COOH-terminal portion of the precursor pre-pro-vasopressin termed CTproAVP [6]. Activation of the AVP system stimulates Cp secretion into the circulation from the posterior pituitary gland in equimolar amounts with AVP [7]. Therefore, Cp directly reflects AVP concentration and can be used as a surrogate biomarker of AVP secretion [8].
Electrohydrodynamic Flow Patterns in a Narrow Electrostatic Precipitator with Longitudinal Wire Electrode for Various Electrode Geometries

Recently narrow electrostatic precipitators (ESPs) have become a subject of interest because of their possible application in diesel engines. In this paper results of 2-dimensional (2D) Particle Image Velocimetry (PIV) measurements of the flow patterns in a narrow ESP for various electrode geometries are presented. The PIV measurements were carried out in the observation plane that is perpendicular to the ESP duct. The ESP was a glass duct, 120 mm long, 30 mm wide and 30 mm high. The electrical electrode set consisted of a single wire discharge electrode and either single or two or four plane collecting electrodes. The discharge electrode, made of a stainless steel wire of a diameter of 0.23 mm and length of 100 mm, was placed longitudinally to the flow. The collecting plane electrodes (30 mm × 120 mm) were made of aluminum tape of a thickness of 50 μm glued on dielectric supporting plates. Depending on the electrode geometry, either a single collecting plane electrode was placed on the bottom wall (geometry 1), either two or four collecting plane electrodes were placed on the bottom and top walls (geometry 2), or on the bottom and side walls (geometry 3), or on all four walls (geometry 4) of the ESP. The DC voltage of 10 kV was applied to the wire electrode through a 10 M resistor, while the collecting electrodes were grounded. Air flow seeded with a cigarette smoke was blown along the ESP duct with an average velocity of 0.9 m/s. The PIV measurements were carried out in a so-called electrode mid-plane, perpendicularly to the wire and the collecting electrodes. The obtained results showed that the particle flow in the ESP had strongly three dimensional (3D) character due to both the applied voltage and narrow cross section of the ESP duct. For the electrode geometries 1, 2 and 3 large and very regular vortex structures were observed in the mid plane. Although our measurements were two dimensional the analysis of the obtained results showed that the observed vortices exhibit three dimensional character, having the form of a spiral vortex moving along the ESP. These spiral vortices may decrease the particles collection efficiency, as it was calculated by Dumitran [1]. However, for the electrode geometry 4 the vortex structures were smaller and irregular, which may results in adifferent particle collection efficiency. The particle collection efficiency for all presented electrode geometries is under investigation. Keywords—Narrow electrostatic precipitator, ESP, EHD flow, 2D PIV, flow measurement Corresponding author: Jerzy Mizeraczyk e-mail address: jmiz@imp.gda. pl Presented at the Sixth International Conference on Applied Electrostatics, ICAES’2008, in September 2008 139 International Journal of Plasma Environmental Science & Technology, Vol.4, No.2, SEPTEMBER 2010
Efficient Load Model of Resistive Mesh Structured Power Network

This paper proposes an efficient load model dedicated to a resistive mesh structured power network. The proposed method approximates an uninterested partition of a large mesh structured circuit, which the partition does not contain storage elements nor internal sources, to a simple load model using only resistors. In the experimental results, the proposed load model showed more than 100 times faster simulation speed with the maximum error less than 3%.
In Defence of Revealed Preference Theory July 2 , 2019

Revealed preference theorists interpret the preferences that feature in standard economic theory behaviourally, as mere descriptions of agents’ actual and hypothetical choice behaviour. This paper defends revealed preference theory against a pervasive line of criticism, according to which the empirical success of revealed preference methodology ultimately relies on appealing to some mental states, in particular the agent’s beliefs. This is then either taken to show that revealed preference theory is incoherent, or that it is unmotivated. Alternative interpretations of preference as a kind of mental state are usually proposed instead. This paper argues that all that is established by these arguments is that revealed preference theorists must accept a limited kind of mentalism in their theory of options, that is, their theory about how the options an agent is choosing between ought to be modelled. I characterize the limited mentalism required, and show that it is consistent both with an essentially behavioural interpretation of preference and with standard revealed preference methodology. I provide evidence that current economic practice is by and large consistent with the concession of a limited kind of mentalism about options, and argue that such a concession does not undermine the core motivations of revealed preference theory.
A Study of Online State-of-Health Estimation Method for In-Use Electric Vehicles Based on Charge Data

The real-time state-of-health (SOH) estimation of lithiumion batteries for electric vehicles (EV) is essential to EV maintenance. According to situations in practical applications such as long EV battery capacity test time, unavailability of regular daily tests, and availability of full-life-cycle charge data of EV recorded on the charging facility big data platform, this paper studies an online in-use EV state-of-health estimation method using iterated extended Gaussian process regression-Kalman filter (GPR-EKF) to incorporate lithium-ion battery data at the macro time scale and the micro time scale based on daily charge data of electric vehicles. This method proposes a kernel function GPR (Gaussian process regression) integrating neutral network with cycles to conduct fitting for data at the macro time scale to determine colored measurement noise; in addition, fragment charge data at the micro time scale is adjusted with real-time iteration to be used as the state equation, which effectively addresses issues of real-time SOC calibration and nonlinearization. The pertinence, effectiveness and real-time performance of the model algorithm in online battery state-of-health estimation is verified by actual data. key words: state of health, fragment charge data, iterated GPR-EKF, inuse EV battery