Miracle-dz/UniFAR-FTUs huggingface dataset:
A Three–Part Framework for Self–Regulated Personality Development across Adulthood

Recently, researchers interested in personality development have begun to acknowledge the roles of motivation and self–regulation for why traits change across adulthood. We propose three preconditions under which individuals may change their own levels of a personality trait through self–directed efforts. Firstly, individuals need to desire changing their trait–related behaviours either as an end in itself or in order to achieve other goals. Secondly, they need to consider behavioural changes feasible and be able to implement the desired changes. Thirdly, behavioural changes need to become habitual in order to constitute a stable trait. After elaborating on these three conditions, we review evidence attesting to the importance of motivation and self–regulation for trait development. We conclude with a discussion of the mutual interdependence of traits and goals, as well as the limits of self–regulated personality change. From our framework, we derive why personality changes across adulthood tend to be small to medium only, namely because they may require that all three preconditions for self–regulated personality change are fulfilled. We provide reasons for why people might not view change as desirable, feasible or fail to maintain it over time. Finally, we propose ideas for potential study designs to research self–regulated personality change. Copyright © 2014 European Association of Personality Psychology
A FKHP-Based Fuzzy Classification Model

This paper introduces a FKHP-based fuzzy classification model with the proposed learning method of FKHP. For this model, the fuzzy partition and the fuzzy classification rules are automatically generated with the method of kernel function and perceptron. During constructing such model, first, patterns in the initial input space are mapped to high dimensional feature space by selecting a suitable kernel function. In the feature space, the hyper-ball which covers all training patterns of a class is founded for every class by the algorithm of FKHP. A hyper-ball is regarded as a fuzzy partition and an IF-THEN rule is created for a fuzzy partition. Experiments with the data sets of standard machine leaning database evaluate the performances of this model with comparison of experiment results of the methods of kernel hyper-ball perceptron and support vector machine. The proposed model has the fast classification training rate, better astringency and high recognition rate
Acquisition of a mental strategy to control a virtual tail via brain–computer interface

ABSTRACT The objective of the present study was to clarify the variation in and properties of mental images and policies used to regulate specific image selection when learning to control a brain–computer interface. Healthy volunteers performed a reaching task with a virtually generated monkey tail-like object on a computer monitor by regulating event-related desynchronization (ERD) on the buttock area of the sensorimotor cortex as recorded by electroencephalogram (EEG). Participants were instructed to find a free image by which the tail was well controlled. Seven participants frequently returned to specific images that were mostly unrelated to a tail, and returned to these images on the last day of training. The ERD levels were greater during use of those selected images versus when selected images were not employed. Our results suggest that individuals adopted a mental strategy where they imagine what would reduce the prediction error between the predicted and actual BCI actions.
Performance Analysis of AODV, DSR and DYMO Protocols using Random Waypoint Mobility Model in MANET

The Mobile Ad Hoc Network is an infrastructure less, multi-hop ad hoc network because the intermediate nodes are used to transmit data from source to destination. This paper represents the simulation based study of network protocols for varying network load and mobility. In this paper three protocols AODV, DSR and DYMO are compared by using random waypoint mobility in few nodes with varying packet sizes in CBR traffic. Different parameters or metrics are used to evaluate the performance of protocols, which are data throughput, end-to-end delay and packet loss with varying data traffic CBR(Constant Bit Ratio) load over UDP using QualNet 5. 0. 2 simulator.
Decoding geometric Goppa codes using an extra place

Decoding geometric Goppa codes can be reduced to solving the key congruence of a received word in an affine ring. If the codelength is smaller than the number of rational points on the curve, then this method can correct up to 1.2 (d*-L)/2-s errors, where d* is the designed minimum distance of the code and s is the Clifford defect. The affine ring with respect to a place P is the set of all rational functions which have no poles except at P, and it is somehow similar to a polynomial ring. For a special kind of geometric Goppa code, namely C/sub Omega /(D,mP), the decoding algorithm is reduced to solving the key equation in the affine ring, which can be carried out by the subresultant sequence in the affine ring with complexity O(n/sup 3/), where n is the length of codewords. >
THE DYNAMICAL POINT OF VIEW OF LOW-DISCREPANCY SEQUENCES

In this overview we show by examples, how to associate certain sequences in the higher-dimensional unit cube to suitable dynamical systems. We present methods and notions from ergodic theory that serve as tools for the study of low-discrepancy sequences and discuss an important technique, cutting- and-stacking of intervals. Communicated by W. G. Nowak Dedicated to the memory of Gerard Rauzy
Small-Cell Lung Cancer Transformation in Patients With Pulmonary Adenocarcinoma: A Case Report and Review of Literature

AbstractDespite the demonstrated benefit from epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) based therapies, EGFR mutant lung adenocarcinoma will eventually acquire drug resistance. Transformation to small-cell lung cancer (SCLC) is considered to be a rare resistance mechanism of EGFR-TKI therapy.We describe a case of a 46-year-old man presenting with refractory cough. Percutaneous transthoracic biopsy was performed and confirmed an EGFR exon 21 L858R lung adenocarcinoma. However, the patient relapsed after successful treatment with gefitinib for 1 year, at which point rebiopsy identified an SCLC and chemotherapy composed of platinum and pemetrexed was started. However, despite the brief success of chemotherapy, our patient died of aggressive cancer progression and complications of chemotherapy.Our case highlights the importance of rebiopsy when managing drug resistance and presents a possible origin of the transformed cells. We also summarize the clinical characteristics of cases involving transformed SCLC from previous studies and discuss whether it could be a new subtype of SCLC.
Teaching Power Electronics with a Design-Oriented and Project-Based Learning Method at the Technical University of Denmark

– Power electronics is a fast developing technology within the electrical engineering field. This paper presents the results and experiences gained from Design-Oriented Project Based Learning of switch-mode power supply design within a power electronics course at the Technical University of Denmark (DTU). Project-based learning (PBL) is known to be a motivating and problem-centered teaching method that not only places students at the core of the teaching and learning activities but also gives students the ability to transfer their acquired scientific knowledge into industrial practices. Students are asked to choose a specification from different power converter applications such as a fuel cell power conditioning converter, a light-emitting diode (LED) driver or a battery charger. Based upon their choice, the students select topology, design magnetic components, calculate input/output filters and design closed-loop controllers in order to fulfill the requirements listed in the chosen specification; thereby meeting the corresponding project’s goals. In this paper, the course teaching plan and teaching methods are introduced, the assessment method is analysed and feedback from the students is studied.
Intelligent Systems to Autonomously Classify Several Arrhythmia Using Information from ECG

This paper is focused on the development of intelligent classifiers in the area of biomedicine, focusing on the problem of diagnosing cardiac diseases based on the electrocardiogram (ECG), or more precisely, on the differentiation of several arrhythmia using a large data set, by an autonomous intelligent system which can be used as an expert system to support human experts in the diagnosis and, moreover, to autonomously display an alarm to the user in case of a dangerous situation. We will study and imitate the ECG treatment methodologies and the features extracted from the electrocardiograms used by the researchers, which obtained the best results in the PhysioNet Challenge. We will extract a great amount of features, partly those used by these researchers and some additional others we considered to be important for the distinction previously mentioned. A new method based on different paradigms of intelligent computation (such as extreme learning machine, support vector machine and feature selection) will be used to select the most relevant characteristics and to obtain a classifier capable of autonomously distinguishing the different types arrhythmia from the ECG signal. Finally, the behavior and performance of the classifier have been tested using data from several cardiac pathologies, obtaining good classification results.
An Extended Intervening Process Model

The rapid on-going demographic shifts in workplaces present challenges to Human Resource Development (HRD) scholars and practitioners, creating a significant demand for diversity initiatives that help organizations harvest diversity. To address inconsistencies in diversity research results and limitations of current diversity intervening process theories, the authors propose an extended intervening process model (EIPM) from the perspective of how group processes underlie the linkage between diversity and performance. The model predicts both positive and negative effects of diversity depending on the intervening roles of group processes and moderation effects of research contextual factors. While extending the knowledge by incorporating three group processes simultaneously, this conceptualization can be regarded as the first theoretical framework that explains both positive and negative effects of diversity found in one study. Implications of the present framework for future research are discussed.
Prefix Tag Clouds

Tag clouds are a popular way to visually represent word frequencies. However, one major limitation is that they do not relate different word forms but treat every form as an individual tag. This results not only in a non-efficient use of screen space but, in particular, leaves the viewer with no indication whether there are other forms of a word or not. To overcome this limitation, we introduce prefix tag clouds: a visualization technique that uses a prefix tree to group different word forms and visualizes the sub trees as tag cloud. The grouping is emphasized by color, while the relative frequencies of the word forms are indicated by font size. A circular tag cloud layout supports the quick identification of the most frequent words and word forms. We show the usefulness of the approach for a large dataset of paper titles from the computer science bibliography DBLP.
Urinary Angiotensinogen Could Be a Prognostic Marker of Renoprotective Effects of Alogliptin in Patients with Type 2 Diabetes

Background. The aims of this study were (1) to examine the renoprotective effects of alogliptin and (2) to establish urinary angiotensinogen (AGT) as a prognostic marker of renoprotective effects of alogliptin in patients with type 2 diabetes (T2D). Methods. In 43 patients with T2D (18 women, 66.1 ± 1.71 years), 25 mg/day of alogliptin was added to the traditional hypoglycemic agents and/or nondrug treatments. Urinary concentrations of albumin (Alb) and AGT, normalized by urinary concentrations of creatinine (Cr) (UAlbCR and UAGTCR, respectively), were measured before and after the 12-week alogliptin treatment. Results. Alogliptin treatment tended to decrease UAlbCR (99.6 ± 26.8 versus 114.6 ± 36.0 mg/g Cr, P = 0.198). Based on % change in UAlbCR, patients were divided into two groups, responders (<−25%) and nonresponders (≥−25%), and a logistic analysis of UAGTCR before treatment showed cutoff value of 20.8 µg/g Cr. When all patients were redivided into two groups, those with higher values of UAGTCR before the treatment (Group H, n = 20) and those with lower values (Group L), Group H showed significantly decreased UAlbCR in response to alogliptin (−14.6 ± 8.6 versus +22.8 ± 16.8%, P = 0.033). Conclusion. Urinary AGT could be a prognostic marker of renoprotective effects of alogliptin in patients with T2D.
Significant Treatment Effect of Bupropion in Patients With Bipolar Disorder but Similar Phase-Shifting Rate as Other Antidepressants

AbstractBupropion is widely used for treating bipolar disorder (BD), and especially those with depressive mood, based on its good treatment effect, safety profile, and lower risk of phase shifting. However, increasing evidence indicates that the safety of bupropion in BD patients may not be as good as previously thought.The aim of this study was to summarize data on the treatment effect and safety profile of bupropion in the treatment of BD via a meta-analysis.Electronic search through PubMed and ClinicalTrials.gov was performed.The inclusion criteria were: (i) studies comparing changes in disease severity before and after bupropion treatment or articles comparing the treatment effect of bupropion in BD patients with those receiving other standard treatments; (ii) articles on clinical trials in humans. The exclusion criteria were (i) case reports/series, and (ii) nonclinical trials.All effect sizes from 10 clinical trials were pooled using a random effects model. We examined the possible confounding variables using meta-regression and subgroup analysis.Bupropion significantly improved the severity of disease in BD patients (P < 0.001), and the treatment effect was similar to other antidepressants/standard treatments (P = 0.220). There were no significant differences in the dropout rate (P = 0.285) and rate of phase shifting (P = 0.952) between BD patients who received bupropion and those who received other antidepressants.We could not perform a detailed meta-analysis of every category of antidepressant, nor could we rule out the possible confounding effect of concurrent psychotropics or include all drug side effects. Furthermore, the number of studies recruited in the meta-analysis was relatively small.Our findings reconfirm the benefits of bupropion for the treatment of bipolar depression, which are similar to those of other antidepressants. However, the rate of phase shifting with bupropion usage was not as low compared to other antidepressants as previously thought, which should serve to remind clinicians of the risk of phase shifting when prescribing bupropion to BD patients regardless of the suggestions of current clinical practice guidelines.
Chemical Determination of Heavy Metals in Pb and Zn Concentrates of Trepça (Kosovo) and Correlations Coeffi cients Study Between Chemical Data

Kosovo ore deposits are located in the Trepca belt which extends for over 80 km. The concentrate produced by the flotation process of the Trepca metallurgical corporation contains a considerable quantity of valuable metals, such as Pb, Zn, Fe and minor accompanying metals such as Cd, Cu, As, Sb, Bi, Ag, Au, etc. The subject of this work was to assess the concentration of major and minor metals in lead and zinc concentrates of Trepca and to study the correlation coefficients between metals. Chemical determination of concentrates was performed by using atomic absorption spectroscopy (AAS). In the content on lead concentrate samples, the following were found: Pb>Fe>Zn> Ag> As>Sb>Cd. In the content of zinc concentrate, the following were found: Zn>Fe>Pb>Ag>As>Cd. The program “Statistica ver. 6.0” has been used for calculations of basic statistical parameters, relationships between data and cluster analysis of R-mode. R-mode cluster analysis on lead concentrate samples showed that Pb has the closest linkages with Fe and they form one branch of the dendogram. On the zinc concentrate samples, Zn has the closest linkages with Fe and they form one branch of the dendogram.
Customer-centered co-design modularization: the skirt design on mobile application

Abstract With the development of mobile technology, customer-centered co-design modularization (CCCM) has been studied by researchers and brand operators. In contrast, in the field of skirt design, CCCM on mobile application has been largely ignored. The purpose of this study is to develop a customer-centered co-design modularization on skirt that enables customers to co-design skirt on mobile application. On the basis of analytic hierarchy theory and interactive genetic algorithm, CCCM is consisted of two categories, such as skirt parts (design attributes) and communicating parts (behavior attributes). Skirt parts are determined by knowledge classification. Communicating parts are supported by recommending, consulting, evaluating among customers, consultant, and expert. The customer-centered co-design modularization was conducted and proved by a mathematical model. Compared to online shopping, the proposed CCCM is a good approach to involve stakeholders (customer, consultant, and expert) to design together who can communicate on mobile application.
Profiling Spectators at a One-day International Cricket Match

Sport events have become major tourism products and promote destinations globally. Since South Africa's re-admission to international cricket in 1991 the country has established itself as one of the leaders in world cricket, boasting some of the finest stadiums in the world. Touring international sides, accompanied by hundreds of national and international tourists in the form of spectators, players, media and medical staff have an impact on the host community. This research argues that it is important to have thorough knowledge of the profile of the cricket spectator since this information is useful not only for marketing purposes but also to establish the nature and impact of these events on the host community. A survey was performed among cricket spectators at a one-day international cricket match in Potchefstroom, South Africa, where South Africa competed against Australia. This survey was used to compile a profile of an average one-day cricket spectator, with the aim to develop current and future marketing strategies.
Deterministic blind beamforming methods

| Deterministic blind beamforming algorithms try to separate superpositions of source signals impinging on a phased antenna array, by using deterministic properties of the signals or the channels such as their constant modulus or directions-of-arrival. Unlike optimal or adaptive methods, the algebraic methods discussed in this review act on a xed block of data and give closed-form expressions for beamformers by focusing on algebraic structures. This typically leads to subspace estimation and generalized eigenvalue problems.
Improved fast Gauss transform User manual

In most kernel based machine learning algorithms and non-parametric statistics the key computational task is to compute a linear combination of local kernel functions centered on the training data, i.e., f(x) = ∑N i=1 qik(x, xi), which is the discrete Gauss transform for the Gaussian kernel. f is the regression/classification function in case of regularized least squares, Gaussian process regression, support vector machines, kernel regression, and radial basis function neural networks. For nonparametric density estimation it is the kernel density estimate. Also many kernel methods like kernel principal component analysis and spectral clustering algorithms involve computing the eigen values of the Gram matrix. Training Gaussian process machines involves the solution of a linear system of equations. Solutions to such problems can be obtained using iterative methods, where the dominant computation is evaluation of f(x).
Understanding Social Influence in Activity Location Choice and Lifestyle Patterns Using Geolocation Data from Social Media

Social media check-in services have enabled people to share their activity-related choices providing a new source of human activity and social networks data. Geo-location data from these services offers us information, in new ways, to understand social influence on individual choices. In this paper, we investigate the extent of social influence on individual activity and life-style choices from social media check-in data. We first collect user check-ins and their social network information by linking two social media systems (Twitter and Foursquare) and analyze the structure of the underlying social network. We next infer user check-in and geo life-style patterns using topic models. We analyze the correlation between the social relationships and individual-level patterns. We investigate whether or not two individuals have similar activity choice and geo life-style patterns if they are socially connected. We find that the similarity between two users, in their check-in behavior and life-style patterns, increases with the increase of the friendship probability.
The lifetime optimization of wireless multimedia sensor networks under uncertain energy consumption

Wireless multimedia sensor networks (WMSN) has a lot of potential applications due to its content-enriched sensing information and flexibility. Energy constraints, low capacity of wireless channel and uncertain factors make the design and optimization of WMSN challenging. In this paper, we consider the lifetime optimization of WMSN subject to energy consumption uncertainty. The uncertain energy consumption in each sensor nodes is modeled as a polyhedral set. The lifetime maximization problem is formulated as a robust optimization problem, the counterpart of which is showed to be a convex problem with linear constraints. We solve the robust counterpart problem in its dual domain with dual decomposition and subgradient method. A partially distributed algorithm is proposed based on the solution of the problem. The algorithm can be implemented in small scale WMSNs with sink node responsible for partially central calculation function.