Miracle-dz/UniFAR-FTUs huggingface dataset:
ASPECTS OF PERSONALITY ASSESSMENT IN THE ACTIVITY OF PROFESSIONAL ATHLETES

Aim. In the complex activity of professional athletes, the sports psychologist and the coach can have the following roles: consultant and implementor of psychological training programs, and scientific researcher, investigating the factors that influence the behavior and performance in sport, by implementing and practicing the tasks that are specific to each sport. In performing these roles, the sports psychologist and the coach use various methods to investigate the variables of interest, among which the methods and techniques to diagnose individual characteristics among professional athletes. Conclusions. A complete assistance endeavor for professional athletes must arrive at a very precise knowledge of the core psychological level (personality style) of an athlete, but also of the specific elements that differentiate that athlete from others. This way, one can unlock the full potential in the athletes' mind in the line of motivation-action-top performing behavior.
The pharmacodynamic effects of ketamine on Central Nervous System of parturients

Ketamine was widely used in obstetric anesthesia soon after its invention. It was gradually less considered, if not forgotten, by medical community with the appearance of new general anaesthetics and more effective neuraxial analgesia. Research in the last 2 decades has reanimated ketamine as a potent analgesic and possible antidepressant. In this review, we will briefly summarize obstetric relevant pharmacology of ketamine; present the previous clinical experiences with ketamine in parturient; and discuss some controversies on its clinical application in obstetric anesthesia and possible future research.
Methodology for Model Generation with Accuracy from DC to RF

This paper presents an example modeling flow for generating a RF CMOS model. Initially, the objectives of this modeling approach are analyzed. Then issues in test structure design are discussed. In the section of model generation, we cover the procedures of DC, AC, 1/f noise and RF modeling. We examine the issues related to measurement, parameter extraction, and optimization. We then focus our discussion on the issues related to advanced CMOS technology: gate tunneling current, measuring gate capacitance with high tunneling current, and de-embedding in s-parameter measurement. We present a method to determine the ratio, between drain and source, of the gate to channel tunneling current. We also briefly introduce the 1/f noise measurement system, which can do on wafer 1/f noise characterization. Finally, we present the fitted model results.
Jernej Azarija Some Results From Algebraic Graph Theory

In this thesis we present some results living in the intersection between graph theory and linear algebra. We introduce the subject of algebraic graph theory presenting some general results from this area. In particular we show how certain algebraic objects such as matrices and polynomials can be used to gain structural information about graphs. We then introduce two graph polynomials namely the chromatic polynomial and its generalization—the Tutte polynomial. We present a counterexample to a conjecture of J. Xu and Z. Liu about the chromatic polynomial and degree sequences. We then turn our attention to matrices associated with graphs namely the adjacency matrix and distance matrix. We present some results in the context of strongly regular graphs. In particular we show a connection between graphs maximizing the number of cycles with length matching their odd girth and Moore graphs. Continuing with strongly regular graphs we present a classificational result for (75, 32, 15, 16) strongly regular graphs. The approach is based on the so called star complement technique developed by Cvetković and Rowlinson. Math. Subj. Class. (2010): 05C12, 05C50, 05C31, 05C75
Health Seekers Understanding Medical Terminology from the Health Care Experts

Recent year’s health service websites are dramatically developed. In such health services Medical term and technical verbal’s play vital role between the health seekers and health care expert’s knowledge has caught up data access. To viaduct this gap, this approach presents a scheme to label question answer (QA) pairs by together utilizing local mining and global learning approaches. Local mining attempts to the persons query can be processed and replayed with healthcare experts that can be no of experts. In local mining map all the user queries and no more same kind of reply from experts have it in local mining. However, it may suffer from data loss and inferior precision, which are caused by the nonappearance of key medical concepts and occurrence of unrelated medical terms. Information technologies are transforming the ways healthcare services are delivered, from patient’s passively embracing their doctor’s orders to patient’s actively seeking online information that concerns their health. Some time that medical terminology not able to understandable normal health seekers. This research presents the new approach that provides the health seekers may convert medical terminologies into their local languages and they can easily understand the Information. Keywords— Home mining, Universal Learning.
Opportunities for Complex Care Programs to Address the Social Determinants of Health

In recognition of the underlying social, economic, and environmental factors that contribute to poor health outcomes, many complex care programs are incorporating strategies to address the social determinants of health (SDOH). This brief explores opportunities to better meet patients’ social needs, including: (1) identifying patients’ non-medical needs; (2) employing non-traditional workers; (3) partnering with community-based organizations and social service agencies; (4) testing new uses for technology to help address social needs; and (5) identifying sustainable funding to support non-medical services. It features organizations that participated in Transforming Complex Care, a national initiative aimed at advancing innovations in complex care led by the Center for Health Care Strategies and supported by the Robert Wood Johnson Foundation.
A Comparative Study of Page Ranking Algorithms for Online Digital Libraries

With the increasing use of academic digital libraries, it becomes more important for authors to have their publications or scientific literature well ranked in order to reach their audience. Web mining is a potential candidate to meet this challenge. These techniques when applied on the search results of a user query, provide them an order so that users can easily navigate through the search results and find the desired information content. This is also called Page Ranking. The ranking mechanisms can arrange the scientific literature or publications in order of their relevance, importance and content score. In this paper, a survey of some prevalent page ranking algorithms for academic digital libraries is being carried out and comparison of these algorithms in context of performance has been done.
Ubiquitous Cloud Computing

With the tremendous development in computing technology, it has become possible for most users to enjoy the benefits of high performance or multipurpose computing. This is achieved through cloud computing, which is a term that generally refers to delivering hosted services (e.g. Platform as a Service) over the Internet. Because, we are interested in designing a general purpose computing platform, we will focus on the trend Infrastructure as a Service (IaaS). Such infrastructure will need to be self adaptive, fault-tolerant and easy to use. A solution is to use a dynamic grid, where diverse computer resources are put together to constitute a uniform resource in terms of processing power and memory (a cloud). We demonstrate how this can be particularly useful in an institution, where an internal cloud is built by harnessing workstations’ processors idle times.
Research on Library Information Consultation Model on Cloud Computing Platform

The rapid development of computer and network technology, the advent of the era of cloud quietly, more and more profound influence and change people's lives. The cloud computing give human life a new force, but also for the social life human beings a whole new trading patterns. The emergence of cloud computing also indicates that the emergence of an unprecedented Information technology and business computing model. In the new form of the Internet, library information and consulting services to gradually shift from the traditional to the information service model in the new network environment. The technology and concept of cloud computing applications in library services, will inevitably affect the library services technology, ideas, ways and means. Combining with the status quo of the current library information consulting services, to create the stage cloud computing platform information consulting services for the feasibility analysis, put forward the construction scheme of library information consulting service in the cloud platform and architecture design.
Inventory Management for Customers with Alternative Lead-time Choices

This paper considers an inventory model where one supplier provides alternative leadtime choices for customers: the short and the long lead-time. We obtain the optimal dynamic inventory-commitment policy for the supplier, that is when to deliver long lead-time requests. We also prove that the optimal inventory replenishment policy is a base-stock type. The optimal commitment levels are independent from the purchasing cost, the salvage value, the long and short lead-time prices; and robust to the inventory holding cost. Further, we compare the profit between the optimal dynamic inventory-commitment policy with the static inventory rationing policy. In addition, we use the customer choice model to characterize the risk-pooling, demand-induction and -cannibalization effects; with both analytical results and numerical experiments, we demonstrate the profit improvement by allowing a dynamic inventory-commitment policy.
Neural Network Architecture for Recognition of Cursive Handwriting

2 Abstract: Handwriting recognition has been a problem that computers are not efficient at. This is obviously due to the varying letter styles that exist. Today, efficient handwriting recognition is limited to ones, using hardware like light pens wherein the strokes are directly detected and the character is recognized. But to convert a handwritten document to digital text, one has to extract the characters and then recognize the extracted character. But the problem with this approach is that there are not many algorithms that could efficiently extract characters from a sentence. Therefore, character recognition using software is still not as efficient as it could be. In this paper, a design for software which could do this job of translating handwritten text to digital text, is proposed. Implementation details of this software is proposed and for this implementation, a new Neural Network Architecture, which is a modified version of the conventional Back Propagation Network is also proposed. Also, the details regarding the automatic preprocessing that are essential along with the shortcomings are also discussed in this paper.
COMPARATIVE EVALUATION OF ARTIFICIAL NEURAL NETWORKS MODELS TO CLASSIFY WELD FLAWS USING PULSE-ECHO ULTRASONIC SIGNALS

The present work evaluates several artificial neural networks models, based on Multilayer Perceptron, Radial Basis Function and Self-organizing Maps, to classify defects in the weld beads attaching metal plates of carbon steel. The database used for training models consists of pulse-echo (A-scan) ultrasonic signals. The artificial neural networks were designed to identify two classes of defects in welds, lack of fusion and longitudinal cracks. Moreover, they can identify defect-free welds. All artificial neural networks were trained and tested with no feature extraction from input signals. Subsequently, they were trained using the Principal Component Analysis technique to feature extraction thereof. The results show that defects classification has occurred with a success rate up to 91% for a Multilayer Perceptron, with two hidden layer model and using Principal Component Analysis technique to feature extraction.
Impact of Television Advertisement on Consumer Buying Behavior : The Moderating Role of Religiosity in the context of Pakistan

The present marketing era shows large dependence on television advertisements which have created serious social and ethical problems due to their materialistic focus. These problems are particularly seen in many Muslim countries including Pakistan in terms of diversion from their cultural and religious values. Main objective of our study is to analyze that to what extant religiosity impacts the buying behavior of Pakistani consumers by watching portrayal of women in television advertisement. For this purpose, relevant data has been collected from 200 respondents through questionnaire. Our survey instrument included a list of 30 statements on a five-point scale. To test our model regression analysis was carried out with the help of SPSS which revealed the positive relationship between television advertisement and consumer buying behavior in the presence of women. However, on the other hand, this relationship becomes negative in the religious minded people. The results of this study are significant. Findings of our study reflect practical implications for marketers to choose appropriate advertisement themes and styles in observance of cultural values and religious beliefs of local society. The study will be helpful for advertising agencies to prevent them from big losses by making blameless advertisements which are accepted by all of the peoples of our society including the religious minded consumers.
Penggunaan Prinsip Apriori untuk Outlier Cleaning pada Process Mining dengan Algoritma α

Process mining telah digunakan untuk membantu dalam penyelesaian masalah pada kehidupan sehari-hari. Discovery adalah salah satu tipe process mining yang membentuk model proses dari event log yang ada. Algortima α adalah salah satu algoritma yang dapat digunakan untuk melakukan discovery process. Algoritma α melakukan pengurutan proses yang terjadi pada event log dan membandingkan semua keterurutan tersebut. Maka dari itu, akan didapatkan informasi proses mana yang merupakan kausalitas dan proses mana yang bersifat paralel. Pada kenyataannya, kesederhanaan konsep ini memberikan masalah pada penerapannya pada data real life. Data reallife memiliki keragaman yang tinggi sehingga mengandung banyak outlier yang akan menjadi data yang mengganggu. Maka dari itu, data outlier tersebut perlu dihilangkan. Salah satu metode yang dapat menghilangkan outlier pada data mining, adalah dengan mengadopsi prinsip apriori. Sehingga kasus dan hubungan aktivitas yang tidak memenuhi syarat batas yang telah ditentukan tidak merusak model secara keseluruhan. Pengujian dilakukan pada data registrasi yang tersimpan dalam event log pada sistem informasi.
Physical Activity and Public Health 12-2004 Comparison of Two Approaches to Structured Physical Activity Surveys of Adolescents

Purpose—To compare the test-retest reliability, convergent validity, and overall feasibility/ usability of activity-based (AB) and time-based (TB) approaches for obtaining self-reported moderate-to-vigorous physical activity (MVPA) from adolescents. Methods—Adolescents (206 females and 114 males) completed two 3-d physical activity recalls using the AB and TB surveys, which contained identical lists of physical activities. The participants wore an MTI Actigraph® accelerometer for the same period. Results—The TB instrument took about 3 min longer to complete (P = 0.022). Overall 2-d testretest correlations for MVPA were similar for the two surveys (r = 0.676 and 0.667), but the girls had higher reliability on the AB survey than the boys (girls: r = 0.713; boys: r = 0.568). The overall 3-d correlations for MVPA surveys and Actigraph counts varied by gender (girls: AB = 0.265 vs TB = 0.314; boys: AB = 0.340 vs TB = 0.277). Correlations for vigorous physical activity and Actigraph counts were higher for the AB than for the TB (r = 0.281 vs 0.162). As the interval between completing the surveys and the days being recalled increased, reliability and validity were lower, especially for the AB survey. Conclusion—For both genders, either approach is acceptable for obtaining MVPA information on a single day, but the TB approach appears to be slightly favored over the AB approach for obtaining multiple days of MVPA. A 3-d recall period appears to be too long for accurate recall of MVPA Address for Correspondence: Robert McMurray, Dept of Exercise and Sport Science, CB#8700, Fetzer Gym, University of North Carolina, Chapel Hill NC 27599–8700; Email: exphys@email.unc. edu. A complete list of activities used for the survey instruments, as well as the instruments and their instructions, can be obtained by contacting the primary author, Robert G. McMurray. NIH Public Access Author Manuscript Med Sci Sports Exerc. Author manuscript; available in PMC 2008 June 8. Published in final edited form as: Med Sci Sports Exerc. 2004 December ; 36(12): 2135–2143. N IH PA Athor M anscript N IH PA Athor M anscript N IH PA Athor M anscript information from either instrument. For both genders, the surveys overestimated activity levels; thus, self-reports should be supplemented with objective data.
On the use of allele frequency data within a Bayesian framework: evaluation of the relative probabilities of alternative stock structure hypotheses for North Pacific minke whales

Genotype frequency information for one or more loci is used within a Bayesian modelling framework to assign relative probabilities to alternative stock-structure hypotheses using the Bayes factor approach. This framework has advantages over maximum-likelihood estimation as it provides the information needed to select amongst hypotheses. For primarily illustrative purposes, the approach is applied to the data for the Adh-1 and Gpi loci for sub-areas 6, 7, 8, 9 and 11 for North Pacific minke whales. The results confirm those of previous studies that there are (at least) two stocks to the east and west of Japan. In contrast, the results support the hypothesis of a single stock in sub-areas 7, 8 and 9 unless a priori the allele frequencies for stocks that are adjacent spatially are likely to be similar. This last result needs to be interpreted with caution as the mutation rate of allozymes is slow and so this caveat might apply in this case.
Modeling and forecasting daily electricity loads via functional clustering and curve linear regression

We propose a methodology for modeling and forecasting daily electricity load. Two main ingredients of our approach are (i) clustering pairs of successive daily load curves into homogeneous sub-groups, and (ii) modeling the dependence between the successive curves within each of such sub-groups via curve linear regression. For the former task, we adopt the k-centers functional clustering (k-CFC) from Chiou and Li (2007) which simultaneously accounts for the dissimilarities between clusters in terms of both the mean and the covariance functions. Besides, we propose some model selection criteria which are applicable to the functional data together with the k-CFC, to identify the number of clusters as well as the optimal clustering of the data. For the latter part of the methodology, the curve linear regression technique from Cho et al. (2013a) plays a significant part, which reduces the curve regression problem to a finite number of scalar linear regression problems via singular value decomposition in a Hilbert space. The combined methodology is applied to a range of simulated datasets as well as to the French electricity load data collected between 1996 and 2009, where various model selection methods are investigated and the SVD-based curve linear regression methods are compared to other competitors.
Mining Framework for Evolving User Profiles Involuntarily

Data mining is one the analytical tools for analyzing information. It allow user to analyze data from many different scope, categorize it, and summarize the relationships. Theoretically, data mining is practice of finding correlation or pattern among dozens of fields in large relational databases. This paper can be defined as the description of the user interests, characteristics, behaviour and as well as preference. User profiling is process of gathering, organize, and interpret the user profile information. Significant work has been carried out for profiling users, In general, the user profiles do not change according to the environment and goals of new users. Here mining the user information at registration and detecting the user behaviours based upon user profiles and satisfying user needs instantly. Then detect masquerades and secures user profiling.
Control Strategies for Personal Rapid Transit System

This paper presents a control system design for personal rapid transit (PRT) system and a configuration of an apparatus to assess its control scheme. The control system can be divided into two parts: one is for the vehicle management system that is related with the supervision of the vehicle operation and the other is for the vehicle operational control scheme that is placed in the lower position of the control hierarchy. In this paper we focus on the vehicle operational control scheme that is one of the important features of PRT to guarantee the PRT system performance. In this paper the operational control scheme that can be implemented by using the commercial process boards is proposed and a simple vehicle control algorithm for test is uploaded to it. The calculation results of the process boards are compared with the simulation results that are provided by a simulation platform that combines Matlab/Simulink and Labview Simulation Interface Toolkit.
Registration of multimodal images of retina

Registration of retinal images provided by different modalities is required to facilitate diagnosis of the optic nerve head and retina. For reliable automatic segmentation of the optic disk, it seems essential to join the image data produced by the Heidelberg Retina Tomograph (HRT) and the standard colour photograph. The proposed method is based on registering both (very different) images using mutual information as the coincidence measure.
1 Dynamic Replication Management Scheme for Cloud Storage May

Nowadays, replication technique is widely used in data center storage systems to prevent data loss. Data popularity is a key factor in data replication as popular files are accessed most frequently and then they become unstable and unpredictable. Moreover, replicas placement is one of key issues that affect the performance of the system such as load balancing, data locality etc. Data locality is a fundamental problem to data-parallel applications that often happens (i.e., a data block should be copied to the processing node when a processing node does not possess the data block in its local storage), and this problem leads to the decrease in performance. To address these challenges, this paper proposes a dynamic replication management scheme based on data popularity and data locality; it includes replica allocation and replica placement algorithms. Data locality, disk bandwidth, CPU processing speed and storage utilization are considered in the proposed data placement algorithm in order to achieve better data locality and load balancing effectively. Our proposed scheme will be effective for large-scale cloud storage. KeywordsReplication, Data Popularity, Data locality, Storage utilization, Disk Bandwidth