Miracle-dz/UniFAR-FTUs huggingface dataset:
Conservative Management of Biceps Brachii Spasticity and Pain with Vibration at the Proper Frequency Range Produced By Oscillating Percussion: Case Report

Objective: Case reports of a patient suffering from biceps brachii spasticity and pain accompanied by limited range of motion in flexion and extension. Clinical features: Patient presented with right arm pain at the glenohumeral area and the elbow. Patient was assessed and treated accordingly. There was glenohumeral pain and muscle fatigue from overuse. Intervention and outcome: A treatment with an instrument generating vibration within specific frequencies to facilitate or inhibit voluntary muscles. The treatment resulted in pain relief and normal range of motion. Conclusion: This case report describes the use of a new technology specific to facilitation and inhibition of voluntary muscles with subjective and symptomatic improvements. *Corresponding author: Richard Roy, Chiropractor, FICC, 7655 Newman Boulevard, Suite 205 LaSalle, QC, H8N 1X7, Canada Tel: 514-364-1756, 514-5026523; E-mail: Drrichardroy@videotron.ca Received January 30, 2014; Accepted February 08, 2014; Published February 10, 2014 Citation: Roy R, Boucher JP (2014) Conservative Management of Biceps Brachii Spasticity and Pain with Vibration at the Proper Frequency Range Produced By Oscillating Percussion: Case Report. J Pain Relief 3: 133. doi:10.4172/21670846.1000133 Copyright: © 2014 Roy R, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Computing predominant local periodicity information in music recordings

The periodic structure of musical events plays a crucial role in the perception of tempo as well as the sensation of note changes and onsets. In this paper, we introduce a novel function that reveals the predominant local periodicity (PLP) in music recordings. Here, our main idea is to estimate for each time position a periodicity kernel that best explains the local periodic nature of previously extracted note onset information and then to accumulate all these kernels to yield a single function. This function, which is also referred to as PLP curve, reveals musically meaningful periodicity information even for non-percussive music with soft and blurred note onsets. Such information is useful not only for stabilizing note onset detection but also for beat tracking and tempo estimation in the case of music with changing tempo.
Effect of Colour Rendering for University Campus Landscape Lighting at Night in a Snowy Region

The importance of the landscape lighting of urban nightscapes is increasing with the urban lifestyle for a 24-hour-life. In addition, the feature of landscape lighting has changed from emphasis on the lighting design for the conventional purpose of safety and the degree of brightness to a lighting plan which matches with the view in the region as well as in consideration of psychological comfort. By the way, snow is sometimes a fearful thing which brings about a major disaster, but at the same time, it has beauty appealing to people’s heart and wonderful attraction. Accordingly, what is sought in a design for landscape lighting in such an environment must be an atmosphere in the space such as an “element which has an effect to alleviate the coldness of snow” or an “element that enables the beauty of snow to be enjoyed”. In this study, we tried to evaluate the effect which the difference in a hue of the light source in landscape lighting gives to urban scenery perception in a snowy region quantitatively and at the same time to examine and consider about “how landscape lighting should be in a snowy region”. We selected six kinds of typical urban landscape lighting as samples. We took photographs of these nightscapes. We made another samples which landscape lighting colour changed to “red”, “orange”, “yellow”, “white” or “skyblue” using computer, and presented to subjects. We applied SD technique to the evaluation for these samples. Also, we analyzed the difference between three samples, using factor analysis. From the results, we found that the landscape lighting of “red” and "orange" provided an active impression to the urban nightscape. On the other hand, the landscape lighting of “skyblue” provided a beautiful impression to the urban nightscape.
QADR with Energy Consumption for DIA in Cloud

: In today’s IT field scalability on storage resources are provided by cloud computing. Moreover data-intensive applications are also developed in this technology. Occurrence of data Corruption on these DIA (data intensive applications) does not meet the QoS Requirements. So in order to satisfy the requirements, we are comparing two algorithms from existing paper one is HQFR which uses the greedy algorithms. HQFR algorithm cannot minimize the cost of data replication and the count of QoS violated data replicas. These two objectives of QADR problem has been achieved by using MCMF algorithm which provides an optimal solution in polynomial time. But when compared to HQFR algorithm its computation time is high, since we have to consider more number of nodes as it is in cloud environment. To find a solution for this time complexity, Combination of nodes– Technique has been introduced in MCMF. Demonstration of these algorithms has been implemented under Windows Azure environment which provides comparatively good results for data replication. Further this implementation has been extended to concern energy consumption in cloud environment.
Classification of Parkinson’s Disease using MRI Images

A novel method for automatic classification of magnetic resonance image (MRI) under categories of normal and Parkinson’s disease (PD) is then classified according to the severity of the medical specialty drawbacks. In recent years, with the advancement in all fields, human suffers from numerous specialty disorders like brain disorder, epilepsy, Alzheimer, Parkinson, etc. Parkinson’s involves the malfunction and death of significant nerve cells within the brain, known as neurons. As metal progresses, the quantity of Dopastat made within the brain decreases, defeat someone, and make them unable to manage movements commonly. In the planned system, T2 (spin-spin relaxation time)—weighted MR images are obtained from the potential PD subjects. For categorizing the MRI knowledge, histogram features and Gray Level Co-occurrence Matrix (GLCM) features are extracted. The features obtained are given as input to the two different classifier techniques namely Support Vector Machine (SVM) and Radial Basis Function Neural Network (RBFNN). The classifiers, classifies the categories into normal or abnormal PD. Abnormal PD is classified into three Parkinson’s diseases. Two different classifiers are used to classify the three subcategories namely Mild, Moderate and Advanced.
Machine learning based approach for fault tolerance in cloud computing

Cloud computing is a rising area that is currently engaged towards many of the IT industries. Bearing with the cloud architecture is the difficult challenge for us. This challenge can be achievable by fault tolerance and monitoring. Methods/Statistical Analysis: Fault Tolerance (FT) facilitates the process or the component to work smoothly even though in the occurrence of the failure. Monitoring is a procedure in which the fault is predicted before it occurs. Proactive and reactive measures can take place to run the cloud environment with tolerance in failure occurrence. Reviewing the potential of FT and monitoring services is to make out the technique that serves in certain purpose. Fault tolerance systems are important for both providers of cloud services and customers. Findings: Based on this idea, this paper provides the different or diverse fault tolerance and monitoring mechanism to improve the reliability in a cloud environment. Applications/Improvements: It presents the information about the various techniques and methods used in the FT and also a future research direction in cloud FT.
Sentiment analysis of informal text using a rule based model

Past research on phrase level sentiment analysis has mainly involved parts of speech tagging; Identifying the different parts of a sentence as nouns, adjectives, adverbs is appropriate and individually considering each such part to compute an aggregate score to decide on the sentiment polarity. Most of these results are binary or ternary, and although they are accurate to an extent, difficulties in working with raw social media content are observed. This paper proposes an alternative approach to sentiment analysis by computing the polarity, the intensity by looking at various phrase level features such as the aspect, and takes into consideration the presence of over one opinion, emoticons and deliberate spelling errors. The algorithm initially identifies the nature of opinion, base or comparative, and then the aspect, the above mentioned features to determine the polarity and strength. The evaluation of this algorithm is done by comparing the results to a baseline produced by manual scoring.
THE INTERNATIONAL JOURNAL OF SCIENCE & TECHNOLEDGE Data Encoding Technique Using Gray Code in Network-on-Chip

: As technology varies the power dissipated by the links of a network-on-chip (NOC) due to noise increases the risk of errors in the communication subsystem. In this paper, we present a data encoding technique using gray code input aimed at reducing the errors and power dissipation by the links of an NOC. The proposed scheme uses the binary to gray conversion at the transmitter and gray to binary conversion at the receiver. An experimental result from both synthetic and real traffic scenarios shows the effectiveness of the proposed schemes which allows to save the power up to 21.6% and reduces the noise or error during the transmission through the encoder using Gray code.
KNOWLEDGE DISCOVERY and SAMPLING TECHNIQUES with DATA MINING for IDENTIFYING TRENDS in DATA SETS

- Complex search and data analysis can be automated using Knowledge Discovery. Extracting hidden knowledge in large volume of raw data can be accomplished by exploiting theoretical and practical issue in Data Mining techniques for Knowledge Discovery. Complex iterative and sequential steps formulate Knowledge Discovery in Databases. The performance of Data mining tools may be degraded due to poor training set and bulky practical data set. The Data Mining process can be optimized by focusing on the efficient method to estimate optimal training set. It is looked upon as a quite challenging problem. In our work, we are aiming to focus on Knowledge Discovery techniques by improving approach with efficiently identifying the sampling dataset and data mining algorithm to mine the sampled data.
Re-Engineering Leadership Communication by Mobile Technology: Comparative Effect on Intra-team Communication, Job Satisfaction and Job Performance

: Through the phase of the 4th Industrial Revolution, the concept of organizational leadership needs to change in line with the rapid of digital climates. Compared to adopting conventional leadership that require leaders and followers to meet face-to-face, organizational leaders today are urged to practice virtual leadership communications, allowing leaders of the organization to continue practicing function of leadership although being away from their employees. Despite various expectations, previous studies have shown that organizational leaders are still less interested in changing conventional leadership practices to digital-based leadership, especially by using mobile technology. This study was conducted to compare mobile technology-based e-leadership practices toward intra-team communication within the organization, job satisfaction and job performance. This cross-sectional survey involved 1033 school leaders in Malaysia. Data were collected through questionnaire, and analysed by SPSS software which is focusing on independent sample t-test and multiple linear regression for hypothesis testing. The findings show that organizational leaders who practice e-leadership at high level have greater impact on intra-team communication, job satisfaction and job performance compare than the school leaders who practicing low-level e-leadership. Since the use of mobile technology in leadership has a major impact on work performance, it is recommended that all leaders of the organization be optimally practicing e-leadership in their leads. To achieve that goal, the responsible parties need to carry out of campaigns, training, incentives and any encourage acts to increase the use of mobile technology in organizational leadership.
Sampling Selection Strategy for Large Scale Deduplication in a Distributed System Using Apache Spark

— The generation of information from a wide range of sources has opened opportunities for the emergence of several new applications such as digital libraries, media streaming etc. that presuppose high quality data to provide reliable services. Data quality is degraded due to the presence of duplicate pairs. So, data deduplication task is necessary to detect and remove duplicates and provide efficient solutions to this problem. In very large datasets, it is very difficult to produce the labeled set from the information provided by the user. A Two-stage sampling selection strategy (T3S) that selects a reduced set of pairs to tune the deduplication process in large datasets has been proposed and is implemented in a distributed environment us-ing Apache Spark. Thus, T3S reduces the labeling effort substantially while achieving superior matching quality when compared with state-of-the-art deduplication methods in large datasets. Also, performing the deduplication in a distributed environment offers a better performance over the centralized system in terms of speed and flexibility. So, in this work, a distributed approach is implemented for the above method using Apache Spark. Also, a comparison is done between T3S and FSDedup. It shows that T3S reduces the training set size by redundancy removal and hence offers better performance than FSDedup.
WHO Classification of of Tumours of the Urinary System and Male Genital Organs

がんプロ : 多様な新ニーズに対応する「がん専門医療人材(がんプロフェッショナル)養成プランプラン」 請求記号 Book-ID タイトル / 編著者 出版年 GP@007.1@B100@1-1 80600063275 パターン認識と機械学習 上 2012 GP@007.1@B100@1-2 80600063283 パターン認識と機械学習 下 (ベイズ理論による統計的予測) 2012 GP@007.1@H100@1 80600063232 はじめてのパターン認識 2012 GP@007.1@M100@1 80600063267 Pythonではじめる機械学習 2017 GP@007.1@O100@1 80600063216 深層学習 (機械学習プロフェッショナルシリーズ) 2015 GP@007.6@W100@1 80600063259 Rではじめるデータサイエンス 2017 GP@007.64@C100@1 80600063089 Rグラフィックスクックブック 2013 GP@007.64@G100@1 80600063097 RStudioではじめるRプログラミング入門 2015 GP@007.64@R100@1 80600063054 IPythonデータサイエンスクックブック 2015 GP@007.64@V100@1(3) 80600063135 Pythonチュートリアル 第3版 2016 GP@417@K100@1 80600063240 データ解析のための統計モデリング入門――一般化線形モデル・階層ベイズモデル・MCMC (確率と 情報の科学) 2012 GP@417@M100@1 80600063291 Pythonによるデータ分析入門 2013 GP@417@T100@1 80600063224 実践 ベイズモデリング -解析技法と認知モデル2017 GP@460@A100@1 80600062902 システム生物学入門 -生物回路の設計原理 2008 GP@461.9@M100@1 80600063186 マレー数理生物学入門 2014
Identificación de modificaciones epigenéticas como posibles biomarcadores de la obesidad y el ictus

DNA methylation is an epigenetic mechanism that regulates gene expression. These epigenetic marks are modified by some pathology such as obesity, and it could be change by stroke. The main objective of this work was to determine the influence of ischemic stroke in the epigenetic pattern of genes involved in cerebrovascular disease and obesity, and their interaction with body mass index and the study of the effect of a hypocaloric diet in these epigenetic changes. In order to realize this objective this work was consisted in a case-control design with four populations. In all populations cases were selected from the Neurology Service of Hospital Donostia, with a first episode of ischemic stroke, evaluated in the first 24 hours for a neurologist of the onset of symptoms. The control groups were constituted by non-vascular neurological disorder patients that were matched by sex and age with the cases. Cases and controls were divided in bothgroups using the BMI classification criteria proposed by the WHO. In the first place it was studied the changes in epigenetic profile of human tumor necrosis factor (TNFα) and paraoxonase (PON) promoters and the relationship with the susceptibility to stroke when considering body composition and dietary intake. In the other hand, it was raised to describe candidate genes with differential epigenetic regulation in patients with ischemic stroke and, to determine the importance of some regions of these candidate genes in different biological processes mediated by BMI. Fist an array was realized and the results were validated with this population and the methylation from regions of four genes was measured in other 115 individuals (PM20D1, CALD1, WT1 and KCNQ1). Modifications in KCNQ1 region was observed and translated in changes of the protein levels. Finally 40 obese patients with and without previous ischemic stroke was included in a 20 week nutritional intervention. Methylation levels of WT1 and KCNQ1 regions were measured, at the beginning and at the end of the treatment. It was observed changes in methylation levels and inflammatory response biomarkers produced by the hypocaloric diet. It can be concluded in general that an ischemic stroke modifies the epigenetic pattern of genes involved in cerebrovascular and metabolic diseases. These changes are depended of previous BMI. In particular, KCNQ1, methylation and secretion levels are revealed as a promising biomarker for stroke and obesity.
A Review on Event Detection Techniques in Wireless Sensor Network

: Different mechanisms for event detection schemes are implemented in an event region detection in a wireless sensor network. Threshold based event detection technique is the basic event detection approach with low computational overhead. Abnormal detection of events are also a major problem in event detection in wsn. Different methods are there to eliminate such faulty recognition of events. Node deployment can be done using grid based system. To detect and refine the event region, a grid-based deployment with threshold based detection scheme can be used effectively.
Loss of Detrusor Smooth Muscle Caveolae Alters alpha-1 Adrenergic Signaling In Overactive Bladders from Spontaneously Hypertensive Rats

Hypothesis / aims of study Caveolae, flask-shaped invaginations on the plasma membrane enriched in cholesterol and sphingolipids, are organized microdomains that play a crucial role in regulating specific receptor−activated signaling events in a variety of cell types, including bladder smooth muscle (BSM) [1]. Compared to control animals, the density of caveolae in BSM cells is significantly reduced in spontaneously hypertensive rats (SHR), an animal model of detrusor overactivity (DO), suggesting that the integrity of these structures is important for normal detrusor function [2]. Although the etiology of DO remains unresolved, alterations in adrenergically-mediated signaling have been proposed in its pathogenesis [3], since α-adrenoceptor (AR) expression is increased in overactive bladders and non-selective α1AR antagonists relieve irritative symptoms in both men and women. Our previous study showed that α−AR-mediated contractions in BSM are sensitive to depletion of caveolae [1]. The purpose of this study was to investigate whether the intrinsic loss of caveolae in SHR bladders might contribute to the development of DO by dysregulation of α1−AR activated pathways.
A Novel Multi-Parameter Tuned Optimizer for Information Retrieval based on Particle Swarm Optimization

Tuning multi-parameter and parameter optimization in Information Retrieval has been a huge area of research and development, especially with BM25F scoring functions having a 2F+1 feature with F fields in the documents. The scoring and ranking function conventionally uses multiple input parameters, to augment the quality of results even at the value of huge calculation time. The searching and ranking documents in the medical literature encompass high recall rates, which are difficult to satisfy with multiple input parameters. The performance of the BM25F depends upon the choice of these F parameters. Particle Swarm Optimization (PSO) searches through the solution- space independently and discovers an optimal solution as opposed to improving and optimizing the gradient; henceforth it can straightforward optimize Mean Average Precision (MAP) a non-differentiable function. In this paper, the usage of PSO to tune multi-parameters is proposed to deal with the gaps in BM25Fscoring function. Also, the advantage of the proposed technique by directly optimizing the MAP has been discussed. Experimental results of quantitative performance metrics MAP and Mean Reciprocal Rank of the proposed PSO-optimized BM25F and most recent ranking algorithms have been compared. The performance measure results demonstrate that the proposed PSO-optimized BM25F performance measure outclasses the standard ranking methods for the OHSUMED data set
SANITARY RISK FACTORS AND MICROBIAL PROFILE IDENTIFICATION BY MALDI-TOF OF STREET READY-TO-EAT MEAT PRODUCTS CONTAMINANTS

Many persons use to eat ready-to-eat meat products sold mostly on the street. The aim of our study was to investigate the microbial contaminants profile of meat products sold on the streets of 5 communes in Abidjan. To reach our goal, a survey was conducted to investigate on the type of customer, meat, the selling points, and the probable causes of food poisoning. Forty samples of each of the three kind of ready-to-eat meat (beef, pork and chicken) were collected in five communes of Abidjan. The microbial quality of the collected meat was determined with conventional methods and confirmed by matrix-assisted laser desorption ionization-time-offlight mass spectrometry. The results of the survey showed a health risk potentially related to the method of cooking and the type of meat. Out of the 600 samples analyzed, 443 strains were isolated. Among the microorganisms, there are Cocci’s (61.61%) mainly Staphylococcus strains, Bacilli (37.28%) and yeast (1.11%). Staphylococcal strains and Enterobacteriaceae are the most predominant and chicken meat is the most contaminated meat product. Also, some strains (Alloiococcus otitis and Ochrobactrum intermedium) were rarely isolated. The meat products contamination constitutes a health risk which could cause significant income loss for both merchant and consumer.
Designing Authentication for Hadoop Cluster using DNA Algorithm

Big Data (BD) generation are exponentially increased and it is necessarily required for modern society. Hadoop Clusters (HC) provides the facilities like Processing, storage and doesn’t have built-in security. But this feature is important as it increases the analysis speed, storage process. HC facilitates storage, processing of data, on the other hand processing of streaming data handled by the Apache Spark. However data storage, processing power, cluster management and data security in HC is not reached up to the mark with increased data. In such situations, HC are scaled out from small scale IT organization and it depends on public cloud centers with lack of data security, communication, computation and operational cost. On the other hand data security in HC is major issue and it uses a separat security mechanisms. This paper proposes New Algorithm Built in Authentication Based on Access (BABA) as a security instance integrated as Hadoop instance for securing data in HC from attackers along with metadata security for avoiding crashes Hadoop. This mechanism provides a secured HC without using other security configurations which will reduce operational cost, computational power, increases data security and providing a better solution for HC.