Wyniki wyszukiwania - Biblioteka Nauki

Ten serwis zostanie wyłączony 2025-02-11.

Nowa wersja platformy, zawierająca wyłącznie zasoby pełnotekstowe, jest już dostępna.
Przejdź na https://bibliotekanauki.pl

Ograniczanie wyników

Znaleziono wyników: 4

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: LBP

Sortuj według:

Ogranicz wyniki do:

Visualisation of Granular Material Concentration Changes, During Silo Discharging Process, Using ECT Large Scale Sensor

100%

Grudzień K. , Chaniecki Z. , Matusiak B. , Romanowski A. , Rybak G. , Sankowski D.

Image Processing & Communications

2012

tom Vol. 17, no. 4

327--338

Measuring changes of bulk materials concentration during gravitational flow, a silo emptying is essential information for the assessment of the behaviour and condition of the material during the emptying of the silo. Parameters obtained during this process are important both in terms of process economics and safety, are the basis for monitoring and diagnostics of the process. Affect the current process, but primarily are the result of their filling the silo, and the process of storing the material. Previous studies, conducted by a team of authors, the laboratory-scale silos and numerical calculations and simulations of its increase, helped build the ECT sensor on a large scale. Results related to the change of scale of the sensor and the actual measurements will be discussed in the article. Proposed by the authors of the paper, the method of visualization, performed in the measuring process, helps to ask about the process and suggests a methodology for dealing with the material stored in the silo.

Prevalence and impacts of low back pain among peasant farmers in South-West Nigeria

88%

Tella B. A. , Akinbo S. R. , Asafa S. A. , Gbiri C. A.

International Journal of Occupational Medicine and Environmental Health

2013

tom 26

nr 4

621-627

Objectives: A relationship between low back pain (LBP) and poor postures has been previously established with a high prevalence observed in many occupations. This study aimed to investigate the prevalence of LBP, associated risk factors and impacts on farmers in South-West Nigeria. Materials and Methods: Six hundred and four farmers completed a 36-item closed-ended questionnaire which was translated to Yoruba language with content validity and back translation done afterwards. The questionnaire sought information on demographic data, 12-month prevalence, severity, history, causes and management of LBP, and its impacts on farm activities and the activities of daily living. Data was analyzed using the Statistical Package for Social Sciences (SPSS) version 17. Data was summarized using descriptive statistics of mean, range, frequency, standard deviation, percentage. Chi² and Mann-Whitney-U test were used to find association between variables. The level of significance was set at α = 0.05. Results: The 12-month prevalence of LBP among the respondents was 74.4%. Low back pain was described as moderate in 53.4%. Prolonged bending (51.3%) was the most related risk factor. A considerable proportion (65.9%) of the respondents were unable to continue some of the previously enjoyed activities. Males had significantly higher (p < 0.05) prevalence, recurrence and duration of LBP than the females. Conclusion: There is a high prevalence of LBP among farmers in South-West Nigeria. Age, sex and years of involvement in farming have a significant influence on the prevalence of LBP.

Texture and gene expression analysis of the MRI brain in detection of Alzheimer’s disease

75%

Bustamam A. , Sarwinda D. , Ardenaswari G.

Journal of Artificial Intelligence and Soft Computing Research

2018

tom Vol. 8, No. 2

111--120

Alzheimer’s disease is a type of dementia that can cause problems with human memory, thinking and behavior. This disease causes cell death and nerve tissue damage in the brain. The brain damage can be detected using brain volume, whole brain form, and genetic testing. In this research, we propose texture analysis of the brain and genomic analysis to detect Alzheimer’s disease. 3D MRI images were chosen to analyze the texture of the brain, and microarray data were chosen to analyze gene expression. We classified Alzheimer’s disease into three types: Alzheimer’s, Mild Cognitive Impairment (MCI), and Normal. In this study, texture analysis was carried out by using the Advanced Local Binary Pattern (ALBP) and the Gray Level Co-occurrence Matrix (GLCM). We also propose the bi-clustering method to analyze microarray data. The experimental results from texture analysis show that ALBP had better performance than GLCM in classification of Alzheimer’s disease. The ALBP method achieved an average value of accuracy of between 75% - 100% for binary classification of the whole brain data. Furthermore, Biclustering method with microarray data shows good performance gene expression, where this information show influence Alzheimer’s disease with total of bi-cluster is 6.

Research of Image Features for Classification of Wear Debris

63%

Juránek R. , Machalik S. , Zemčik P.

Machine Graphics and Vision

2011

tom Vol. 20, No. 4

479-493

The wear debris of engineering equipment (such as combustion engines, gearboxes, etc.) consists of metal particles which can be obtained from lubricants used in the equipment. The analysis of wear particles is very important for early detection and prevention of failures. The analysis is often done using classication of individual wear particles obtained by analytical ferrography. In this paper, we present a study of feature extraction methods for a classication of wear particles based on visual similarity. The main contribution of the paper is the comparison of nine selected feature types in the context of three state-of-the-art learning models. Another contribution is the large public database of particle images which can be used for further experiments. The paper describes the dataset, presents the methods of classication, demonstrates the experimental results, and draws conclusions.