scholarly journals Acidentes de trabalho: análises estatísticas na agricultura, indústria e construção civil

ForScience ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. e00729
Author(s):  
Ketelin Rúbia Almeida Silva ◽  
Júlia Silva De Jesus ◽  
André Henrique Garcia Silva ◽  
Talita Emidio Andrade Soares ◽  
Denilson Junio Marques Soares
Keyword(s):  
Hazardous Materials ◽  
Workplace Safety ◽  
Work Hours ◽  
Statistical Hypothesis ◽  
Federal System ◽  
Statistical Hypothesis Testing ◽  
Work Safety ◽  
Agriculture Industry ◽  
National Databases ◽  
And Training

Quando um acidente ocorre no local e no tempo de trabalho ele é chamado de acidente de trabalho, que pode ser ocasionado por diversos fatores como fadiga, estresse, uso de materiais perigosos ou mesmo por imprudências do empregador ou do empregado. Estudos indicam a existência de atividades mais propensas a este tipo de acidente e que a prevenção, a conscientização e o treinamento dos trabalhadores são fatores essenciais para a sua redução. O objetivo deste trabalho foi verificar se existem diferenças estatisticamente significativas entre as médias de acidentes de trabalho de três expressivos grupamentos de atividades: agricultura, indústria e construção civil. Trata-se de um estudo analítico-descritivo, produzido a partir de dados quantitativos obtidos em bases de dados nacionais, oriundas do Sistema Federal de Inspeção do Trabalho e referentes aos anos compreendidos entre 2010 e 2016. Observou-se que a construção civil apresentou uma média de acidentes superior às demais e que as médias obtidas pela agricultura e indústria não apresentaram diferenças à 5% de nível de significância. Espera-se que este trabalho sirva como um instrumento de difusão e alerta aos perigos que a falta de investimento em segurança do trabalho pode ocasionar, especialmente no campo da construção civil. Palavras-chave: Acidentes de trabalho. Segurança do trabalho. Testes de hipóteses. Construção civil.   Accidents at work: statistical analysis on agriculture, industry and construction Abstract When an accident occurs in the workplace and during work hours it is called an accident at work, which can be occasioned by several factors such as fatigue, stress, use of hazardous materials or even by imprudences of the employer or the employee. Studies indicate that there are activities that are more prone to accidents and that prevention, awareness and training of workers are essential factors in reducing these eventualities. The objective of this study was to verify if there are significant differences between the averages of accidents at work in three expressive groups of activities: agriculture, industry and construction through an analytical-descriptive study, produced from quantitative data obtained in national databases, from the Federal System of Labor Inspection and referring to the years 2010 to 2016. It was observed that the construction presented na average of accidents higher than the others and that the averages obtained by agriculture and industry did not show differences at 5% of significance. It is expected that this work can be used as an instrument of diffusion and alert to the dangers the lack of investment in work safety may cause, especially in the field of construction. Keywords: Accidents at work. Workplace safety. Statistical hypothesis testing. Construction.

scholarly journals Hiring Disable People to Avoid Staff Turnover and Enhance Sustainability of Production

2021 ◽  
Vol 13 (19) ◽  
pp. 10577
Author(s):  
Tomasz Chajduga ◽  
Manuela Ingaldi
Keyword(s):  
Independent Living ◽  
People With Disabilities ◽  
Disabled Persons ◽  
Statistical Hypothesis ◽  
Staff Turnover ◽  
Statistical Hypothesis Testing ◽  
New Employees ◽  
Disabled Employees ◽  
The Disabled ◽  
And Training

Companies struggle with maintaining staff turnover at a low and sustainable level. Hiring a new employee means costs for the company, such as recruitment, medical examinations, and training, etc. In addition, new employees must learn to perform their tasks properly and quickly, which can take up to several months. Disabled employees change jobs less frequently due to the fact that it is difficult for them to find a new one. For employers, such an employee, if they have the same skills as a fully functional employee, can therefore be very valuable, for example, in relation to the reduction of costs associated with frequent employment of new employees. In addition, employing people with disabilities also brings social benefits related to counteracting social exclusion and the possibility of independent living by such people. The human factor is an element that greatly influences sustainable production. The objective of the research was to verify the potential of hiring disabled employees in order to reduce staff turnover. In other words, the analysis has been designed to understand if hiring disabled staff, making expenditures to prepare the workplace and training for them could be potentially efficient in terms of obtaining a higher level of sustainability of the employment in the company. The research has the form of direct (in-depth) interviews in the case of representatives of the companies involved, and a paper questionnaire for the disabled subjects (employees). It took place from September to December 2020 and was the basis for the statistical hypothesis testing. The conducted research was based on a statistical U Mann–Whitney test and fi Yule index calculation. The results showed that people with disabilities remain employed by the same employer longer in comparison to “able-bodied” employees, meaning it may be worthwhile to make extra expenditures to hire disabled persons who could become loyal employees for years. Moreover, it has been statistically proven that the key factor influencing the attractiveness of a given job for a disabled person depends on the type of disability they have.

scholarly journals Thermodynamics of order and randomness in dopant distributions inferred from atomically resolved imaging

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Lukas Vlcek ◽  
Shize Yang ◽  
Yongji Gong ◽  
Pulickel Ajayan ◽  
Wu Zhou ◽  
...  
Keyword(s):  
Effective Interaction ◽  
Mean Field ◽  
Interaction Model ◽  
Optimization Techniques ◽  
Statistical Hypothesis ◽  
Structure Property ◽  
Statistical Hypothesis Testing ◽  
Stochastic Representation ◽  
Scanning Transmission ◽  
Complex Structural

AbstractExploration of structure-property relationships as a function of dopant concentration is commonly based on mean field theories for solid solutions. However, such theories that work well for semiconductors tend to fail in materials with strong correlations, either in electronic behavior or chemical segregation. In these cases, the details of atomic arrangements are generally not explored and analyzed. The knowledge of the generative physics and chemistry of the material can obviate this problem, since defect configuration libraries as stochastic representation of atomic level structures can be generated, or parameters of mesoscopic thermodynamic models can be derived. To obtain such information for improved predictions, we use data from atomically resolved microscopic images that visualize complex structural correlations within the system and translate them into statistical mechanical models of structure formation. Given the significant uncertainties about the microscopic aspects of the material’s processing history along with the limited number of available images, we combine model optimization techniques with the principles of statistical hypothesis testing. We demonstrate the approach on data from a series of atomically-resolved scanning transmission electron microscopy images of MoxRe1-xS2 at varying ratios of Mo/Re stoichiometries, for which we propose an effective interaction model that is then used to generate atomic configurations and make testable predictions at a range of concentrations and formation temperatures.

scholarly journals Cutaneous Squamous Cell Carcinoma in Patients with Hidradenitis Suppurativa

Cancers ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 1153
Author(s):  
Elysia Racanelli ◽  
Abdulhadi Jfri ◽  
Amnah Gefri ◽  
Elizabeth O’Brien ◽  
Ivan Litvinov ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Hidradenitis Suppurativa ◽  
Case Reports ◽  
Rare Complication ◽  
Cox Proportional Hazards ◽  
Cutaneous Squamous Cell Carcinoma ◽  
Statistical Hypothesis ◽  
Statistical Hypothesis Testing

Background: Cutaneous squamous cell carcinoma (cSCC) is a rare complication of hidradenitis suppurativa (HS). Objectives: To conduct a systematic review and an individual patient data (IPD) meta-analysis to describe the clinical characteristics of HS patients developing cSCC and determine predictors of poor outcome. Methods: Medline/PubMed, Embase, and Web of Science were searched for studies reporting cSCC arising in patients with HS from inception to December 2019. A routine descriptive analysis, statistical hypothesis testing, and Kaplan–Meier survival curves/Cox proportional hazards regression models were performed. Results: A total of 34 case reports and series including 138 patients were included in the study. The majority of patients were males (81.6%), White (83.3%), and smokers (n = 22/27 reported) with a mean age of 53.5 years. Most patients had gluteal (87.8%), Hurley stage 3 HS (88.6%). The mean time from the diagnosis of HS to the development of cSCC was 24.7 years. Human papillomavirus was identified in 12/38 patients tested. Almost 50% of individuals had nodal metastasis and 31.3% had distant metastases. Half of the patients succumbed to their disease. Conclusions: cSCC is a rare but life-threatening complication seen in HS patients, mainly occurring in White males who are smokers with severe, long-standing gluteal HS. Regular clinical examination and biopsy of any suspicious lesions in high-risk patients should be considered. The use of HPV vaccination as a preventive and possibly curative method needs to be explored.

scholarly journals sepal: identifying transcript profiles with spatial patterns by diffusion-based modeling

2021 ◽  
Author(s):  
Alma Andersson ◽  
Joakim Lundeberg
Keyword(s):  
Spatial Patterns ◽  
Expression Profiles ◽  
Synthetic Data ◽  
Real Data ◽  
Cell Types ◽  
Statistical Hypothesis ◽  
Supplementary Information ◽  
Statistical Hypothesis Testing ◽  
Transcriptomics Data ◽  
Transcript Profiles

Abstract Motivation Collection of spatial signals in large numbers has become a routine task in multiple omics-fields, but parsing of these rich datasets still pose certain challenges. In whole or near-full transcriptome spatial techniques, spurious expression profiles are intermixed with those exhibiting an organized structure. To distinguish profiles with spatial patterns from the background noise, a metric that enables quantification of spatial structure is desirable. Current methods designed for similar purposes tend to be built around a framework of statistical hypothesis testing, hence we were compelled to explore a fundamentally different strategy. Results We propose an unexplored approach to analyze spatial transcriptomics data, simulating diffusion of individual transcripts to extract genes with spatial patterns. The method performed as expected when presented with synthetic data. When applied to real data, it identified genes with distinct spatial profiles, involved in key biological processes or characteristic for certain cell types. Compared to existing methods, ours seemed to be less informed by the genes’ expression levels and showed better time performance when run with multiple cores. Availabilityand implementation Open-source Python package with a command line interface (CLI), freely available at https://github.com/almaan/sepal under an MIT licence. A mirror of the GitHub repository can be found at Zenodo, doi: 10.5281/zenodo.4573237. Supplementary information Supplementary data are available at Bioinformatics online.

High‐resolution velocity determination: Statistical phase correlation and image processing

Geophysics ◽  
1996 ◽  
Vol 61 (4) ◽  
pp. 1115-1127 ◽  
Author(s):  
Igor B. Morozov ◽  
Scott B. Smithson
Keyword(s):  
Image Processing ◽  
High Resolution ◽  
Phase Correlation ◽  
Statistical Hypothesis ◽  
Original Form ◽  
Velocity Spectrum ◽  
Statistical Hypothesis Testing ◽  
Simple Estimate ◽  
Optimal Velocity ◽  
Velocity Spectra

We address three areas of the problem of the stacking velocity determination: (1) the development of a new high‐resolution velocity determination technique, (2) the choice of an optimal velocity trial scenario, and (3) a unified approach to the comparison of time‐velocity spectra produced by various methods. We present a class of high‐resolution coherency measures providing five‐eight times better velocity resolution than conventional measures. The measure is based on the rigorous theory of statistical hypothesis testing and on the statistics of directional data. In its original form, our method analyzes only the phase distributions of the data, thus making unnecessary careful spherical divergence corrections and other normalization procedures. Besides the statistical one, we develop an “instantaneous” version of the conventional coherency measure. This measure is based on the concept of the trace envelope, thus eliminating the need for an averaging procedure. Finally, we design a hybrid high‐resolution coherency measure, incorporating the latter and the statistical one. Carrying out a systematic comparison of various measures of coherency, we present a simple estimate of an attainable velocity resolution. Based on this estimate, we define an optimal velocity grid, providing uniform coverage of all details of the time‐velocity spectrum. To facilitate quantitative comparisons of different coherency functions, we develop a unified normalization approach, based on techniques known in image processing. Described methods are tested on synthetic and field data. In both cases, we obtained a remarkable improvement in the time‐velocity resolution. The methods are general, very simple in implementation, and robust and reliable in application.

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