Measurement of fabric handle characteristics based on the Quick-Intelligent Handle Evaluation System for Fabrics (QIHES-F)

2018 ◽  
Vol 89 (16) ◽  
pp. 3374-3386
Author(s):  
Yi Sun ◽  
Mingyue Zhang ◽  
Gui Liu ◽  
Zhaoqun Du
Keyword(s):  
Evaluation System ◽  
Regression Models ◽  
Prediction Models ◽  
American Association ◽  
Subjective Evaluation ◽  
Fabric Handle ◽  
Wide Range ◽  
Objective Tests ◽  
The Relationship ◽  
Force Displacement

A quick-intelligent handle evaluation system for fabrics (QIHES-F) was developed to evaluate tactile perceptions of fabric by measuring thickness and multiple mechanical properties of fabrics via a single testing process. The main aim dealt with in this study was to establish optimal and suitable regression models by a stepwise regression method based on the QIHES-F and human sensations of fabrics, thereby estimating total handle values effectively. Subjective evaluation by American Association of Textile Chemists and Colorists EP5-2007 and objective tests by QIHES-F of a wide range of 50 fabrics were conducted, to predict fabric handle from four primary handle characteristics and total handle values. Five prediction models corresponding to the fullness, stiffness, roughness, tightness and total handle of fabrics were built based on featured indexes to analyze the relationship between the subjective handle and experimental curve parameters. The indexes were featured from the force-displacement curves of QIHES-F. The results show that these featured indexes can be treated as indicators to characterize fabric properties, and that the five corresponding prediction models can predict handle characteristics of fabrics reliably, as the Pearson’s coefficients and adjusted coefficients are high. They indicate that QIHES-F can directly and accurately obtain fabric handle values and can evaluate the grades of fabric quality.

scholarly journals The differential role of practical and emotional support in infant feeding experience in the UK

2021 ◽  
Vol 376 (1827) ◽  
Author(s):  
S. Myers ◽  
A. E. Page ◽  
E. H. Emmott
Keyword(s):  
Social Support ◽  
Infant Feeding ◽  
Regression Models ◽  
Cox Regression ◽  
Nuclear Family ◽  
Maternal Child Health ◽  
Feeding Experience ◽  
Wide Range ◽  
The Uk ◽  
The Relationship

Social support is a known determinant of breastfeeding behaviour and is generally considered beneficial. However, social support encompasses a myriad of different supportive acts, providing scope for diverse infant feeding outcomes. Given the vulnerability of postpartum mental health, this paper aims to explore both how support prolongs breastfeeding and which forms of support promote the positive experience of all infant feeding. Using survey data collected online from 515 UK mothers with infants aged 0–108 weeks, Cox regression models assessed the relationship between receiving different types of support, support need and breastfeeding duration. Quasi-binomial logistic regression models assessed the relationship between receiving support, infant feeding mode and maternal experience of infant feeding. Rates of negative infant feeding experience indicate the widespread need for support: e.g. 38% of currently, 47% of no longer and 31% of never breastfeeding women found infant feeding stressful. Overall, practical support via infant feeding broadly predicted shorter breastfeeding durations and poorer feeding experience; results in relation to other forms of support were more complex. Our findings indicate different forms of support have different associations with infant feeding experience. They also highlight the wide range of individuals beyond the nuclear family on which postpartum mothers in the UK rely. This article is part of the theme issue ‘Multidisciplinary perspectives on social support and maternal–child health’.

scholarly journals The Comfort Properties of Two Differential-Shrinkage Polyester Warp Knitted Fabrics

2016 ◽  
Vol 16 (2) ◽  
pp. 90-99 ◽  
Author(s):  
Qing Chen ◽  
Xuhong Miao ◽  
Haiwen Mao ◽  
Pibo Ma ◽  
Gaoming Jiang
Keyword(s):  
Evaluation System ◽  
Subjective Evaluation ◽  
Knitted Fabric ◽  
Polyester Yarn ◽  
Mechanical Pressure ◽  
Knitted Fabrics ◽  
Vapour Permeability ◽  
Fabric Handle ◽  
Differential Shrinkage ◽  
Warp Knitted Fabric

AbstractSingle-layered warp knitted fabrics were produced by the 60D/36F (containing 36 filaments) polyester yarn with differential shrinkage (DS) property in this study. Due to the differential shrinkage property, the fabric becomes curly and bulkier, simulating cotton fabric in terms of its appearance and fabric handle. The performance and appearance of these DS polyester warp knitted fabrics were evaluated objectively and subjectively. The testing results demonstrated that the DS polyester warp knitted fabric had better abrasion property, worse pilling resistance due to the mechanical property of polyester yarn when compared with 100% cotton warp knitted fabric. Meanwhile, lower water vapour permeability and air resistance were found for DS polyester warp knitted fabric resulting from the dense structure of yarn shrinkage after heat-moisture treatment. Besides, the fabric handle was evaluated by Kawabata evaluation system and subject to trial under dry and wet fabric condition. DS polyester warp knitted fabrics provide better recovery under low stress mechanical pressure. The subjective evaluation result shows that the warp knitted fabrics made of DS polyester had similar handle against cotton warp knitted fabric in terms of prickle, smooth, comfort and dry feeling in both dry and wet testing conditions.

Use of a General Dose–Response Model for Rockfish Fecundity–Length Relationships

1990 ◽  
Vol 47 (6) ◽  
pp. 1148-1156 ◽  
Author(s):  
Laura J. Richards ◽  
Jon T. Schnute
Keyword(s):  
Dose Response ◽  
Regression Models ◽  
Data Sets ◽  
Response Model ◽  
Exact Inference ◽  
Special Cases ◽  
Wide Range ◽  
Length Data ◽  
The Relationship ◽  
General Method

In this paper we describe a general method for determining the relationship between fecundity and another fish attribute, such as size or age. Our methods include linear and logarithmic regression models as special cases and are applicable to a wide range of situations. The model we propose is based on the univariate form of the Schnute–Jensen dose–response model. However, we extend the Schnute–Jensen analysis by describing exact inference regions obtained from likelihood contours, to which we assign nominal probability levels. We also provide a method for obtaining an inference band for the predicted curve. We examine the issue of model adequacy as it relates to fecundity–length data from two rockfish (Sebastes) species. We show that the extra complexity of our model is justified, as none of the traditional regression models are appropriate for all three of our data sets. Further, we use inference bands to distinguish fecundity–length relationships for quillback rockfish (S. maliger) from two areas, but we are unable to distinguish one of these relationships from a similar relationship for copper rockfish (S. caurinus).

scholarly journals A Blood–Bone–Tooth Model for Age Prediction in Forensic Contexts

Biology ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1312
Author(s):  
Helena Correia Dias ◽  
Licínio Manco ◽  
Francisco Corte Real ◽  
Eugénia Cunha
Keyword(s):  
Regression Models ◽  
Sanger Sequencing ◽  
Prediction Models ◽  
Predictive Ability ◽  
Linear Regression Models ◽  
Absolute Deviation ◽  
Tissue Models ◽  
The Relationship ◽  
Multiple Linear Regression Models ◽  
Age Prediction

The development of age prediction models (APMs) focusing on DNA methylation (DNAm) levels has revolutionized the forensic age estimation field. Meanwhile, the predictive ability of multi-tissue models with similar high accuracy needs to be explored. This study aimed to build multi-tissue APMs combining blood, bones and tooth samples, herein named blood–bone–tooth-APM (BBT-APM), using two different methodologies. A total of 185 and 168 bisulfite-converted DNA samples previously addressed by Sanger sequencing and SNaPshot methodologies, respectively, were considered for this study. The relationship between DNAm and age was assessed using simple and multiple linear regression models. Through the Sanger sequencing methodology, we built a BBT-APM with seven CpGs in genes ELOVL2, EDARADD, PDE4C, FHL2 and C1orf132, allowing us to obtain a Mean Absolute Deviation (MAD) between chronological and predicted ages of 6.06 years, explaining 87.8% of the variation in age. Using the SNaPshot assay, we developed a BBT-APM with three CpGs at ELOVL2, KLF14 and C1orf132 genes with a MAD of 6.49 years, explaining 84.7% of the variation in age. Our results showed the usefulness of DNAm age in forensic contexts and brought new insights into the development of multi-tissue APMs applied to blood, bone and teeth.

The Stepwise Regression Model between Munsell Color Index and Clothing Pressure of Knitted Tight-Fit Sports Pants

2011 ◽  
Vol 58-60 ◽  
pp. 243-248
Author(s):  
Jian Da Cao ◽  
Xuan Run Wu ◽  
Yan Chen
Keyword(s):  
Regression Models ◽  
Stepwise Regression ◽  
Color Index ◽  
Prediction Models ◽  
Correlation Coefficients ◽  
Dynamic Surface ◽  
Surface Wetness ◽  
Clothing Pressure ◽  
Predicted Values ◽  
The Relationship

Through testing the Munsell color index and the static fabric pressure of the tight pants, the paper has established eight related model with the method of the stepwise regression analysis. The result shows that (a) The static garment pressure of the stretch knitted pants can be predicted with stepwise regression models which are using the Munsell color index and fabric specifications as input parameters, and prediction models have the high adjusted correlation coefficients, and have smaller errors between predicted values and measured values. (b) The static garment pressure of the stretch knitted pants has some relationship with dynamic surface wetness of fabrics. (c)The relationship between the static garment pressure of the stretch knitted pants and the weft density is not obvious.

scholarly journals The Differential Role of Practical and Emotional Support in Infant Feeding Experience in the UK

2020 ◽  
Author(s):  
Sarah Myers ◽  
Abigail Emma Page ◽  
Emily H Emmott
Keyword(s):  
Social Support ◽  
Infant Feeding ◽  
Regression Models ◽  
Cox Regression ◽  
Nuclear Family ◽  
Feeding Mode ◽  
Young Infants ◽  
Feeding Experience ◽  
Wide Range ◽  
The Relationship

Social support is a known determinant of breastfeeding behaviour and is generally considered beneficial. However, social support encompasses a myriad of different supportive acts, providing scope for diverse consequences. Given the vulnerability of postpartum mental health, it is crucial to understand not only how support prolongs breastfeeding, but which forms of support promote the positive experience of all infant feeding. Using survey data collected from 515 UK mothers with young infants, we ran cox regression models to assess the relationship between receiving different types of support, support need, and the duration breastfeeding. Quasi-binomial logistic regression models assessed the relationship between receiving support, infant feeding mode, and maternal experience of infant feeding. Overall, infant feeding support broadly predicted shorter breastfeeding durations and poorer feeding experience; results in relation to other forms of support were more complex. 38% of breastfeeding women found it stressful and 42% emotionally draining and rates were higher in non-breastfeeding women, emphasising the widespread need for support. Our findings endorse the contention that social support should not be treated as a univariate entity with uniform outcomes, as well as highlight the wide range of individuals beyond the nuclear family on which mothers in a high-income setting rely.

scholarly journals Relationship between General Jump Types and Spike Jump Performance in Elite Female and Male Volleyball Players

2021 ◽  
Vol 11 (3) ◽  
pp. 1105
Author(s):  
Philip X. Fuchs ◽  
Julia Mitteregger ◽  
Dominik Hoelbling ◽  
Hans-Joachim K. Menzel ◽  
Jeffrey W. Bell ◽  
...  
Keyword(s):  
Regression Models ◽  
Prediction Models ◽  
Performance Testing ◽  
Jump Height ◽  
Jump Performance ◽  
Arm Swing ◽  
Great Reliability ◽  
Volleyball Players ◽  
The Relationship ◽  
Squat Jumps

In performance testing, it is well-established that general jump types like squat and countermovement jumps have great reliability, but the relationship with volleyball spike jumps is unclear. The objectives of this study were to analyze the relationship between general and spike jumps and to provide improved models for predicting spike jump height by general jump performance. Thirty female and male elite volleyball players performed general and spike jumps in a randomized order. Two AMTI force plates (2000 Hz) and 13 Vicon MX cameras (250 Hz) captured kinematic and kinetic data. Correlation and stepwise-forward regression analyses were conducted at p < 0.05. Simple regression models with general jump height as the only predictor for spike jumps revealed 0.52 ≤ R2 ≤ 0.76 for all general jumps in both sexes (p < 0.05). Alternative models including rate of force development and impulse improved predictions during squat jumps from R2 = 0.76 to R2 = 0.92 (p < 0.05) in females and from R2 = 0.61 to R2 = 0.71 (p < 0.05) in males, and during countermovement jumps with arm swing from R2 = 0.52 to R2 = 0.78 (p < 0.01) in males. The findings include improved prediction models for spike jump height based on general jump performance. The derived formulas can be applied in general jump testing to improve the assessment of sport-specific spike jump performance.

The Problem of Identifying and Modeling the Relationship between Monetary Factor and Inflation in the Russian Economy

2008 ◽  
pp. 61-76
Author(s):  
A. Porshakov ◽  
A. Ponomarenko
Keyword(s):  
Money Supply ◽  
Money Demand ◽  
Russian Economy ◽  
Long Run ◽  
Complex Approach ◽  
Wide Range ◽  
Long Time ◽  
The Relationship ◽  
Supply Side Factors

The role of monetary factor in generating inflationary processes in Russia has stimulated various debates in social and scientific circles for a relatively long time. The authors show that identification of the specificity of relationship between money and inflation requires a complex approach based on statistical modeling and involving a wide range of indicators relevant for the price changes in the economy. As a result a model of inflation for Russia implying the decomposition of inflation dynamics into demand-side and supply-side factors is suggested. The main conclusion drawn is that during the recent years the volume of inflationary pressures in the Russian economy has been determined by the deviation of money supply from money demand, rather than by money supply alone. At the same time, monetary factor has a long-run spread over time impact on inflation.

Study of the infl uence of information and communication tools on the psychophysiological state of schoolchildren on the basis of subjective evaluation

2020 ◽  
pp. 47-55
Author(s):  
Aleksandra Rakhmanova ◽  
Georgiy Loginov ◽  
Vladimir Dolich ◽  
Nataliya Komleva ◽  
Galina Rakhmanova
Keyword(s):  
High School ◽  
Emotional State ◽  
Statistical Significance ◽  
Subjective Evaluation ◽  
Educational Process ◽  
The Body ◽  
Leningrad Region ◽  
Communication Tools ◽  
Information And Communication ◽  
The Relationship

The relevance of the article is determined by the existence of contradictions between the need to introduce innovative technologies into the educational process at school, as an integral attribute of modern education, and the negative influence of factors on the physical and psycho-emotional state of health of students related to the use of information and communication tools (computers, phones, headphones). The goal of the study was to assess the relationship between the timing of the use of information and communication tools and the frequency of functional and psycho-emotional complaints in groups of middle and high school schoolchildren. 400 schoolchildren of the Saratov Region, the Moscow Region, Leningrad Region and the Republic of Dagestan were surveyed, who made up two groups of research: middle-school schoolchildren (grades 5–6) and high-school schoolchildren (grades 10–11 The survey was carried out by means of the standardized formalized cards which included the questions considering usage time of computers and mobile phones, complaints to a headache, hands pain, other pain and/or feeling of discomfort from visual organ and the organs of hearing, as well as a psycho-emotional state. Statistical analysis of the data was performed using the STATISTICA application software program by StatSoft Inc (USA). To compare the frequencies of a binary feature, a fourfold table of absolute frequencies was constructed and the level of statistical significance for the exact Fisher’s two-tailed test criterion was determined. The study was conducted according to the requirements of bioethics, after signing informed consent statement by teenagers and their parents. The study examined the relationship between the timing of the use of information and communication tools and the frequency of complaints in groups of schoolchildren. The results of the study should be taken into account when developing and implementing preventive measures to prevent negative effects of computers and mobile devices on the body of students.

COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

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