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Yarns of 20 tex were rotor spun from a set of American cottons of diverse origins that provided a wide range of fibre properties. A fabric of plain weave with 27.6 ends per cm and 26.8 picks per cm was woven from each cotton. Where quantities of cotton permitted, rotor yarns of 42 tex were spun and woven into plain weave fabric having 18.5 ends per cm and 17.7 picks per cm.

Each cotton was tested using a high volume instrument (HVI). The greigestate fabrics were characterised in terms of tensile properties, tear strength, abrasion resistance, stiffness, and air permeability. The results of correlation and regression analyses of greigestate fabric properties on HVI fiber properties are presented and discussed.

This research is an initial outcome of studies into the prediction of rotor spun yarn parameters conducted collaboratively by the Cotton Industry Research Institute (VUB) of the Czech Republic, and the Agricultural Research Service of the U.S. Department of Agriculture.

In this study, the response surface methodology is used to predict the mechanical properties of yarn, their unevenness and hairiness by using the high-volume instrument (HVI) properties of raw cotton and the parameters of the spinning process. Therefore, five different blends of cotton are processed and spun into ring yarns (Nm13, Nm19, Nm 21, Nm31 and Nm37). Each count is spun at five twist levels (450, 500, 650, 750 and 850 trs/m).

The models that are developed by using response surface regression with many iterations on a Minitab16 statistical software predict very well the different yarn properties since the R² values obtained are very important. In addition, these models show that metric number and twist have the highest effect on the four studied parameters

The purpose of this paper is to understand the impacts of oleoyl glycine on biodegradation, friction and wear performances of a mineral lubricating oil.

The biodegradabilities of a neat oil and its formulations with oleoyl glycine were evaluated on a biodegradation tester and the microbial characters in the biodegradation sewage observed through a microscope. Also, the friction and wear performances of neat oil and the formulated oil were determined on a four‐ball tribometer. The morphologies and tribochemical features of the worn surfaces were analyzed by scanning electron microscopy and X‐ray photoelectron spectroscopy.

Oleoyl glycine markedly enhanced biodegradation of unreadily biodegradable mineral oil and effectively improved its anti‐wear and friction‐reducing abilities. The enhancement of biodegradability of the mineral oil was preliminarily ascribed to the increment of microbial populations in the biodegradation processes, while the improvement of anti‐wear and friction‐reducing abilities was mainly attributed to the formation of a boundary adsorption film of oleoyl glycine on the friction surfaces.

Oleoyl glycine is a biodegradable and low eco‐toxic compound. The authors' work has shown that oleoyl glycine is effective in improving biodegradability and tribological performances of mineral lubricants. Enhancing biodegradability of petroleum‐based lubricants by additives is a new attempt. The paper has significance for improving ecological and tribological performances of mineral lubricants, even for developing petroleum‐based biodegradable lubricants.

The purpose of this paper is to predict a global quality index of a ring spun yarn whose count Ne is ranging between 7.8 (76.92 tex) and 22.2 (27 tex). To fulfill this goal, a hybrid model based on artificial neural network (ANN) and fuzzy logic has been established. Fiber properties, yarn count and twist level are used as inputs to train the hybrid model and the output would be a quality index which includes the major physical properties of ring spun yarn.

The hybrid model has been developed by means of the application of two soft computing approaches. These techniques are ANN which allows the authors to predict four important yarn properties, namely: tenacity, breaking elongation, unevenness and hairiness and fuzzy expert system which investigates spinner experience to give each combination of the four yarn properties an index ranging from 0 to 1. The prediction of the model accuracy was estimated using statistical performance criteria. These criteria are correlation coefficient, root mean square error, mean absolute error and mean relative percent error.

The obtained results show that the constructed hybrid model is able to predict yarn quality from the chosen input variables with a reasonable degree of accuracy.

Until now, there is no sufficiently information to evaluate and predict the global yarn quality from raw materials characteristics and process parameters. Therefore, this present paper’s aim is to investigate spinner experience and their understanding about both the impact of various parameters on yarn properties and the relationship between these properties and the global yarn quality to predict a quality index.

Like all other natural fibers, the physical properties of cotton also vary owing to changes in the related genetic and environmental factors, which ultimately affect both the mechanics involved in yarn spinning and the quality of the yarn produced. However, information is lacking about the degree of influence that those properties impart on the spinnability of cotton fiber and the strength of the final yarn. This paper aims to discuss this issue.

This paper proposes the application of discriminant analysis as a multivariate regression tool to develop the causal relationships between six cotton fiber properties, i.e. fiber strength (FS), fiber fineness (FF), upper half mean length (UHML), uniformity index (UI), reflectance degree and yellowness and spinning consistency index (SCI) and yarn strength (YS) along with the determination of the respective contributive roles of those fiber properties on the considered dependent variables.

Based on the developed discriminant function, it can be revealed that FS, UI, FF and reflectance degree are responsible for higher YS. On the other hand, with increasing values of UHML and fiber yellowness, YS would tend to decrease. Similarly, SCI would increase with higher values of FS, UHML, UI and reflectance degree, and its value would decrease with increasing FF and yellowness.

The discriminant functions can effectively envisage the contributive role of each of the considered cotton fiber properties on SCI and YS. The discriminant analysis can also be adopted as an efficient tool for investigating the effects of various physical properties of other natural fibers on the corresponding yarn characteristics.

Cotton fibre lots are graded and selected for yarn spinning based on their quality value which is a function of certain fibre properties. Cotton grading and selection has created a domain of emerging interest among the researchers. Several researchers have addressed the said issue using a few exponents of multi-criteria decision-making (MCDM) technique. The purpose of this study is to demonstrate a cotton selection problem using a recently developed measurement of alternatives and ranking according to compromise solution (MARCOS) method which can handle almost any decision problem involving a finite number of alternatives and multiple conflicting decision criteria.

The MARCOS method of the MCDM technique was deployed in this study to rank 17 cotton fibre lots based on their quality values. Six apposite fibre properties, namely, fibre bundle strength, elongation, fineness, upper half mean length, uniformity index and short fibre content are considered as the six decision criteria assigning weights previously determined by an earlier researcher using analytic hierarchy process.

Among the 17 alternatives, C9 secured rank 1 (the best lot) with the highest utility function (0.704) and C7 occupied rank 17 (the worst lot) with the lowest utility function (0.596). Ranking given by MARCOS method showed high degree of congruence with the earlier approaches, as evidenced by high rank correlation coefficients (Rs > 0.814). During sensitivity analyses, no occurrence of rank reversal is observed. The correlations between the quality value-based ranking and the yarn tenacity-based rankings are better than many of the traditional methods. The results can be improved further by adopting other efficient method of weighting the criteria.

The properties of raw cotton have significant impact on the quality of final yarn. Compared to the traditional methods, MCDM is reported as the most viable solution in which fibre parameters are given their due importance while formulating a single index known as quality value. The present study demonstrates the application of a recently developed exponent of MCDM in the name of MARCOS for the first time to address a cotton fibre selection problem for textile spinning mills. The same approach can also be extended to solve other decision problems of the textile industry, in general.

Novelty of the present study lies in the fact that the MARCOS is a very recently developed MCDM method, and this is a maiden application of the MARCOS method in the domain of textile, in general, and cotton industry, in particular. The approach is very simple, highly effective and quite flexible in terms of number of alternatives and decision criteria, although highly robust and stable.

The purpose of this study is to investigate the effects of weft yarn diameter and pick density on the properties of surface roughness (SMD) of 3/1 (Z) twill-woven fabrics in three measurement directions weft (0°), the warp (90°) and the diagonal (45°).

Nine 3/1 (Z) twill samples were prepared with two factors and three levels and their roughness values were measured in the weft (0°), warp (90°) and diagonal (45°) directions of 3/1 (Z) twill fabrics using the Kawabata-FB4 instrument. Analysis of variance (ANOVA) is used to determine the effect of weft yarn diameter and pick density on SMD properties and comparisons were done in the weft (0°), the warp (90°) and the diagonal (45°) directions.

From experimental analysis, weft yarn diameter and pick density affect SMD of 3/1 (Z) twill-woven fabrics in both diagonal (45°) and weft (0°) directions but slightly affect warp (90°) direction. Maximum SMD values were observed in diagonal (45°) directions and the minimum was in warp (90°) directions of fabrics. Weft yarn diameter and pick density are statistically significant on SMD values of 3/1 (Z) twill-woven fabrics for three directions at a 95% confidence interval. Parameter variation in weft directions of 3/1 (Z) twill-woven fabrics also varies SMD values in three directions measurements

The findings of this study can be usually used for textile technology, industries and laboratories to create a basic understanding for measuring roughness properties of 3/1 (Z) twill fabric. It is also possible to identify the surface characterizations in different directions of measurement for their usage in some specific areas of end application like consumer goods, home textiles, technical textiles, etc.

Modeling helps to determine how structural parameters of fabric affect the surface of a fabric and also identify the way they influence fabric properties. Moreover, it helps to estimate and evaluate without the complexity and time-consuming experimental procedures. The purpose of this study is to develop and select the best regression model equations for the prediction and evaluation of surface roughness of plain-woven fabrics.

In this study, a linear and quadratic regression model was developed for the prediction and evaluation of surface roughness of plain-woven fabrics, and the capability in accuracy and reliability of the two-model equation was determined by the root mean square error (RMSE). The Design-Expert AE11 software was used for developing the two model equations and analysis of variance “ANOVA.” The count and density were used for developing linear model equation one “SMD1” as well as for quadratic model equation two “SMD2.”

From results and findings, the effects of count and density and their interactions on the roughness of plain-woven fabric were found statistically significant for both linear and quadratic models at a confidence interval of 95%. The count has a positive correlation with surface roughness, while density has a negative correlation. The correlations revealed that models were strongly correlated at a confidence interval of 95% with adjusted R² of 0.8483 and R² of 0.9079, respectively. The RMSE values of the quadratic model equation and linear model equation were 0.1596 and 0.0747, respectively.

Thus, the quadratic model equation has better capability accuracy and reliability in predictions and evaluations of surface roughness than a linear model. These models can be used to select a suitable fabric for various end applications, and it was also used for tests and predicts surface roughness of plain-woven fabrics. The regression model helps to reduce the gap between the subjective and objective surface roughness measurement methods.

This study aims to propose a fuzzy linear regression (FLR) model to deal with the vagueness or fuzziness of the underlying relationship between silk cocoon and yarn quality.

Shell ratio percentage, defective cocoon percentage and cocoon volume are considered as significant independent variables to predict the quality of silk cocoons. Input and output parameters of the FLR model are considered as non-fuzzy, but the underlying relationship between the variables is assumed to be fuzzy.

The fuzzy regression model shows its superiority against conventional multiple linear regression model for estimation of silk cocoon characteristics. It is inferred that the fuzziness in underlying relationship between the parameters can be handled efficiently by FLR model.

A rigorous experimental work has been carried out on 40 lots of mulberry silk cocoons to generate real-world data set to characterize silk cocoons’ quality in a fuzzy environment.

Housing market is predominantly driven by supply and demand, and the measurement of housing supply plays a crucial role in understanding market dynamics. One such measure is the number of building permits (BPs) issued. Despite the importance of BPs as an economic indicator, direct links have yet to be drawn between BP and housing value index (HVI). The purpose of this paper is to establish links between HVI and BP.

Trials were conducted using data at the national, state and metropolitan statistical area (MSA) levels. For each trial, the Granger causality test was used first to identify causal relationships between HVI and BP. Subsequently, the vector autoregression model was implemented in an attempt to observe impulse–response relationships and to create a forecast for HVI.

Bidirectional causal relationships were observed between HVI and BP at the national, state and MSA levels. The number of issued BPs proves to be an indicator for HVI. Impulse response functions indicate that HVI responds negatively to an increase in BP in the short term of 4–7 months but positively to an increase in BP with a lag of 10–12 months.

To the best of the authors’ knowledge, this paper is the first in the body of knowledge that establishes the number of issued BPs as an indicator for housing value. The results drawn using impulse–response function are also novel and had not been observed in previous studies.