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The purpose of this paper is to develop a workforce optimisation model that maximises the profitability of a knowledge-based service organisation in the quaternary sector.

An optimisation model that allocates resources from different skillsets and seniority to projects that are delivered from several geographies has been developed in this paper. With the objective of maximising the profitability of a pipeline of projects, the model selects which projects to accept and which not to and indicates how many resources to hire for (or layoff from) each skillset-seniority-geography combination.

The paper discusses the model and its scalable nature. Through hypothetical scenarios, it is shown that the model, using a simple non-linear algorithm, converges to optimal solutions.

The model depends on inputs that are exogenously supplied by the organisation. The applicability of the outcome is dependent on them. However, on the other hand, it allows for the alignment of the outcomes with the strategic objective of the organisation.

The paper discusses the multi-dimensional nature of effective human resource allocation problem. It not only maximises profitability but also allows organisations to strategically screen projects. With proper calibration and minor modifications, the model may be used to allocate resources across the knowledge-based industry.

The paper integrates the demand and supply-side problems of workforce allocation to projects in a novel way to form a tractable model that is pragmatic and applicable.

The use and importance of mélange yarn in apparel sector is increasing day by day. With the gradual increase in market share, achieving the desired quality level of mélange yarn remains a challenge for yarn manufacturing industry. The purpose of this paper is to investigate the effect of raw material (dyed fiber percentage in the mixing), important spinning process variable (yarn twist multiplier) and productivity (spindle rpm of ring frame) on properties of cotton mélange spun yarn.

Box and Behnken Design of experiment has been used to investigate the important yarn quality parameters like evenness, imperfection, hairiness, breaking strength and breaking elongation of blow room blended cotton mélange yarn. The quadratic regression model is used to derive the statistical inferences about sensitivity of the yarn quality parameters to the different process variables. The response surfaces are constructed for depicting the geometric representation of yarn quality parameters plotted as a function of process variables.

The study shows that shade depth and spindle speed have significant effects on the mélange yarn unevenness and imperfections. Mélange yarn strength and hairiness are significantly affected by shade depth and yarn twist multiplier (TM). Yarn elongation at break is only influenced by the spindle speed. A darker shade is responsible for higher yarn unevenness, imperfection, hairiness and lower yarn strength. A higher spindle speed is also liable for deterioration of yarn quality.

Many spinning industries are planning to convert their existing spindles from normal gray yarn production to mélange yarn manufacturing. The outcome of this study will lead to achieve better mélange yarn quality and productivity by the industry.

Research on mélange yarn is itself scant. This study is exclusively conducted to analyze the individual and interactive effect of various process parameters on the mélange yarn quality.

In recent years, rough set theory has evolved as one of the most promising classification techniques. One of the cardinal uses of rough set theory is its application for rule generation. The purpose of this paper is to propose a real-time fabric inspection technique. This work deals with the multi-class classification of fabric defects using rough set theory.

This technique focuses on the classification of fabric defects using the effective decision rules envisaged by rough set theory. In the proposed work, the six features of 50 images have been used for multiclass classification of fabric defects.

In this work, 40 images were used for generation of decision rules and 10 unseen images were used for validation out of which nine images are accurately predicted by the proposed technique.

The proposed method accurately identified 9 out of 10 testing defects. The obtained decision rules provide an insight about the classification method which ensures that the prediction accuracy can be improved further by framing more robust decision rules with the help of a large training data set. Thus, with the support of modern computational systems this method is potent in getting recognition from the textile industry as a real-time classification technique.

Bending and shear rigidities of woven fabrics depend on fibre, yarn and fabric-related parameters. However, there is lack of research efforts to understand how bending and shear rigidities change in woven fabrics having similar areal density. The purpose of this paper is to investigate the change in bending and shear rigidities in plain woven fabrics having similar areal density.

A total of 18 fabrics were woven (9 each for 100 per cent cotton and 100 per cent polyester) keeping the areal density same. Yarns of 20, 30 and 40 Ne were used in warp and weft wise directions and fabric sett was adjusted to attain the desired areal density.

When warp yarns become finer, keeping weft yarns same, bending rigidity remains unchanged but shear rigidity increases in warp wise direction. When weft yarns are made finer, keeping the warp yarns same, both the bending and shear rigidities of fabric increase in warp wise direction. Similar results for fabric bending and shear rigidities were obtained in transpose direction. There is a strong association between fabric shear rigidity and number of interlacement points per unit area of fabric even when fabric areal density is same.

Very limited research has been reported on the low-stress mechanical properties of woven fabrics having similar areal density. A novel attempt has been made in this research work to investigate the bending and shear rigidities of woven fabrics having similar areal density. Besides, it has been shown that it is possible to design a set of woven fabrics having similar bending rigidity but different shear rigidity.

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.

An intelligence machine is a computer program that can learn from experience, i.e. modifies its processing on the basis of newly acquired information and thereafter makes decisions in a rightfully sensible manner when presented with inputs. Examples of such machine learning systems are artificial neural networks (ANNs), support vector machines (SVMs), fuzzy logic, evolutionary computation, etc. The prediction of cotton yarn properties from constituent fibre properties is quite significant from a technological point of view. Regardless of the relentless efforts made by researchers, the exact relationship between fibre and yarn properties has not yet been decisively recognized. The intelligence machine, which is a potent data-modeling tool in capturing complex input-output relationships, seems to be the right approach to decipher the fibre-to-yarn relationship. In this work, various cotton yarns properties, such as strength, elongation, evenness and hairiness, have been predicted from fibre properties by using different intelligence models, such as ANNs, SVMs and adaptive neuro fuzzy inference systems (ANFIS). A k-fold cross validation technique is applied to assess the expected generalization accuracies of these models. A comparison of the prediction efficiencies among these models shows that the performances of the SVM model has better accuracies than the other models.

A bio-sensor has been developed in this study for the purpose of point-of-care diagnostics. Point-of-care-diagnostic is a type of diagnosis where the diagnostic centre, i.e. the diagnosis kit is made available at the location of the patient when the patient needs immediate action. In this process of diagnosis a compact, portable, integrated kit must be available which can diagnose the disease of the patient by testing various analytes.

Using a fully experimental methodology, a blood glucose sensor is made by conducting carbon interdigitated electrode (IDE) on a flexible substrate. IDEs are used to increase the effective capacitance of the structure, as well as the effective electroactive area of the sensor. Interdigitated structure permits two-electrode sticks with “each other” and “infuse” together. As a consequence, the distance between electrodes can be tuned to a much smaller value than traditional thin-film architectures. Narrowing the distance between electrodes allows for fast ion diffusion that offers better rate capability and efficiency in power density. The fabricated device exhibits a remarkable value of sensitivity in the order of 2.741 µA mM-1 cm⁻².

A highly sensitive, portable and inexpensive blood glucose sensor has been developed in this context.

This research study can be a scope for future research in the field of bio-sensors.

The airline passenger industry in India was a mess in 2013, but the low-cost carrier IndiGo was making money. This relatively new company had managed to work against the odds and grab market share from longer-established flyers. Still, the weak rupee, depreciated by 15%, was sending a chill wind through the aviation sector, and growth plans would have to include opening new destinations. This meant hiring more employees, opening more ticketing stations, and increasing costs. Could the airline continue its climb, or would it be prudent to prepare for a hard landing?

The purpose of this paper is to give an approach for categorization of diverse textile designs using their textural features as extracted from their gray images by means of multi-class least-square support vector machines (LS-SVM).

In this work, the authors endeavor to devise a pattern recognition system based on LS-SVM which performs a multi-class categorization of three basic woven designs namely plain, twill and sateen after analyzing their features.

The result establishes that LS-SVM is able to classify the fabric design with a reasonable degree of accuracy and it outperforms the standard SVM.

The algorithmic simplicity of LS-SVM resulting from replacement of inequality constraints by equality ones and ability of handling noisy data by accommodating an error variable in its algorithm make it eminently suitable for textile pattern recognition. This paper offers a maiden application of LS-SVM in textile pattern recognition.