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The purpose of this paper is to enhance the accuracy of classification of streaming big data sets with lesser processing time. This kind of social analytics would contribute to society with inferred decisions at a correct time. The work is intended for streaming nature of Twitter data sets.

It is a demanding task to analyse the increasing Twitter data by the conventional methods. The MapReduce (MR) is used for quickest analytics. The online feature selection (OFS) accelerated bat algorithm (ABA) and ensemble incremental deep multiple layer perceptron (EIDMLP) classifier is proposed for Feature Selection and classification. Three Twitter data sets under varied categories are investigated (product, service and emotions). The proposed model is compared with Particle Swarm Optimization, Accelerated Particle Swarm Optimization, accelerated simulated annealing and mutation operator (ASAMO). Feature Selection algorithms and classifiers such as Naïve Bayes, support vector machine, Hoeffding tree and fuzzy minimal consistent class subset coverage with the k-nearest neighbour (FMCCSC-KNN).

The proposed model is compared with PSO, APSO, ASAMO. Feature Selection algorithms, and classifiers such as Naïve Bayes (NB), support vector machine (SVM), Hoeffding Tree (HT), and Fuzzy Minimal Consistent Class Subset Coverage with the K-Nearest Neighbour (FMCCSC-KNN). The outcome of the work has achieved an accuracy of 99%, 99.48%, 98.9% for the given data sets with the processing time of 0.0034, 0.0024, 0.0053, seconds respectively.

A novel framework is proposed for Feature Selection and classification. The work is compared with the authors’ previously developed classifiers with other state-of-the-art Feature Selection and classification algorithms.

The purpose of this paper is to make available to the parachute industry tools to predict behaviour of certain textile materials. In addition to this, it is desired to reveal and explain the basic requirement criteria for proper textile material selection. The strength of an assembly as a whole is directly dependent on the strengths of the various joints and seams required to assemble the larger structure. Keeping in mind the complex problem of parachute construction, this research seeks to enlighten the industry about the performance of seams in nylon woven canopy fabrics. Five factors have been studied: different types of weave (plain, rip-stop and twill), density (number of stitches per centimetre), different rows of stitches with lapped seams, different types of stitches (lock stitch, chain stitch and zig-zag) and seam direction (warp, weft and bias direction). Two responses have been analysed, the seam breaking force and the seam efficiency (per cent ratio of seam strength to fabric strength). The test results were subjected to an analysis of variance and the seam strength proved to vary significantly not only with the primary parameters, but with the interactions of the primary parameters as well. That is seam strength (and seam efficiency) changes with each primary parameter but it changes in a different manner when other parameters change. Multiple regressions have been used to construct preliminary predictor equations for seam strength and efficiency, and investigations to provide better equations are in progress.

ANOVA techniques and statistical regression equations were formed.

The work has concluded that twill weave 9 with chain stitch has the maximum seam strength, which makes canopies made with 2/1 twill weave and stitched with lapped seam with four rows of chain stitch optimum for heavy supply droppings with a single use parachute(s). It is evident from the results that twill weave with lock stich has the maximum seam efficiency. This makes the canopies stitched with twill fabric, constructed with lapped seams and four rows of stitches ideal for parachutes to be used multiple times. The brake parachutes on aircrafts and parachutes used by sky divers and air combat soldiers can use parachutes whose canopies can be used many times made out of the above mentioned weave and stitch specification.

Original work was conducted from the woven fabrics.

Parachutes are equipment that is repeatedly used as and when needed. Some of them are used for as many as 60 jumps. The property of the canopy fabric gets deteriorated with use. It is evaluated by destructive tensile and bursting strength. This study aims to focus on the nondestructive evaluation of the canopy fabric's fitness by testing air permeability and relating it with bursting strength. Predictor equations were developed to determine bursting strength from air permeability values.

ANOVA techniques and statistical regression equations were formed.

A series of samples containing five parachutes fabrics was used seven times, and their air permeability and bursting strength were determined to find the extent to the effect of reuse of parachute fabrics on their bursting strength and air permeability determination. It was found that there was a progressive drop in bursting strength and an increase in air permeability. An investigation of the extent of determination in terms of bursting strength and an increase in air permeability following the sense of five different types of parachute fabrics is reported.

The work focuses on the prediction of bursting strength to textile materials only and may not apply to other materials like membranes and sheets. The process of determining air permeability is relatively simpler and faster.

The bursting strength can be predicted for used parachutes, which are otherwise subjected to destructive testing.

The men using the parachutes can be assured of the superior flawless performance of the parachute as equipment and also contribute to the saving of resources due to nondestructive testing, 100% evaluation of all parachutes is possible.

This article describes the nature of the test procedure and discusses the means of introducing it to users of parachutes. It is accepted that the method must undergo field evaluation and possible modification before it can become a routine tool of parachute using organizations.

The purpose of this paper is to assess food safety knowledge, choice behavior and health hazard concerns of ethnic Goan pork sausage consumers through various consumer food safety indices and also to evaluate socio-demographic influences on various consumer food safety indices.

Primary data in the form of 210 questionnaire responses were collected from Goan pork sausage consumers. Based on the responses, seven various consumer food safety indices were derived. The cumulative score of all the indices were normalized (0–1). The association between socio-demographic variables and consumer indices were evaluated using linear regression analysis, Pearson's correlation matrix, principal component analysis (PCA) and correspondence analysis (CA).

The results of the analysis indicate that the consumption of Goan pork sausages is most prevalent amongst younger people with high education having higher income. A majority of consumers have high score for the four consumer indices. The results of correlation analysis, regression analysis, PCA and CA revealed a significant association of consumer indices with socio-demographic variables.

The results of this study will provide a way forward for the evaluation of existing overall food safety concerns in the target population and in the future would be used for the promotion of good handling practices and health of Goan pork sausage consumers.

This chapter juxtaposes human trafficking of children by their families with structural disadvantage characterizing cycles of violence to suggest that carceral approaches alone cannot break trajectories of crime. It highlights contexts where familial violence is culturally normalized and where the victim–offender binary is challenged. The devadasi system in a district in the Northern region of the State of Karnataka, India, and stories of women and communities in urban India provide a context for the discussion. This chapter posits the need to explore community corrections, restorative and transitional justice, and social justice in criminal justice administration to build safe families and communities for children.

This study aims to investigate the factors influencing the reduction in strength of sewing threads after stitching (after-stitch strength reduction) in different fabric assemblies, including nonwoven fabrics. This research expands upon previous studies, which primarily focused on woven fabrics.

A full-factorial experimental design was employed, considering three factors: fabric assembly type (woven, nonwoven, composite), yarn type (spun polyester, core-spun polyester) and thread count (30 Tex, 60 Tex). Also, the stitching parameters stitch density (3 spc, 5 spc) and needle count (75 Nm, 100 Nm) were considered. The predictive regression model was also developed with a second-degree order. Tensile testing was conducted on unravelled threads before and after stitching to assess strength reduction.

Fabric assembly type significantly impacted after-stitch strength reduction, with the composite assembly exhibiting the highest reduction. Yarn type played a crucial role in the composite assembly; core-spun yarn experienced higher strength reduction compared to spun polyester yarn. Thread count consistently affected strength reduction across all fabric assemblies, with finer threads experiencing higher reductions. Stitch density generally had a positive correlation with strength reduction for woven and nonwoven assemblies, but no significant difference was observed in the composite assembly. Needle count did not significantly impact strength reduction for any fabric assembly.

The findings can guide the selection of appropriate sewing threads, yarn types and sewing parameters for different fabric assemblies, including nonwoven fabrics, to optimize seam performance and garment durability.

This study contributes to the understanding of how fabric structure, yarn characteristics and sewing parameters interact to influence sewing thread strength reduction in diverse fabric assemblies, including nonwoven fabrics, which were not previously considered in similar studies. This broader investigation provides valuable insights applicable to a wider range of sewing applications.

The purpose of this paper is to analyse the certified emission reduction (CERs) disclosure and reporting practices followed by Indian firms.

The study is based on all 131 Indian firms who received the CERs under the CDM of UNFCCC. The content analysis is being used to examine the recognition, measurement, presentation and disclosure of CERs within the financial statements.

The study found that there is generally no uniformity of accounting for CERs. The firms adopted a diversity of accounting practices. More specifically, majority of companies (40.46 per cent) recognised CERs as the other income; a very high non-disclosure rate (91.60 per cent) for valuation of CERs inventories was found as only four companies (3.05 per cent) provided accounting treatment for CERs inventories at lower of cost or net realisable value followed by three companies (2.29 per cent) accounted for these inventories at Net realisable value at the end of the reporting period; similarly, a very high non-disclosure rate (92.36 per cent) for how companies account for expenses incurred in earning these credits was found.

The study will be useful for a wide array of audiences ranging from accounting standard setter to the auditors. The present analysis is based on secondary data, as we examined only annual reports of the sample companies to know how they recognise their earned CERs within the financial statements. So, we did not cover the opinions of various key persons of companies like an accountant, auditors etc. which could be a limitation of this study in validating CERs disclosure practices followed by the Indian firms.

To the best of the author's knowledge, the present study is a first of its kind to analyse the carbon credit disclosure practices in the context of a developing country.