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Quality management of products is an important part of manufacturing process. One way to manage and assure product quality is to use machine learning algorithms based on relationship among various process steps. The purpose of this paper is to integrate manufacturing, inspection and after-sales service data to make full use of machine learning algorithms for estimating the products’ quality in a supervised fashion. Proposed frameworks and methods are applied to actual data associated with heavy machinery engines.

By following Lenzerini’s formula, manufacturing, inspection and after-sales service data from various sources are integrated. The after-sales service data are used to label each engine as normal or abnormal. In this study, one-class classification algorithms are used due to class imbalance problem. To address multi-dimensionality of time series data, the symbolic aggregate approximation algorithm is used for data segmentation. Then, binary genetic algorithm-based wrapper approach is applied to segmented data to find the optimal feature subset.

By employing machine learning-based anomaly detection models, an anomaly score for each engine is calculated. Experimental results show that the proposed method can detect defective engines with a high probability before they are shipped.

Through data integration, the actual customer-perceived quality from after-sales service is linked to data from manufacturing and inspection process. In terms of business application, data integration and machine learning-based anomaly detection can help manufacturers establish quality management policies that reflect the actual customer-perceived quality by predicting defective engines.

This chapter reviews successful pedagogical interventions in teaching literacy in languages of teaching and learning in the upper primary grades in low- and middle-income countries (LMICs) and explores how researchers and teachers decolonized classroom spaces to ensure student achievement of learning outcomes. Themes emerging from the analysis of data are biliteracy interventions, interventions in official and national languages, teacher professional development, and ecological interventions. Results of the review indicate that researchers decolonized classroom spaces by using student linguistic repertoires, evidence-based pedagogical strategies student cultural capital and engaging families and communities. The review also reveals that more research needs to be conducted on teaching and learning in upper primary grades. The linguistic diversity of LMICs provides rich contexts for more research in bilingual education and L2 acquisition which could be useful worldwide as stakeholders in the education process explore the best ways to improve learning outcomes in schools.

The purpose of this paper is to illuminate enablers, barriers and vignettes of South Asian women leaders and possible paths to increase the influence and leadership of women in South Asia.

Navratna, the nine precious gems of ancient Indian literature are used to frame reflections on South Asian women leaders, and the Global Gender Gap Report of 2015 is used to give context to five barriers and five enablers to women’s leadership in the region. Illustrative vignettes of South Asian women in leadership roles are presented. These vignettes have been selected based on a case study approach of South Asian women leaders.

Five enablers that may help empower women towards greater leadership and influence are proposed: involving men in what should change, greater economic participation by women, supportive family, country- and context-specific leadership training, and finally grassroots advocacy, mentoring and role models.

The paper shines new light on women leaders whose sparking excellence in their specific field illuminate paths for others to follow and thus contributes to promoting research on multifaceted women leaders in South-Asia.

This paper aims to study the value of informal care (IC) time from the perspective of caregivers using two alternative contingent valuation tools – willingness to pay (WTP) and willingness to accept (WTA) – and to identify the variables that affect the stated values.

The authors used data from a multi-centre study of 610 adult caregivers conducted in two Spanish regions in 2013. The existence of “protest zeros” and “economic zeros” because of the severe budgetary constraints of the households was also considered. Two-part multivariate models were used to analyse the main factors that explained the declared values of WTA and WTP.

The average WTP and WTA were €3.12 and €5.98 per hour of care, respectively (€3.2 and €6.3 when estimated values for “protest zeros” and “economic zeros” were considered). Some explanatory variables of WTA and WTP are coincident (place of residence and intensity of care time), whereas other variables only help to explain WTP values (household and negative coping with caregiving) or WTA values (age and burden of care). Some nuances are also identified when comparing the results obtained without protest and economic zeros with the estimated values of these special zeros.

Studies analysing the determinants of WTP and WTA in IC settings are very scarce. This paper seeks to provide information to fill this gap. The results indicate that the variables that explain the value of IC from one perspective may differ from the variables that explain it from an alternative perspective. Given the relevance of contextual factors, studies on the topic should be expanded, and care should be taken with the extrapolation of results across countries and settings.