Machine learning and design of experiments with an application to product innovation in the chemical industry

Industrial statistics plays a major role in the areas of both quality management and innovation. However, existing methodologies must be integrated with the latest tools from the field of Artificial Intelligence. To this end, a background on the joint application of Design of Experiments (DOE) and Machine Learning (ML) methodologies in industrial settings is presented here, along with a case study from the chemical industry. A DOE study is used to collect data, and two ML models are applied to predict responses which performance show an advantage over the traditional modeling approach. Emphasis is placed on causal investigation and quantification of prediction uncertainty, as these are crucial for an assessment of the goodness and robustness of the models developed. Within the scope of the case study, the models learned can be implemented in a semi-automatic system that can assist practitioners who are inexperienced in data analysis in the process of new product development.     

From a ‘sort of Muslim’ to ‘proud to be Alevi’: the Alevi religion and identity project combatting the negative identity among second-generation Alevis in the UK

ABSTRACT

This article explains how the negative identity of second-generation Alevi-Kurds in the UK has been transmitted intergenerationally, linked to their history of persecuted exclusion in Turkey and to the transnational settlement of Alevi migrants in the UK, and how this sense of marginalization and invisibility in the receiving country can be addressed. Education is identified as a starting point for the underachievement and disaffection of Alevi pupils, which can lead them into more serious trouble and descent into the rainbow underclass. In the quest to tackle this identity issue, a unique collaborative action research project was set up between an Alevi community centre, local schools and a university to develop the world’s first Alevi lessons as part of the compulsory Religious Education curriculum in British schools. The Alevi Religion and Identity Project is described and evaluated in terms of its outcomes, especially its contribution towards a more positive Alevi identity as a reflection of a vibrant community.     

Estimation of Nonparametric Models With Simultaneity

We introduce methods for estimating nonparametric, nonadditive models with simultaneity. The methods are developed by directly connecting the elements of the structural system to be estimated with features of the density of the observable variables, such as ratios of derivatives or averages of products of derivatives of this density. The estimators are therefore easily computed functionals of a nonparametric estimator of the density of the observable variables. We consider in detail a model where to each structural equation there corresponds an exclusive regressor and a model with one equation of interest and one instrument that is included in a second equation. For both models, we provide new characterizations of observational equivalence on a set, in terms of the density of the observable variables and derivatives of the structural functions. Based on those characterizations, we develop two estimation methods. In the first method, the estimators of the structural derivatives are calculated by a simple matrix inversion and matrix multiplication, analogous to a standard least squares estimator, but with the elements of the matrices being averages of products of derivatives of nonparametric density estimators. In the second method, the estimators of the structural derivatives are calculated in two steps. In a first step, values of the instrument are found at which the density of the observable variables satisfies some properties. In the second step, the estimators are calculated directly from the values of derivatives of the density of the observable variables evaluated at the found values of the instrument. We show that both pointwise estimators are consistent and asymptotically normal.     

Imagined interactions,family money management patterns and coalitions,and attitudes toward money and credit

This study explores the imagined interactions college students have with their parents about money and credit, their attitudes toward credit and money, the ways they say their parents deal with financial decisions, and the communication coalitions regarding finances they perceive existing within their family. Students’ imagined interaction pleasantness is greatest when parents jointly form a plan for paying off credit card debt and lowest when parents argue. When family coalitions exist, students report more frequent imagined interactions. Imagined interaction frequency and pleasantness are related to credit and money attitudes.

A Multivariate Indicator to Compute Middle Class Population

Social Indicators Research - Monitoring progress towards the Sustainable Development Goals by 2030 requires the global community to disaggregate targets along socio-economic lines, but little has...     

Reclaiming ourselves through Testimonio pedagogy: reflections on a curriculum design lab in teacher education

This article details one teacher preparation course centering Latin American Testimonio narratives of struggle/survival amid structural oppression for use in secondary curriculum. As our class of predominantly Latina/o students and two Latina instructors engaged Testimonio pedagogy, we fashioned a hopeful alternative to our own experiences of intergenerational oppression. While research indicates that the experiences and histories of pre-service Teachers of Color lend pedagogical strength and critical consciousness to teacher education, three Latina pre-service students highlight the ways in which Testimonio became more than a pedagogical approach. Testimonio’s collectivity, resistance, hope, and assertions of voice and dignity moved through them not as educators first but as (great-grand)daughters of oppressed though still-resilient People(s). Testimonio emboldened these Latina pre-service educators to recognize and validate their own inherited multiliteracies, (re)claim their connectedness to land, and articulate their visions for more equitable schooling. This work advances research into the essentiality of engaging race and ethnicity in K-12 and teacher education curriculum and pedagogy.     

A permutation approach for testing heterogeneity in two-sample categorical variables

In many sciences researchers often meet the problem of establishing if the distribution of a categorical variable is more concentrated, or less heterogeneous, in population P ₁ than in population P ₂. An approximate nonparametric solution to this problem is discussed within the permutation context. Such a solution has similarities to that of testing for stochastic dominance, that is, of testing under order restrictions, for ordered categorical variables. Main properties of given solution and a Monte Carlo simulation in order to evaluate its degree of approximation and its power behaviour are examined. Two application examples are also discussed.