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The purpose of this paper is to report on a collaborative project and study implemented by two teacher educators in an elementary education program. To prepare teacher candidates for field experiences and practicum in a diverse (bilingual) urban school, the program uses coursework to impart asset-based pedagogies and practices.

In this mixed-method case study, this paper examined the awareness and perspectives of preservice teachers (n = 26) to cultural and linguistic diversity and relevant teaching and learning practices. In particular, this study gauged their engagement with multicultural children’s literature in a collaborative interclass activity. The data sources included beginning and end of semester survey responses, notes on participant interactions during the mid-semester collaborative interclass activity and participant retrospective reflections about the activity.

This paper found that teacher candidates showed increased awareness and positive shifts in perspectives. This study also ascertaind that, in learning to become culturally (and linguistically) responsive and sustaining teachers, they benefited from collaborative peer work that focused on learning about multicultural children’s literature, analyzing it and planning to integrate it into their classrooms.

Studies show that culturally relevant literature in schools is beneficial; however, teacher candidates often lack knowledge of such literature and how to use it. This need is especially critical and relevant when learning about and implementing culturally relevant and sustaining practices. The collaborative undertaking discussed in this study fills this gap through co-teaching and interclass activity that brings preservice teachers as a cohort to collaboratively learn about, discuss, reflect on and plan lessons as they prepare to work with students from different backgrounds than their own.

The way in which clients or their consultants undertake to select firms to tender for a given project is a highly complex process and can be very problematic. This is also true for public authorities as, for them, ‘compulsory competitive tendering’ is a relatively new concept. Despite its importance, contractors' prequalification is often based on heuristic techniques combining experience, judgement and intuition of the decision makers. This, primarily, stems from the fact that prequalification is not an exact science. For any project, the right choice of the contractor is one of the most important decisions that the client has to make. Therefore, it is envisaged that the development of an effective decision‐support model for contractor prequalification can yield significant benefits to the client. By implication, such a model can also be of considerable use to contractors: a model of this nature is an effective marketing tool for contractors to enhance their chances of success to obtain new work. To this end, this work offers a decision‐support model that predicts whether or not a contractor should be selected for tendering projects. The focus is on local authorities because, in the absence of a viable universal selection system, there are significant variations in the way they conduct prequalification. The model is based on the use of artificial neural networks (ANN) and uses data relating to 42 local authorities (clients). With the aid of a questionnaire and a scaling system, the prequalification attributes that are considered to be important by clients are identified. The survey indicates significant variations in the level of importance given to different attributes. Statistical methods are adopted to generate additional data representing disqualified instances. Following a preprocessing exercise, the data form the basis of the input and output layers for training the neural‐net model. An independent set of data is subjected to a similar preprocessing for testing the model. Tests reveal that the model has a highly satisfactory predictive accuracy and that the ANN technique is a viable tool for the prediction of success or failure of the contractor to qualify to tender for local authority projects.

The purpose of this paper is to discuss key elements of lean supply (LS) in light of core concepts in the Industrial Marketing and Purchasing Group (IMP) perspective.

First, the authors examine the literature on LS and identify and discuss important characteristics and key elements of LS. Second, the authors present key concepts in the IMP Perspective, in particular the dyad versus network levels, and the ARA model, capturing activities, resources, and actors. Third, the authors cross-fertilize the concepts from these two streams of research.

The authors identify 12 key LS elements. Relating these to core IMP frameworks, they identify areas of LS that can be expanded. First, the authors found that key elements in LS mainly focus on the dyadic level and that the network level is addressed to a much lesser extent and primarily captures serial “chain” connections among relationships. Second, it was found that key elements in LS predominantly focus on the activity layer and pay much less attention to resources and actors.

The authors suggest that LS theory and practice can benefit from taking a network perspective, and by paying more attention to resource and actor concepts and issues. The study is purely theoretical.

To the best of the authors’ knowledge, no previous studies combine LS and the IMP perspective. The authors add to LS by elaborating how 12 key elements in LS can be expanded.

The pandemic impacted consumers' shopping processes, leading them to approach the online channel for grocery shopping for the first time. The paper contributes to the retailing literature by identifying different grocery shopper segments willing to switch online moved by heterogeneous motivations. Integrating the technology acceptance model 2 (TAM-2) and the protection motivation theory (PMT), this study identifies technology-related and Covid-related motivations jointly impacting channel switching.

A mixture regression model was estimated on the 370 valid questionnaires, filled out by Italian shoppers, delivering four internally consistent segments.

The results reveal the existence of four segments willing to switch towards the online channel for grocery shopping in the aftermath of the pandemic. Utilitarian shoppers would switch online as they consider the online channel useful and easy to use. Responsive shoppers will prefer the online channel driven by the fear of being infected in-store. Novel enthusiasts show interest in the online channel to not catch the virus and cope with emotional fear, although they consider online shopping as an enjoyable and useful activity as well. Smart shoppers consider online shopping as an easy-to-use alternative for their grocery purchases.

This paper identifies technology-related and Covid-related motivations jointly impacting shoppers' channel switching to online and presents a novel method – i.e. mixture regression – allowing for the identification of shopper segments motivated by different reasons, both emotional and utilitarian, to switch towards the online channel for their grocery shopping. Among other motivations, the fear of Covid-19 is identified as a relevant motivation to switch to online.

The purpose of this paper is to present a novel concentration estimation model for improving the accuracy and robustness of low-cost electronic noses (e-noses) with metal oxide semiconductor sensors in indoor air contaminant monitoring and overcome the potential sensor drift.

In the quantification model, a piecewise linearly weighted artificial neural network ensemble model (PLWE-ANN) with an embedded self-calibration module based on a threshold network is studied.

The nonlinear estimation problem of sensor array-based e-noses can be effectively transformed into a piecewise linear estimation through linear weighted neural networks ensemble activated by a threshold network.

In this paper, a number of experimental results have been presented, and it also demonstrates that the proposed model has very good accuracy and robustness in real-time indoor monitoring of formaldehyde.

This bibliography covers materials published during 1976, with some 1975 entries omitted from last year's listing. Citations from a number of foreign countries are included if published in English. A few items were not available for annotation. The growing interest in library use instruction is evident from the fact that the number of entries has doubled over those included in the bibliography for 1975.

The complexity of atmospheric corrosion, further compounded by the effects of climate change, makes existing models inappropriate for corrosion prediction. The commonly used kinetic model and dose-response functions are restricted in their capacity to represent the non-linear behaviour of corrosion phenomena. The application of artificial intelligence (AI)-driven machine learning algorithms to corrosion data can better represent the corrosion mechanism by considering the dynamic behaviour due to changing climatic conditions. Effective use of materials, coating systems and maintenance strategies can then be made with such a corrosivity model. Accurate corrosion prediction will help to improve climate change resilience of the social, economic and energy infrastructure in line with the UN Sustainable Development Goals (SDGs) 7 (Affordable and Clean Energy), 9 (Industry, Innovation and Infrastructure) and 13 (Climate Action). This chapter discusses atmospheric corrosion prediction in relation to the SDGs and the influence of AI in overcoming the challenges.