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Organizations in many domains generate a considerable amount of heterogeneous data every day. Such data can be processed to enhance these organizations’ decisions in real time. However, storing and processing large and varied datasets (known as big data) is challenging to do in real time. In machine learning, streaming feature selection has always been considered a superior technique for selecting the relevant subset features from highly dimensional data and thus reducing learning complexity. In the relevant literature, streaming feature selection refers to the features that arrive consecutively over time; despite a lack of exact figure on the number of features, numbers of instances are well-established. Many scholars in the field have proposed streaming-feature-selection algorithms in attempts to find the proper solution to this problem. This paper presents an exhaustive and methodological introduction of these techniques. This study provides a review of the traditional feature-selection algorithms and then scrutinizes the current algorithms that use streaming feature selection to determine their strengths and weaknesses. The survey also sheds light on the ongoing challenges in big-data research.

This chapter analyzes what happens to media use when everyday life is suddenly disrupted, focusing on how the COVID-19 pandemic transformed work, socializing, communication and everyday living. The empirical case is changing media use in Norway during the pandemic, building on a qualitative questionnaire survey conducted in early lockdown, and follow-up interviews eight months later. Expanding on the ideas of destabilization of media repertoires developed in the former chapter, this analysis discusses transforming media repertoires as more digital, as less mobile (but still smartphone-centric) and as essentially social. The chapter further explains new concepts for pandemic media use practices, such as doomscrolling and Zoom fatigue.

Tracing the development of a parallel-engaged pedagogy of care that extended and adapted the critical and transformative pedagogies of Freire, De Sousa Santos and hooks to the South African context. The development of this transformative pedagogy addresses the local conditions of an architectural design studio at a postcolonial, post-Apartheid and post “Fees must Fall” protests South African university. This pedagogy used practice-based design research to build a more conscious, critical and careful design practice in both students and educators.

The pedagogy was developed through participatory action research, over five years, from 2019 to 2023 including two years of the COVID-19 pandemic. Parallel and active engagement of students and educators within a nurturing and caring environment evolved from year to year, through a conscious and critical reflection on the process. Student surveys, reflective essays and focus groups unearth the impact of the parallel-engaged pedagogy of care.

The parallel-engaged pedagogy of care was shown to support and scaffold students becoming more conscious, critical and careful in their design practices validating diverse lived experiences as generative for design and important for social justice and transformative equity.

The parallel-engaged pedagogy of care is part of a global shift to more transformative pedagogies that address student diversity and decoloniality.

Through dismantling traditional hierarchical teaching modes, the pedagogy is more student-led, agile and adaptable. Through centring and demonstrating care in the pedagogy, students are encouraged to develop both self-care and care in their design practice. This is especially critical in the South African context where the cultural capital of the institution, with its roots in colonial and Apartheid education differs from that of the majority of students of colour.

This work presents a novel approach by considering teaching learning based optimization (TLBO) and radial basis function neural networks (RBFNs) for building a classifier for the databases with missing values and irrelevant features. The least square estimator and relief algorithm have been used for imputing the database and evaluating the relevance of features, respectively. The preprocessed dataset is used for developing a classifier based on TLBO trained RBFNs for generating a concise and meaningful description for each class that can be used to classify subsequent instances with no known class label. The method is evaluated extensively through a few bench-mark datasets obtained from UCI repository. The experimental results confirm that our approach can be a promising tool towards constructing a classifier from the databases with missing values and irrelevant attributes.

The use of long-term anti-psychotic medication for borderline personality disorder contravenes prescribing guidelines in the UK. There is evidence to suggest clozapine can be beneficial yet anecdotally it is prescribed almost exclusively in locked settings. A single study suggests a substantial proportion of psychiatrists disapprove of this practice. The purpose of this paper is to articulate concerns about the use of clozapine for “BPD” that are absent from current literature.

This paper summarises the reflections and experiences of the authors lived experience, academic and clinical backgrounds.

The published literature is uniformly positive when describing the prescription of clozapine for those diagnosed with BPD; however, this in no way reflects the experience of the authors. There is no body of material reflecting a study showing that a substantial number of psychiatrists have issues with this practice.

While it is a fact that there is a discrepancy between psychiatrists attitudes towards clozapine prescription for “BPD” and the published literature, the described concerns in this paper are based solely on the authors’ experiences and observations.

Those seeking literature to articulate concerns about the use of clozapine with this population will likely be disheartened by the paucity of published literature.

To the best of the authors’ knowledge, this paper is the first to raise substantial concerns about the use of clozapine for those diagnosed with “BPD” and the circumstances in which it is prescribed.

The purpose of this paper is to examine how automation in the ever-changing technological landscape is increasing integrated into, and has become a significant presence in, our personal lives.

Through post qualitative inquiry, the authors provide a contemplation of automation and its effect on creativity, as a contemporary expression of dis/locations, the simulacrum, performative work and a toxic digital presence in socio-cultural-technical spaces.

The authors discuss how we behave, contribute, explore, interact and communicate within and across automated digital platforms, has salience for understanding and questioning the ways that dominant discourses in the contemporary construction and enactment of subjectivity, creativity and agency are being modulated by the machine.

This paper offers a nuanced consideration of creativity, by considering the way creativity is being performed and situated within the effects of automation and its role in dis/locations, performative work and its potential as a the simulacrum in socio-cultural-technical spaces.