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Police training is in a period of transition, requiring new recruits to already have, or work towards, a policing degree. However, recruitment procedures have not significantly changed in the past few decades. With psychometric testing commonplace in North America and Australasia to help ensure the right recruits are selected, this research seeks to understand if police trainers feel there is scope for a similar process in the United Kingdom (UK).

Twenty-five police training staff across four different police forces took part in a total of six focus groups to discuss views on this and other areas of recruitment.

Results indicated that police trainers are concerned about the quality and aptitude of recruits. Support was given for the introduction of formal psychometric testing to prevent unsuitable candidates from successfully joining and/or to give trainers better insight into the personalities of their students.

There was general concern from female trainers that the police environment new recruits entered still bore elements of covert sexism. Trainers' views on reforms to police recruitment, the implications of this and areas for future study are discussed.

Cost overrun in infrastructure projects is a constant concern, with a need for a proper solution. The current estimation practice needs improvement to reduce cost overruns. This study aimed to find possible statistical modelling techniques that could be used to develop cost models to produce more reliable cost estimates.

A bibliographic literature review was conducted using a two-stage selection method to compile the relevant publications from Scopus. Then, Visualisation of Similarities (VOS)-Viewer was used to develop the visualisation maps for co-occurrence keyword analysis and yearly trends in research topics.

The study found seven primary techniques used as cost models in construction projects: regression analysis (RA), artificial neural network (ANN), case-based reasoning (CBR), fuzzy logic, Monte-Carlo simulation (MCS), support vector machine (SVM) and reference class forecasting (RCF). RA, ANN and CBR were the most researched techniques. Furthermore, it was observed that the model's performance could be improved by combining two or more techniques into one model.

The research was limited to the findings from the bibliometric literature review.

The findings provided an assessment of statistical techniques that the industry can adopt to improve the traditional estimation practice of infrastructure projects.

This study mapped the research carried out on cost-modelling techniques and analysed the trends. It also reviewed the performance of the models developed for infrastructure projects. The findings could be used to further research to develop more reliable cost models using statistical modelling techniques with better performance.

Over the past decade, criminology in Australia, Canada and other settler colonies has increasingly engaged with activist challenges to the penal system. These anti-carceral engagements have been levelled at its laws, institutions and agents. Following a long history of criminology explicating and buttressing penal institutions, the criminological gaze slowly transitioned in the 1970s to a more critical lens, shifting focus from the people who are criminalised to the harms of the apparatus that criminalises. However, the focus remained steadfastly on institutions and dominant players – until much more recently. The COVID-19 pandemic has revealed the strength of activist organisations and grassroots movements in affecting change and shaping debates in relation to the penal system. This chapter will explore the role of activism in informing criminological scholarship during the pandemic period and how criminologists, in turn, have increasingly recognised the need to build alliances and collaborations with grassroots activists and engage in their own activism. The chapter focuses primarily on Australian and Canadian criminology and its growing imbrication with the prison abolition movement, especially in the shadow of ongoing colonial violence. It considers how activist scholars, including ourselves, attempt to build movements for structural change in the criminal system and beyond.

The textile industry holds immense significance in the economy of any nation, particularly in the production of synthetic yarn and fabrics. Consequently, the pursuit of acquiring high-quality products at a reduced cost has become a significant concern for countries. The primary objective of this research is to leverage data mining and data intelligence techniques to enhance and refine the production process of texturized yarn by developing an intelligent operating guide that enables the adjustment of production process parameters in the texturized yarn manufacturing process, based on the specifications of raw materials.

This research undertook a systematic literature review to explore the various factors that influence yarn quality. Data mining techniques, including deep learning, K-nearest neighbor (KNN), decision tree, Naïve Bayes, support vector machine and VOTE, were employed to identify the most crucial factors. Subsequently, an executive and dynamic guide was developed utilizing data intelligence tools such as Power BI (Business Intelligence). The proposed model was then applied to the production process of a textile company in Iran 2020 to 2021.

The results of this research highlight that the production process parameters exert a more significant influence on texturized yarn quality than the characteristics of raw materials. The executive production guide was designed by selecting the optimal combination of production process parameters, namely draw ratio, D/Y and primary temperature, with the incorporation of limiting indexes derived from the raw material characteristics to predict tenacity and elongation.

This paper contributes by introducing a novel method for creating a dynamic guide. An intelligent and dynamic guide for tenacity and elongation in texturized yarn production was proposed, boasting an approximate accuracy rate of 80%. This developed guide is dynamic and seamlessly integrated with the production database. It undergoes regular updates every three months, incorporating the selected features of the process and raw materials, their respective thresholds, and the predicted levels of elongation and tenacity.

While there are many guides available on how to construct academic texts, the actual experience of writing can often remain unspoken. In this chapter, I share my personal experience of academic writing in entrepreneurship research, including the enabling and constraining aspects. My aim is to make academic writing more visible and encourage open discussion about this important activity. I approach academic writing as a comprehensive experience, encompassing various dimensions such as embodied, emotional and social aspects. I reflect on the influence of my body, physical conditions, emotions and writing habits on my writing process. Additionally, I consider the impact of external expectations and the context of working in entrepreneurship research, and how they shape my writing style. By examining these different dimensions of my writing experience, I hope to provide insight into the multifaceted nature of academic writing in entrepreneurship research.

While known by many names, qualifying exams function as gatekeepers to graduate student advancement to PhD candidacy, yet there has been little formal study on best qualifying exam practices particularly in biomedical and related STEM PhD programs. The purpose of this study is to examine the current state of qualifying exams through an examination of the literature and exploration of university-wide policies.

The authors conducted a literature review of studies on qualifying exams and completed an external evaluation of peer institutions’ and internal institutional qualifying exam requirements to inform our discussion of qualifying exams practices in PhD training at a research-intensive US institutions.

This study identified the need for more research on qualifying exams to establish evidence-based best practices. The authors found a wide variety of qualifying exam formats, with little evidence in support for specific formats. The authors also found little evidence that student expectations are made clear. The lack of evidence-based best practices coupled with insufficient clarity for students has a real potential to disadvantage PhD students, particularly first generation, underrepresented minority, international and/or other trainees who are not privileged or socialized to navigate training environments with vague landmarks such as the qualifying exams.

There are very few studies that evaluate qualifying exams in US doctoral education, particularly in STEM fields, and to the authors’ knowledge, there has been no analysis of campus-wide policies on qualifying exams reported. The lack of evidence for best practices and the need for to evaluate the implementation and effectiveness of qualifying exams are discussed.