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This chapter examines the teaching practice of the author in the Faculty of Education, University of Malta, taking sessions in smart learning as part of technology-enhanced learning (TEL) study units in Bachelors of Education and Masters in Teaching and Learning degree programs between 2017 and 2019. My teaching sessions ran concurrent with undertaking separate doctoral research investigating how participants experience “smart learning journeys.” Smart learning journeys in the research were conceptualized as real-world journeys, with geo-spatially relevant points of interest forming a journey of locations related to a topic of learning, providing context-aware content via digital interactions. Research was not connected to teaching practice, though students who took TEL units also participated in the same smart learning journey activity as part of their syllabus.

Though teaching sessions were not part of my research, my classroom practice modified as a result of emerging research findings, and my teaching benefited as I gained deeper understanding about smart learning activities and the role of the learner in them. Using dialogic learning methods and techniques inspired from my research interview methodology, class sessions became noticeably more effective as students engaged directly in discovering their own learning from having participated in the smart learning journey.

The purpose of this work is to investigate the performance of non‐contaminating metal cutting environments and investigate the associated tool chip interface conditions. The work benchmarks flood coolant characteristics and considers gaseous cutting environments as possible alternatives.

Cutting trials were undertaken for a range of cutting environments. Flood coolant was investigated as was dry cutting, compressed air, room temperature nitrogen and liquid nitrogen environments. A range of cutting variables was measured in order to document the effect of cutting environment.

The gaseous component of the liquid nitrogen environment limited the adhesion on the tool face to a region along the flank edge of the tool, shifting rake face conditions from seizure to that of sliding. Tighter chip curl, shorter contact lengths, reduced adhesion and lower feed forces are evidence that liquid nitrogen is acting as a “liquid inert barrier” beneath the chip within the tool/chip interface.

Only one tool work combination has been investigated. More tool work combinations will need to be investigated.

The work demonstrated that it is possible to use environmentally safe environments during metal cutting operations. This reduces the exposure of the environment and machine tool operatives to compounds which have been shown to have detrimental effects on the environment and human health.

The work has led to presenting a hypothesis that liquid nitrogen acts as a “liquid inert barrier” beneath the chip within the tool/chip interface.

Purpose – This chapter examines the process of radicalization, deradicalization, and support for intelligence agencies in a few well-known cases of terrorists who turned into informants.

Methodology/Approach – Five cases studies are utilized to demonstrate the process of engagement in, disengagement from, and revolt against terrorist groups. Existing literature on radicalization and deradicalization is set against the context of these case studies.

Findings – By drawing upon the experiences of terrorists who turned into informants, it is possible to prove theories on radicalization and deradicalization. In particular, the process of cognitive radicalization presumes that extremist beliefs can also be rejected (deradicalization), while the process of behavioral radicalization presumes that terrorists can distance themselves from extremist behaviors (disengagement).

Originality/Value – Scholarship has traditionally focused on “underdogs” of all kinds, with a less keen interest in elites or the actors operating on their behalf. The work of informants has often remained in a dimly lit corner of academic research. This chapter helps illuminate the path undertaken by terrorists who become informants for Western security apparatus.

New environmental legislation is forcing companies to realign their use of metalworking fluids in favour of non‐polluting cutting environments that will return acceptable tool wear rates and reduced costs. Studies have been undertaken to determine the effectiveness of various environments on tool wear, in order to either reduce or even eliminate totally, the dependency on flood coolants. Industrially reproducible cutting tests were devised, where an EN32 case hardening steel material was turned in a range of different cutting environments and tool life measured. Low oxygen gaseous environments were compared with conventional cutting environments and a 55 per cent flank wear reduction has been recorded using uncoated tooling.

Cutting fluids despite playing an important role in metal cutting have considerable environmental impact. Inert gaseous metal cutting environments were investigated with the aim of removing soluble oil cutting fluids from metal cutting operations.Design/methodology/approach – Industrially reproducible cutting tests were devised, where an austenitic stainless steel and En32 low carbon steel material was milled in a range of different cutting environments. Tool life was measured for tests carried out in a number of gaseous environments and results were then compared with test results from conventional flood cutting environments.Findings – Low oxygen gaseous environments were compared with conventional cutting environments and a considerable flank wear reduction has been recorded using CVD coated tooling. Additionally flood coolant environments have been seen to promote chemical wear after the initial breakdown of coatings leading to rapid flank wear during milling of both En32 and austenitic stainless steel.Research limitations/implications – Only a limited number of work/tool material combinations have been investigated. A more detailed and exhaustive investigation is required to ascertain the scope of the improvements for a range of tool work combinations. This will assist in understanding the underlying reasoning for the tool life enhancement reported.Practical implications – All experimentation carried out is industrially reproducible. This work, therefore, proposes an environmentally clean alternative to the use of emulsified oils in metal cutting operations in order to exploit cost savings and improved operator working environments.Originality/value – Distinct operational performance improvements have been demonstrated in the form of extended tool life for metal cutting operations performed in a non‐polluting cutting environment. These findings could herald widespread advantages within the metal cutting community.

As more stringent environmental legislation is enforced throughout Europe manufacturing businesses, employing metal cutting processes, can no longer ignore the growing importance of environmental aspects relating to cutting fluids. Businesses, through market forces, are being forced into offering a “clean solution” to the metal cutting processes which they operate. Cutting fluids despite playing an important role in metal cutting, have considerable environmental impact. There is a need therefore to understand the role of cutting fluids within the cutting process in order to evaluate possible environmentally friendly alternatives to the use of cutting fluids. In order to achieve this the operating environment in which the process is being carried out, and the consequences of removing the cutting fluid from the process altogether has to be assessed. This paper therefore, reflects on the role of cutting fluid and the implications of their use. Viable methods of reducing cutting fluid consumption are also reported, together with efficient methods of cutting fluid utilisation (e.g. minimum quantity delivery systems). Finally, the difficulties experienced in removing cutting fluids from the metal cutting process are highlighted through the consideration of dry cutting technologies.

The use of some Class B fuels in small‐bore high‐speed engines is accompanied by rapid wear of the piston, piston rings, and cylinder liner. This wear has been attributed in the past to corrosive properties of the sulphur contained in the fuel, but investigations at the laboratory of the British Internal Combustion Engine Research Association, at Slough, have indicated that the abrasive property of the carbonaceous deposits from the burnt fuel is a major factor in the problem.

The purpose of this paper is to examine Toshiba’s fraudulent financial reporting in relation to the fraud diamond (pressure, opportunity, rationalisation and capability).

A quantitative empirical research, analysing secondary data from Toshiba’s published annual reports before restatement, from 2008–2014 has been used. A simultaneous equations approach was used to test the hypothesis. Excel software was used to analyse secondary data and to carry out correlation analysis and descriptive statistics analysis.

This study uncovers evidence that pressure proxied by return on assets (ROA), the opportunity proxied by ineffective monitoring (BDOUT), rationalisation proxied by audit opinion (AO) and capability proxied by board member changes (BCHANGE) had moderate to strong relationship to financial statement fraud (FSF) (proxied by Beneish M-score model). However, ROA has a negative and significant effect on Toshiba’s FSF. BDOUT, AO and BCHANGE have positive and significant effect on Toshiba’s FSF. Furthermore, there is no multicollinearity problem within the four variables. Overall, this study has statistically proven that all dimensions of fraud diamond are required for the explanation of Toshiba’s accounting scandal.

Although a few studies discuss the four dimensions (fraud diamond), none, to our surprise, exists which explain the circumstances led Toshiba’s high-level executives to commit fraud. This study is the first thorough investigation of Toshiba’s accounting scandal that uses all four dimensions to explain Toshiba’s FSF.

This paper aims to investigate the efficiency and effectiveness of alternative credit‐scoring models for consumer loans in the banking sector. In particular, the focus is upon the financial risks associated with both the efficiency of alternative models in terms of correct classification rates, and their effectiveness in terms of misclassification costs (MCs).

A data set of 630 loan applicants was provided by an Egyptian private bank. A two‐thirds training sample was selected for building the proposed models, leaving a one‐third testing sample to evaluate the predictive ability of the models. In this paper, an investigation is conducted into both neural nets (NNs), such as probabilistic and multi‐layer feed‐forward neural nets, and conventional techniques, such as the weight of evidence measure, discriminant analysis and logistic regression.

The results revealed that a best net search, which selected a multi‐layer feed‐forward net with five nodes, generated both the most efficient classification rate and the most effective MC. In general, NNs gave better average correct classification rates and lower MCs than traditional techniques.

By reducing the financial risks associated with loan defaults, banks can achieve a more effective management of such a crucial component of their operations, namely, the provision of consumer loans.

The use of NNs and conventional techniques in evaluating consumer loans within the Egyptian private banking sector utilizes rigorous techniques in an environment which merits investigation.

This paper aims to distinguish whether the decision-making process of the Islamic financial houses in the UK can be improved through the use of credit scoring modeling techniques as opposed to the currently used judgmental approaches. Subsidiary aims are to identify how scoring models can reclassify accepted applicants who later are considered as having bad credit and how many of the rejected applicants are later considered as having good credit, and highlight significant variables that are crucial in terms of accepting and rejecting applicants, which can further aid the decision-making process.

A real data set of 487 applicants is used consisting of 336 accepted credit applications and 151 rejected credit applications made to an Islamic finance house in the UK. To build the proposed scoring models, the data set is divided into training and hold-out subsets. The training subset is used to build the scoring models, and the hold-out subset is used to test the predictive capabilities of the scoring models. Seventy per cent of the overall applicants will be used for the training subset, and 30 per cent will be used for the testing subset. Three statistical modeling techniques, namely, discriminant analysis, logistic regression (LR) and multilayer perceptron (MP) neural network, are used to build the proposed scoring models.

The findings reveal that the LR model has the highest correct classification (CC) rate in the training subset, whereas MP outperforms other techniques and has the highest CC rate in the hold-out subset. MP also outperforms other techniques in terms of predicting the rejected credit applications and has the lowest misclassification cost above other techniques. In addition, results from MP models show that monthly expenses, age and marital status are identified as the key factors affecting the decision-making process.

This contribution is the first to apply credit scoring modeling techniques in Islamic finance. Also in building a scoring model, the authors' application applies a different approach by using accepted and rejected credit applications instead of good and bad credit histories. This identifies opportunity costs of misclassifying credit applications as rejected.