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For several decades, universities have been trying to implement new technologies in their teaching methods, intending to create skills for the twenty-first century. In the literature, this process is called digital transformation. This chapter is based on an integrative revision and solid work of the authors in their university, providing students with technological devices, such as laptops, tablets, and other gadgets to invest in digital education skills. Concluding that investing in digital education is crucial for improving the student experience and preparing students for the future workforce.

Social bookmarking is an online tool that can enable students to develop their skills in finding, sharing and (re)organising online information. Research has demonstrated that it has the potential to impact positively on students’ digital literacies – their ability to use the Internet critically to support their learning – and particularly on the kinds of online research skills that are vital to supporting inquiry-based approaches to learning and teaching in history. This chapter provides a detailed overview of how online social bookmarking tools have been used to support the development of students’ digital literacies in history in a number of UK higher education institutions. The general approach which has been adopted is based on constructivist principles and requires students to develop their skills and appreciation of the Internet as a venue for scholarly research in order to strengthen their inquiry skills in preparation for more independent work at higher levels of study. The chapter presents evaluative data that has been collected from students who have used social bookmarking to support inquiry activities within modules and as part of their independent learning activities. We also report staff reflections on the usefulness of social bookmarking to support student learning in history and make some recommendations for the practical application of such tools elsewhere. These include the potential significant impact of social bookmarking on students’ ability to interact productively and creatively with online resources in the course of their learning; the usefulness of the tool in supporting collaborative working and sharing materials; the need to ensure that students receive adequate training in using social bookmarking and that their work receives adequate credit (which will, in turn, increase motivation).

L. Dee Fink proposes that different and more significant kinds of learning should be created in higher education to transition student outcomes from simply “learning” to “significant learning,” and these new types of learning should be situated within significant learning experiences (Fink, 2003). Fink also identified a taxonomy of significant learning that included six components: integration, foundational knowledge, application, human dimension, caring, and learning how to learn. Using Fink’s Taxonomy of Significant Learning as a framework, the authors will share the development of a course on navigating the US Healthcare System that resulted in significant learning outcomes for students completing the first semester of a four-year Doctor of Pharmacy curriculum. Each learning experience will link to a component of the taxonomy and will serve as the mechanism for the authors to share the development and implementation associated with each aspect of the semester-long course. The assessment structure of the course is described in detail. The authors present one or more learning experiences to illustrate each component of Fink’s Taxonomy. Finally, lessons learned from the development and implementation of the course are presented to guide programs considering implementation of a similar significant learning experience.

Self-harm is a cause of concern for health-care professionals. The Short-Term Assessment of Risk and Treatability (START) is a short-term assessment instrument used to rate the likelihood of risk behaviours, including self-harm. As result of the assessment, interventions that are implemented to reduce the risk of self-harm may reduce the strength of the predictive validity of a risk assessment tool. The aim of this study was explore the impact of risk management interventions on the capacity of START to predict self-harm. It was predicted that the interventions would weaken the ability of START to predict self-harm in patients who received the intervention.

Secondary analysis of routinely collected data in a large sample of women in an inpatient secure care setting. Demographic and clinical information, self-harm episodes, safety management interventions and START assessments were extracted and used to build an anonymous database.

START significantly predicted self-harm in those with and without the safety management intervention. However, the strength of the predictive validity was smaller in those who received the intervention compared to those without.

The results suggest that the implementation of safety management interventions needs to be taken into account when assessing future risk of self-harm.

To the best of the authors’ knowledge, this is the first study to explore the impact of safety management interventions on the predictive validity of START in a large sample of women.

A major difficulty identified many years ago in psychiatric care is the shortage of appropriate instruments with which to carry out valid and reliable therapeutic assessments which are behaviourally based and therefore appropriate for use in a variety of contexts. The aim of this project was to ascertain the utility of a forensic nursing risk assessment tool - Behavioural Status Index (BEST-Index). The paper aims to discuss these issues.

A multi-site cross-sectional survey was undertaken using mixed method design. Quantitative data was generated using BEST-Index to allow comparisons across three different levels of security (high, medium and low) in Scotland and Ireland. Qualitative data were gathered from patients and multi-disciplinary team (MDT) members using semi-structured interviews and questionnaire.

Measured over an 18-month period, there was a statistically significant improvement in behaviour, when comparing patients in high and medium secure hospitals. Two key themes emerged from patient and staff perspectives: “acceptance of the process” and “production and delivery of information”, respectively. The wider MDT acknowledge the value of nursing risk assessment, but require adequate information to enable them to interpret findings. Collaborating with patients to undertake risk assessments can enhance future care planning.

Studies using cross-section can only provide information at fixed points in time.

The BEST-Index assessment tool is well established in clinical practice and has demonstrated good utility.

This project has served to highlight the unique contribution of BEST-Index to both staff and patients alike and confirm its robustness and versatility across differing levels of security in Scottish and Irish forensic mental health services.

On 8 October 1992 the ISHM‐Benelux Chapter organised a one‐day conference entitled ‘New Trends in Electronic Packaging and Interconnection’ which took place in the Holiday Inn, Gent, Belgium. This conference was attended by 45 participants from the Benelux countries and Great Britain.

Denial-of-service (DoS) attacks develop unauthorized entry to various network services and user information by building traffic that creates multiple requests simultaneously making the system unavailable to users. Protection of internet services requires effective DoS attack detection to keep an eye on traffic passing across protected networks, freeing the protected internet servers from surveillance threats and ensuring they can focus on offering high-quality services with the fewest response times possible.

This paper aims to develop a hybrid optimization-based deep learning model to precisely detect DoS attacks.

The designed Aquila deer hunting optimization-enabled deep belief network technique achieved improved performance with an accuracy of 92.8%, a true positive rate of 92.8% and a true negative rate of 93.6.

The introduced detection approach effectively detects DoS attacks available on the internet.

Phishing is a serious cybersecurity problem, which is widely available through multimedia, such as e-mail and Short Messaging Service (SMS) to collect the personal information of the individual. However, the rapid growth of the unsolicited and unwanted information needs to be addressed, raising the necessity of the technology to develop any effective anti-phishing methods.

The primary intention of this research is to design and develop an approach for preventing phishing by proposing an optimization algorithm. The proposed approach involves four steps, namely preprocessing, feature extraction, feature selection and classification, for dealing with phishing e-mails. Initially, the input data set is subjected to the preprocessing, which removes stop words and stemming in the data and the preprocessed output is given to the feature extraction process. By extracting keyword frequency from the preprocessed, the important words are selected as the features. Then, the feature selection process is carried out using the Bhattacharya distance such that only the significant features that can aid the classification are selected. Using the selected features, the classification is done using the deep belief network (DBN) that is trained using the proposed fractional-earthworm optimization algorithm (EWA). The proposed fractional-EWA is designed by the integration of EWA and fractional calculus to determine the weights in the DBN optimally.

The accuracy of the methods, naive Bayes (NB), DBN, neural network (NN), EWA-DBN and fractional EWA-DBN is 0.5333, 0.5455, 0.5556, 0.5714 and 0.8571, respectively. The sensitivity of the methods, NB, DBN, NN, EWA-DBN and fractional EWA-DBN is 0.4558, 0.5631, 0.7035, 0.7045 and 0.8182, respectively. Likewise, the specificity of the methods, NB, DBN, NN, EWA-DBN and fractional EWA-DBN is 0.5052, 0.5631, 0.7028, 0.7040 and 0.8800, respectively. It is clear from the comparative table that the proposed method acquired the maximal accuracy, sensitivity and specificity compared with the existing methods.

The e-mail phishing detection is performed in this paper using the optimization-based deep learning networks. The e-mails include a number of unwanted messages that are to be detected in order to avoid the storage issues. The importance of the method is that the inclusion of the historical data in the detection process enhances the accuracy of detection.