Ionic liquids via reaction of the zwitterionic 1,3-dimethylimidazolium-2-carboxylate with protic acids. Overcoming synthetic limitations and establishing new halide free protocols for the formation of ILs

The previously reported preparation of 1,3-dimethylimidazolium salts by the reaction of 1,3-dialkylimidazolium-2-carboxylate zwitterions with protic acids has been reinvestigated in detail, leading to the identification of two competing reactions: isomerisation and decarboxylation. The ability to control both pathways allows this methodology to be used as an effective, green, waste-free approach to readily prepare a wide range of ionic liquids in high yields. Additionally, this reaction protocol opens new possibilities in the formation of other imidazolium salts, whose syntheses were previously either very expensive (due to ion exchange protocols involving metals like Ag) or difficult to achieve (due to multiple extractions and large quantities of hard to remove inorganic by-products).

Classifying Network Protocols: A ‘2 Way’ Flow Approach

The identification and classification of network traffic and protocols is a vital step in many quality of service and security systems. Traffic classification strategies must evolve, alongside the protocols utilising the Internet, to overcome the use of ephemeral or masquerading port numbers and transport layer encryption. This research expands the concept of using machine learning on the initial statistics of flow of packets to determine its underlying protocol. Recognising the need for efficient training/retraining of a classifier and the requirement for fast classification, the authors investigate a new application of k-means clustering referred to as 'two-way' classification. The 'two-way' classification uniquely analyses a bidirectional flow as two unidirectional flows and is shown, through experiments on real network traffic, to improve classification accuracy by as much as 18% when measured against similar proposals. It achieves this accuracy while generating fewer clusters, that is, fewer comparisons are needed to classify a flow. A 'two-way' classification offers a new way to improve accuracy and efficiency of machine learning statistical classifiers while still maintaining the fast training times associated with the k-means.

Wholeness of healing: an innovative Student-Selected Component introducing United Kingdom medical students to the spiritual dimension in healthcare.

Objective: This Student Selected Component (SSC) was designed to equip United Kingdom (UK) medical students to engage in whole-person care. The aim was to explore students' reactions to experiences provided, and consider potential benefits for future clinical practice.

Methods: The SSC was delivered in the workplace. Active learning was encouraged through facilitated discussion with and observation of clinicians, the palliative team, counselling services, hospital chaplaincy and healing ministries; sharing of medical histories by patients; and training in therapeutic communication. Assessment involved reflective journals, literature appraisal, and role-play simulation of the doctor-patient consultation. Module impact was evaluated by analysis of student coursework and a questionnaire.

Results: Students agreed that the content was stimulating, relevant, and enjoyable and that learning outcomes were achieved. They reported greater awareness of the benefit of clinicians engaging in care of the "whole person" rather than "the disease." Contributions of other professions to the healing process were acknowledged, and students felt better equipped for discussion of spiritual issues with patients. Many identified examples of activities which could be incorporated into core teaching to benefit all medical students.

Conclusion: The SSC provided relevant active learning opportunities for medical students to receive training in a whole-person approach to patient care.