University of Salford

About: University of Salford is a education organization based out in Salford, Manchester, United Kingdom. It is known for research contribution in the topics: Population & Thin film. The organization has 13049 authors who have published 22957 publications receiving 537330 citations. The organization is also known as: University of Salford Manchester & The University of Salford Manchester.

Enhancing Students' Employability through Business Simulation.

Abstract: Purpose – The purpose of this paper is to introduce an approach to business simulation with less dependence on business simulation software to provide innovative work experience within a programme of study, to boost students’ confidence and employability.Design/methodology/approach – The paper is based on analysis of existing business simulation literature, which is synthesised with contemporary pedagogic trends and the outputs of the authors’ longitudinal research on improving the effectiveness of business simulation as a teaching method.Findings – The use of business simulation as a pedagogic tool can be considerably extended beyond built‐in functionality to match the needs of various business‐related disciplines. Learning from their own mistakes enabled students to appreciate the gap between theory and its application.Research limitations/implications – Business simulation can provide an innovative provision of work experience for students, if its design utilises continuous formative feedback and refle...

BIM adoption within Australian small and medium-sized enterprises (SMEs) : an innovation diffusion model

Abstract: Despite the envisaged benefits of BIM adoption for SMEs, BIM in SMEs has remained an underrepresented area within the available academic literature. This study proposes and draws upon a framework grounded on innovation diffusion theory (IDT) to provide an illuminating insight into the current state of BIM and the main barriers to BIM adoption within Australian SMEs. Based on analyses of 135 questionnaires completed by SMEs through partial least squares structural equation modelling (PLS-SEM) and grounded on the proposed framework, the current state of BIM adoption and barriers to BIM adoption for SMEs are discussed. The findings show that currently around 42% of Australian SMEs use BIM in Level 1 and Level 2 with only around 5% have tried Level 3. It comes to light that lack of knowledge within SMEs and across the construction supply chain is not a major barrier for Australian SMEs. In essence, the main barriers stem from the risks associated with an uncertain return on investment (ROI) for BIM as perceived by key players in SMEs. The findings also show the validity of the framework proposed for explaining BIM adoption in Australian SMEs.

An Evaluation of Suture Materials Used in Tendon Surgery

Abstract: The following suture materials have been evaluated for their suitability for use in flexor tendon repairs: 4/0 gauge monofilament and multifilament stainless steel, mono-filament nylon, monofilament polypropylene, monofilament polybutestor, braided polyester, braided polyglycolic acid and a monofilament polyglyconate. These were investigated for their tensile strength (both knotted and unknotted), their extension to failure and knot-holding properties. Stainless steel and monofilament polyglyconate appeared to be the most suitable in that they had high tensile strengths, both knotted and unknotted, and had good knot-holding security. The only disadvantages are that stainless steel is difficult to use and monofilament polyglyconate is absorbable. Polypropylene and braided polyester, although having lower tensile strengths, are reasonable alternatives.

Leveraging machine learning techniques for Windows ransomware network traffic detection

Abstract: Ransomware has become a significant global threat with the ransomware-as-a-service model enabling easy availability and deployment, and the potential for high revenues creating a viable criminal business model. Individuals, private companies or public service providers e.g. healthcare or utilities companies can all become victims of ransomware attacks and consequently suffer severe disruption and financial loss. Although machine learning algorithms are already being used to detect ransomware, variants are being developed to specifically evade detection when using dynamic machine learning techniques. In this paper we introduce NetConverse, a machine learning evaluation study for consistent detection of Windows ransomware network traffic. Using a dataset created from conversation-based network traffic features we achieved a True Positive Rate (TPR) of 97.1% using the Decision Tree (J48) classifier.