Framework to Maintain Specialisations in a General Degrees Structure: An economical high-value degree structure

Structuring a degree is a common activity for course developers. Analyzing appropriate subjects and year levels, establishing pre- and co-requisite, and benchmarking against similar degrees are common academic activities. However, the degree structure itself has not had significant changes until now. A degree often lacks flexibility and cohesion and arguably may even lose the main concept of making students highly skilled in the selected labor market more employable. After examining different degree structures, approaches, and employability incentives, we identified a degree structure that can divide each subject into components. Subjects' learning activities, tutorials, and assessments are tailored to align more closely with employment skills. We then proposed breaking all subjects into components relative to year levels, such as majors, minors, streams, and more. This sub-division of work can be performed to any degree. Particular advantages come with a general degree with standard core units and majors—creating learning activities closer to the major and offering students a more robust academic scaffold of their subjects. In addition, higher Education providers benefit by having a cost-efficient degree with minimum overhead to pass the benefits onto students. We discussed several examples from engineering, business, and information technology. Showing how learning opportunities can be divided per degree and subjects into degrees, majors, streams, and specializations. Students studying this framework will have developed skills firmly built on each other, enabling specialization in employment careers and academically. Closing the gap between employment and graduation

Assessment Re-Think: Income-Generating and Industry-Based Assessments

Assessments are the fundamental media between students and educators. This paper aims to evaluate how to create assessments, how students learn from them, and how to link them to the industry and entrepreneurism. The implementation plan postulates how students can generate income from income-generation assessments or business innovation assessments. In this paper, we discuss the involvement of modern industry in assessment. We examine evidence from approximately 100 assessments detailed in 32 subject outlines. We employ a descriptive, pragmatic research methodology to consider whether they can be aligned more with industry expectations and expected duties. We propose a framework to connect with industry and create student income-generating projects. This proposed income-generating assessments framework recommended industry-based assessments with which students can not only earn marks towards a subject but potentially earn an income based on it. This paper extends the idea of peer learning to expert or industry learning: an approach that did not employ in higher education. Our approach supports educators in keeping the assessment up-to-date, enabling students to add more value to their learning of industry products and procedures. Students can directly contribute to the product and procedures and learn from the strategies actively employed in the workplace

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication

Racial Inclusion in Education: An Australian Context

Racism in various forms exists worldwide. In Australia, racism is inextricably linked to the history of Australian immigrants and early setters. Although the Australian education system has adopted inclusive education, evidence shows several incidents of racial exclusion. With the public education system experiencing an increased cultural diversity in student population, schools are required to develop inclusive education policies. While policies related to disability inclusion have been in practice for many years, only recently has there been an increasing awareness of racial inclusion. This research paper explores the importance of racial inclusion in education by examining the causes and effects of racial exclusion in the Australian education context. This paper considers existing practices at the national level and in schools to explore racial discrimination. It identifies the factors contributing towards racism and proposes a framework employing key strategies at the macro, meso and micro levels to achieve racial inclusion in education. It also suggests opportunities based on research to strengthen the response against racism

Designing and evaluating a big data analytics approach for predicting students’ success factors

Abstract Reducing student attrition in tertiary education plays a significant role in the core mission and financial well-being of an educational institution. The availability of big data source from the Learning Management System (LMS) can be analysed to help with the attrition issues. This study aims to use an integrated Design Science Research (DSR) methodology to develop and evaluate a novel Big Data Analytical Solution (BDAS) as an educational decision support artefact. The BDAS as DSR artefact utilises Artificial Intelligence (AI) approaches to predict potential students at risk. Identifying students at risk helps to take timely intervention in the learning process to improve student academic progress for increasing their retention rate. To evaluate the performance of the predictive model, we compare the accuracy of the collection of representational AI algorithms in the literature. The study utilized an integrated DSR methodology founded on the similarities of DSR and design based research (DBR) to design and develop the proposed BDAS employing an specific evaluation framework that works on real data scenarios. The BDAS does not only aimto replace any existing practice but also support educators to implement a variety of pedagogical practices for improving students’ academic performance

Trusted time-based verification model for automatic man-in-the-middle attack detection in cybersecurity

Due to the prevalence and constantly increasing risk of cyber-attacks, new and evolving security mechanisms are required to protect information and networks and ensure the basic security principles of confidentiality, integrity, and availability&mdash;referred to as the CIA triad. While confidentiality and integrity can be achieved using Secure Sockets Layer (SSL)/Transport Layer Security (TLS) certificates, these depend on the correct authentication of servers, which could be compromised due to man-in-the-middle (MITM) attacks. Many existing solutions have practical limitations due to their operational complexity, deployment costs, as well as adversaries. We propose a novel scheme to detect MITM attacks with minimal intervention and workload to the network and systems. Our proposed model applies a novel inferencing scheme for detecting true anomalies in transmission time at a trusted time server (TTS) using time-based verification of sent and received messages. The key contribution of this paper is the ability to automatically detect MITM attacks with trusted verification of the transmission time using a learning-based inferencing algorithm. When used in conjunction with existing systems, such as intrusion detection systems (IDS), which require comprehensive configuration and network resource costs, it can provide a robust solution that addresses these practical limitations while saving costs by providing assurance

An Enhanced Inference Algorithm for Data Sampling Efficiency and Accuracy Using Periodic Beacons and Optimization

Transferring data from a sensor or monitoring device in electronic health, vehicular informatics, or Internet of Things (IoT) networks has had the enduring challenge of improving data accuracy with relative efficiency. Previous works have proposed the use of an inference system at the sensor device to minimize the data transfer frequency as well as the size of data to save network usage and battery resources. This has been implemented using various algorithms in sampling and inference, with a tradeoff between accuracy and efficiency. This paper proposes to enhance the accuracy without compromising efficiency by introducing new algorithms in sampling through a hybrid inference method. The experimental results show that accuracy can be significantly improved, whilst the efficiency is not diminished. These algorithms will contribute to saving operation and maintenance costs in data sampling, where resources of computational and battery are constrained and limited, such as in wireless personal area networks emerged with IoT networks

Hybrid Routing for Man-in-the-Middle (MITM) Attack Detection in IoT Networks

Affordable and expandable low power networks such as 5G and Low Power Wide Area Networks (LPWAN) in the public and private network areas have improved network bandwidth capacities and processing performance. Internet of Things (IoT) technologies are increasing in popularity with numerous applications and devices being developed for smart environments and health-related applications. This raises security concerns in these networks, as many IoT devices handle confidential information such as IP/MAC addresses, which could be used to identify a user\u27s location. As a result, there is vulnerability to data tampering by man-in-the-middle (MITM) attacks, which feature two observable characteristics: (1) there is a measurable delay in the session and (2) has unusual travel times compared to prior normal transactions. To improve the detection of these attacks, this paper proposes a novel scheme using a hybrid routing mechanism, which involves appointing dedicated nodes for enforcing routing between IoT devices and users with minimal intervention and workload to the network. The function of dedicated devices with more computational and battery power can provide three advantages: (1) determine secured paths within the network by avoiding suspicious nodes and networks, (2) provide stable travel times (less fluctuations) for a trusted time server (TTS) to improve the accuracy of estimated travel times, and (3) provide packet inspection for security checks. This proposed solution contributes towards increasing the security of IoT networks by enabling the real-time detection of intruders