Creating Responsive Information Systems with the Help of SSADM

In this paper, a program for a research is outlined. Firstly, the concept of responsive information systems is defined and then the notion of the capacity planning and software performance engineering is clarified. Secondly, the purpose of the proposed methodology of capacity planning, the interface to information systems analysis and development methodologies (SSADM), the advantage of knowledge-based approach is discussed. The interfaces to CASE tools more precisely to data dictionaries or repositories (IRDS) are examined in the context of a certain systems analysis and design methodology (e.g. SSADM)

Paterson\u27s Curse management handbook

Paterson’s curse (Echium plantagineum) is one of the most damaging weeds to the Australian meat and wool industries. It infests an estimated 33 million hectares in southern Australia, at an annual cost to the sheep industry of $250 million in lost pasture productivity, control costs and wool contamination. In WA, it is found on about 5000 agricultural properties infesting some 500,000 ha. It is an extremely invasive weed, that reduces pasture productivity and stock carrying capacity by competing with and excluding more beneficial pasture species. The weed is also toxic to stock. It is important to implement control measures on isolated patches of Paterson’s curse, to limit the spread of the weed into new areas.https://researchlibrary.agric.wa.gov.au/bulletins/1091/thumbnail.jp

Going SOLO to assess novice programmers

This paper explores the programming knowledge of novices using Biggs' SOLO taxonomy. It builds on previous work of Lister et al. (2006) and addresses some of the criticisms of that work. The research was conducted by studying the exam scripts for 120 introductory programming students, in which three specific questions were analyzed using the SOLO taxonomy. The study reports the following four findings: when the instruction to students used by Lister et al. - "In plain English, explain what the following segment of Java code does" - is replaced with a less ambiguous instruction, many students still provide multistructural responses; students are relatively consistent in the SOLO level of their answers; student responses on SOLO reading tasks correlate positively with performance on writing tasks; postgraduates students manifest a higher level of thinking than undergraduates. Copyright 2008 ACM

Reliably Classifying Novice Programmer Exam Responses using the SOLO Taxonomy

Abstract: Past papers of the BRACElet project have described an approach to teaching and assessing students where the students are presented with short pieces of code, and are instructed to explain, in plain English, what the code does. The student responses to these types of questions can be analysed according to the SOLO taxonomy. Some students display an understanding of the code as a single, functional whole, while other students cannot âsee the forest for the treesâ . However, classifying student responses into the taxonomy is not always straightforward. This paper analyses the reliability of the SOLO taxonomy as a means of categorising student responses. The paper derives an augmented set of SOLO categories for application to the programming domain, and proposes a set of guidelines for researchers to use

Conversion of electrical energy from one form to another, and its management through multichip module structures

The design and fabrication of a highly integrated, intelligent integral horsepower, three-phase induction motor drive based on multichip module (MCM) technology is described. A conventional three-phase induction motor is transformed into a stand-alone variable-speed drive by way of MCM technology. This solid-state controller-known as a multichip power module (MCPM)-uses known good die (KGD) to obtain minimal footprint, volume, and mass, while maximizing efficiency, reliability, and manufacturability. This is done by integrating the low-power control and high-power sections onto a single substrate. In accordance with one embodiment of the present invention, an integrated circuit assembly formed on a single substrate is capable of transforming and controlling AC power input to DC power output responsive to input signals. In accordance with another embodiment, an integrated circuit assembly on a single substrate is capable of receiving direct current power and controlling it and transforming it to alternating current power in single phase or multiphase form having variable magnitude and/or variable frequency. In accordance with a further embodiment, an integrated circuit assembly on a single substrate is capable of receiving alternating current power and controlling it and transforming it to alternating current power in single phase or multiphase form having variable magnitude and/or variable frequency

Federated Learning on Heterogenous Data using Chest CT

Large data have accelerated advances in AI. While it is well known that population differences from genetics, sex, race, diet, and various environmental factors contribute significantly to disease, AI studies in medicine have largely focused on locoregional patient cohorts with less diverse data sources. Such limitation stems from barriers to large-scale data share in medicine and ethical concerns over data privacy. Federated learning (FL) is one potential pathway for AI development that enables learning across hospitals without data share. In this study, we show the results of various FL strategies on one of the largest and most diverse COVID-19 chest CT datasets: 21 participating hospitals across five continents that comprise >10,000 patients with >1 million images. We present three techniques: Fed Averaging (FedAvg), Incremental Institutional Learning (IIL), and Cyclical Incremental Institutional Learning (CIIL). We also propose an FL strategy that leverages synthetically generated data to overcome class imbalances and data size disparities across centers. We show that FL can achieve comparable performance to Centralized Data Sharing (CDS) while maintaining high performance across sites with small, underrepresented data. We investigate the strengths and weaknesses for all technical approaches on this heterogeneous dataset including the robustness to non-Independent and identically distributed (non-IID) diversity of data. We also describe the sources of data heterogeneity such as age, sex, and site locations in the context of FL and show how even among the correctly labeled populations, disparities can arise due to these biases

Deep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT

The Coronavirus disease 2019 (COVID-19) presents open questions in how we clinically diagnose and assess disease course. Recently, chest computed tomography (CT) has shown utility for COVID-19 diagnosis. In this study, we developed Deep COVID DeteCT (DCD), a deep learning convolutional neural network (CNN) that uses the entire chest CT volume to automatically predict COVID-19 (COVID+) from non-COVID-19 (COVID−) pneumonia and normal controls. We discuss training strategies and differences in performance across 13 international institutions and 8 countries. The inclusion of non-China sites in training significantly improved classification performance with area under the curve (AUCs) and accuracies above 0.8 on most test sites. Furthermore, using available follow-up scans, we investigate methods to track patient disease course and predict prognosis

Eight years of computing education papers at NACCQ

The 157 computing education papers from the past eight NACCQ conferences are categorised and summarised by a group of researchers from multiple institutions, with steps taken to measure and improve the consistency of classification. The papers are set predominantly in programming subjects, hardware/architecture/systems/ network subjects, and capstone projects. The bulk of the papers are about teaching/learning techniques, assessment techniques, teaching/learning tools, curriculum, and educational technology. Most of the papers are set within single subjects, a few in multiple subjects within a single program or department, and fewer still in a range of subjects across the whole institution or multiple institutions. Nearly a quarter of the papers either expound a position or outline a proposal; a large but diminishing proportion report on something such as a change of curriculum or approach; and a large and increasing proportion are clearly research papers, focusing on the analysis of data to answer an explicit research question

Inequitable provision of optimal services for patients with chronic heart failure: A national geo-mapping study

Objective: To compare the location and accessibility of current Australian chronic heart failure (CHF) management programs and general practice services with the probable distribution of the population with CHF. Design and setting: Data on the prevalence and distribution of the CHF population throughout Australia, and the locations of CHF management programs and general practice services from 1 January 2004 to 31 December 2005 were analysed using geographic information systems (GIS) technology. Outcome measures: Distance of populations with CHF to CHF management programs and general practice services. Results: The highest prevalence of CHF (20.3–79.8 per 1000 population) occurred in areas with high concentrations of people over 65 years of age and in areas with higher proportions of Indigenous people. Five thousand CHF patients (8%) discharged from hospital in 2004–2005 were managed in one of the 62 identified CHF management programs. There were no CHF management programs in the Northern Territory or Tasmania. Only four CHF management programs were located outside major cities, with a total case load of 80 patients (0.7%). The mean distance from any Australian population centre to the nearest CHF management program was 332 km (median, 163 km; range, 0.15–3246 km). In rural areas, where the burden of CHF management falls upon general practitioners, the mean distance to general practice services was 37 km (median, 20 km; range, 0–656 km). Conclusion: There is an inequity in the provision of CHF management programs to rural Australians

Response of a barrier estuary to climate change and river regulation using attractor analysis: Snowy River estuary, Australia

The evolution of the entrance channel of the Snowy River estuary in response to river regulation and climate change is predicted. The predictions are made in terms of the physical attractors that define possible long-term states of the estuary entrance condition. The classification of these attractors shows the dependence of the entrance stability on the catchment inflows and the present entrance depth. The Snowy River estuary in south-eastern Australia is a barrier estuary with an unstable entrance that tends to closure. The classification from the attractor map shows that the estuary entrance has changed from predominantly stable to a predominantly unstable state attributable to diversion of water from the upper catchment. The introduction of a series of environmental flow regimes, commencing in 2002, has returned 8% rising to 21% of the mean annual natural flow, but this study shows that the releases provide limited improvement in entrance stability. Additionally, the predicted effects of climate change for this region include increased mean sea level (MSL), decreased annual rainfall, and increased incidence of storms. These changes will decrease stability, primarily through the rise in MSL. The rise in sea level will increase the plan area of the tidal basin, increasing the tidal prism, and hence drawing in more marine sand. The application to the Snowy River estuary provides a proof of concept of the attractor classification to support estuary management