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Classification schemes make things happen. The Australian Disability Discrimination Act (DDA), which derives its classification system from the World Health Organization's International Classification of Functioning, Disability and Health (ICF), legislates for adjustments to support the inclusion of people with disability. This study explores how students with disability enrolled in a university experience the systems intended to facilitate their studying “on the same basis” as students without disability.

Through an online questionnaire and interviews comprising open and closed questions made available to students registered with the disability services unit of a university and follow-up interviews with a small number of students, students’ views of their own disability and effects on their participation in learning were gathered, alongside reports of their experiences of seeking support in their learning. Interview data and responses to open-ended questions were analysed using a priori and emergent coding.

The findings demonstrate that students are aware of the workings of the classification scheme and that most accept them. However, some students put themselves outside of the scheme, often as a way to exercise autonomy or to assert their “ability”, while others are excluded from it by the decisions of academic staff. Thus, the principles of fairness and equity enshrined in legislation and policy are weakened.

Through the voices of students with disability, it is apparent that, even though a student's classification according to the DDA and associated university policy remains constant, the outcomes of the workings of the scheme may reveal inconsistencies, emerging from the complexity of bureaucracy, processes and the exercises of power.

The purpose of this study is to develop a model for automated classification of old digitised texts to the Universal Decimal Classification (UDC), using machine-learning methods.

The general research approach is inherent to design science research, in which the problem of UDC assignment of the old, digitised texts is addressed by developing a machine-learning classification model. A corpus of 70,000 scholarly texts, fully bibliographically processed by librarians, was used to train and test the model, which was used for classification of old texts on a corpus of 200,000 items. Human experts evaluated the performance of the model.

Results suggest that machine-learning models can correctly assign the UDC at some level for almost any scholarly text. Furthermore, the model can be recommended for the UDC assignment of older texts. Ten librarians corroborated this on 150 randomly selected texts.

The main limitations of this study were unavailability of labelled older texts and the limited availability of librarians.

The classification model can provide a recommendation to the librarians during their classification work; furthermore, it can be implemented as an add-on to full-text search in the library databases.

The proposed methodology supports librarians by recommending UDC classifiers, thus saving time in their daily work. By automatically classifying older texts, digital libraries can provide a better user experience by enabling structured searches. These contribute to making knowledge more widely available and useable.

These findings contribute to the field of automated classification of bibliographical information with the usage of full texts, especially in cases in which the texts are old, unstructured and in which archaic language and vocabulary are used.

In this paper, we examine the comparative advantage of Korea and China while focusing on their technology level. The three digit SITC (Standard International Trade Classification) data is classified by technology level and the revealed comparative advantage (RCA) is derived from 1992-2009 by using UN COMTRADE data. For careful interpretation of the comparative advantage and technology levels, we also examined intra-industry trade and unit values of bilateral Korea-China trade, and semi-conductor industry technology. We found that the revealed comparative advantage has moved from low technology products to high technology products in Korea. China still maintains a comparative advantage in low technology products such as textiles and clothing, but at the same time, China’s high and medium-high technology products have recently gained a comparative advantage. The perception that China only has a comparative advantage for labor intensive products with low technology should be changed based on our analysis. However, China’s advancement in technology should not be overestimated. When comparing the unit value of basic materials of Korea’s and China’s exports, we found that Korea’s export product prices are on average higher than that of China’s, although the gap is reducing. A wider technology gap between Korea and China still exists in the semi-conductor industry, which is one of the most advanced high technology industries throughout the world.

Machine Learning is an intelligent methodology used for prediction and has shown promising results in predictive classifications. One of the critical areas in which machine learning can save lives is diabetes prediction. Diabetes is a chronic disease and one of the 10 causes of death worldwide. It is expected that the total number of diabetes will be 700 million in 2045; a 51.18% increase compared to 2019. These are alarming figures, and therefore, it becomes an emergency to provide an accurate diabetes prediction.

Health professionals and stakeholders are striving for classification models to support prognosis of diabetes and formulate strategies for prevention. The authors conduct literature review of machine models and propose an intelligent framework for diabetes prediction.

The authors provide critical analysis of machine learning models, propose and evaluate an intelligent machine learning-based architecture for diabetes prediction. The authors implement and evaluate the decision tree (DT)-based random forest (RF) and support vector machine (SVM) learning models for diabetes prediction as the mostly used approaches in the literature using our framework.

This paper provides novel intelligent diabetes mellitus prediction framework (IDMPF) using machine learning. The framework is the result of a critical examination of prediction models in the literature and their application to diabetes. The authors identify the training methodologies, models evaluation strategies, the challenges in diabetes prediction and propose solutions within the framework. The research results can be used by health professionals, stakeholders, students and researchers working in the diabetes prediction area.

Previous dynamic prediction models rarely handle multi-period data with different intervals, and the large-scale patient hospital records are not effectively used to improve the prediction performance. This paper aims to focus on the prediction of cardiovascular disease using the improved long short-term memory (LSTM) model.

A new model based on the traditional LSTM was proposed to predict cardiovascular disease. The irregular time interval is smoothed to obtain the time parameter vector, and it is used as the input of the forgetting gate of LSTM to overcome the prediction obstacle caused by the irregular time interval.

The experimental results show that the dynamic prediction model proposed in this paper obtained a significant better classification performance compared with the traditional LSTM model.

In this paper, the authors improved the LSTM by smoothing the irregular time between different medical stages of the patient to obtain the temporal feature vector.

Economies of scale drive container ship owners towards ordering larger vessels. Terminals need to ensure a safe (un)loading operation of these vessels, which can only be guaranteed if the mooring equipment is not overloaded (lines, fenders and bollards) and if the motions of the vessel remain below set limits, under external forces. This paper aims to focus on the passing vessel effect as a potential disturbing factor in the Port of Antwerp.

Motion criteria for allowing safe (un)loading of container vessels are established by considering the container handling process and existing international standards (PIANC). A case study simulation is presented where the behaviour of the moored vessel under ship passages is evaluated. Starting from a representative event, the effect of changes in passing speed and distance is discussed.

The study illustrates the influence of passing velocity and distance on the behaviour of the moored vessel, showing that when passing speeds are higher and/or distances lower than the reference event, safety limits are potentially exceeded. Possible mitigating measures, including the use of stiffer mooring lines and/or a change in arrangement, are discussed.

This paper serves as a basis for future research on safety criteria and optimisation of the mooring equipment and configuration to deal with passing vessel effects.

The presented results can be used by ship and terminal designers to gain familiarity with passing vessel effects and adopt suggested best practice.

By restricting the motions of the passing vessels, the focus and general well-being of the crane operator is enhanced, as is the safety of workers.

The paper provides a unique combination of container fleet observation, safety criteria establishment and case study application.