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The identification of malaria infection using microscope images of blood smears is considered as a 'gold standard'. The diagnosis of malaria needs expert microscopists which are scarce in remote areas where malaria is endemic. Therefore, it is desirable to automate the repetitive task of pathogen detection in the blood samples received as microscope images. This study provides an easy to use and deploy method for implementing a malaria pathogen detection software- the Intelligent Suite. The Intelligent Suite features a graphical user interface (GUI) implemented using 'cvui' library to interact with the OpenVINO's inference engine for model optimisation and deployment across several inference devices. The intelligent Suite uses a custom YOLO-mp-3l model trained on Darknet framework for detection of malaria pathogen in thick smear microscope images. Moreover, the Intelligent Suite provides user interface for inference device/mode selection, alter model parameters, and generate detection reports along with the model performance metrics. The Intelligent Suite was executed on a CPU computer with model inference running on a plug-and-play Neural Compute Stick (NCS2) and performance reported. [ABSTRACT FROM AUTHOR]

Australia, like many other countries around the world, is undergoing a transition toward net zero emissions. It requires changes and development in many sectors, which not only bring benefits but also challenges. The rapid growth in renewable energy sources (RESs) is necessary to decarbonise electricity generation but negatively affects grid stability. Residential buildings also contribute to this issue through specific load profiles and the high penetration of rooftop photovoltaic (PV) installations. Maintaining grid balance will be crucial for further emissions reductions. One of the potential solutions can be the replacement of conventional heating and cooling systems in houses with solutions capable of storing energy and shifting the electrical load. As presented in this paper, heat pumps and hydronic systems can significantly improve the electrical load of a typical South Australian household when they are controlled by algorithms reacting to the current grid conditions and household-generated electricity compared to conventional solutions. TRNSYS 18 simulations of air source and ground source heat pump systems with smart control based on measured electricity consumption and domestic hot water usage data showed the possibility of total energy consumption reduction, shifting the load from peak periods towards periods of excessive RES generation and increasing self-consumption of rooftop PV electricity. These improvements reduce the amount of emissions generated by such a household and allow for further development of other sectors. [ABSTRACT FROM AUTHOR]

The data management process is characterised by a set of tasks where data quality management (DQM) is one of the core components. Data quality, however, is a multidimensional concept, where the nature of the data quality issues is very diverse. One of the most widely anticipated data quality challenges, which becomes particularly vital when data come from multiple data sources which is a typical situation in the current data-driven world, is duplicates or non-uniqueness. Even more, duplicates were recognised to be one of the key domain-specific data quality dimensions in the context of the Internet of Things (IoT) application domains, where smart grids and health dominate most. Duplicate data lead to inaccurate analyses, leading to wrong decisions, negatively affect data-driven and/or data processing activities such as the development of models, forecasts, simulations, have a negative impact on customer service, risk and crisis management, service personalisation in terms of both their accuracy and trustworthiness, decrease user adoption and satisfaction, etc. The process of determination and elimination of duplicates is known as deduplication, while the process of finding duplicates in one or more databases that refer to the same entities is known as Record Linkage. To find the duplicates, the data sets are compared with each other using similarity functions that are usually used to compare two input strings to find similarities between them, which requires quadratic time complexity. To defuse the quadratic complexity of the problem, especially in large data sources, record linkage methods, such as blocking and sorted neighbourhood, are used. In this paper, we propose a six-step record linkage deduplication framework. The operation of the framework is demonstrated on a simplified example of research data artifacts, such as publications, research projects and others of the real-world research institution representing Research Information Systems (RIS) domain. To make the proposed framework usable we integrated it into a tool that is already used in practice, by developing a prototype of an extension for the well-known DataCleaner. The framework detects and visualises duplicates thereby identifying and providing the user with identified redundancies in a user-friendly manner allowing their further elimination. By removing the redundancies, the quality of the data is improved therefore improving analyses and decision-making. This study makes a call for other researchers to take a step towards the "golden record" that can be achieved when all data quality issues are recognised and resolved, thus moving towards absolute data quality. [ABSTRACT FROM AUTHOR]

Data migration is required to run data-intensive applications. Legacy data storage systems are not capable of accommodating the changing nature of data. In many companies, data migration projects fail because their importance and complexity are not taken seriously enough. Data migration strategies include storage migration, database migration, application migration, and business process migration. Regardless of which migration strategy a company chooses, there should always be a stronger focus on data cleansing. On the one hand, complete, correct, and clean data not only reduce the cost, complexity, and risk of the changeover, it also means a good basis for quick and strategic company decisions and is therefore an essential basis for today's dynamic business processes. Data quality is an important issue for companies looking for data migration these days and should not be overlooked. In order to determine the relationship between data quality and data migration, an empirical study with 25 large German and Swiss companies was carried out to find out the importance of data quality in companies for data migration. In this paper, we present our findings regarding how data quality plays an important role in a data migration plans and must not be ignored. Without acceptable data quality, data migration is impossible. [ABSTRACT FROM AUTHOR]

Big Data can help users attain a competitive advantage, and evidence suggests that by utilising Big Data, organisations can generate insight that can help strengthen their decision-making capabilities. However, a key issue remains that much data is trapped in legacy systems, and is hence not being appropriately retrieved and utilised. This paper builds on the existing literature base to investigate the challenges and issues organisations face in utilising Big Data. Through results of a survey with 97 respondents, this work shows that these issues can be categorised into six areas, including issues of format and structure of the data, as well as identification of the key need for a framework and architecture for organising Big Data. [ABSTRACT FROM AUTHOR]

Background There is a large burden of psychological distress in low and middle-income countries, and culturally relevant interventions must be developed to address it. This requires an understanding of how distress is experienced. We conducted a qualitative grounded theory study to understand how mothers experience and manage distress in Dhanusha, a low- resource setting in rural Nepal. We also explored how distressed mothers interact with their families and the wider community. Methods Participants were identified during a cluster-randomised controlled trial in which mothers were screened for psychological distress using the 12-item General Health Questionnaire (GHQ-12). We conducted 22 semi-structured interviews with distressed mothers (GHQ-12 score ≥5) and one with a traditional healer (dhami), as well as 12 focus group discussions with community members. Data were analysed using grounded theory methods and a model was developed to explain psychological distress in this setting. Results We found that distress was termed tension by participants and mainly described in terms of physical symptoms. Key perceived causes of distress were poor health, lack of sons, and fertility problems. Tension developed in a context of limited autonomy for women and perceived duty towards the family. Distressed mothers discussed several strategies to alleviate tension, including seeking treatment for perceived physical health problems and tension from doctors or dhamis, having repeated pregnancies until a son was delivered, manipulating social circumstances in the household, and deciding to accept their fate. Their ability to implement these strategies depended on whether they were able to negotiate with their in-laws or husbands for resources. Conclusions Vulnerability, as a consequence of gender and social disadvantage, manifests as psychological distress among mothers in Dhanusha. Screening tools incorporating physical symptoms of tension should be envisaged, along with interventions to address gender inequity, support marital relationships, and improve access to perinatal healthcare. [ABSTRACT FROM AUTHOR]