Case-based reasoning for context-aware solutions supporting personalised asthma management

Context-aware solutions have the potential to address the personalisation required for implementing asthma management plans. However, they have limitations to aid people with asthma when their triggers and symptoms are poorly known or changing. Case-Based Reasoning can address these limitations as it can effectively deal with personal constraints in problems that involve evolving context adaptation. This research work proposes to use Case-Based Reasoning together with Context-Aware Reasoning to aid the personalisation of asthma management plans at specific stages of the condition when the triggers and symptoms are not completely known or evolving. The proposal was implemented and evaluated using historical weather and air pollution data and two control cases that were defined based on a set of interviews. Finally, the benefits and challenges of the proposal are presented and analysed based on the results of the evaluation

Holding on for too long? An experimental study on inertia in entrepreneurs’ and non-entrepreneurs’ disinvestment choices

Disinvestment, in the sense of project termination and liquidation of assets including the cession of a venture, is an important realm of entrepreneurial decision-making. This study presents the results of an experimental investigation modeling the choice to disinvest as a dynamic problem of optimal stopping in which the patterns of decisions are analyzed with entrepreneurs and non-entrepreneurs. Our experimental results reject the standard net present value approach as an account of observed behavior. Instead, most individuals seem to understand the value of waiting. Their choices are weakly related to the disinvestment triggers derived from a formal optimal stopping benchmark consistent with real options reasoning. We also observe a pronounced ‘psychological inertia’, i.e., most individuals hold on to a losing project for even longer than real options reasoning would predict. The study provides evidence for entrepreneurs and non-entrepreneurs being quite similar in their behavior.Real-Options, Disinvestment, Exit Behavior, Experimental Economics, Agribusiness, Agricultural and Food Policy, Agricultural Finance, Institutional and Behavioral Economics,

ADAPT: Approach to Develop context-Aware solutions for Personalised asthma managemenT

The creation of sensors allowing the collection of a high amount of data has been possible thanks to the evolution of information and communication technology. These data must be properly interpreted to deliver meaningful information and services. Context-aware reasoning plays an important role in this task, and it is considered as a hot topic to study in the development of solutions that can be categorised under the scope of Intelligent Environments. This research work studies the use of context-aware reasoning as a tool to provide support in the asthma management process. The contribution of this study is presented as the Approach to Develop context-Aware solutions for Personalised asthma managemenT (ADAPT), which can be used as a guideline to create solutions supporting asthma management in a personalised way. ADAPT proposes context-aware reasoning as an appropriate tool to achieve the personalisation that is required to address the heterogeneity of asthma. This heterogeneity makes people with asthma have different triggers provoking their exacerbations and to experience different symptoms when their exacerbations occur, which is considered as the most challenging characteristic of the condition when it comes to implementing asthma treatments. ADAPT context dimensions are the main contribution of the research work as they directly address the heterogeneity of asthma management by allowing the development of preventive and reactive features that can be customised depending on the characteristics of a person with asthma. The approach also provides support to people not knowing their triggers properly through case-based reasoning, and includes virtual assistant as a complementing technology supporting asthma management. The comprehensive nature of ADAPT motivates the study of the interaction between context-aware reasoning and case-based reasoning in Intelligent Environments, which is also reported as a key contribution of the research work. The inclusion of people with asthma, carers and experts in respiratory conditions in the experiments of the research project was possible to achieve thanks to the collaboration formed with Asthma UK

Code Prompting: a Neural Symbolic Method for Complex Reasoning in Large Language Models

Large language models (LLMs) have scaled up to unlock a wide range of complex reasoning tasks with the aid of various prompting methods. However, current prompting methods generate natural language intermediate steps to help reasoning, which can cause imperfect task reduction and confusion. To mitigate such limitations, we explore code prompting, a neural symbolic prompting method with both zero-shot and few-shot versions which triggers code as intermediate steps. We conduct experiments on 7 widely-used benchmarks involving symbolic reasoning and arithmetic reasoning. Code prompting generally outperforms chain-of-thought (CoT) prompting. To further understand the performance and limitations of code prompting, we perform extensive ablation studies and error analyses, and identify several exclusive advantages of using symbolic promptings compared to natural language. We also consider the ensemble of code prompting and CoT prompting to combine the strengths of both. Finally, we show through experiments how code annotations and their locations affect code prompting

Inference as a data management problem

Inference over OWL ontologies with large A-Boxes has been researched as a data management problem in recent years. This work adopts the strategy of applying a tableaux-based reasoner for complete T-Box classification, and using a rule-based mechanism for scalable A-Box reasoning. Specifically, we establish for the classified T-Box an inference framework, which can be used to compute and materialise inference results. The inference we focus on is type inference in A-Box reasoning, which we define as the process of deriving for each A-Box instance its memberships of OWL classes and properties. As our approach materialises the inference results, it in general provides faster query processing than non-materialising techniques, at the expense of larger space requirement and slower update speed. When the A-Box size is suitable for an RDBMS, we compile the inference framework to triggers, which incrementally update the inference materialisation from both data inserts and data deletes, without needing to re-compute the whole inference. More importantly, triggers make inference available as atomic consequences of inserts or deletes, which preserves the ACID properties of transactions, and such inference is known as transactional reasoning. When the A-Box size is beyond the capability of an RDBMS, we then compile the inference framework to Spark programmes, which provide scalable inference materialisation in a Big Data system, and our evaluation considers up to reasoning 270 million A-Box facts. Evaluating our work, and comparing with two state-of-the-art reasoners, we empirically verify that our approach is able to perform scalable inference materialisation, and to provide faster query processing with comparable completeness of reasoning.Open Acces

ADAPT: Approach to Develop context-Aware solutions for Personalised asthma managemenT

People with asthma have heterogeneous triggers and symptoms, which they need to be aware of in order to implement the strategies to manage their condition. Context-aware reasoning has the potential to provide the personalisation that is required to address the heterogeneity of asthma by helping people to define the information that is relevant considering the characteristics of their condition and delivering services based on this information. This research work proposes the Approach to Develop context-Aware solutions for Personalised asthma managemenT (ADAPT), whose aim is to facilitate the creation of solutions allowing the required customisation to address the heterogeneity of asthma. ADAPT is the result of the constant interaction with people affected by asthma throughout the research project, which was possible to achieve thanks to the collaboration formed with the Centre for Applied Research of Asthma UK. ADAPT context dimensions facilitate the development of preventive and reactive features that can be configured depending on the characteristics of the person with asthma. The approach also provides support to people not knowing their triggers through case-based reasoning and includes virtual assistant as a complementing technology supporting asthma management. ADAPT is validated by people with asthma, carers and experts in respiratory conditions, who evaluated a mobile application that was built based on the approach

Use-cases on evolution

This report presents a set of use cases for evolution and reactivity for data in the Web and Semantic Web. This set is organized around three different case study scenarios, each of them is related to one of the three different areas of application within Rewerse. Namely, the scenarios are: “The Rewerse Information System and Portal”, closely related to the work of A3 – Personalised Information Systems; “Organizing Travels”, that may be related to the work of A1 – Events, Time, and Locations; “Updates and evolution in bioinformatics data sources” related to the work of A2 – Towards a Bioinformatics Web

Allergen Control in Asthma

Asthma is one of the most prevalent chronic diseases especially among children so that it continues to be a public health problem. Even though genetics is an important factor for asthma, dramatic increase of the asthma recently is related with environmental triggers and lifestyle factors. Understanding of the interaction of multiple factors causing asthma is absolutely necessary for the planning interventions strategies. Exposure to allergens is a key factor for asthma morbidity. Environmental exposure leads allergen sensitization for genetically predisposed individuals and persisting of exposure is a risk element for asthma and other allergic diseases as well. Evidences suggest that environmental triggers avoidance and control interventions preclude asthma attacks, decrease the frequency of symptoms, and the need for drugs. Thus, environmental control should be focused in the management of asthma. Identifying and controlling of indoor and outdoor environmental triggers is the cornerstone for a successful asthma management. Figuring out the reasoning factors and developing primary preventive precautions are necessary to decrease asthma development frequency throughout the world