CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

Deep ensemble multitask classification of emergency medical call incidents combining multimodal data improves emergency medical dispatch

[EN] The objective of this work was to develop a predictive model to aid non-clinical dispatchers to classify emergency medical call incidents by their life-threatening level (yes/no), admissible response delay (undelayable, minutes, hours, days) and emergency system jurisdiction (emergency system/primary care) in real time. We used a total of 1 244 624 independent incidents from the Valencian emergency medical dispatch service in Spain, compiled in retrospective from 2009 to 2012, including clinical features, demographics, circumstantial factors and free text dispatcher observations. Based on them, we designed and developed DeepEMC2, a deep ensemble multitask model integrating four subnetworks: three specialized to context, clinical and text data, respectively, and another to ensemble the former. The four subnetworks are composed in turn by multi-layer perceptron modules, bidirectional long short-term memory units and a bidirectional encoding representations from transformers module. DeepEMC2 showed a macro F1-score of 0.759 in life-threatening classification, 0.576 in admissible response delay and 0.757 in emergency system jurisdiction. These results show a substantial performance increase of 12.5 %, 17.5 % and 5.1 %, respectively, with respect to the current in-house triage protocol of the Valencian emergency medical dispatch service. Besides, DeepEMC2 significantly outperformed a set of baseline machine learning models, including naive bayes, logistic regression, random forest and gradient boosting (¿ = 0.05). Hence, DeepEMC2 is able to: 1) capture information present in emergency medical calls not considered by the existing triage protocol, and 2) model complex data dependencies not feasible by the tested baseline models. Likewise, our results suggest that most of this unconsidered information is present in the free text dispatcher observations. To our knowledge, this study describes the first deep learning model undertaking emergency medical call incidents classification. Its adoption in medical dispatch centers would potentially improve emergency dispatch processes, resulting in a positive impact in patient wellbeing and health services sustainability.This work has been supported by the Valencian agency for security and emergency response project A1800173041, the Ministry of Science, Innovation and Universities of Spain program FPU18/06441 and the EU Horizon 2020 project InAdvance 825750Ferri-Borredà, P.; Sáez Silvestre, C.; Felix-De Castro, A.; Juan-Albarracín, J.; Blanes-Selva, V.; Sánchez-Cuesta, P.; Garcia-Gomez, JM. (2021). Deep ensemble multitask classification of emergency medical call incidents combining multimodal data improves emergency medical dispatch. Artificial Intelligence in Medicine. 117:1-13. https://doi.org/10.1016/j.artmed.2021.102088S11311

An agent-based approach to assess drivers’ interaction with pre-trip information systems.

This article reports on the practical use of a multi-agent microsimulation framework to address the issue of assessing drivers’ responses to pretrip information systems. The population of drivers is represented as a community of autonomous agents, and travel demand results from the decision-making deliberation performed by each individual of the population as regards route and departure time. A simple simulation scenario was devised, where pretrip information was made available to users on an individual basis so that its effects at the aggregate level could be observed. The simulation results show that the overall performance of the system is very likely affected by exogenous information, and these results are ascribed to demand formation and network topology. The expressiveness offered by cognitive approaches based on predicate logics, such as the one used in this research, appears to be a promising approximation to fostering more complex behavior modelling, allowing us to represent many of the mental aspects involved in the deliberation process

Alternative sweetener from curculigo fruits

This study gives an overview on the advantages of Curculigo Latifolia as an alternative sweetener and a health product. The purpose of this research is to provide another option to the people who suffer from diabetes. In this research, Curculigo Latifolia was chosen, due to its unique properties and widely known species in Malaysia. In order to obtain the sweet protein from the fruit, it must go through a couple of procedures. First we harvested the fruits from the Curculigo trees that grow wildly in the garden. Next, the Curculigo fruits were dried in the oven at 50 0C for 3 days. Finally, the dried fruits were blended in order to get a fine powder. Curculin is a sweet protein with a taste-modifying activity of converting sourness to sweetness. The curculin content from the sample shown are directly proportional to the mass of the Curculigo fine powder. While the FTIR result shows that the sample spectrum at peak 1634 cm–1 contains secondary amines. At peak 3307 cm–1 contains alkynes

Unintended and accidental medical radiation exposures in radiology: guidelines on investigation and prevention

This paper sets out guidelines for managing radiation exposure incidents involving patients in diagnostic and interventional radiology. The work is based on collation of experiences from representatives of international and national organizations for radiologists, medical physicists, radiographers, regulators, and equipment manufacturers, derived from an International Atomic Energy Agency Technical Meeting. More serious overexposures can result in skin doses high enough to produce tissue reactions, in interventional procedures and computed tomography, most notably from perfusion studies. A major factor involved has been deficiencies in training of staff in operation of equipment and optimization techniques. The use of checklists and time outs before procedures commence, and dose alerts when critical levels are reached during procedures can provide safeguards to reduce risks of these effects occurring. However, unintended and accidental overexposures resulting in relatively small additional doses can take place in any diagnostic or interventional X-ray procedure and it is important to learn from errors that occur, as these may lead to increased risks of stochastic effects. Such events may involve the wrong examinations, procedural errors, or equipment faults. Guidance is given on prevention, investigation and dose calculation for radiology exposure incidents within healthcare facilities. Responsibilities should be clearly set out in formal policies, and procedures should be in place to ensure that root causes are identified and deficiencies addressed. When an overexposure of a patient or an unintended exposure of a foetus occurs, the foetal, organ, skin and/or effective dose may be estimated from exposure data. When doses are very low, generic values for the examination may be sufficient, but a full assessment of doses to all exposed organs and tissues may sometimes be required. The use of general terminology to describe risks from stochastic effects is recommended rather than calculation of numerical values, as these are misleading when applied to individuals

Uncoupling inequality: Reflections on the ethics of benchmarks for digital media

Our collaboration seeks to demonstrate shared interrogation by exploring the ethics of machine learning benchmarks from a socio-technical management perspective with insight from public health and ethnic studies. Benchmarks, such as ImageNet, are annotated open data sets for training algorithms. The COVID-19 pandemic reinforced the practical need for ethical information infrastructures to analyze digital and social media, especially related to medicine and race. Social media analysis that obscures Black teen mental health and ignores anti-Asian hate fails as information infrastructure. Despite inadequately handling non-dominant voices, machine learning benchmarks are the basis for analysis in operational systems. Turning to the management literature, we interrogate cross-cutting problems of benchmarks through the lens of coupling, or mutual interdependence between people, technologies, and environments. Uncoupling inequality from machine learning benchmarks may require conceptualizing the social dependencies that build structural barriers to inclusion

GPT Models in Construction Industry: Opportunities, Limitations, and a Use Case Validation

Large Language Models(LLMs) trained on large data sets came into prominence in 2018 after Google introduced BERT. Subsequently, different LLMs such as GPT models from OpenAI have been released. These models perform well on diverse tasks and have been gaining widespread applications in fields such as business and education. However, little is known about the opportunities and challenges of using LLMs in the construction industry. Thus, this study aims to assess GPT models in the construction industry. A critical review, expert discussion and case study validation are employed to achieve the study objectives. The findings revealed opportunities for GPT models throughout the project lifecycle. The challenges of leveraging GPT models are highlighted and a use case prototype is developed for materials selection and optimization. The findings of the study would be of benefit to researchers, practitioners and stakeholders, as it presents research vistas for LLMs in the construction industry.Comment: 58 pages, 20 figure

Using BPM to improve it service management: an incident management case study

Business process management is a methodology focused on the continuous improvement of business processes, providing for this a collection of best practices. These best practices enable the redesign of business processes to meet the desired performance. By using this methodology, organisations can improve their business processes to achieve their objectives. IT service management defines the management of IT operations as a service. There are several IT service management frameworks available, consisting in best practices that propose standardizing these processes for the respective operations. By adopting these frameworks, organisations can align IT with their business objectives. The objective of this research is to understand how business process management can be applied for the improvement of IT service management processes. To achieve this goal, a case study is conducted for the improvement of the time performance of an incident management process, as it is a process that, to the best of our knowledge, has not been analysed for this objective. The results obtained identified three best practices – activity automation, activity elimination and integral technology – as the best suited for the improvement of the time performance of the analysed incident management process. Using a simulation tool for business processes, it was revealed that the employment of these best practices in the analysed incident management process eliminates the effort required in the 1st support level and reduces in 10.7% the average processing time in the 2nd support level.A gestão de processos de negócio é uma metodologia focada na melhoria contínua de processos de negócio, indicando para isso um conjunto de melhores práticas. Estas melhores práticas permitem o redesenho dos processos de negócio para obter o desempenho desejado. Através da aplicação desta metodologia, as organizações conseguem melhorar os seus processos de negócio para alcançarem os seus objectivos de negócio. A gestão de serviços de TI define a gestão das operações de TI como um serviço. Existem divesas frameworks para gestão de serviços de TI, consistindo em melhores práticas que propõem processos-padrão de TI para as respectivas operações. Com a adopção de frameworks, as organizações conseguem alinhar as TI com os seus objectivos de negócio. O objectivo desta investigação é perceber como é que a gestão de processos de negócio pode ser aplicada para a melhoria de processos de gestão de serviços de TI. Para atingir este objectivo, é conduzido um caso de estudo para a melhoria de desempenho do tempo num processo de gestão de incidentes, sendo este um processo que, de acordo com o conhecimento adquirido, ainda não foi analisado com este objectivo. Os resultados obtidos identificaram três melhores práticas – automação de atividades, eliminação de atividades e introdução de novas tecnologias – como as mais ajustadas para a melhoria de desempenho do tempo no processo de gestão de incidentes analisado. Recorrendo a uma ferramenta de simulação de processos de negócio, foi revelado que a aplicação destas melhores práticas no processo de gestão de incidentes analisado elimina o esforço necessário no 1º nível de suporte e reduz em 10.7% o tempo médio de processamento no 2º nível de suporte

ICSEA 2021: the sixteenth international conference on software engineering advances

The Sixteenth International Conference on Software Engineering Advances (ICSEA 2021), held on October 3 - 7, 2021 in Barcelona, Spain, continued a series of events covering a broad spectrum of software-related topics. The conference covered fundamentals on designing, implementing, testing, validating and maintaining various kinds of software. The tracks treated the topics from theory to practice, in terms of methodologies, design, implementation, testing, use cases, tools, and lessons learnt. The conference topics covered classical and advanced methodologies, open source, agile software, as well as software deployment and software economics and education. The conference had the following tracks: Advances in fundamentals for software development Advanced mechanisms for software development Advanced design tools for developing software Software engineering for service computing (SOA and Cloud) Advanced facilities for accessing software Software performance Software security, privacy, safeness Advances in software testing Specialized software advanced applications Web Accessibility Open source software Agile and Lean approaches in software engineering Software deployment and maintenance Software engineering techniques, metrics, and formalisms Software economics, adoption, and education Business technology Improving productivity in research on software engineering Trends and achievements Similar to the previous edition, this event continued to be very competitive in its selection process and very well perceived by the international software engineering community. As such, it is attracting excellent contributions and active participation from all over the world. We were very pleased to receive a large amount of top quality contributions. We take here the opportunity to warmly thank all the members of the ICSEA 2021 technical program committee as well as the numerous reviewers. The creation of such a broad and high quality conference program would not have been possible without their involvement. We also kindly thank all the authors that dedicated much of their time and efforts to contribute to the ICSEA 2021. We truly believe that thanks to all these efforts, the final conference program consists of top quality contributions. This event could also not have been a reality without the support of many individuals, organizations and sponsors. We also gratefully thank the members of the ICSEA 2021 organizing committee for their help in handling the logistics and for their work that is making this professional meeting a success. We hope the ICSEA 2021 was a successful international forum for the exchange of ideas and results between academia and industry and to promote further progress in software engineering research