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Using text mining to diagnose and classify epilepsy in children
Publication . Luis Pereira; Rijo, Rui; Silva, Catarina; Agostinho, Margarida
Epilepsy diagnosis can be an extremely complex process, demanding considerable time and effort from physicians and healthcare infrastructures. Physicians need to classify each specific type of epilepsy based on different data, e.g., types of seizures, events and exams' results. This work presents a text mining approach to support medical decisions relating to epilepsy diagnosis and classification in children. We propose a text mining process that, using patient medical records, applies ontologies and named entities recognition as preprocessing steps, then applying K-Nearest Neighbors as a white-box lazy method to classify each instance. Results on real medical records suggest that the proposed framework shows good performance and clear interpretations, albeit the reduced volume of available training data.
Data Acquisition and Monitoring System for Legacy Injection Machines
Publication . Silva, Bruno; Sousa, João; Alenya, Guillem
Nowadays, companies must embrace the concept of Digitalization and Industry 4.0 to remain competitive in the market. The reality is that most of them do not have their industrial devices prepared to access their data on a real-time basis. As most companies do not have the possibility to renew all their legacy devices and because these devices are still very productive, a retrofit solution is of high interest. In this work, we propose an affordable procedure that allows data collection and monitoring of older injection machines, as a contribution towards legacy devices integration. The developed system neither requires additional proprietary modules, nor contractual annual fees for different devices, sharing the same interface across different machine manufacturers and also contributing to uniform data collection. Evaluation was carried out in a real shop floor, monitoring the injection parameters for different machine models, validating the effectiveness of the developed system.
Selective reporting - a half signalling load algorithm for distributed sensing
Publication . Gameiro, Atílio; Ribeiro, Carlos; Quaresma, José
Spectrum sensing is a powerful tool of the cognitive cycle to help circumvent the apparent spectrum scarcity faced by wireless transmission systems. To overcome the challenging issues faced by the localized sensing, multiple cognitive radios can cooperate to explore the multiuser diversity and generate a more reliable decision on the presence of a signal in the frequencies of interest. In such a cooperative sensing scenario, a common reporting channel is needed for the transmission of the information of each element. As the number of elements that participate in the sensing operation increases, so does the bandwidth demanded for the reporting channel, quickly becoming the limiting factor in this scenario. To tackle the issue of reducing the sensing report overhead, this paper introduces a new cooperative sensing scheme that introduces silence periods in the reporting and, relying on information theory principles, explores the information present in these periods to reduce by 50% the sensing reporting overhead while maintaining the same performance of standard reporting schemes. Numerical and experimental results confirm the theoretical analysis and show the predicted reduction in reporting overhead and performance preservation.
Practices and Challenges in Portuguese Early Childhood Intervention: A Descriptive Study
Publication . Costeira , Cristina; Lopes, Inês; Lopes, Saudade; Pedrosa, Vanda Varela; Custódio, Susana; Cioga, Elisabete; Silva, Cândida G.
Background/Objectives: Early Childhood Intervention (ECI) services are critical for supporting children with developmental needs and their families. Despite an established legislative framework, challenges related to accessibility, equity, resources, and standardization of practices persist. This study aimed to describe the perspectives of early intervention professionals in Portugal regarding current barriers, facilitators, and priority areas for improvement within the system. Methods: A descriptive study was conducted involving 82 professionals working in early intervention in Portugal. Data were collected using a survey specifically developed by the research team, grounded in a comprehensive literature review and professional expertise. The instrument was validated through a Delphi Panel with two rounds involving six experts in ECI. Data from open-ended questions were analyzed using content analysis, identifying categories and sub-categories to describe the responses, and descriptive statistics for the closed-ended questions. Results: Professionals highlighted the need to update the National ECI System (SNIPI), improve accessibility, and ensure equitable access to early intervention services. Participants reported limited resources, a lack of standardization in practices, and emphasized the importance of professional training and continuous professional development. The findings also pointed to the urgent need for investment and functional and structural restructuring of early intervention services. Various barriers and facilitators were identified. Conclusions: The study provides valuable insights into the perspectives of early intervention professionals, identifying critical areas for policy improvement, resource allocation, and practice standardization.
Global optimization framework for solar building design
Publication . Silva, N.; Alves, N.; Pascoal-Faria, P.
The generative modeling paradigm is a shift from static models to flexible models. It describes a modeling process using functions, methods and operators. The result is an algorithmic description of the construction process. Each evaluation of such an algorithm creates a model instance, which depends on its input parameters (width, height, volume, roof angle, orientation, location). These values are normally chosen according to aesthetic aspects and style. In this study, the model’s parameters are automatically generated according to an objective function. A generative model can be optimized according to its parameters, in this way, the best solution for a constrained problem is determined. Besides the establishment of an overall framework design, this work consists on the identification of different building shapes and their main parameters, the creation of an algorithmic description for these main shapes and the formulation of the objective function, respecting a building’s energy consumption (solar energy, heating and insulation). Additionally, the conception of an optimization pipeline, combining an energy calculation tool with a geometric scripting engine is presented. The methods developed leads to an automated and optimized 3D shape generation for the projected building (based on the desired conditions and according to specific constrains). The approach proposed will help in the construction of real buildings that account for less energy consumption and for a more sustainable world.