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- Estudo de conservação sob atmosfera controlada na qualidade da cereja cv. Satin.Publication . Espírito Santo, Christophe; Belane, Yuran; Resende, Mafalda; Caseiro, Catarina; Beato, Helena; Reis, João; Brandão, Inês; Silveira, Ana; Riscado, Ana; Baptista, Cátia; Pintado, Cristina Miguel; Veloso, Abel; Ferreira, Dora; Andrade, Luís P.; Nunes, José; Simões, Maria Paula; Morais, Diogo Cerqueira; Teixeira, Maria Cristina Canavarro; Gaspar, Pedro Dinis; Silva, Pedro Dinho daA cerejeira (Prunus avium L.) é uma espécie pertencente à subfamília das Prunóideas e a produção de cereja apresenta elevada importância económica na região da Beira Interior, que, embora não seja a região com maior área de produção é a principal região de produção de Portugal. A cereja apresenta um elevado teor de compostos bioativos como vitamina C, fibra, antocianinas, quercetina e carotenóides relacionados com a prevenção de doenças cardiovasculares, diabetes e cancro (McCune et al., 2011; Wang et al., 2016). No entanto, este fruto não climatérico deteriora-se rapidamente após a colheita apresentando alterações na cor da pele, acastanhamento do pedúnculo, desidratação, amolecimento da polpa, diminuição da acidez e apodrecimento (Dugan & Roberts, 1997; Wang et al., 2016). A refrigeração, combinada com a utilização de atmosferas controladas, visa o atraso da deterioração e o consequente prolongamento da vida útil alargando o período de oferta. Esta técnica consiste no armazenamento a baixa temperatura num ambiente com uma concentração elevada de CO2, uma concentração baixa de O2 e uma humidade relativa elevada (Andrade et al., 2019). Os valores indicados na bibliografia relativos à concentração de CO2 variam entre 5% e 20% (Gross et al., 2016) e, para a concentração de O2, encontram-se entre 1% (Gross et al., 2016) e 10% (Ben-Yehoshua et al., 2005)
- Experimental study of the consequences of controlled atmosphere conservation environment on cherry characteristicsPublication . Andrade, Luís P.; Nunes, José; Simões, Maria Paula; Morais, Diogo Cerqueira; Teixeira, Maria Cristina Canavarro; Espírito Santo, Christophe; Gaspar, Pedro Dinis; Silva, Pedro Dinho da; Resende, Mafalda; Caseiro, Catarina; Baeto, Helena; Belane, Yuran; Ferreira, DoraCherry is a highly perishable fruit widely appreciated that is only commercialized during a short period. The post-harvest control and monitoring of this fruit is central and essential for optimal consumption in its highest state of quality. The conservation process aimed to inhibit the microbial propagation is usually accomplished by low temperatures and/or variable atmosphere composition. This paper describes experimental tests conducted in different refrigeration chambers located in industrial and laboratorial facilities. The latter one includes modified atmosphere and controlled atmosphere chambers. The tests were performed with four different concentration of O2 and CO2 in the controlled atmosphere chamber. Fruit samples extracted from each chamber were analyzed at specific residence times and several organoleptic characteristics were analyzed. The results show that the modified and controlled atmosphere maintain the fruit quality in terms of size, color, appearance and firmness, thus increasing their shelf life and food safety.
- Identification of activities of daily living through data fusion on motion and magnetic sensors embedded on mobile devicesPublication . Pires, Ivan; Garcia, Nuno M.; Pombo, Nuno; Flórez-Revuelta, Francisco; Spinsante, Susanna; Teixeira, Maria Cristina CanavarroSeveral types of sensors have been available in off‐the‐shelf mobile devices, including motion, magnetic, vision, acoustic, and location sensors. This paper focuses on the fusion of the data acquired from motion and magnetic sensors, i.e., accelerometer, gyroscope and magnetometer sensors, for the recognition of Activities of Daily Living (ADL). Based on pattern recognition techniques, the system developed in this study includes data acquisition, data processing, data fusion, and classification methods like Artificial Neural Networks (ANN). Multiple settings of the ANN were implemented and evaluated in which the best accuracy obtained, with Deep Neural Networks (DNN), was 89.51%. This novel approach applies L2 regularization and normalization techniques on the sensors’ data proved it suitability and reliability for the ADL recognition.
- Android Library for Recognition of Activities of Daily Living: Implementation Considerations, Challenges, and SolutionsPublication . Pires, Ivan; Teixeira, Maria Cristina Canavarro; Pombo, Nuno; Garcia, Nuno M.; Flórez-Revuelta, Francisco; Spinsante, Susanna; Goleva, Rossitza; Zdravevski, EftimBackground: Off-the-shelf-mobile devices have several sensors available onboard that may be used for the recognition of Activities of Daily Living (ADL) and the environments where they are performed. This research is focused on the development of Ambient Assisted Living (AAL) systems, using mobile devices for the acquisition of the different types of data related to the physical and physiological conditions of the subjects and the environments. Mobile devices with the Android Operating Systems are the least expensive and exhibit the biggest market while providing a variety of models and onboard sensors. Objective: This paper describes the implementation considerations, challenges and solutions about a framework for the recognition of ADL and the environments, provided as an Android library. The framework is a function of the number of sensors available in different mobile devices and utilizes a variety of activity recognition algorithms to provide a rapid feedback to the user. Methods: The Android library includes data fusion, data processing, features engineering and classification methods. The sensors that may be used are the accelerometer, the gyroscope, the magnetometer, the Global Positioning System (GPS) receiver and the microphone. The data processing includes the application of data cleaning methods and the extraction of features, which are used with Deep Neural Networks (DNN) for the classification of ADL and environment. Throughout this work, the limitations of the mobile devices were explored and their effects have been minimized. Results: The implementation of the Android library reported an overall accuracy between 58.02% and 89.15%, depending on the number of sensors used and the number of ADL and environments recognized. Compared with the results available in the literature, the performance of the library reported a mean improvement of 2.93%, and they do not differ at the maximum found in prior work, that based on the Student’s t-test. Conclusion: This study proves that ADL like walking, going upstairs and downstairs, running, watching TV, driving, sleeping and standing activities, and the bedroom, cooking/kitchen, gym, classroom, hall, living room, bar, library and street environments may be recognized with the sensors available in off-the-shelf mobile devices. Finally, these results may act as a preliminary research for the development of a personal digital life coach with a multi-sensor mobile device commonly used daily.