Abstract
Falls, especially in the elderly, are one of the main factors of hospitalization. Time-consuming intervention can be fatal or cause irreversible damages to the victims. On the other hand, there is currently a significant amount of smart clothing equipped with various sensors, particularly gyroscopes and accelerometers, which can be used to detect accidents. The creation of a tool that automatically detects eventual falls allows helping the victims as soon as possible. This works focuses in the automatic fall detection from sensors signals using long short-term memory networks. To train and test this approach, the Sisfall dataset is used, which considers the simulation of 23 adults and 15 older people. These simulations are based on everyday activities and the falls that may result from their execution. The results indicate that the procedure provides an accuracy score of 97.1% on the test set.

Abstract
Congestive heart failure (CHF) is a severe condition that affects the pumping power of your cardiac muscle. In this work, long-term memory (LSTM) and Bidirectional LSTM (BiLSTM) networks were used to identify congestive heart failure human beings using datasets from the PhysioNET. Two approaches were adopted, the first considers beating signals directly to feed the LSTM networks, and the second one used features signals extracted from the beating signals. The BiLSTM considering features signals obtain the best results reaching an accuracy of 90%.

Abstract
A classifier using a Long Short-Term Memory (LSTM) network to identify human beings infected with Covid-19 is proposed in this work. This classifier has significant advantages over current testing methods: it is fast, contactless, and requires few monetary resources. The data considered for this study was extracted from the Coswara dataset using 140 individuals (70 healthy and 70 infected with Covid-19). This dataset contains respiratory signals, such as people counting numbers, coughing, or breathing. The classifier uses non-linear time sequence features extracted from the signals after a preprocessing stage. The classifier was able to discriminate whether a human is infected with Covid-19 with an accuracy of 92.1%, specificity of 85.7%, and sensitivity of 98.6% using 5-fold Cross-Validation. Based on the results obtained, the classifier can be used as an alternative for the Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests.

Abstract
Nowadays, independent older people stay alone for long periods, which increases the risk of being seriously damaged after a fall without the quick attendance of medical services. Several smart clothing equipment was created to detect these falls using sensors such as accelerometers and gyroscopes, allowing a short intervention to the victims. This work considers the Sisfall database, where 23 adults and 15 older people performed several daily living simulations. The signals registered by three sensors were used to train a Long Short-Term Memory network and a Bi-Long Short-Term Memory network to detect everyday activities and falls. Several experiments were performed, where the BiLSTM model outperforms the LSTM model with a mean accuracy of 99.21% on the testing set.

Abstract
Alto Douro Wine Region is located in the Northeast of Portugal and is classified by UNESCO as a World Heritage Site. Snaked by the Douro River, the region has been producing wines for over 2000 years, with the world-famous Porto wine standing out. The vineyards, in that region, are built in a territory marked by steep slopes and the almost inexistence of flat land and water. The vineyards that cover the great slopes rise from the Douro River and form an immense terraced staircase. All these ingredients combined make the right key for exploring precision agriculture techniques. In this study, a preliminary approach allowing to perform terrace vineyards identification is presented. This is a key-enabling task towards the achievement of important goals such as production estimation and multi-temporal crop evaluation. The proposed methodology consists in the use of Convolutional Neural Networks (CNNs) to classify and segment the terrace vineyards, considering a high-resolution dataset acquired with remote sensing sensors mounted in unmanned aerial vehicles (UAVs).

Abstract
Listeria monocytogenes has been referred to as a concern microorganism in cheese making due to its ability to survive and grow in a wide range of environmental conditions, such as refrigeration temperatures, low pH and high salt concentration at the end of the production process. Since cheese may be a potential hazard for consumers, especially high-risk consumers (e.g., pregnant, young children, the elderly, people with medical conditions), efforts of the dairy industry have been aimed at investigating new conservation techniques based on natural additives to meet consumers' demands on less processed foods without compromising the food safety. Thus, the aim of this study was to evaluate the efficacy of Myrtus communis L. (myrtle) and Rosmarinus officinalis L. (rosemary) essential oils (EO) against Listeria monocytogenes ATCC 679 spiked in sheep cheese before ripening. After the cheesemaking process, the samples were stored at 8 degrees C for 2 h, 1 d, 3 d, 14 d and 28 d. The composition of EO was identified by gas chromatography-mass spectrometry (GC-MS) analysis. Constituents such as 1,8-cineole, limonene, methyl-eugenol, alpha-pinene, alpha-terpineol, alpha-terpinolene and beta-pinene were present in both EO, accounting for 44.61% and 39.76% from the total of chemical compounds identified for myrtle and rosemary EO, respectively. According to the chemical classification, both EO were mainly composed of monoterpenes. Minimum inhibitory concentration (MIC) against L. monocytogenes was obtained at 31.25 mu L/mL to myrtle EO and at 0.40 mu L/mL to rosemary EO. Then, cheeses were inoculated with L. monocytogenes (Ca. 6 log CFU/mL) and EO was added at MIC value. The addition of rosemary and myrtle EO displayed lower counts of L. monocytogenes (p < 0.01) (about 1-2 log CFU/g) during the ripening period compared to control samples. Ripening only influences (p < 0.001) the growth of L. monocytogenes in control samples. Since rosemary and myrtle EO do not exert any negative impact on the growth of native microflora (p > 0.05), their use as natural antimicrobial additives in cheese demonstrated a potential for dairy processors to assure safety against L. monocytogenes.