A Genetic Programming Approach for Computer Vision: Classifying High-level Image Features from Convolutional Layers with an Evolutionary Algorithm

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceComputer Vision is a sub-field of Artificial Intelligence that provides a visual perception component to computers, mimicking human intelligence. One of its tasks is image classification and Convolutional Neural Networks (CNNs) have been the most implemented algorithm in the last few years, with few changes made to the fully-connected layer of those neural networks. Nonetheless, recent research has been showing their accuracy could be improved in certain cases by implementing other algorithms for the classification of high-level image features from convolutional layers. Thus, the main research question for this document is: To what extent does the substitution of the fully-connected layer in Convolutional Neural Networks for an evolutionary algorithm affect the performance of those CNN models? The proposed two-step approach in this study does the classification of high-level image features with a state-of-the-art GP-based algorithm for multiclass classification called M4GP. This is conducted using secondary data with different characteristics, to better benchmark the implementation and to carefully investigate different outcomes. Results indicate the new learning approach yielded similar performance in the dataset with a low number of output classes. However, none of the M4GP models was able to surpass the results of the fully-connected layers in terms of test accuracy. Even so, this might be an interesting route if one has a powerful computer and needs a very light classifier in terms of model size. The results help to understand in which situation it might be beneficial to perform a similar experimental setup, either in the context of a work project or concerning a novel research topic

Synthesis of Catechol Derived Rosamine Dyes and Their Reactivity toward Biogenic Amines

Functional organic dyes play a key role in many fields, namely in biotechnology and medical diagnosis. Herein, we report two novel 2,3- and 3,4-dihydroxyphenyl substituted rosamines (3 and 4, respectively) that were successfully synthesized through a microwave-assisted protocol. The best reaction yields were obtained for rosamine 4, which also showed the most interesting photophysical properties, specially toward biogenic amines (BAs). Several amines including n- and t-butylamine, cadaverine, and putrescine cause spectral changes of 4, in UV–Vis and fluorescence spectra, which are indicative of their potential application as an effective tool to detect amines in acetonitrile solutions. In the gas phase, the probe response is more expressive for spermine and putrescine. Additionally, we found that methanolic solutions of rosamine 4 and n-butylamine undergo a pink to yellow color change over time, which has been attributed to the formation of a new compound. The latter was isolated and identified as 5 (9−aminopyronin), whose solutions exhibit a remarkable increase in fluorescence intensity together with a shift toward more energetic wavelengths. Other 9-aminopyronins 6a, 6b, 7a, and 7b were obtained from methanolic solutions of 4 with putrescine and cadaverine, demonstrating the potential of this new xanthene entity to react with primary amines.Financial support from PT national funds (FCT/MCTES, Fundação para a Ciência e a Tecnologia and Ministério da Ciência, Tecnologia e Ensino Superior) through the project PTDC/QUI-QOR/29426/2017. The research team would like to thank the projects UIDB/50006/2020, PTDC/QUI-QIN/28142/2017 and Grant BU263P18 (from the Junta de Castilla y León, Consejería de Educación y Cultura y Fondo Social Europeo). F. M. -S. gratefully acknowledges FCT (Portugal’s Foundation for Science and Technology) within grant DFA/BD/9136/2020. A.M.G.S. and A.L. thank FCT for the program DL 57/2016 – Norma transitória

MAMMALS IN PORTUGAL : A data set of terrestrial, volant, and marine mammal occurrences in P ortugal

Mammals are threatened worldwide, with 26% of all species being includedin the IUCN threatened categories. This overall pattern is primarily associatedwith habitat loss or degradation, and human persecution for terrestrial mam-mals, and pollution, open net fishing, climate change, and prey depletion formarine mammals. Mammals play a key role in maintaining ecosystems func-tionality and resilience, and therefore information on their distribution is cru-cial to delineate and support conservation actions. MAMMALS INPORTUGAL is a publicly available data set compiling unpublishedgeoreferenced occurrence records of 92 terrestrial, volant, and marine mam-mals in mainland Portugal and archipelagos of the Azores and Madeira thatincludes 105,026 data entries between 1873 and 2021 (72% of the data occur-ring in 2000 and 2021). The methods used to collect the data were: live obser-vations/captures (43%), sign surveys (35%), camera trapping (16%),bioacoustics surveys (4%) and radiotracking, and inquiries that represent lessthan 1% of the records. The data set includes 13 types of records: (1) burrowsjsoil moundsjtunnel, (2) capture, (3) colony, (4) dead animaljhairjskullsjjaws, (5) genetic confirmation, (6) inquiries, (7) observation of live animal (8),observation in shelters, (9) photo trappingjvideo, (10) predators dietjpelletsjpine cones/nuts, (11) scatjtrackjditch, (12) telemetry and (13) vocalizationjecholocation. The spatial uncertainty of most records ranges between 0 and100 m (76%). Rodentia (n=31,573) has the highest number of records followedby Chiroptera (n=18,857), Carnivora (n=18,594), Lagomorpha (n=17,496),Cetartiodactyla (n=11,568) and Eulipotyphla (n=7008). The data setincludes records of species classified by the IUCN as threatened(e.g.,Oryctolagus cuniculus[n=12,159],Monachus monachus[n=1,512],andLynx pardinus[n=197]). We believe that this data set may stimulate thepublication of other European countries data sets that would certainly contrib-ute to ecology and conservation-related research, and therefore assisting onthe development of more accurate and tailored conservation managementstrategies for each species. There are no copyright restrictions; please cite thisdata paper when the data are used in publications.info:eu-repo/semantics/publishedVersio

Mammals in Portugal: a data set of terrestrial, volant, and marine mammal occurrences in Portugal

Mammals are threatened worldwide, with ~26% of all species being included in the IUCN threatened categories. This overall pattern is primarily associated with habitat loss or degradation, and human persecution for terrestrial mammals, and pollution, open net fishing, climate change, and prey depletion for marine mammals. Mammals play a key role in maintaining ecosystems functionality and resilience, and therefore information on their distribution is crucial to delineate and support conservation actions. MAMMALS IN PORTUGAL is a publicly available data set compiling unpublished georeferenced occurrence records of 92 terrestrial, volant, and marine mammals in mainland Portugal and archipelagos of the Azores and Madeira that includes 105,026 data entries between 1873 and 2021 (72% of the data occurring in 2000 and 2021). The methods used to collect the data were: live observations/captures (43%), sign surveys (35%), camera trapping (16%), bioacoustics surveys (4%) and radiotracking, and inquiries that represent less than 1% of the records. The data set includes 13 types of records: (1) burrows | soil mounds | tunnel, (2) capture, (3) colony, (4) dead animal | hair | skulls | jaws, (5) genetic confirmation, (6) inquiries, (7) observation of live animal (8), observation in shelters, (9) photo trapping | video, (10) predators diet | pellets | pine cones/nuts, (11) scat | track | ditch, (12) telemetry and (13) vocalization | echolocation. The spatial uncertainty of most records ranges between 0 and 100 m (76%). Rodentia (n =31,573) has the highest number of records followed by Chiroptera (n = 18,857), Carnivora (n = 18,594), Lagomorpha (n = 17,496), Cetartiodactyla (n = 11,568) and Eulipotyphla (n = 7008). The data set includes records of species classified by the IUCN as threatened (e.g., Oryctolagus cuniculus [n = 12,159], Monachus monachus [n = 1,512], and Lynx pardinus [n = 197]). We believe that this data set may stimulate the publication of other European countries data sets that would certainly contribute to ecology and conservation-related research, and therefore assisting on the development of more accurate and tailored conservation management strategies for each species. There are no copyright restrictions; please cite this data paper when the data are used in publications

COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study

Background: The ISARIC prospective multinational observational study is the largest cohort of hospitalized patients with COVID-19. We present relationships of age, sex, and nationality to presenting symptoms. Methods: International, prospective observational study of 60 109 hospitalized symptomatic patients with laboratory-confirmed COVID-19 recruited from 43 countries between 30 January and 3 August 2020. Logistic regression was performed to evaluate relationships of age and sex to published COVID-19 case definitions and the most commonly reported symptoms. Results: ‘Typical’ symptoms of fever (69%), cough (68%) and shortness of breath (66%) were the most commonly reported. 92% of patients experienced at least one of these. Prevalence of typical symptoms was greatest in 30- to 60-year-olds (respectively 80, 79, 69%; at least one 95%). They were reported less frequently in children (≤ 18 years: 69, 48, 23; 85%), older adults (≥ 70 years: 61, 62, 65; 90%), and women (66, 66, 64; 90%; vs. men 71, 70, 67; 93%, each P < 0.001). The most common atypical presentations under 60 years of age were nausea and vomiting and abdominal pain, and over 60 years was confusion. Regression models showed significant differences in symptoms with sex, age and country. Interpretation: This international collaboration has allowed us to report reliable symptom data from the largest cohort of patients admitted to hospital with COVID-19. Adults over 60 and children admitted to hospital with COVID-19 are less likely to present with typical symptoms. Nausea and vomiting are common atypical presentations under 30 years. Confusion is a frequent atypical presentation of COVID-19 in adults over 60 years. Women are less likely to experience typical symptoms than men

Optical Sensing of Nitrogen, Phosphorus and Potassium: A Spectrophotometrical Approach toward Smart Nutrient Deployment

The feasibility of a compact, modular sensing system able to quantify the presence of nitrogen, phosphorus and potassium (NPK) in nutrient-containing fertilizer water was investigated. Direct UV-Vis spectroscopy combined with optical fibers were employed to design modular compact sensing systems able to record absorption spectra of nutrient solutions resulting from local producer samples. N, P, and K spectral interference was studied by mixtures of commercial fertilizer solutions to simulate real conditions in hydroponic productions. This study demonstrates that the use of bands for the quantification of nitrogen with linear or logarithmic regression models does not produce analytical grade calibrations. Furthermore, multivariate regression models, i.e., Partial Least Squares (PLS), which consider specimens interference, perform poorly for low absorbance nutrients. The high interference present in the spectra has proven to be solved by an innovative self-learning artificial intelligence algorithm that is able to find interference modes among a spectral database to produce consistent predictions. By correctly modeling the existing interferences, analytical grade quantification of N, P, and K has proven feasible. The results of this work open the possibility of real-time NPK monitoring in Micro-Irrigation Systems

Point-of-Care Using Vis-NIR Spectroscopy for White Blood Cell Count Analysis

Total white blood cells count is an important diagnostic parameter in both human and veterinary medicines. State-of-the-art is performed by flow cytometry combined with light scattering or impedance measurements. Spectroscopy point-of-care has the advantages of miniaturization, low sampling, and real-time hemogram analysis. While white blood cells are in low proportions, while red blood cells and bilirubin dominate spectral information, complicating detection in blood. We performed a feasibility study for the direct detection of white blood cells counts in canine blood by visible-near infrared spectroscopy for veterinary applications, benchmarking current chemometrics techniques (similarity, global and local partial least squares, artificial neural networks and least-squares support vector machines) with self-learning artificial intelligence, introducing data augmentation to overcome the hurdle of knowledge representativity. White blood cells count information is present in the recorded spectra, allowing significant discrimination and equivalence between hemogram and spectra principal component scores. Chemometrics methods correlate white blood cells count to spectral features but with lower accuracy. Self-Learning Artificial Intelligence has the highest correlation (0.8478) and a small standard error of 6.92 × 109 cells/L, corresponding to a mean absolute percentage error of 25.37%. Such allows the accurate diagnosis of white blood cells in the range of values of the reference interval (5.6 to 17.8 × 109 cells/L) and above. This research is an important step toward the existence of a miniaturized spectral point-of-care hemogram analyzer

Perchloroethylene gas-phase degradation over titania-coated transparent monoliths

Perchloroethylene (PCE) has been detected as one of the major pollutants in indoor air of wastewater treatment plants (WWTPs) with closed facilities. Its hazardousness requires the complete PCE removal from air. For this reason, gas-phase photooxidation of PCE was studied in a continuous-flow tubular photoreactor under simulated solar radiation. Since negligible degradation of PCE was observed by photolysis, photocatalytic oxidation (PCO) experiments were carried out employing a catalytic bed of cellulose acetate monoliths (CAM) coated with TiO2 (TiO2-CAM). The three TiO2-CAM samples tested (3, 6, and 9 TiO2 dip-coating layers), showed that the catalytic activity increases considerably with the number of layers (similar to 92% for the 9-layered TiO2-CAM). Different conditions of feed flow rate, pollutant concentration, feed relative humidity, and incident irradiance were tested for the 9-layered TiO2-CAM photocatalyst in order to assess the most relevant operating parameters. Taking into consideration the small path of the photoreactor employed (0.16 m length), PCO of PCE showed interesting results, achieving degradation efficiencies between 90 and similar to 98%, depending on the operating conditions used. The mathematical modelling of PCE kinetics through PCO suggested that there is no competition between PCE and H2O molecules to the surface active sites, even considering them as independent molecules that target distinct surface active sites (Langmuir-Hinshelwood bimolecular competitive two types of sites rate model). The validation of the PCO mathematical model at lab-scale allowed predictive simulations for PCO of PCE that can be used for possible scale-up a unit aiming at completely mineralization of PCE under solar irradiation. Finally, identification of PCE by-products allowed the complete formulation of a feasible reaction mechanism