Prediction of high-performance concrete compressive strength through a comparison of machine learning techniques

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceHigh-performance concrete (HPC) is a highly complex composite material whose characteristics are extremely difficult to model. One of those characteristics is the concrete compressive strength, a nonlinear function of the same ingredients that compose HPC: cement, fly ash, blast furnace slag, water, superplasticizer, age, and coarse and fine aggregates. Research has shown time and time again that concrete strength is not determined just by the water-to-cement ratio, which was for years the go to metric. In addition, traditional methods that attempt to model HPC, such as regression analysis, do not provide sufficient prediction power due to nonlinear proprieties of the mixture. Therefore, this study attempts to optimize the prediction and modeling of the compressive strength of HPC by analyzing seven different machine learning (ML) algorithms: three regularization algorithms (Lasso, Ridge and Elastic Net), three ensemble algorithms (Random Forest, Gradient Boost and AdaBoost), and Artificial Neural Networks. All techniques were built and tested with a dataset composed of data from 17 different concrete strength test laboratories, under the same experimental conditions, which enabled a fair comparison amongst them and between different previous studies in the field. Feature importance analysis and outlier analysis were also performed, and all models were subject to a Wilcoxon Signed-Ranks Test to ensure statistically significant results. The final results show that the more complex ML algorithms provided greater accuracy than the regularization techniques, with Gradient Boost being the superior model amongst them, providing more accurate predictions than the sate-of-the-art. Better results were achieved using all variables and without removing outlier observations

I. Film: Using Electrospinning to Create Bioactive Films

This work was supported by the Portuguese Foundation for Science and Technology (FCT) through the project UID/Multi/04044/2013 and PAMI – ROTEIRO/0328/2013 (Nº 022158). In addition, the authors acknowledge the funding from the project Multifunctional Films for Intelligent and Active Applications (iFILM), project in consortium n. 17921, from the Portuguese National Innovation Agency. The authors gratefully acknowledge to all the consortium partners for their contribute on the project development.With increasing competition between companies, they are increasingly striving to produce higher quality products and to improve their production processes, and the food industry is no different. In order to preserve food for longer, this type of company has dedicated its attention to intelligent packaging, capable of preserving food for longer and of acting as a sensor of the state of food. To develop a film capable of fulfilling the new ambitions of the companies various manufacturing processes have been studied. This document gives an overview of the electrospinning process, capable of producing nano-fibres of biological materials. This document aims to serve as a study base for the I.film project through the study of the process that will provide the polymer film with the necessary biological characteristics.info:eu-repo/semantics/publishedVersio

Análise dos periódicos qualis/CAPES: visão geral da área de ensino em Ciências e Matemática

Resumo: No Brasil, a Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) propõe à produção científica, avaliação da qualidade dos periódicos, por meio de um sistema denominado Qualis. Buscamos descrever os periódicos da área de Ensino na especificidade de Educação/Ensino em Ciências e/ou Matemática, presente na edição Qualis CAPES (2013-2016). A partir do estudo dos periódicos A1 a B1 da área de Ensino. Aferimos 506 títulos, desconsiderando duplicidades na plataforma Sucupira. A Análise foi qualitativa e descritiva com uso do Formulário Google. Os dados pesquisados foram relacionados à fundação, disponibilização pela internet, bases de indexadores e citações. Encontramos um quadro de 82 revistas com escopo voltado a área de Ensino de Ciências e Matemática. Do total de periódicos, 46 em Ciências, 27 em Matemática e 9 periódicos Mistos. Os dados foram organizados conforme a caracterização das revistas. Realizamos análise e discussão, e por fim sugerimos recomendações sobre a qualidade.Palavras-chave: Periódicos; Ensino de Ciências; Educação Matemática; Qualis. Analysis of qualis/CAPES periodicals: overview of the area in of Science and Mathematics educationAbstract: In Brazil, the Coordination of Improvement of Higher Education Personnel (CAPES in portuguese) proposes to the scientific production, evaluation of the quality of journals, through a system called Qualis. We seek to describe the journals of the Teaching area in the specificity of Education / Teaching in Science and / or Mathematics, present in the edition Qualis CAPES (2013-2016). From the study of the periodicals A1 to B1 of the Teaching area. We checked 506 titles, disregarding duplications on the Sucupira Platform. Qualitative and descriptive analysis using the Google Form. The data surveyed were related to the foundation, availability by intenet, bases of indexers and citations. We found a table of 82 journals with scope focused on the area ofScience and Mathematics Teaching. Of the total periodicals, 46 in Science, 27 in Mathematics and 9 in Mixed Periodicals. The data were organization according to the characterization of the journals. We carry out analysis and discussion, and finally we suggest recommendations on quality.Keywords: Periodicals; Science Education; Mathematical Education; Qualis.

Determinants of the income velocity of money in Portugal: 1891–1998

This paper performs a long-run time series analysis of the behaviour of the income velocity of money in Portugal between 1891 and 1998 by assessing the importance of both macroeconomic and institutional factors and looking for particularities in the Portuguese case. We estimate two cointegration vectors for the income velocity of money, macroeconomic variables and institutional variables. It is apparent that one of these vectors reflects the relationship between income velocity and macroeconomic variables, while the other reflects the relationship between income velocity and institutional variables. Moreover, a regression analysis reveals that the usual U-shaped pattern is displayed with a relatively late inflection point located around 1970, which is consistent with the Spanish case. It is further noted that this is a feature of countries with a late economic and institutional development process.info:eu-repo/semantics/publishedVersio

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

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Photography-based taxonomy is inadequate, unnecessary, and potentially harmful for biological sciences

The question whether taxonomic descriptions naming new animal species without type specimen(s) deposited in collections should be accepted for publication by scientific journals and allowed by the Code has already been discussed in Zootaxa (Dubois & Nemésio 2007; Donegan 2008, 2009; Nemésio 2009a–b; Dubois 2009; Gentile & Snell 2009; Minelli 2009; Cianferoni & Bartolozzi 2016; Amorim et al. 2016). This question was again raised in a letter supported by 35 signatories published in the journal Nature (Pape et al. 2016) on 15 September 2016. On 25 September 2016, the following rebuttal (strictly limited to 300 words as per the editorial rules of Nature) was submitted to Nature, which on 18 October 2016 refused to publish it. As we think this problem is a very important one for zoological taxonomy, this text is published here exactly as submitted to Nature, followed by the list of the 493 taxonomists and collection-based researchers who signed it in the short time span from 20 September to 6 October 2016

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery