Knowledge maps as support tool for managing scientific competences: a case study at a portuguese research institute

In a research organization, finding someone who is an expert in a field and that can take up a given role, defining areas of excellence, or employing a new member all require understanding the competences that are available in-house. This work explores the idea of using knowledge or competence maps as support tools for managing scientific competences. We implemented a use case at the Institute of Electronics and Informatics Engineering of Aveiro, a research institute at the University of Aveiro, but the methodology we proposed can be adapted to virtually any research organization. Knowledge maps are visual representations of information that can be designed with variable granularities with respect to the knowledge assets of an organization. From a research management perspective, knowledge maps support the discovery of research competences and provide an instant overview of a topic by showing the main areas at a glance. This solution explored in this work employed data mining approaches for gathering information from public databases and presenting it using knowledge maps. Other visualization tools, such as bar graphs, tables, filters and search functionalities, were created and integrated into a web platform. When put together, these components could turn the platform into a key component for the administration of a research organization.publishe

Lossy-to-Lossless Compression of Biomedical Images Based on Image Decomposition

The use of medical imaging has increased in the last years, especially with magnetic resonance imaging (MRI) and computed tomography (CT). Microarray imaging and images that can be extracted from RNA interference (RNAi) experiments also play an important role for large-scale gene sequence and gene expression analysis, allowing the study of gene function, regulation, and interaction across a large number of genes and even across an entire genome. These types of medical image modalities produce huge amounts of data that, for several reasons, need to be stored or transmitted at the highest possible fidelity between various hospitals, medical organizations, or research units

A deep learning-based dirt detection computer vision system for floor-cleaning robots with improved data collection

Floor-cleaning robots are becoming increasingly more sophisticated over time and with the addition of digital cameras supported by a robust vision system they become more autonomous, both in terms of their navigation skills but also in their capabilities of analyzing the surrounding environment. This document proposes a vision system based on the YOLOv5 framework for detecting dirty spots on the floor. The purpose of such a vision system is to save energy and resources, since the cleaning system of the robot will be activated only when a dirty spot is detected and the quantity of resources will vary according to the dirty area. In this context, false positives are highly undesirable. On the other hand, false negatives will lead to a poor cleaning performance of the robot. For this reason, a synthetic data generator found in the literature was improved and adapted for this work to tackle the lack of real data in this area. This synthetic data generator allows for large datasets with numerous samples of floors and dirty spots. A novel approach in selecting floor images for the training dataset is proposed. In this approach, the floor is segmented from other objects in the image such that dirty spots are only generated on the floor and do not overlap those objects. This helps the models to distinguish between dirty spots and objects in the image, which reduces the number of false positives. Furthermore, a relevant dataset of the Automation and Control Institute (ACIN) was found to be partially labelled. Consequently, this dataset was annotated from scratch, tripling the number of labelled images and correcting some poor annotations from the original labels. Finally, this document shows the process of generating synthetic data which is used for training YOLOv5 models. These models were tested on a real dataset (ACIN) and the best model attained a mean average precision (mAP) of 0.874 for detecting solid dirt. These results further prove that our proposal is able to use synthetic data for the training step and effectively detect dirt on real data. According to our knowledge, there are no previous works reporting the use of YOLOv5 models in this application.publishe

Abstract computation in schizophrenia detection through artificial neural network based systems

Schizophrenia stands for a long-lasting state of mental uncertainty that may bring to an end the relation among behavior, thought, and emotion; that is, it may lead to unreliable perception, not suitable actions and feelings, and a sense of mental fragmentation. Indeed, its diagnosis is done over a large period of time; continuos signs of the disturbance persist for at least 6 (six) months. Once detected, the psychiatrist diagnosis is made through the clinical interview and a series of psychic tests, addressed mainly to avoid the diagnosis of other mental states or diseases. Undeniably, the main problem with identifying schizophrenia is the difficulty to distinguish its symptoms from those associated to different untidiness or roles. Therefore, this work will focus on the development of a diagnostic support system, in terms of its knowledge representation and reasoning procedures, based on a blended of Logic Programming and Artificial Neural Networks approaches to computing, taking advantage of a novel approach to knowledge representation and reasoning, which aims to solve the problems associated in the handling (i.e., to stand for and reason) of defective information.This work is funded by National Funds through the FCT, Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within projects PEstOE/EEI/UI0752/2014 and PEst-OE/QUI/UI0619/2012

Gellan gum injectable hydrogels for cartilage tissue engineering applications: in vitro studies and preliminary in vivo evaluation

Gellan gum is a polysaccharide that we have previously proposed for applications in the cartilage tissue engineering field. In this work, gellan gum hydrogels were tested for their ability to be used as injectable systems using simple processing methods, able to deliver and maintain chondrocytes by in situ gelation, and support cell viability and production of extracellular matrix (ECM). Rheological measurements determined that the sol–gel transition occurred near the body temperature at 39ºC, upon temperature decrease, in approximately 20 s. Gellan gum discs shows a storage compression modulus of around 80 kPa at a frequency of 1Hz by dynamic mechanical analysis. Human articular chondrocytes were encapsulated in the gels, cultured in vitro for total periods of 56 days, and analyzed for cell viability and ECM production. Calcein AM staining showed that cell kept viable after 14 days and the histological analysis and real-time quantitative polymerase chain reaction revealed that hyaline-like cartilage ECM was synthesized. Finally, the in vivo performance of the gellan gum hydrogels, in terms of induced inflammatory reaction and integration into the host tissue, was evaluated by subcutaneous implantation in Balb/c mice for 21 days. Histological analysis showed a residual fibrotic capsule at the end of the experiments. Dynamic mechanical analysis revealed that the gels were stable throughout the experiments while evidencing a tendency for decreasing mechanical properties, which was consistent with weight measurements. Altogether, the results demonstrate the adequacy of gellan gum hydrogels processed by simple methods for noninvasive injectable applications toward the formation of a functional cartilage tissue-engineered construct and originally report the preliminary response of a living organism to the subcutaneous implantation of the gellan gum hydrogels. These are the two novel features of this work.J. T. Oliveira would like to acknowledge the Portuguese Foundation for Science and Technology for his grant (SFRH/BD17135/2004). The authors would like to thank the patients at Hospital de S. Marcos, Braga, Portugal for the donation of the biological samples, and the medical staff for their help and support. The authors would also like to thank the Institute for Health and Life Sciences, University of Minho, Braga, Portugal, for allowing the use of their research facilities. This work was carried out under the scope of the European NoE EXPERTISSUES (NMP3-CT-2004 500283) and partially supported by the European Project HIPPOCRATES (STRP 505758-1)

Uncovering archaeological sites in airborne LiDAR data with data-centric artificial intelligence

Mapping potential archaeological sites using remote sensing and artificial intelligence can be an efficient tool to assist archaeologists during project planning and fieldwork. This paper explores the use of airborne LiDAR data and data-centric artificial intelligence for identifying potential burial mounds. The challenge of exploring the landscape and mapping new archaeological sites, coupled with the difficulty of identifying them through visual analysis of remote sensing data, results in the recurring issue of insufficient annotations. Additionally, the top-down nature of LiDAR data hinders artificial intelligence in its search, as the morphology of archaeological sites blends with the morphology of natural and artificial shapes, leading to a frequent occurrence of false positives. To address this problem, a novel data-centric artificial intelligence approach is proposed, exploring the available data and tools. The LiDAR data is pre-processed into a dataset of 2D digital elevation images, and the known burial mounds are annotated. This dataset is augmented with a copy-paste object embedding based on Location-Based Ranking. This technique uses the Land-Use and Occupation Charter to segment the regions of interest, where burial mounds can be pasted. YOLOv5 is trained on the resulting dataset to propose new burial mounds. These proposals go through a post-processing step, directly using the 3D data acquired by the LiDAR to verify if its 3D shape is similar to the annotated sites. This approach drastically reduced false positives, attaining a 72.53% positive rate, relevant for the ground-truthing phase where archaeologists visit the coordinates of proposed burial mounds to confirm their existence.This work was supported by the Project Odyssey: Platform for Automated Sensing in Archaeology Co-Financed by COMPETE 2020 and Regional Operational Program Lisboa 2020 through Portugal 2020 and FEDER under Grant ALG-01-0247-FEDER-070150.info:eu-repo/semantics/publishedVersio

Shielding AZ91D-1%Ca from corrosion through ultrasound melt treatment: a study for stent design

Magnesium-based materials show great potential for producing biodegradable stents, but their high corrosion rates are a roadblock. This study investigates whether ultrasound melt treatment can change the corrosion response of an extruded AZ91D-1.0%Ca (wt.%) in Earle's Balanced Salt Solution by tailoring the intermetallics' morphology in the as-extruded state. The results showed that the wires from ultrasound-treated ingots corroded faster than non-treated ones in immersion for up to 6 hours. This trend shifted for longer periods, and ultrasound-treated material showed lower corrosion rates and uniform corrosion, while the non-treated material displayed localized corrosion signs. Tensile testing of the wires demonstrated that immersion in EBSS lowered the tensile strength and elongation at fracture due to material degradation, regardless of the processing route. Nonetheless, this decline was sharper in the non-treated material. These findings suggest that ultrasound melt processing can be a promising method for improving the corrosion resistance of magnesium-based materials, paving the way for their use in manufacturing biodegradable stents.This work was supported by Portuguese FCT under the project UIDB/04436/2020, the doctoral grant PD/BD/140094/2018 and SFRH/BD/145285/2019

Electrochemical degradation of Diclofenac on catalysts based on CNT and M/CNT modified electrodes

Diclofenac (DCF) - a nonsteroidal anti-inflammatory drug - is one of the most frequently detected pharmaceutical compounds in the aquatic environment. Because of its negative effects on public health and environment, this emerging pollutant monitoring and removal from wastewater are of high importance. Electrochemical oxidation has attracted growing interest in the last years, due to its versatility, high efficiency and environment compatibility.Electrochemical oxidation of DCF has been studied using multiwalled carbon nanotubes (CNT) and monometallic catalysts based on carbon nanotubes (M/CNT), at different pH media. The electroreactivity of DCF on modified electrodes and the kinetic parameters of the redox reactions were determined using cyclic voltammetry. Electrodegradation of DCF in aqueous medium was carried out using long-term electrolyses. The products of these electrolyses were identified and quantified by HPLC-MS, GC-MS, HPLC-UV-RID and IC.BioTecNorte (operation NORTE-01-0145-FEDER-000004) and “AIProcMat@N2020 -Advanced Industrial Processes and Materials for a Sustainable Northern Region of Portugal 2020”, NORTE-01-0145-FEDER- 000006, supported by the Northern Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). This work also has been funded by national funds (FCT, Fundação para a Ciência e a Tecnologia), through the projects: PTDC/AAGTEC/5269/2014, Centre of Chemistry (UID/QUI/00686/2013 and UID/QUI/0686/2016) and LSRE-LCM (POCI-01-0145-FEDER-006984)info:eu-repo/semantics/publishedVersio

Atribuciones causales y nivel educativo familiar en la comprensión del desempeño escolar en alumnos portugueses

Este artigo analisa o contributo específico do nível educativo familiar e das atribuições causais para o bom e fraco rendimento escolar dos alunos na explicação do seu desempenho nas disciplinas de Língua Portuguesa e de Matemática numa amostra de 2.082 alunos do ensino público de 11 escolas de Portugal, incluindo as Regiões Autónomas dos Açores e da Madeira. As atribuições causais foram avaliadas por meio do Questionário das Atribuições Causais para os Resultados Escolares (QARE). O nível educativo familiar foi estimado levando em conta o nível escolar do progenitor com maior habilitação escolar. As classificações obtidas pelos alunos nas disciplinas de língua portuguesa e matemática foram também examinadas. Os resultados apontam para correlações estatisticamente significativas, destacando as atribuições na capacidade para a explicação do rendimento escolar, situação que contrasta com a atribuição dos níveis de rendimento a variáveis externas ao aluno. A análise de regressão permite associar 34,5% da variância no rendimento conjunto em Língua Portuguesa e em Matemática no ensino básico, assim como 21,3% no ensino secundário, às dimensões atribucionais e ao nível educativo familiar. Implicações educacionais são derivadas a partir dos achados.This paper analyzes the contribution of both family educational level and causal attributions in explaining students´ academic performance in Mathematics and Portuguese Language in a sample of 2.082 public school students from mainland Portugal and the Azores and Madeira regions. Causal attributions were assessed by the Causal Attributions Questionnaire for School Results (QARE). The family educational level was estimated taking into account the educational level of the parent with higher academic degree. The academic grades obtained by students in mathematics and portuguese language were also considered. The results show statistically significant correlations between variables. Internal causal attributions such as capacity and study methods were predominant to explain school performance in the sample and stood out in contrast to external attributions explanations. Regression analyses revealed that 34.5% of the variance in the combined achievement on Portuguese Language and Mathematics in primary education and 21.3% in secondary education could be explained by both the attributional dimensions and family education level. Some educational implications are present taking these data.Este artículo analiza la contribución específica del nivel educativo familiar y de las atribuciones causales para el buen y el flaco rendimiento escolar de los alumnos en la explicación de su desempeño en las disciplinas de Lengua Portuguesa y Matemáticas en una muestra de 2.082 alumnos de enseñanza pública de 11 escuelas de Portugal, incluyendo las Regiones Autónomas de Açores y Madeira. Las atribuciones causales fueron evaluadas por medio del “Questionário das Atribuições Causais para os Resultados Escolares” (QARE). El nivel educativo familiar fue estimado teniendo en cuenta el nivel escolar del progenitor con mayor habilitación escolar. Las clasificaciones obtenidas por los alumnos en las disciplinas de lengua portuguesa y matemáticas fueron también examinadas. Los resultados señalaron correlaciones estadísticamente significativas, destacando las atribuciones en la capacidad para la explicación del rendimiento escolar, situación que contrasta con la atribución de los niveles de rendimiento a variables externas al alumno. El análisis de regresión permite asociar 34.5% de la variancia en el rendimiento conjunto de la Lengua Portuguesa y las Matemáticas en la enseñanza básica, así como 21.3% en la enseñanza secundaria, a las dimensiones de atribución y al nivel educativo familiar. Implicaciones educacionales son derivadas a partir de los hallazgos.Editora Universitária São Francisc

Zeolite structures loading with an anticancer compound as drug delivery systems

The authors are thankful to Dr. A. S. Azevedo for collecting the powder diffraction data.Two different structures of zeolites, faujasite (FAU) and Linde type A (LTA), were studied to investigate their suitability for drug delivery systems (DDS). The zeolites in the sodium form (NaY and NaA) were used as hosts for encapsulation of α-cyano-4- hydroxycinnamic acid (CHC). CHC, an experimental anticancer drug, was encapsulated in both zeolites by diffusion in liquid phase. These new drug delivery systems, CHC@zeolite, were characterized by spectroscopic techniques (FTIR, 1H NMR, 13C and 27Al solidstate MAS NMR, and UV−vis), chemical analysis, powder X-ray diffraction (XRD) and scanning electron microscopy (SEM). The effect of the zeolites and CHC@zeolite drug deliveries on HCT-15 human colon carcinoma cell line viability was evaluated. Both zeolites alone revealed no toxicity to HCT-15 cancer cells. Importantly, CHC@zeolite exhibit an inhibition of cell viability up to 585-fold, when compared to the non-encapsulated drug. These results indicate the potential of the zeolites for drug loading and delivery into cancer cells to induce cell deathO.M. and R.A. are recipients of fellowships (SFRH/BD/36463/2007, SFRH/BI/51118/2010) from Fundação para a Ciência e a Tecnologia (FCT, Portugal). This work was supported by the FCT projects refs PEst-C/ QUI/UI0686/2011, PEst-C/CTM/LA0011/2011, and PTDC/ SAU-FCF/104347/2008, under the scope of “Programa Operacional Temático Factores de Competitividade” (COMPETE) of “Quadro Comunitário de Apoio III” and cofinanced by Fundo Comunitário Europeu FEDER, and the Centre of Chemistry and Life and Health Sciences Research Institute (University of Minho, Portugal)