Efficient and close targets in data envelopment analysis (DEA)

This paper draws attention for the fact that traditional Data Envelopment Analysis (DEA) models do not provide the closest possible targets (or peers) to inefficient units, and presents a procedure to obtain such targets. It focuses on non-oriented efficiency measures both measured in relation to a Free Disposal Hull (FDH) technology and in relation to a convex technology. The approaches developed for finding close targets are applied to a sample of Portuguese bank branches

Finding closest targets in non-oriented DEA models: the case of convex and non-convex technologies

This paper draws attention for the fact that traditional Data Envelopment Analysis (DEA) models do not provide the closest possible targets (or peers) to inefficient units, and presents a procedure to obtain such targets. It focuses on non-oriented efficiency measures (which assume that production units are able to control, and thus change, inputs and outputs simultaneously) both measured in relation to a Free Disposal Hull (FDH) technology and in relation to a convex technology. The approaches developed for finding close targets are applied to a sample of Portuguese bank branches

Sistema de Deteção de Transações Fraudulentas no e-commerce através de Machine Learning

O crescimento exponencial do comércio eletrónico trouxe inúmeras vantagens e oportunidades ao facilitar o estilo de vida dos seres humanos. No entanto, deu também origem a um grave problema: a fraude online. Com o propósito de colmatar este problema, este trabalho aborda a necessidade de desenvolver sistemas de deteção de fraude complexos no âmbito do comércio eletrónico. Após uma revisão abrangente da literatura, foram identificadas e implementadas técnicas que contribuíram para a melhoria dos projetos existentes, permitindo uma análise comparativa mais precisa. Neste contexto, os algoritmos de RF, LR, SVM, KNN, DT, LSTM e CNN, por serem os mais adequados a sistemas de classificação pela sua versatilidade e capacidade de aprender padrões complexos nos dados, foram aplicados a três conjuntos de dados distintos. Para avaliar rigorosamente os modelos propostos, o conjunto de dados foi dividido em 70% de dados para treino e os restantes 30% para teste. Cada um dos conjuntos de dados apresenta características específicas, de forma a avaliar o impacto de técnicas de oversampling e undersampling. Os algoritmos foram aplicados também aos mesmos conjuntos com os dados normalizados, para inferir quais os modelos que beneficiam desta normalização. Os resultados demonstraram que os modelos RF e CNN apresentaram um desempenho superior em comparação com os restantes algoritmos testados. Estes algoritmos foram posteriormente otimizados com a exploração dos hiper-parâmetros respetivos, o que permitiu melhorar o desempenho do modelo e, por sua vez, alcançar resultados de maior qualidade. A utilização de inteligência artificial na deteção de fraude no comércio eletrónico é fundamental para proteger os interesses tanto das empresas como dos consumidores. Este trabalho teve como foco principal contribuir para o avanço dos sistemas de deteção de transações fraudulentas ao fornecer informações sobre pontos positivos e negativos de vários algoritmos de machine learning no contexto do problema em questão.The exponential growth of e-commerce has brought numerous advantages and opportunities by facilitating the lifestyle of human beings. However, it has also given rise to a serious problem: online fraud. With the purpose of solving this problem, this work addresses the imperative need to develop complex fraud detection systems within the scope of electronic commerce. After a systematic review of the literature, different techniques were identified and implemented that contributed to the improvement of existing projects, allowing for a more accurate comparative analysis. In this context, the RF, LR, SVM, KNN, DT, LSTM and CNN algorithms, as they are the most suitable for classification systems due to their versatility and ability to learn complex patterns in data, were applied to three distinct datasets. To rigorously evaluate the proposed models, the dataset was divided into 70% training data and the remaining 30% to testing data. Each of the datasets consists in specific characteristics, in order to evaluate the impact of oversampling and undersampling techniques. The algorithms were also applied to the same datasets with normalized data, to infer which models benefit from this normalization. The results demonstrated that the RF and CNN algorithms presented superior performance compared to the remaining algorithms tested. These algorithms were subsequently optimized by exploring the respective hyper-parameters, which allowed improving the model's performance and, in turn, achieving higher quality results. The use of artificial intelligence to detect fraud in e-commerce is essential to protect the interests of both companies and consumers. This work's main focus was to contribute to the advancement of fraudulent purchase detection systems by providing information about the positive and negative points of various machine learning algorithms in the context of the problem in question

Influence of a Concurrent Exercise Training Intervention during Pregnancy on Maternal and Arterial and Venous Cord Serum Cytokines: The GESTAFIT Project

The aim of the present study was to analyze the influence of a supervised concurrent exercise-training program, from the 17th gestational week until delivery, on cytokines in maternal (at 17th and 35th gestational week, and at delivery) and arterial and venous cord serum. Fifty-eight Caucasian pregnant women (age: 33.5 +/- 4.7 years old, body mass index: 23.6 +/- 4.1kg/m(2)) from the GESTAFIT Project (exercise (n = 37) and control (n = 21) groups) participated in this quasi-experimental study (per-protocol basis). The exercise group followed a 60-min 3 days/week concurrent (aerobic-resistance) exercise-training from the 17th gestational week to delivery. Maternal and arterial and venous cord serum cytokines (fractalkine, interleukin (IL)-1 beta, IL-6, IL-8, IL-10, interferon (IFN)-gamma, and tumor necrosis factor (TNF)-alpha) were assessed using Luminex xMAP technology. In maternal serum (after adjusting for the baseline values of cytokines), the exercise group decreased TNF-alpha (from baseline to 35th week, p = 0.02), and increased less IL-1 beta (from baseline to delivery, p = 0.03) concentrations than controls. When adjusting for other potential confounders, these differences became non-significant. In cord blood, the exercise group showed reduced arterial IL-6 and venous TNF-alpha (p = 0.03 and p = 0.001, respectively) and higher concentrations of arterial IL-1 beta (p = 0.03) compared to controls. The application of concurrent exercise-training programs could be a strategy to modulate immune responses in pregnant women and their fetuses. However, future research is needed to better understand the origin and clearance of these cytokines, their role in the maternal-placental-fetus crosstalk, and the influence of exercise interventions on them

Pesquisa de anticorpos contra o vírus da imunodeficiência humana tipos 1 e 2 em amostras de sangue seco coletadas em papel filtro

Human Immunodeficiency Vírus Type 1 and 2 antibodies detection was performed in 457 dried whole blood spots samples (S&S 903). Q-Preven HIV 1+2 was the screening test used. The results were compared with the gold standard serum tests by ELISA (Cobas Core e Axsym HIV1/2 gO) and imunofluorescence was the definitive confirmatory test. The samples were obtained from the Hospital Nossa Senhora da Conceição in Porto Alegre, RS - Brazil, through whole blood transfer to filter paper card and sent to Caxias do Sul, RS - Brazil where the tests were performed. The dried whole blood spot stability was evaluated with two different panels. The first one was composed of five negative and five positive samples stored at room temperature, 4 ºC, -20 ºC and -70 ºC, while the second was composed of two negative and three positive samples stored at 37 ºC (humidityForam realizados 457 testes para detectar anticorpos contra o Vírus da Imunodeficiência Humana tipos 1 e 2, em amostras de sangue total seco coletadas em papel filtro (S&S 903), com o teste de triagem Q-Preven HIV 1+2, comparando-se com os resultados dos testes de triagem no soro (Cobas Core e Axsym HIV1/2 gO), sendo a imunofluorescência indireta o teste confirmatório. As amostras foram obtidas no Hospital Conceição em Porto Alegre, pela transferência de sangue total para cartão de papel filtro e encaminhadas para Caxias do Sul para a realização dos testes. Foi analisada a estabilidade da amostra em papel filtro com a utilização de dois painéis: o primeiro com cinco amostras negativas e cinco positivas armazenadas por seis semanas à temperatura ambiente, 4 ºC, -20 ºC e -70 ºC; o segundo com duas negativas e três positivas armazenadas por seis semanas com avaliações semanais a 37 ºC (umidad

Improving Sustainability through Corrosion Resistance of Reinforced Concrete by Using a Manufactured Blended Cement and Fly Ash

The objective of this paper is to report the improvement of sustainability through the increase of reinforced concrete corrosion resistance by using a blended cement and fly ash. Different reinforced concrete mixtures were prepared with partial substitution of a manufactured blended cement with fly ash from a thermal power plant in Andorra (Teruel, Spain). These mixtures were manufactured using three different water/cement ratios (0.46, 0.59, and 0.70) and three substitution percentages of cement by fly ash (0%, 25%, and 50%). The test cylinders underwent an accelerated carbonation process and exposure to different chloride levels, with the aim of characterizing the corrosion level of the different mixtures. The addition of local FA matched or even improved the resistance of the control mixture against carbonation and chlorides.This work was economically supported by Conacyt: National Scholarship Call: 290604 (No. 309129), mixed scholarship: Scholarship 2012–2013 for foreign mobility (290674), Basic Science Project 155363, Universidad Autónoma de Nuevo León and Universidad de Alicante

Optimization of Au:CuO thin films by plasma surface modification for high-resolution LSPR gas sensing at room temperature

In this study, thin films composed of gold nanoparticles embedded in a copper oxide matrix (Au:CuO), manifesting Localized Surface Plasmon Resonance (LSPR) behavior, were produced by reactive DC magnetron sputtering and post-deposition in-air annealing. The effect of low-power Ar plasma etching on the surface properties of the plasmonic thin films was studied, envisaging its optimization as gas sensors. Thus, this work pretends to attain the maximum sensing response of the thin film system and to demonstrate its potential as a gas sensor. The results show that as Ar plasma treatment time increases, the host CuO matrix is etched while Au nanoparticles are uncovered, which leads to an enhancement of the sensitivity until a certain limit. Above such a time limit for plasma treatment, the CuO bonds are broken, and oxygen is removed from the film’s surface, resulting in a decrease in the gas sensing capabilities. Hence, the importance of the host matrix for the design of the LSPR sensor is also demonstrated. CuO not only provides stability and protection to the Au NPs but also promotes interactions between the thin film’s surface and the tested gases, thereby improving the nanocomposite film’s sensitivity. The optimized sensor sensitivity was estimated at 849 nm/RIU, which demonstrates that the Au-CuO thin films have the potential to be used as an LSPR platform for gas sensors.This research was sponsored by the Portuguese Foundation for Science and Technology (FCT) in the framework of the Strategic Funding UIDB/04650/2020 and by the project CO2Plasmon with reference EXPL/CTM-REF/0750/2021. M.P. acknowledges her Ph.D. Scholarship from FCT, with reference SFRH/BD/137076/2018. Diana I. Meira acknowledges her Ph.D. Scholarship from FCT, with reference SFRH/BD/143262/2019