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The promotion of electric mobility has the potential to reduce several impacts of the road transport sector, such as increased emissions of greenhouse gas (GHG) and air pollutants and natural resource depletion. As such, decision makers and other stakeholders around the world have invested significant efforts in the transition to sustainable urban mobility based on the use of electric vehicles. However, there are few studies that investigate the potential benefits of electric mobility in all its facets; i.e., most focus on analyzing the benefits of replacing conventional cars while leaving aside other means of transportation with greater use and, consequently, higher emissions, such as, primarily, public transport and freight transport vehicles. Thus, this study aims to develop an action plan focused on electric mobility—APOEM, which stipulates gradual implementation steps in a time horizon of 5 years. For this purpose, a city with potential for electric mobility implementation called Atibaia, in the countryside of the State of São Paulo, in Brazil, is investigated. The results show that, in an ambitious scenario of electric vehicle deployment in the City of Atibaia, which presents energy supply compatible with demand, it would be possible to reduce a total of 30,788.70 tons of GHG in 5 years, and, as for emissions of air pollutants, it would be possible to reduce 140.38 tons of CO, 36.38 tons of NMHC, 1.58 tons of RCHO, 130.20 tons of NOx, 3.54 tons of MP and 0.75 tons of SO2 in 5 years. It is also worth noting that, in order for electrification to generate a greater contribution to the environment, it is necessary that electric mobility be generated from renewable energy sources. [ABSTRACT FROM AUTHOR]

Metrics are key elements that can give us valuable information about the effectiveness of agile software development processes, particularly considering the Scrum environment. This study aims to learn about the metrics adopted to assess agile development processes and explore the impact of how the role performed by each member in Scrum contributed to increasing/reducing the perception of the importance of these metrics. The impact of years of experience in Scrum on this perception was also explored. To this end, a quantitative study was conducted with 191 Scrum professionals in companies based in Portugal. The results show that the Scrum role is not a determining factor, while individuals with more years of experience have a higher perception of the importance of metrics related to team performance. The same conclusion is observed for the business value metric of the product backlog and the percentage of test automation in the testing phase. The findings allow for extending the knowledge about Scrum project management processes and their teams, in addition to offering important insights into the implementation of metrics for software engineering companies that adopt Scrum. [ABSTRACT FROM AUTHOR]

In view of the GHG reduction targets to be met, Brazilian researchers are looking for cleaner alternatives to energy sources. These alternatives are primarily to be applied in the transport sector, which presents high energy consumption, as well as high CO2 emissions. In this sense, this research developed an LCI study considering two bus alternatives for the city of Rio de Janeiro: diesel-powered internal combustion buses (ICEB) and a hydrogen-powered polymer fuel cell hybrid bus (FCHB). For the FCHB, three hydrogen production methods were also included: water electrolysis (WE), ethanol steam reforming (ESR) and natural gas steam reforming (NGSR). The research was aimed at estimating energy consumption, including the percentage of energy that is renewable, as well as CO2 emissions. The results show diesel as the energy source with the highest emissions as well as the highest fossil energy consumption. Regarding the alternatives for hydrogen production, water electrolysis stood out with the lowest emissions. [ABSTRACT FROM AUTHOR]

Motion-driven electromagnetic energy harvesting is a well-suited technological solution to autonomously power a broad range of autonomous devices. Although different harvester configurations and mechanisms have been already proposed to perform effective tuning and broadband harvesting, no methodology has proven to be effective to maximize the harvester performance for unknown and time-varying patterns of mechanical power sources externally exciting the harvesters. This paper provides, for the first time, a radically new concept of energy harvester to maximize the harvested energy for time-varying excitations: the self-adaptive electromagnetic energy harvester. This research work aims to analyze the electric energy harvesting gain when self-adaptive electromagnetic harvesters, using magnetic levitation architectures, are able to autonomously adapt their architecture as variations in the excitation patterns occur. This was accomplished by identifying the optimal harvester length for different excitation patterns and load resistances. Gains related to electric current and power exceeding 100 can be achieved for small-scale harvesters. The paper also describes comprehensive case studies to verify the feasibility of the self-adaptive harvester, considering the energy demand from the adaptive mechanism, namely the sensing, processing and actuation systems. These successful results highlight the potential of this innovative methodology to design highly sophisticated energy harvesters, both for a small- and large-scale power supply. [ABSTRACT FROM AUTHOR]

Flowering is an important agronomic trait that presents non-additive gene action. Genome-enabled prediction allow incorporating molecular information into the prediction of individual genetic merit. Artificial neural networks (ANN) recognize patterns of data and represent an alternative as a universal approximation of complex functions. In a Genomic Selection (GS) context, the ANN allows automatically to capture complicated factors such as epistasis and dominance. The objectives of this study were to predict the individual genetic merits of the traits associated with the flowering time in the common bean using the ANN approach, and to compare the predictive abilities obtained for ANN and Ridge Regression Best Linear Unbiased Predictor (RR-BLUP). We used a set of 80 bean cultivars and genotyping was performed with a set of 384 SNPs. The higher accuracy of the selective process of phenotypic values based on ANN output values resulted in a greater efficacy of the genomic estimated breeding value (GEBV). Through the root mean square error computational intelligence approaches via ANN, GEBV were shown to have greater efficacy than GS via RR-BLUP. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is an incurable and highly debilitating condition characterized by the progressive degeneration and/or death of nerve cells, which leads to manifestation of disabilities in cognitive functioning. In recent years, the development of biosensors for determination of AD's main biomarkers has made remarkable progress, particularly based on the tremendous advances in nanoscience and nanotechnology. The unique and outstanding properties of nanomaterials (such as graphene, carbon nanotubes, gold, silver and magnetic nanoparticles, polymers and quantum dots) have been contributing to enhance the electrochemical and optical behavior of transducers while offering a suitable matrix for the immobilization of biological recognition elements. Therefore, optical and electrochemical immuno- and DNA-biosensors with higher sensitivity, selectivity and longer stability have been reported. Nevertheless, strategies based on the detection of multiple analytes still need to be improved, as they will play a crucial role in minimizing misdiagnosis. This review aims to provide insights into the conjugation of nanomaterials with different transducers highlighting their crucial role in the construction of biosensors for detection of AD main biomarkers. [ABSTRACT FROM AUTHOR]

Genomic selection (GS) aims to incorporate molecular information directly into the prediction of individual genetic merit. Regularized quantile regression (RQR) can be used to fit models for all portions of a probability distribution of the trait, enabling the conditional quantile that "best" represents the functional relationship between dependent and independent variables to be chosen. The objective of this study was to predict the individual genetic merits of the traits associated with flowering time (DFF—days to first flower; DTF—days to flower) in the common bean using RQR and to compare the predictive abilities obtained from Random Regression Best Linear Unbiased Predictor (RR-BLUP), Bayesian LASSO (BLASSO), BayesB, and RQR for predicting the genetic merit. GS was performed using 80 genotypes of common beans genotyped for 380 single nucleotide polymorphism (SNP) markers. Considering the "best" RQR fit models (RQR0.3 for DFF, and RQR0.2 for DTF), the gains in predictive ability in relation to BLASSO, BayesB, and RR-BLUP were 18.75%, 22.58%, and 15.15% for DFF, respectively, and 15.20%, 24.65%, and 12.55% for DTF, respectively. The potential cultivars selected, considering the RQR "best" models, were among the 5% of cultivars with the lowest genomic estimated breeding value (GEBV) for the DFF and DTF traits—the IAC Imperador, IPR Colibri, Capixaba Precoce, and IPR Andorinha were included in the list of early cycle cultivars. [ABSTRACT FROM AUTHOR]