Increasing the photocatalytic and fungicide activities of Ag3PO4 microcrystals under visible-light irradiation

The present study reports for the first time the performance of silver phosphate (Ag₃PO₄) microcrystals as photocatalyst (degradation of Rodamine B-RhB) and antifungal agent (against Candida albicans–C. albicans) under visible-light irradiation (455 nm). Ag₃PO₄ microcrystals were synthesized by a simple co-precipitation (CP) method at room temperature. The structural and electronic properties of the as-synthetized Ag₃PO₄ have been investigated before and after 4 cycles of RhB degradation under visible light using X-ray diffraction (XRD), micro-Raman spectroscopy, UV–Vis spectrophotometer and field emission scanning electron microscopy (FE-SEM) images. The antifungal activity was analyzed in planktonic cells and 48h-biofilm of C. albicans by colony forming units (CFU) counting, confocal laser and FE-SE microscopies. Statistical analysis was carried out using SPSS software. Morphological and structural modifications of Ag₃PO₄ were observed upon recycling. After 4 recycles, the material maintained its photodegradation property; an eightfold increase in the efficiency of Ag₃PO₄ was observed in planktonic cells and a two fold increase in biofilm when irradiated under visible light. Thus, higher antifungal effectiveness against C. albicans was obtained when associated with visible-light irradiation.     

A multivariate fuzzy c-means method

Fuzzy c-means (FCMs) is an important and popular unsupervised partitioning algorithm used in several application domains such as pattern recognition, machine learning and data mining. Although the FCM has shown good performance in detecting clusters, the membership values for each individual computed to each of the clusters cannot indicate how well the individuals are classified. In this paper, a new approach to handle the memberships based on the inherent information in each feature is presented. The algorithm produces a membership matrix for each individual, the membership values are between zero and one and measure the similarity of this individual to the center of each cluster according to each feature. These values can change at each iteration of the algorithm and they are different from one feature to another and from one cluster to another in order to increase the performance of the fuzzy c-means clustering algorithm. To obtain a fuzzy partition by class of the input data set, a way to compute the class membership values is also proposed in this work. Experiments with synthetic and real data sets show that the proposed approach produces good quality of clustering.     

Is floating photovoltaic better than conventional photovoltaic? Assessing environmental impacts

Photovoltaic (PV) solar energy installations are growing all over the world as a promising renewable alternative to generate electricity. However, many studies have highlighted some drawbacks associated with the installation and operation of conventional solar energy power plants. Thus, floating photovoltaic (FPV) systems have been emerging as a new concept in solar energy to lessen negative environmental impacts caused by allocation of conventional PV facilities. This paper is an overview of the potential negative and positive environmental impacts caused by photovoltaic systems with particular interest on large-scale conventional and floating photovoltaic. This study addresses and compares the impacts at all phases of project implementation, which covers planning, construction, and operation and decommissioning, focusing on ambient located in the tropics. The overall impacts associated with project allocation such as deforestation (for the project implementation and site accessing), bird mortality, erosion, runoff, and change in microclimate are expected to have higher magnitudes for the implementation of conventional PV facilities. The results highlight advantages of FPV over conventional PV during the operational and decommissioning phases as well. Though, further studies are required to assess both qualitative and quantitative aspects of installations in similar areas.     

A segmentation algorithm for automated tracking of fast swimming unlabelled cells in three dimensions

Recent advances in microscopy and cytolabelling methods enable the real time imaging of cells as they move and interact in their real physiological environment. Scenarios in which multiple cells move autonomously in all directions are not uncommon in biology. A remarkable example is the swimming of marine spermatozoa in search of the conspecific oocyte. Imaging cells in these scenarios, particularly when they move fast and are poorly labelled or even unlabelled requires very fast three-dimensional time-lapse (3D+t) imaging. This 3D+t imaging poses challenges not only to the acquisition systems but also to the image analysis algorithms. It is in this context that this work describes an original automated multiparticle segmentation method to analyse motile translucent cells in 3D microscopical volumes. The proposed segmentation technique takes advantage of the way the cell appearance changes with the distance to the focal plane position. The cells translucent properties and their interaction with light produce a specific pattern: when the cell is within or close to the focal plane, its two-dimensional (2D) appearance matches a bright spot surrounded by a dark ring, whereas when it is farther from the focal plane the cell contrast is inverted looking like a dark spot surrounded by a bright ring. The proposed method analyses the acquired video sequence frame-by-frame taking advantage of 2D image segmentation algorithms to identify and select candidate cellular sections. The crux of the method is in the sequential filtering of the candidate sections, first by template matching of the in-focus and out-of-focus templates and second by considering adjacent candidates sections in 3D. These sequential filters effectively narrow down the number of segmented candidate sections making the automatic tracking of cells in three dimensions a straightforward operation.     

Soft component adaptation

Component adaptation is widely recognised to be one of the crucial problems in Component-Based Software Engineering (CBSE). We present here a formal methodology for the soft adaptation of components presenting mismatching interaction behaviours. The notions of access rights (associating components with the services they are allowed to use) and subservicing (providing alternative services in place of those requested by components lacking the required access rights) are exploited to feature a secure and flexible adaptation of third-party components.     

Short communication: The role of genotypes from animals without phenotypes in single-step genomic evaluations

In a 2-step genomic system, genotypes of animals without phenotypes do not influence genomic prediction of other animals, but that might not be the case in single-step systems. We investigated the effects of including genotypes from culled bulls on the reliability of genomic predictions from single-step evaluations. Four scenarios with a constant amount of phenotypic information and increasing numbers of genotypes from culled bulls were simulated and compared with respect to prediction reliability. With increasing numbers of genotyped culled bulls, there was a corresponding increase in prediction reliability. For instance, in our simulation scenario the reliability for selection candidates was twice as large when all culled bulls from the last 4 generations were included in the analysis. Single-step evaluations imply the imputation of all nongenotyped animals in the pedigree. We showed that this imputation was increasingly more accurate as increasingly more genotypic information from the culled bulls was taken into account. This resulted in higher prediction reliabilities. The extent of the benefit from including genotypes from culled bulls might be more relevant for small populations with low levels of reliabilities.     

Using Twitter® as source of information for dietary market research: a study on veganism and plant-based diets

Social media has experienced rapid growth in recent years and has been subjected of discussions on a wide range of topics including new lifestyles and eating habits, providing a great opportunity to obtain spontaneous consumer information. In this sense, the present work aimed to understand the perception of Twitter^® users about themes of veganism and plant-based diets. The social networking data mining methodology was applied to measure the relationship between both terms. The significant differences found were analysed using the global chi-square (χ²) test, and their sources of variation were investigated by the chi-square per cell. The results indicate that the vegan group's posts are more related to the categories of recipes, trends, criticism and negative comments about veganism, being more often citing sources when compared to the other group. The results of the plant-based diet group are more significantly related to the impacts of nutrition, physical activity and consumer health. In conclusion, Twitter^® has proved to be an interesting tool for obtaining data on (re) produced food publications on social media and their results can guide the market and the academic environment in creating new products, services and marketing strategies to answer the needs of specific consumers.