A DSS For Reservoirs Operation Based On The Execution Of Formal Models

Controlling the evolution of a reservoir in a flood episode can be critical, especially in Mediterranean basins where the concentration time is very short. In this situation, an automatic software tool can help the reservoir manager to make a quick decision. In this paper we present a DSS based on the execution of formal models by means of model checking. This technique exhaustively explores the different execution branches of a model of the system. The DSS addresses two flood control tasks. First, the DSS simulates the evolution of the reservoir (the water stored and released) when applying a predefined flood control strategy. Second, and probably the most important task, the DSS generates, in a short time, new flood control strategies based on a prediction of the water inflow and the goals and constraints imposed by the dam manager. This task is associated with the concept of risk in its technical sense; that is, the probability of occurrence of an extreme event and the damage cost associated. The DSS returns a set of maneuvers over the dam gates that control the evolution of the dam level following the goals and constrains imposed by the dam manager. Currently, the DSS includes hybrid model of a specific dam located in the city of Marbella. This model describes the continuous discharge curves and the discrete opening degrees of the dam gates. Other models of Andalusian dams will be included soon. In addition, there are discrete models of two flood control strategies (Dordogne and MEV) that can be applied. Moreover in order to generate new strategies, the DSS uses a non-deterministic model that simulates the dam manager. We present some case studies where we evaluate the performance of the tool and the suitability of the control strategies generated for different historical flood episodes

Childhood injury after a parental cancer diagnosis

A parental cancer diagnosis is psychologically straining for the whole family. We investigated whether a parental cancer diagnosis is associated with a higher-than-expected risk of injury among children by using a Swedish nationwide register-based cohort study. Compared to children without parental cancer, children with parental cancer had a higher rate of hospital contact for injury during the first year after parental cancer diagnosis (hazard ratio [HR] = 1.27, 95% confidence interval [CI] = 1.22-1.33), especially when the parent had a comorbid psychiatric disorder after cancer diagnosis (HR = 1.41, 95% CI = 1.08-1.85). The rate increment declined during the second and third year after parental cancer diagnosis (HR = 1.10, 95% CI = 1.07-1.14) and became null afterwards (HR = 1.01, 95% CI = 0.99-1.03). Children with parental cancer also had a higher rate of repeated injuries than the other children (HR = 1.13, 95% CI = 1.12-1.15). Given the high rate of injury among children in the general population, our findings may have important public health implications.NonePublishe

Mach-Zehnder interferometer based on all-fiber multimode interference device for DPSK signal demodulation

Differential Phase Shift Keying (DPSK) modulation format has been shown as a robust solution for next-generation optical transmission systems. One key device enabling such systems is the delay interferometer, converting the signal phase information into intensity modulation to be detected by the photodiodes. Usually, Mach-Zehnder interferometer (MZI) is used for demodulating DPSK signals. In this paper, we developed an MZI which is based on all-fiber Multimode Interference (MI) structure: a multimode fiber (MMF) located between two single-mode fibers (SMF) without any transition zones. The standard MZI is not very stable since the two beams go through two different paths before they recombine. In our design the two arms of the MZI are in the same fiber, which will make it less temperature-sensitive than the standard MZI. Performance of such MZI will be analyzed from transmission spectrum. Finally such all-fiber MI-based MZI (MI-MZI) is used to demodulate 10 Gbps DPSK signals. The demodulated signals are analyzed from eye diagram and bit error rate (BER)

Bioinformatic approaches for the genetic and phenotypic characterization of a Saccharomyces cerevisiae wine yeast collection

The objective of the present study was to compare genetic and phenotypic variation of 103 Saccharomyces cerevisiae strains isolated from winemaking environments. We used bioinformatics approaches to identify genetically similary strains with specific phenotypes and to estimate a strain's biotechnological potential. 
A S. cerevisiae collection, comprising 440 strains that were obtained from winemaking environments in Portugal has been constituted during the last years. All strains were genetically characterized by a set of eleven highly polymorphic microsatellites and showed unique allelic combinations. Using neural networks, a subset of 103 genetically most diverse strains was chosen for phenotypic analysis, that included growth in synthetic must media at various temperatures, utilization of carbon sources (glucose, ribose, arabinose, xylose, saccharose, galactose, rafinose, maltose, glycerol, potassium acetate and pyruvic acid), growth in ethanol containing media, evaluation of osmotic and oxidative stress resistance, H2S production and utilization of different nitrogen sources. Using supervised data mining approaches we have found that genotype represented with presence/absence of eleven microsatellites relates well with geographical location (performance evaluation using leave-out-out technique resulted in high performance scores; e.g., area under ROC curve was above 0.8 for a number of standard machine learning approaches tested). To find relations between phenotypes and genotypes, we used a two-step approach which first hierarchically clusters the strains according to their phenotype, and then tests if the resulting sub-clusters are identifiable using strain’s genetic data. Several groups of strains with similar phenotype profiles and common features in genotype were identified this way, and they are subject to further investigations. 

Financially supported by the programs POCI 2010 (FEDER/FCT, POCTI/AGR/56102/2004) and AGRO (ENOSAFE, Nº 762).
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Impact of facial conformation on canine health: Brachycephalic Obstructive Airway Syndrome

The domestic dog may be the most morphologically diverse terrestrial mammalian species known to man; pedigree dogs are artificially selected for extreme aesthetics dictated by formal Breed Standards, and breed-related disorders linked to conformation are ubiquitous and diverse. Brachycephaly–foreshortening of the facial skeleton–is a discrete mutation that has been selected for in many popular dog breeds e.g. the Bulldog, Pug, and French Bulldog. A chronic, debilitating respiratory syndrome, whereby soft tissue blocks the airways, predominantly affects dogs with this conformation, and thus is labelled Brachycephalic Obstructive Airway Syndrome (BOAS). Despite the name of the syndrome, scientific evidence quantitatively linking brachycephaly with BOAS is lacking, but it could aid efforts to select for healthier conformations. Here we show, in (1) an exploratory study of 700 dogs of diverse breeds and conformations, and (2) a confirmatory study of 154 brachycephalic dogs, that BOAS risk increases sharply in a non-linear manner as relative muzzle length shortens. BOAS only occurred in dogs whose muzzles comprised less than half their cranial lengths. Thicker neck girths also increased BOAS risk in both populations: a risk factor for human sleep apnoea and not previously realised in dogs; and obesity was found to further increase BOAS risk. This study provides evidence that breeding for brachycephaly leads to an increased risk of BOAS in dogs, with risk increasing as the morphology becomes more exaggerated. As such, dog breeders and buyers should be aware of this risk when selecting dogs, and breeding organisations should actively discourage exaggeration of this high-risk conformation in breed standards and the show ring

Data-Driven Methodology for Coliving Spaces and Space Profiling Based on Post-Occupancy Evaluation through Digital Trail of Users

Sustainable spaces are those that are optimized, accessible, promote user experience and aim to reduce CO2 emissions while enhancing users’ well-being and comfort. The purpose of this paper is to present a methodology that was developed during the COVID-19 pandemic to understand and improve the use of coliving spaces based on remote Post-Occupancy Evaluation (POE) analysis of the digital trail generated by the users. Applying the POE methodology based on data collection from IT infrastructure enabled to identify opportunities to improve the future design of human-centered spaces. The residential market, design-wise traditional for centuries, is now facing a high-speed adaptation to the changing needs, accelerated by the COVID-19 crisis. New ways of living and shared spaces like Coliving are escalating. Technology is both an enabler of this shift in housing and the solution to operating and managing these new buildings. This paper demonstrates, through the case study of a Coliving space located in Madrid, Spain, the benefits of implementing data analysis of the digital trail collected from in-built IT systems such as smart locks, Wi-Fi networks and electric consumption devices. The conclusion is that analysing the available data from the digital infrastructure of coliving buildings can enable practitioners to improve the future design of residential spaces

Personality detection from text, based on the MBTI model

Personality is a person's distinguishing set of behaviours, ways of perception and emotional patters. It also plays a key role in everyday life, and the addition of personality awareness across various fields may be of great benefit. The idea of obtaining a person's personality type without having to go through lengthy and at times biased traditional methods of questionnaires and interviews is thus of interest. With the growing popularity of online social networking sites, it is no longer difficult to get a hold of text generated by users of the various platforms. And with the advances in Artificial Intelligence (AI), it is now possible to make use of machine learning algorithms to detect personality. In this project, personality detection based on the Myers–Briggs Type Indicator (MBTI) personality model is explored using various machine learning algorithms. Data is first pre-processed and prepared to train the various machine learning algorithms that will form the classification models. The performance of each model is then recorded by testing them against data that has not been used for training the models. The model that performed the best can thus be evaluated and improvements can be made upon the model to increase accuracy in future work.Bachelor of Engineering (Computer Science

Assessing the effect of project-based learning on college students\u27 learning outcomes

The study investigated the effect of Project-based Learning (PBL) on learning approach, performance accuracy, and attitude towards the PBL activity. Qualitative evaluation on the course topic on memory was also obtained from the groups who underwent the PBL activity. The PBL was employed in a lesson on memory in two sections of students taking a course in general psychology. The results indicate that the students shift more to deep approach in their learning regardless whether they underwent PBL or not. It was found that the classes receiving the PBL activity on memory had significantly higher performance accuracy in the test and had higher attitude as compared with the other classes. The benefits of PBL and the phases of employing it in a general psychology course are explained in the paper