Assessing land–ocean connectivity via submarine groundwater discharge (SGD) in the Ria Formosa Lagoon (Portugal): combining radon measurements and stable isotope hydrology

Natural radioactive tracer-based assessments of basin-scale submarine groundwater discharge (SGD) are well developed. However, SGD takes place in different modes and the flow and discharge mechanisms involved occur over a wide range of spatial and temporal scales. Quantifying SGD while discriminating its source functions therefore remains a major challenge. However, correctly identifying both the fluid source and composition is critical. When multiple sources of the tracer of interest are present, failure to adequately discriminate between them leads to inaccurate attribution and the resulting uncertainties will affect the reliability of SGD solute loading estimates. This lack of reliability then extends to the closure of local biogeochemical budgets, confusing measures aiming to mitigate pollution. Here, we report a multi-tracer study to identify the sources of SGD, distinguish its component parts and elucidate the mechanisms of their dispersion throughout the Ria Formosa – a seasonally hypersaline lagoon in Portugal. We combine radon budgets that determine the total SGD (meteoric + recirculated seawater) in the system with stable isotopes in water (δ2H, δ18O), to specifically identify SGD source functions and characterize active hydrological pathways in the catchment. Using this approach, SGD in the Ria Formosa could be separated into two modes, a net meteoric water input and another involving no net water transfer, i.e., originating in lagoon water re-circulated through permeable sediments. The former SGD mode is present occasionally on a multi-annual timescale, while the latter is a dominant feature of the system. In the absence of meteoric SGD inputs, seawater recirculation through beach sediments occurs at a rate of  ∼  1.4  ×  106 m3 day−1. This implies that the entire tidal-averaged volume of the lagoon is filtered through local sandy sediments within 100 days ( ∼  3.5 times a year), driving an estimated nitrogen (N) load of  ∼  350 Ton N yr−1 into the system as NO3−. Land-borne SGD could add a further  ∼  61 Ton N yr−1 to the lagoon. The former source is autochthonous, continuous and responsible for a large fraction (59 %) of the estimated total N inputs into the system via non-point sources, while the latter is an occasional allochthonous source capable of driving new production in the system.

Ensemble Stream Model for Data-Cleaning in Sensor Networks

Ensemble Stream Modeling and Data-cleaning are sensor information processing systems have different training and testing methods by which their goals are cross-validated. This research examines a mechanism, which seeks to extract novel patterns by generating ensembles from data. The main goal of label-less stream processing is to process the sensed events to eliminate the noises that are uncorrelated, and choose the most likely model without over fitting thus obtaining higher model confidence. Higher quality streams can be realized by combining many short streams into an ensemble which has the desired quality. The framework for the investigation is an existing data mining tool. First, to accommodate feature extraction such as a bush or natural forest-fire event we make an assumption of the burnt area (BA*), sensed ground truth as our target variable obtained from logs. Even though this is an obvious model choice the results are disappointing. The reasons for this are two: One, the histogram of fire activity is highly skewed. Two, the measured sensor parameters are highly correlated. Since using non descriptive features does not yield good results, we resort to temporal features. By doing so we carefully eliminate the averaging effects; the resulting histogram is more satisfactory and conceptual knowledge is learned from sensor streams. Second is the process of feature induction by cross-validating attributes with single or multi-target variables to minimize training error. We use F-measure score, which combines precision and accuracy to determine the false alarm rate of fire events. The multi-target data-cleaning trees use information purity of the target leaf-nodes to learn higher order features. A sensitive variance measure such as f-test is performed during each node’s split to select the best attribute. Ensemble stream model approach proved to improve when using complicated features with a simpler tree classifier. The ensemble framework for data-cleaning and the enhancements to quantify quality of fitness (30% spatial, 10% temporal, and 90% mobility reduction) of sensor led to the formation of streams for sensor-enabled applications. Which further motivates the novelty of stream quality labeling and its importance in solving vast amounts of real-time mobile streams generated today.

Nonlinear combining and compression in multicore fibers

We demonstrate numerically light-pulse combining and pulse compression using wave-collapse (self-focusing) energy-localization dynamics in a continuous-discrete nonlinear system, as implemented in a multicore fiber (MCF) using one-dimensional (1D) and 2D core distribution designs. Large-scale numerical simulations were performed to determine the conditions of the most efficient coherent combining and compression of pulses injected into the considered MCFs. We demonstrate the possibility of combining in a single core 90% of the total energy of pulses initially injected into all cores of a 7-core MCF with a hexagonal lattice. A pulse compression factor of about 720 can be obtained with a 19-core ring MCF.

Designing intrinsically photostable low band gap polymers:a smart tool combining EPR spectroscopy and DFT calculations

A rapid and efficient method to identify the weak points of the complex chemical structure of low band gap (LBG) polymers, designed for efficient solar cells, when submitted to light exposure is reported. This tool combines Electron Paramagnetic Resonance (EPR) using the 'spin trapping method' coupled with density functional theory modelling (DFT). First, the nature of the short life-time radicals formed during the early-stages of photo-degradation processes are determined by a spin-trapping technique. Two kinds of short life-time radical (R and R′O) are formed after 'short-duration' illumination in an inert atmosphere and in ambient air, respectively. Second, simulation allows the identification of the chemical structures of these radicals revealing the most probable photochemical process, namely homolytical scission between the Si atom of the conjugated skeleton and its pendent side-chains. Finally, DFT calculations confirm the homolytical cleavage observed by EPR, as well as the presence of a group that is highly susceptible to photooxidative attack. Therefore, the synergetic coupling of a spin trapping method with DFT calculations is shown to be a rapid and efficient method for providing unprecedented information on photochemical mechanisms. This approach will allow the design of LBG polymers without the need to trial the material within actual solar cell devices, an often long and costly screening procedure.

Evolution of classifiers for pitch estimation of piano music using cartesian genetic programming

Pitch Estimation, also known as Fundamental Frequency (F0) estimation, has been a popular research topic for many years, and is still investigated nowadays. The goal of Pitch Estimation is to find the pitch or fundamental frequency of a digital recording of a speech or musical notes. It plays an important role, because it is the key to identify which notes are being played and at what time. Pitch Estimation of real instruments is a very hard task to address. Each instrument has its own physical characteristics, which reflects in different spectral characteristics. Furthermore, the recording conditions can vary from studio to studio and background noises must be considered. This dissertation presents a novel approach to the problem of Pitch Estimation, using Cartesian Genetic Programming (CGP).We take advantage of evolutionary algorithms, in particular CGP, to explore and evolve complex mathematical functions that act as classifiers. These classifiers are used to identify piano notes pitches in an audio signal. To help us with the codification of the problem, we built a highly flexible CGP Toolbox, generic enough to encode different kind of programs. The encoded evolutionary algorithm is the one known as 1 + , and we can choose the value for . The toolbox is very simple to use. Settings such as the mutation probability, number of runs and generations are configurable. The cartesian representation of CGP can take multiple forms and it is able to encode function parameters. It is prepared to handle with different type of fitness functions: minimization of f(x) and maximization of f(x) and has a useful system of callbacks. We trained 61 classifiers corresponding to 61 piano notes. A training set of audio signals was used for each of the classifiers: half were signals with the same pitch as the classifier (true positive signals) and the other half were signals with different pitches (true negative signals). F-measure was used for the fitness function. Signals with the same pitch of the classifier that were correctly identified by the classifier, count as a true positives. Signals with the same pitch of the classifier that were not correctly identified by the classifier, count as a false negatives. Signals with different pitch of the classifier that were not identified by the classifier, count as a true negatives. Signals with different pitch of the classifier that were identified by the classifier, count as a false positives. Our first approach was to evolve classifiers for identifying artifical signals, created by mathematical functions: sine, sawtooth and square waves. Our function set is basically composed by filtering operations on vectors and by arithmetic operations with constants and vectors. All the classifiers correctly identified true positive signals and did not identify true negative signals. We then moved to real audio recordings. For testing the classifiers, we picked different audio signals from the ones used during the training phase. For a first approach, the obtained results were very promising, but could be improved. We have made slight changes to our approach and the number of false positives reduced 33%, compared to the first approach. We then applied the evolved classifiers to polyphonic audio signals, and the results indicate that our approach is a good starting point for addressing the problem of Pitch Estimation.

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Combining multispecies home range and distribution models aids assessment of MPA effectiveness

Marine protected areas (MPAs) are today's most important tools for the spatial management and conservation of marine species. Yet, the true protection that they provide to individual fish is unknown, leading to uncertainty associated with MPA effectiveness. In this study, conducted in a recently established coastal MPA in Portugal, we combined the results of individual home range estimation and population distribution models for 3 species of commercial importance and contrasting life histories to infer (1) the size of suitable areas where they would be fully protected and (2) the vulnerability to fishing mortality of each species. Results show that the relationship between MPA size and effective protection is strongly modulated by both the species' home range and the distribution of suitable habitat inside and outside the MPA. This approach provides a better insight into the true potential of MPAs in effectively protecting marine species, since it can reveal the size and location of the areas where protection is most effective and a clear, quantitative estimation of the vulnerability to fishing throughout an entire MPA.

Machine learning techniques for implicit interaction using mobile sensors

Interactions in mobile devices normally happen in an explicit manner, which means that they are initiated by the users. Yet, users are typically unaware that they also interact implicitly with their devices. For instance, our hand pose changes naturally when we type text messages. Whilst the touchscreen captures finger touches, hand movements during this interaction however are unused. If this implicit hand movement is observed, it can be used as additional information to support or to enhance the users’ text entry experience. This thesis investigates how implicit sensing can be used to improve existing, standard interaction technique qualities. In particular, this thesis looks into enhancing front-of-device interaction through back-of-device and hand movement implicit sensing. We propose the investigation through machine learning techniques. We look into problems on how sensor data via implicit sensing can be used to predict a certain aspect of an interaction. For instance, one of the questions that this thesis attempts to answer is whether hand movement during a touch targeting task correlates with the touch position. This is a complex relationship to understand but can be best explained through machine learning. Using machine learning as a tool, such correlation can be measured, quantified, understood and used to make predictions on future touch position. Furthermore, this thesis also evaluates the predictive power of the sensor data. We show this through a number of studies. In Chapter 5 we show that probabilistic modelling of sensor inputs and recorded touch locations can be used to predict the general area of future touches on touchscreen. In Chapter 7, using SVM classifiers, we show that data from implicit sensing from general mobile interactions is user-specific. This can be used to identify users implicitly. In Chapter 6, we also show that touch interaction errors can be detected from sensor data. In our experiment, we show that there are sufficient distinguishable patterns between normal interaction signals and signals that are strongly correlated with interaction error. In all studies, we show that performance gain can be achieved by combining sensor inputs.

Combining micro and macro data in hedonic price indexes

This paper proposes arithmetic and geometric Paasche quality-adjusted price indexes that combine micro data from the base period with macro data on the averages of asset prices and characteristics at the index period.

The three-dimensional single-bin-size bin packing problem: combining metaheuristic and machine learning approaches

The Three-Dimensional Single-Bin-Size Bin Packing Problem is one of the most studied problem in the Cutting & Packing category. From a strictly mathematical point of view, it consists of packing a finite set of strongly heterogeneous “small” boxes, called items, into a finite set of identical “large” rectangles, called bins, minimizing the unused volume and requiring that the items are packed without overlapping. The great interest is mainly due to the number of real-world applications in which it arises, such as pallet and container loading, cutting objects out of a piece of material and packaging design. Depending on these real-world applications, more objective functions and more practical constraints could be needed. After a brief discussion about the real-world applications of the problem and a exhaustive literature review, the design of a two-stage algorithm to solve the aforementioned problem is presented. The algorithm must be able to provide the spatial coordinates of the placed boxes vertices and also the optimal boxes input sequence, while guaranteeing geometric, stability, fragility constraints and a reduced computational time. Due to NP-hard complexity of this type of combinatorial problems, a fusion of metaheuristic and machine learning techniques is adopted. In particular, a hybrid genetic algorithm coupled with a feedforward neural network is used. In the first stage, a rich dataset is created starting from a set of real input instances provided by an industrial company and the feedforward neural network is trained on it. After its training, given a new input instance, the hybrid genetic algorithm is able to run using the neural network output as input parameter vector, providing as output the optimal solution. The effectiveness of the proposed works is confirmed via several experimental tests.

Combining molecular alterations and functional imaging in metastatic castration resistant prostate cancer treated with taxanes

The treatment of metastatic castration-resistant prostate cancer (mCRPC) is currently characterized by several drugs with different mechanisms of action, such as new generation hormonal agents (abiraterone, enzalutamide), chemotherapy (docetaxel, cabazitaxel), PARP inhibitors (olaparib) and radiometabolic therapies (radium-223, LuPSMA). There is an urgent need to identify biomarkers to guide personalized therapy in mCRPC. In recent years, the status of androgen receptor (AR) gene detected in liquid biopsy has been associated with outcomes in patients treated with abiraterone or enzalutamide. More recently, plasma tumor DNA (ptDNA) and its changes during treatment have been identified as early indicators of response to anticancer treatments. Recent works also suggested a potential role of tumor-related metabolic parameters of 18Fluoro-Choline Positron Emission Tomography (F18CH-PET)-computed tomography (CT) as a prognostic tool in mCRCP. Other clinical features, such as the presence of visceral metastases, have been correlated with outcome in mCRPC patients. Recent studies conducted by our research group have designed and validated a prognostic model based on the combination of molecular characteristics (ptDNA levels), metabolic features found in basal FCH PET scans (metabolic tumor volume values, MTV), clinical parameters (absence or presence of visceral metastases), and laboratory tests (serum lactate dehydrogenase levels, LDH). Within this PhD project, 30 patients affected by mCRPC, pre-treated with abiraterone or enzalutamide, candidate for taxane-based treatments (docetaxel or cabazitaxel), have been prospectively evaluated. The prognostic model previously described was applied to this population, to interrogate its prognostic power in a more advanced cohort of patients, resulting in a further external validation of the tool.

Boosting microbial fuel cell performance by combining with an external supercapacitor

Microbial Fuel Cells (MFC) technology finds space as a promising technology as a green alternative power-generating device, by the possibility to convert organic matter directly into electricity by microbially catalysed reactions, especially for the potential of the simultaneous treatment of wastewaters. Despite the studies that were carried out over the decades, MFCs still provide insufficient power and current densities in order to be commercially attractive in the energy market. Scientific community today pursues two main strategies in order to increase the overall performance output of the MFC. The first is to support the cells with an external supercapacitor (SC), which is able to accept and deliver charge much faster than normal capacitors, thanks to the use of an electrostatic double-layer capacitance, in combination with pseudocapacitance. The second is to implement directly the SC into the MFC, by using carbon electrodes with high surface area, similar to the SC. Both strategies are eventually supported by the use of charge boosters, respect to the application of the MFC. Galvanostatic measures for the MFC and SCs are performed at different currents, alone and by integration of both devices. The SCs used have a capacitance respectively of 1F, 3F and 6F. Subsequently, a stack of MFCs is assembled and paired to a 3F SC, in order to power an ambient diffuser, able to spray at intervals with a can and a controller. In conclusion, the use of a SC in parallel to the MFCs increases the overall performance of the system. The SC remove the discharge current limit of the MFC and increases the energy and power delivered by the system, allowing it to power for a certain time the ambient diffuser successfully. The key factor highlighted by the final experiment was the insufficient charging time of the SC, resulting finally in a voltage that is inadequate to power the device. Further studies are therefore necessary to improve the performance of the MFCs.

Integrated Proteomics Identified Up-regulated Focal Adhesion-mediated Proteins In Human Squamous Cell Carcinoma In An Orthotopic Murine Model.

Understanding the molecular mechanisms of oral carcinogenesis will yield important advances in diagnostics, prognostics, effective treatment, and outcome of oral cancer. Hence, in this study we have investigated the proteomic and peptidomic profiles by combining an orthotopic murine model of oral squamous cell carcinoma (OSCC), mass spectrometry-based proteomics and biological network analysis. Our results indicated the up-regulation of proteins involved in actin cytoskeleton organization and cell-cell junction assembly events and their expression was validated in human OSCC tissues. In addition, the functional relevance of talin-1 in OSCC adhesion, migration and invasion was demonstrated. Taken together, this study identified specific processes deregulated in oral cancer and provided novel refined OSCC-targeting molecules.

Histopathological Alterations In The Prostates Of Mongolian Gerbils Exposed To A High-fat Diet And Di-n-butyl Phthalate Individually Or In Combination.

Both high-fat diet and exposure to endocrine-disrupting chemicals have been implicated in susceptibility to pathological prostate lesions, but the consequences of combining the two have not yet been examined. We evaluated the effects of gestational and postnatal exposure to a high-fat diet (20% fat) and low doses of di-n-butyl phthalate (DBP; 5mg/kg/day), individually or in combination, on the tissue response and incidence of pathological lesions in the ventral prostate of adult gerbils. Continuous intake of a high-fat diet caused dyslipidemia, hypertrophy, and promoted the development of inflammatory, premalignant and malignant prostate lesions, even in the absence of obesity. Life-time DBP exposure was obesogenic and dyslipidemic and increased the incidence of premalignant prostate lesions. Combined exposure to DBP and a high-fat diet also caused prostate hypertrophy, but the effects were less severe than those of individual treatments; combined exposure neither induced an inflammatory response nor altered serum lipid content.

Hipotaxe e Gramaticalização: uma Análise das Construções de Tempo e de Condição

This paper studies complex sentences with temporal hypotatic clauses and with conditional hypotatic clauses in order to investigate the degree of grammaticalization shown by these two kinds of utterances. Our hypothesis is that the more the hypotatic clause is integrated to the nuclear clause, the greater is the degree of grammaticalization. Such degree of integration was measured according to three groups of factors, and the results show that, regarding two of the variables evaluated, the conditional clauses are the most integrated to their nucleus, but, in another rank of evaluation, the temporal clauses are the most integrated ones. Considering that this study is based on a functionalist view, the results may be interpreted according to the principle that there is a competition of motivations in the use of language, so that each utterance reflects the balance of such forces.