EGCL: an extended G-Code Language with flow control, functions and mnemonic variables

In the context of computer numerical control (CNC) and computer aided manufacturing (CAM), the capabilities of programming languages such as symbolic and intuitive programming, program portability and geometrical portfolio have special importance -- They allow to save time and to avoid errors during part programming and permit code re-usage -- Our updated literature review indicates that the current state of art presents voids in parametric programming, program portability and programming flexibility -- In response to this situation, this article presents a compiler implementation for EGCL (Extended G-code Language), a new, enriched CNC programming language which allows the use of descriptive variable names, geometrical functions and flow-control statements (if-then-else, while) -- Our compiler produces low-level generic, elementary ISO-compliant Gcode, thus allowing for flexibility in the choice of the executing CNC machine and in portability -- Our results show that readable variable names and flow control statements allow a simplified and intuitive part programming and permit re-usage of the programs -- Future work includes allowing the programmer to define own functions in terms of EGCL, in contrast to the current status of having them as library built-in functions

Aedes aegypti Pharate First Instar Aseasonal Quiescence Cues Anticipatory Plasticity with Implications for Urban Vector Ecology and Control

The eggs of the dengue fever vector Aedes aegypti possess the ability to undergo an extended quiescence period hosting a fully developed first instar larvae within its chorion. As a result of this life history stage, pharate larvae can withstand months of dormancy inside the egg where they depend on stored reserves of maternal origin. This adaptation known as pharate first instar quiescence, allows A. aegypti to cope with fluctuations in water availability. An examination of this fundamental adaptation has shown that there are trade-offs associated with it. Aedes aegypti mosquitoes are frequently associated with urban habitats that may contain metal pollution. My research has demonstrated that the duration of this quiescence and the extent of nutritional depletion associated with it affects the physiology and survival of larvae that hatch in a suboptimal habitat; nutrient reserves decrease during pharate first instar quiescence and alter subsequent larval and adult fitness. The duration of quiescence compromises metal tolerance physiology and is coupled to a decrease in metallothionein mRNA levels. My findings also indicate that even low levels of environmentally relevant larval metal stress alter the parameters that determine vector capacity. My research has also demonstrated that extended pharate first instar quiescence can elicit a plastic response resulting in an adult phenotype distinct from adults reared from short quiescence eggs. Extended pharate first instar quiescence affects the performance and reproductive fitness of the adult female mosquito as well as the nutritional status of its progeny via maternal effects in an adaptive manner, i.e., anticipatory phenotypic plasticity results as a consequence of the duration of pharate first instar quiescence and alternative phenotypes may exist for this mosquito with quiescence serving as a cue possibly signaling the environmental conditions that follow a dry period. M findings may explain, in part, A. aegypti’s success as a vector and its geographic distribution and have implications for its vector capacity and control.

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.

Challenges and Opportunities of Machine Learning for Clinical and Omics Data

Clinical and omics data are a promising field of application for machine learning techniques even though these methods are not yet systematically adopted in healthcare institutions. Despite artificial intelligence has proved successful in terms of prediction of pathologies or identification of their causes, the systematic adoption of these techniques still presents challenging issues due to the peculiarities of the analysed data. The aim of this thesis is to apply machine learning algorithms to both clinical and omics data sets in order to predict a patient's state of health and get better insights on the possible causes of the analysed diseases. In doing so, many of the arising issues when working with medical data will be discussed while possible solutions will be proposed to make machine learning provide feasible results and possibly become an effective and reliable support tool for healthcare systems.

Information Usefulness To Support Dialogue Management in Healthcare

The main objective of the framework we are proposing is to help the physician obtain information about the patient's condition in order to reach the \emph{correct} diagnosis as soon as possible. In our proposal, the number of interactions between the physician and the patient is reduced to a strict minimum on the one hand and, on the other hand, it is made possible to increase the number of questions to be asked if the uncertainty about the diagnosis persists. These advantages are due to the fact that (i) we implement a reasoning component that allows us to predict a symptom from another symptom without explicitly asking the patient, (ii) we consider non-binary values for the weights associated with the symptoms, we introduce a dataset filtering process in order to choose which partition should be used with respect to some particular characteristics of the patient, and, in addition, (iv) it was added new functionality to the framework: the ability to detect further future risks of a patient already knowing his pathology. The experimental results we obtained are very encouraging

Embedded representations of social interactions

Social interactions have been the focus of social science research for a century, but their study has recently been revolutionized by novel data sources and by methods from computer science, network science, and complex systems science. The study of social interactions is crucial for understanding complex societal behaviours. Social interactions are naturally represented as networks, which have emerged as a unifying mathematical language to understand structural and dynamical aspects of socio-technical systems. Networks are, however, highly dimensional objects, especially when considering the scales of real-world systems and the need to model the temporal dimension. Hence the study of empirical data from social systems is challenging both from a conceptual and a computational standpoint. A possible approach to tackling such a challenge is to use dimensionality reduction techniques that represent network entities in a low-dimensional feature space, preserving some desired properties of the original data. Low-dimensional vector space representations, also known as network embeddings, have been extensively studied, also as a way to feed network data to machine learning algorithms. Network embeddings were initially developed for static networks and then extended to incorporate temporal network data. We focus on dimensionality reduction techniques for time-resolved social interaction data modelled as temporal networks. We introduce a novel embedding technique that models the temporal and structural similarities of events rather than nodes. Using empirical data on social interactions, we show that this representation captures information relevant for the study of dynamical processes unfolding over the network, such as epidemic spreading. We then turn to another large-scale dataset on social interactions: a popular Web-based crowdfunding platform. We show that tensor-based representations of the data and dimensionality reduction techniques such as tensor factorization allow us to uncover the structural and temporal aspects of the system and to relate them to geographic and temporal activity patterns.

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.

Big Data approaches as a support for precision livestock farming techniques

With the advent of new technologies it is increasingly easier to find data of different nature from even more accurate sensors that measure the most disparate physical quantities and with different methodologies. The collection of data thus becomes progressively important and takes the form of archiving, cataloging and online and offline consultation of information. Over time, the amount of data collected can become so relevant that it contains information that cannot be easily explored manually or with basic statistical techniques. The use of Big Data therefore becomes the object of more advanced investigation techniques, such as Machine Learning and Deep Learning. In this work some applications in the world of precision zootechnics and heat stress accused by dairy cows are described. Experimental Italian and German stables were involved for the training and testing of the Random Forest algorithm, obtaining a prediction of milk production depending on the microclimatic conditions of the previous days with satisfactory accuracy. Furthermore, in order to identify an objective method for identifying production drops, compared to the Wood model, typically used as an analytical model of the lactation curve, a Robust Statistics technique was used. Its application on some sample lactations and the results obtained allow us to be confident about the use of this method in the future.

Robotic Manipulation of DLOs for Wiring Harness Assembly: a Machine Learning Approach

In recent times, a significant research effort has been focused on how deformable linear objects (DLOs) can be manipulated for real world applications such as assembly of wiring harnesses for the automotive and aerospace sector. This represents an open topic because of the difficulties in modelling accurately the behaviour of these objects and simulate a task involving their manipulation, considering a variety of different scenarios. These problems have led to the development of data-driven techniques in which machine learning techniques are exploited to obtain reliable solutions. However, this approach makes the solution difficult to be extended, since the learning must be replicated almost from scratch as the scenario changes. It follows that some model-based methodology must be introduced to generalize the results and reduce the training effort accordingly. The objective of this thesis is to develop a solution for the DLOs manipulation to assemble a wiring harness for the automotive sector based on adaptation of a base trajectory set by means of reinforcement learning methods. The idea is to create a trajectory planning software capable of solving the proposed task, reducing where possible the learning time, which is done in real time, but at the same time presenting suitable performance and reliability. The solution has been implemented on a collaborative 7-DOFs Panda robot at the Laboratory of Automation and Robotics of the University of Bologna. Experimental results are reported showing how the robot is capable of optimizing the manipulation of the DLOs gaining experience along the task repetition, but showing at the same time a high success rate from the very beginning of the learning phase.

Can We Predict The Clinical Outcomes Of Genitourinary Trauma Patients And Offer Them An Evidence-based Support? The Case For Telemedicine Network.

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Development And Characterization Of A Cationic Lipid Nanocarrier As Non-viral Vector For Gene Therapy.

The aim of the present work was to produce a cationic solid lipid nanoparticle (SLN) as non-viral vector for protein delivery. Cationic SLN were produced by double emulsion method, composed of softisan(®) 100, cetyltrimethylammonium bromide (CTAB), Tween(®) 80, Span(®) 80, glycerol and lipoid(®) S75 loading insulin as model protein. The formulation was characterized in terms of mean hydrodynamic diameter (z-ave), polydispersity index (PI), zeta potential (ZP), stability during storage time, stability after lyophilization, effect of toxicity and transfection ability in HeLa cells, in vitro release profile and morphology. SLN were stable for 30days and showed minimal changes in their physicochemical properties after lyophilization. The particles exhibited a relatively slow release, spherical morphology and were able to transfect HeLa cells, but toxicity remained an obstacle. Results suggest that SLN are nevertheless promising for delivery of proteins or nucleic acids for gene therapy.

Matrix Support And Institutional Support: Analyzing Their Construction.

This is an analysis of the theoretical and practical construction of the methodology of Matrix Support by means of studies on Paideia Support (Institutional and Matrix Support), which is an inter-professional work of joint care in recent literature and official documents of the Unified Health System (SUS). An attempt was made to describe methodological concepts and strategies. A comparative analysis of Institutional Support and Matrix Support was also conducted using the epistemological framework of Field and Core Knowledge and Practices.

Proposta metodológica utilizando ferramentas de qualidade na avaliação do processo de pulverização

The present work aimed to create a methodology to evaluate the pulverization process with the use of quality tools. It was listed the primary factors, secondary factors, tertiary factors and, with the check list tool support, the list was elaborated. It was evaluated the factors labor, agriculture machine, material and method of 32 pulverization process before pesticide application, in that each factor received a punctuation, having as total sum of 750 points. The medium punctuation to the factors labor, agriculture machine, material and method was 78; 211; 49; 20 and 94 points, respectively. The sum of the factors points for the 32 processes, the minimum value found was 230 and maximum was 620 points. With the proposed methodology, can be identify which common causes of the processes can affect its result.

Análise cinemática tridimensional do salto em distância de atletas de alto nível em competição

Universidade Estadual de Campinas . Faculdade de Educação Física

Effect of insertion method on knoop hardness of high viscous glass ionomer cements

The aim of this study was to assess the Knoop hardness of three high viscous glass ionomer cements: G1 - Ketac Molar; G2 - Ketac Molar Easymix (3M ESPE) and G3 - Magic Glass ART (Vigodent). As a parallel goal, three different methods for insertion of Ketac Molar Easymix were tested: G4 - conventional spatula; G5 - commercial syringe (Centrix) and G6 - low-cost syringe. Ten specimens of each group were prepared and the Knoop hardness was determined 5 times on each specimen with a HM-124 hardness machine (25 g/30 s dwell time) after 24 h, 1 and 2 weeks. During the entire test period, the specimens were stored in liquid paraffin at 37ºC. Significant differences were found between G3 and G1/G2 (two-way ANOVA and Tukey's post hoc test; p<0.01). There was no significant difference in the results among the multiple ways of insertion. The glass ionomer cement Magic Glass ART showed the lowest hardness, while the insertion technique had no significant influence on hardness.