Design of high stroke electrostatic micropumps: a charge control approach with ring electrodes

A novel design strategy to avoid pull-in occurrence in electrostatic micro electro mechanical systems is proposed. It combines charge control with ring electrodes, on a circular geometry. This idea is introduced here for the design of efficient and reliable high stroke electrostatic diaphragm micropumps, while it has a broad potential applicability. A minimal lumped one degree-of-freedom model is derived and used to introduce and demonstrate the proposed approach for a circular plate geometry. Finite element models are subsequently adopted for a more detailed device modelling. As expected, charge control exhibits a stabilizing effect with respect to voltage drive, but not sufficient to achieve a full-range stability for the considered geometry. When the electrode area is properly defined, stability range can be extended up to gap closure in the central part of the membrane. In this configuration, the increase in voltage required for full-range device drive would be relevant, while in charge control the penalty is considerably lower. Finally, loading conditions and geometrical parameters for an optimized actuation are suggested.     

Markerless human articulated tracking using hierarchical particle swarm optimisation

In this paper, we address markerless full-body articulated human motion tracking from multi-view video sequences acquired in a studio environment. The tracking is formulated as a multi-dimensional non-linear optimisation and solved using particle swarm optimisation (PSO), a swarm-intelligence algorithm which has gained popularity in recent years due to its ability to solve difficult non-linear optimisation problems. We show that a small number of particles achieves accuracy levels comparable with several recent algorithms. PSO initialises automatically, does not need a sequence-specific motion model and recovers from temporary tracking divergence through the use of a powerful hierarchical search algorithm (HPSO). We compare experimentally HPSO with particle filter (PF), annealed particle filter (APF) and partitioned sampling annealed particle filter (PSAPF) using the computational framework provided by Balan et al. HPSO accuracy and consistency are better than PF and compare favourably with those of APF and PSAPF, outperforming it in sequences with sudden and fast motion. We also report an extensive experimental study of HPSO over ranges of values of its parameters.     

Effects of a new multielement buffer on production, ruminal environment, and blood minerals of lactating dairy cows

Twelve multiparous Holstein cows were used to evaluate the capacity of a multielement compound consisting mainly of northupite and sylvite to alleviate low milk fat percent. Possible mechanisms of action were assessed. Cows were arranged in a 4 X 4 Latin square design replicated three times. Basal diet was 55% concentrate:45% forage fed ad libitum. Dietary treatments were control, NaHCO3 at 1% of diet DM, and multielement buffer at 1 and 3% of diet DM. Feed intake and milk production were similar for all treatments. Milk fat percentages for the four treatments were 2.97, 3.21, 3.43, and 3.67%, respectively. A shift toward a higher molar percent of ruminal acetate and a lower molar percent of valerate appeared to coincide with changes in milk fat percentage. Milk protein percentage also was increased by supplemental multielement buffer. Ruminal fluid acidity was reduced by NaHCO3 and multielement buffer. Extent of in situ digestion of forage DM and cellulose was improved when cows consumed a buffering agent. Rate of corn silage digestion tended toward improvement. As mineral buffer was consumed, concentrations of Mg and K increased in ruminal fluid and blood. As dietary Na intake increased, ruminal and plasma Cl concentrations were depressed and plasma S and Ca were elevated.     

Near-recursive optical flow from weighted image differences.

This correspondence derives a formal link between temporally weighted frame differences, or disturbance fields, which carry limited information suitable for motion detection, and the optic flow (OF), which carries richer information on local image motion. We use this link to derive a novel, simple, near-recursive optic flow algorithm based on a recursive-filter formulation. Most quantities involved are computed recursively, using only data from the current and previous frame. We can limit expensive OF calculations to pixels where motion magnitude is sufficiently high using image differences which the algorithm computes anyway. Experimental results with well-known synthetic, ground-truthed test sequences and standard performance metrics indicate good quantitative performance. Tests with real sequences suggest similar or better performance than a well-known, similar algorithm due to Lucas and Kanade (LK).     

Measurement of O and Ti atom displacements in TiO2 during flash sintering experiments

In‐situ flash experiments on rutile TiO₂ were performed at the synchrotron at the Brookhaven National Laboratory. Pair distribution function analysis of total X‐ray scattering measurements yielded mean‐square atomic displacements of oxygen and titanium atoms during the progression of the 3 stages of flash. The displacements are measured to be far greater for oxygen atoms than for titanium atoms. These large displacements may signal an “elastic softening” of the lattice, which, recently, has been predicted as a precursor to the onset of flash.     

A learn‐and‐construct framework for general mixed‐integer programming problems

In this paper, we propose a new framework for finding an initial feasible solution from a mixed‐integer programming (MIP) model. We call it learn‐and‐construct since it first exploits the structure of the model and its linear relaxation solution and then uses this knowledge to try to produce a feasible solution. In the learning phase, we use an unsupervised learning algorithm to cluster entities originating the MIP model. Such clusters are then used to decompose the original MIP in a number of easier sub‐MIPs that are solved by using a black box solver. Computational results on three well‐known problems show that our procedure is characterized by a success rate larger than both the feasibility pump heuristic and a state‐of‐the‐art MIP solver. Furthermore, our approach is more scalable and uses less computing time on average.     

Exploiting OpenMP and OpenACC to accelerate a geometric approach to molecular docking in heterogeneous HPC nodes

The Journal of Supercomputing - In drug discovery, molecular docking is the task in charge of estimating the position of a molecule when interacting with the docking site. This task is usually used...     

A dual–polarimetric approach to earthquake damage assessment

In this study, a novel physical approach is proposed to detect damages due to earthquakes using dual polarimetric (DP) coherent Synthetic Aperture Radar (SAR) imagery. An optimization method, aimed at enhancing scattering basis differences between measurements collected before and after the event, is designed exploiting Lagrange optimization of the difference between two polarimetric covariance matrices. A meaningful showcase is presented to demonstrate the soundness of the proposed approach that consists of processing Sentinel–1 C–band scenes related to 2016 Central Italy Earthquake. The proposed approach, which is contrasted with the conventional coherence based single– and dual–polarization approaches, results in the best sensitivity to damages.     

Supervised machine learning in multimodal learning analytics for estimating success in project‐based learning

Multimodal learning analytics provides researchers new tools and techniques to capture different types of data from complex learning activities in dynamic learning environments. This paper investigates the use of diverse sensors, including computer vision, user‐generated content, and data from the learning objects (physical computing components), to record high‐fidelity synchronised multimodal recordings of small groups of learners interacting. We processed and extracted different aspects of the students' interactions to answer the following question: Which features of student group work are good predictors of team success in open‐ended tasks with physical computing? To answer this question, we have explored different supervised machine learning approaches (traditional and deep learning techniques) to analyse the data coming from multiple sources. The results illustrate that state‐of‐the‐art computational techniques can be used to generate insights into the "black box" of learning in students' project‐based activities. The features identified from the analysis show that distance between learners' hands and faces is a strong predictor of students' artefact quality, which can indicate the value of student collaboration. Our research shows that new and promising approaches such as neural networks, and more traditional regression approaches can both be used to classify multimodal learning analytics data, and both have advantages and disadvantages depending on the research questions and contexts being investigated. The work presented here is a significant contribution towards developing techniques to automatically identify the key aspects of students success in project‐based learning environments, and to ultimately help teachers provide appropriate and timely support to students in these fundamental aspects.