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Technical skill is essential for good outcomes in cardiac surgery. However, no objective methods exist to measure dexterity while performing surgery. The purpose of this study was to validate sensor-based hand motion analysis (HMA) of technical dexterity while performing a graft anastomosis within a validated simulator. Surgeons at various training levels performed an anastomosis while wearing flexible sensors (BioStamp nPoint, MC10 Inc) with integrated accelerometers and gyroscopes on each hand to quantify HMA kinematics. Groups were stratified as experts (n = 8) or novices (n = 18). The quality of the completed anastomosis was scored using the 10 Point Microsurgical Anastomosis Rating Scale (MARS10). HMA parameters were compared between groups and correlated with quality. Logistic regression was used to develop a predictive model from HMA parameters to distinguish experts from novices. Experts were faster (11 ± 6 minutes vs 21 ± 9 minutes; P =.012) and used fewer movements in both dominant (340 ± 166 moves vs 699 ± 284 moves; P =.003) and nondominant (359 ± 188 moves vs 567 ± 201 moves; P =.02) hands compared with novices. Experts' anastomoses were of higher quality compared with novices (9.0 ± 1.2 MARS10 vs 4.9 ± 3.2 MARS10; P =.002). Higher anastomosis quality correlated with 9 of 10 HMA parameters, including fewer and shorter movements of both hands (dominant, r = −0.65, r = −0.46; nondominant, r = −0.58, r = −0.39, respectively). Sensor-based HMA can distinguish technical dexterity differences between experts and novices, and correlates with quality. Objective quantification of hand dexterity may be a valuable adjunct to training and education in cardiac surgery training programs. [Display omitted] [ABSTRACT FROM AUTHOR]

Predictive PI (PPI) controllers have demonstrated to exceed traditional PID controllers when they are applied to systems with long delays. This work proposes a new controller structure and tuning that we call Generalized Predictive PI (GPPI) controller which provides greater design flexibility than PI and PPI strategies. To realize a fair comparison, the design and tuning rules for discrete PI and PPI controllers were developed using optimal arguments based on the root-locus, for critically damped response before a step change in the reference. Experimental results, using industrial equipment, have illustrated the tuning methodology and the performance of the proposed controller under real conditions. Flow and water level process in a laboratory flume were considered. For these processes, First Order Plus Time Delay (FOPTD) models are used. The GPPI control results are encouraging, reducing the settling time plus a very small overshoot before step change in the reference regarding the PI and PPI strategies, up to 41.03% for the flow control loop and up to 54.21% for the level control loop. The discrete analysis of the strategies in the Z plane was performed, allowing for a direct translation to recursive equations that can then be programmed into a Programmable Logic Controller (PLC), other industrial controllers such as Distributed Control Systems (DSC), or microcontrollers, such as Arduino, Raspberry or FPGA. This is an important result, since it demonstrates that the increased complexity of the proposed controller does not hamper its implementation in industrial controller systems. In this work, we used a Rockwell ControlLogix \protect \relax \special {t4ht=®} PLC with Structured Text programming language. • Systems with long time delays are particularly challenging for a PID controller. • The Predictive PI controller has been previously proposed as a PID alternative. • The Generalized PPI controller proposed here extends on the PPI controller.. • The Generalized PPI controller offers more performance design freedom. • The Generalized PPI controller introduces a limited increase in complexity. • Experimental verification shows superior performance of the GPPI controller. [ABSTRACT FROM AUTHOR]

The explicit consideration of a communication channel model in a feedback control loop is known to be constrained by a fundamental limitation for stabilizability on the channel signal-to-noise ratio (SNR) when the linear time invariant (LTI) plant model is unstable. The LTI modelling approach for real, usually nonlinear, processes compromises accuracy versus complexity of the resulting model. This in turn introduces a gap between the proposed model and the real process, which is known as the model uncertainty. In this paper we then study SNR limitations by considering the continuous-time scenario and the case of an additive coloured Gaussian noise (ACGN) channel with bandwidth limitation, for which we then quantify the infimal SNR subject to the simultaneous presence of plant, channel and noise model uncertainties. We observe, for the special case of memoryless additive white Gaussian noise (AWGN) channels, that the obtained SNR limitation subject to plant model uncertainty can be redefined as a channel capacity limitation for stabilization. • Signal transmission through communication channels is increasingly relevant in Control Theory. • Many relevant communication features can be considered: power, channel bandwidth, time delay, etc. • Control Theory require analysis tools for studying model uncertainty and communication features simultaneously. • The SNR approach can provide such analysis tool, and in turn better inform the controller design. [ABSTRACT FROM AUTHOR]

Many studies have established the strong connections between aboveground biomass and lidar height metrics; however, these relationships are site-specific. Field data required to derive these relationships are not readily available in many cases. We developed a model to estimate plot-level aboveground biomass density (AGBD) directly from large-footprint lidar waveform measurements. An individual tree-based aboveground biomass (AGB)-height allometric relationship was scaled up to the plot level using lidar-waveform sensed tree height and crown size distribution characteristics. The AGBD was estimated based on a waveform/foliage profile-weighted height-based allometric equation. The AGBD-height scaling exponent was then built on the allometric relationships of tree height with stem diameter and crown volume with tree height. Global vegetation structure data analysis demonstrated that one general model (scaling exponent ~ 1.6–1.8) works reasonably well across all global forest biomes except boreal forests (scaling exponent ~ 0.9). We applied the model to estimate aboveground biomass in two distinct geographic regions: temperate deciduous/conifer forests in the northeastern USA and a montane conifer forest in Sierra National Forest in California. Local vegetation structural data analysis leads to a consistent height scaling exponent for these two distinct biomes, slightly different from the global data analysis results. This model produced optimal AGBD estimates using the local height scaling exponent value. Adequate AGBD estimates with the general height scaling exponent value were also provided by our model. Our analysis suggests one general allometric relationship between plot-level AGBD and large-footprint lidar waveforms. Integrating local structure allometric relationships improve the predictive accuracy of the model. Our model outperformed the lidar height metrics-based approach for AGBD estimates and overcame the biomass underestimation problem using height metrics for high biomass regions. This model could potentially serve as a general and robust model for monitoring forest carbon stocks using large-footprint lidar waveform measurements such as the Global Ecosystem Dynamics Investigation (GEDI) mission at the continental and global scales. The model could be a framework for integrating a demography-based terrestrial ecosystem model and GEDI global mission measurements to improve global carbon stock and flux estimates. • The model uses lidar waveform to estimate plot-level AGBD. • Tested the model's generality at global scale. • The model produced AGBD in two distinct biomes with high accuracy. • The model outperformed lidar-height based approach. [ABSTRACT FROM AUTHOR]

Abstract: In pattern recognition, feature selection is a very important task for supervised classification. The problem consists in, given a dataset where each object is described by a set of features, finding a subset of the original features such that a classifier that runs on data containing only these features would reach high classification accuracy. A useful way to find this subset of the original features is through testor theory. A testor is defined as a subset of the original features that allows differentiating objects from different classes. Testors are very useful particularly when object descriptions contain both numeric and non-numeric features. Computing testors for feature selection is a very complex problem due to exponential complexity, with respect to the number of features, of algorithms based on testor theory. Hardware implementation of testor computing algorithms helps to improve their performance taking advantage of parallel processing for verifying if a feature subset is a testor in a single clock cycle. This paper introduces an efficient hardware–software platform for computing irreducible testors for feature selection in pattern recognition. Results of implementing the proposed platform using a FPGA-based prototyping board are presented and discussed. [Copyright &y& Elsevier]

Abstract: In the present paper we study the fundamental limitations on stability that arise when an additive coloured Gaussian noise (ACGN) channel is explicitly considered in a linear time invariant (LTI) control feedback loop. By considering a linear setting we can express the fundamental limitation on stability as a lower bound in closed-form on the ACGN channel signal-to-noise ratio (SNR). Outside the context of networked control, the obtained lower bound can be interpreted as an performance limitation. [ABSTRACT FROM AUTHOR]

Abstract: In the present paper we obtain a closed-form solution for a class of continuous-time algebraic Riccati equations (AREs) with vanishing state weight. The ARE in such a class solves a minimum energy control problem. The obtained closed-form solution is used to prove a link between two independent fundamental limitation results in control over networks. [Copyright &y& Elsevier]

Abstract: Fundamental limitations in feedback control is a well established area of research. In recent years it has been extended to the study of limitations imposed by the consideration of a communication channel in the control loop. Previous results characterised these limitations in terms of a minimal data transmission rate necessary for stabilisation. In this paper a signal-to-noise ratio (SNR) approach is used to obtain a tight condition for the linear time invariant output feedback stabilisation of a continuous-time, unstable, non minimum phase (NMP) plant with time-delay over an additive Gaussian coloured noise communication channel. By working on a linear setting the infimal SNR for stabilisability is defined as the infimal achievable norm between the channel noise input and the channel signal input. The result gives a guideline in estimating the severity of the fundamental SNR limitation imposed by the plant unstable poles, NMP zeros, time-delay as well as the channel NMP zeros, bandwidth, and channel noise colouring. [Copyright &y& Elsevier]

A novel combustion diagnostics system based on flame radiation measurement and spectral emissions analysis is presented. The key aspect of this study is the proper estimation of the continuous component or baseline of spectral signals containing spurious discontinuities. We compare both, a PCA based method against an alternative method based on an Artificial Neural Networks model. The accuracy and computational cost for baseline estimation in practical industrial applications is analyzed, in order to incorpore the radiation measurement as a variable suitable for control decisions. Several experiments in industrial facilities demonstrate the suitability of radiation measurement via flame spectrum analysis, in particular the relation between the proposed optical variable and the process operational conditions in the case of an oil boiler for steam production and a ladle furnace preheating process. As results, the methodology based on principal components analysis (PCA) provides high accuracy for baseline estimation in flame spectra, with metrics of RMSE ≈ 0.1 · 10 - 4 and GFC > 0.99 with fast computation times, smaller than typical flame flickering. [ABSTRACT FROM AUTHOR]

This paper presents the results of two novel approaches to measure the soot propensity of a flame and their comparison with the Line of Sight Attenuation (LOSA) method. Both approaches are based on the detection of the Smoke Point Height (SPH), concept used to determine when a flame is in a sooting state. The first approach is based on the detection of morphological changes in the flame, identified through their amplification via the Eulerian Video Magnification algorithm. Results show an effective amplification of the flame geometry, allowing the visualization of variations on the flame tip unable to be detected by the naked human eye and therefore the detection of SPH. The second approach is based on the application of Artificial Intelligence models to classify flame images regarding their sooting propensity, taking advantage of the knowledge acquired from a referential data set. Both approaches provide an accurate classification when compared to the conventional method of LOSA. Furthermore, both approaches show a greater implementation potential in practical combustion devices than the conventional method of LOSA, due to their reduced hardware and technical requirements. [ABSTRACT FROM AUTHOR]

In this paper we apply independent component analysis (ICA) followed by second order blind identification (SOBI) to an atrial fibrillation (AF) 12-Iead electrocardiogram (ECG) recording in order to extract the source that represents atrial activity (AA) (ICA-SOBI method). Still, there is no assurance that only one source obtained from this method will contain AA, and thus we aim to select the most representative source of AA. The novelty in this paper is the proposal of three parameters to select the most representative source of AA. These parameters are correlation coefficient with lead V1 (CV1), peak factor (PF) and spectral concentration (SC). The first two parameters are introduced as new indicators, addressing features overlooked by the SC even when they are present in AA during AF. For synthesized data, at least two of the three parameters select the same representation of AA in 93.3% of the cases. For real data (218 ECG recordings), we observe that PF presents, in 89.5% of the cases, values between 2 and 4.5 for the selected sources, ensuring a well-defined range of values for AA. The actual values of CV1 and SC were scattered throughout their possible ranges (0-1 for CV1 and 0.08-0.7 for SC), and the correlation coefficient between these variables was found to be p = 0.58. We compared our results with three known algorithms: QRST cancellation, principal components analysis (PCA) and ICA-SOBI. The results obtained from this comparison show that our proposed methods to select the best representation of AA in general outperform the three above-mentioned algorithms. [ABSTRACT FROM AUTHOR]

Objective: To demonstrate the efficiency of vitrifying mature human oocytes for different clinical indications. Design: Descriptive case series. Setting: Cryobiology laboratory, Centro Colombiano de Fertilidad y Esterilidad–CECOLFES LTDA. (Bogotá, Colombia). Patient(s): Oocyte vitrification was offered as an alternative management for patients undergoing infertility treatment because of ovarian hyperstimulation syndrome, premature ovarian failure, natural ovarian failure, male factor, poor response, or oocyte donation. Mature oocytes were obtained from 33 donor women and 40 patients undergoing infertility treatment. Intervention(s): Oocytes were retrieved by ultrasound-guided transvaginal aspiration and vitrified with the Cryotops method, with 30% ethylene glycol, 30% dimethyl sulfoxide, and 0.5 mol/L sucrose. Viability was assessed 3 hours after thawing. The surviving oocytes were inseminated by intracytoplasmic sperm injection. Fertilization was evaluated after 24 hours. The zygotes were further cultured in vitro for up to 72 hours until time of embryo transfer. Main Outcome Measure(s): Recovery, viability, fertilization, and pregnancy rates. Result(s): Oocyte vitrification with the Cryotop method resulted in high rates of recovery, viability, fertilization, cleavage, and ongoing pregnancy. Conclusion(s): Vitrification with the Cryotop method is an efficient, fast, and economical method for oocyte cryopreservation that offers high rates of survival, fertilization, embryo development, and ongoing normal pregnancies, providing a new alternative for the management of female infertility. [Copyright &y& Elsevier]