Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of Electrostatic and Piezoelectric Sensor Arrays for Determining the Velocity and Concentration Profiles and Size Distribution of Pneumatically Conveyed Bulk Solids

One way countries around the world are increasing the proportion of renewable fuels for electricity generation is to convert coal fired power stations to co-fired (biomass/coal fired) or converting coal fired power stations to burn only biomass fuels. This however has led to measurement challenges monitoring the complex multi-phase flow of the pulverised fuels entering the furnace due to the complex shape of biomass particles. To meet these measurement challenges a novel electrostatic sensor array and piezoelectric sensor array have been developed. The electrostatic sensor array consists of an array of electrostatic electrode pairs that span the diameter of the pipe. Consequently the electrostatic sensor array is capable of determining the particle velocity and concentration profiles as well as detecting specific flow regimes such as roping. The piezoelectric impact sensor array consists of an array of piezoelectric individual impact sensors that span the diameter of the pipe. The piezoelectric sensor array is capable of determining the particle concentration and size distribution profiles. Experimentation has been carried out on laboratory scale pneumatic conveying systems using a variety of materials such as coal, biomass, coal/biomass blends and plastic shot. Experiments using the electrostatic sensor array have shown that it is indeed capable of determining the particle velocity and concentration profiles in both dilute developed and undeveloped flows. Analysis of the standard deviation of the velocity profiles as well as the correlation coefficient profiles have indicated that parts of the pipe cross section have a more stable flow compared to others. Data obtained through on-line and off-line experimentation using the piezoelectric sensor array has shown that through selective frequency filtering of the impact signal particle size can be determined assuming the particle velocity and the mechanical properties of the conveyed pulverised materials are known. By using a threshold voltage to determine when an impact has occurred on each element of the piezoelectric sensor array the particle concentration profile has been determined. The concentration profiles measured by the piezoelectric sensor array were verified using the electrostatic sensor array.
Sensor Compatibility for Biomass Change Estimation Using Remote Sensing Data Sets: Part of NASA's Carbon Monitoring System Initiative

Time series of remote sensing data offers the opportunity to predict changes in vegetation extent and to estimate forest parameter change such as biomass. However, as sensors and technology advance, it is important to ensure that estimates obtained from different time periods or using different, but related, instruments are consistent in order to have confidence in detected change. This study compares estimates of biomass from small-footprint discrete-return LiDAR data and medium-footprint full-waveform LiDAR for Howland Experimental Forest, Maine, USA. Data were collected from both sensors during Summer 2009. Similar results were found using the same height metric with R2 = 0.67, SE = 58.5 Mg ha-1 and R2 = 0.52, SE = 58.1 Mg ha-1, respectively. The predicted model of the relationship between LiDAR metrics and biomass was applied to data captured in 2003. Identified areas of change corresponded well with a map of forest management operations of varying intensities. Where sensitivity to change allows, vegetation age estimated using time series of Landsat observations, combined with biomass estimates, allows growth curves to be produced to monitor the effect of pests or disease, recovery rates following disturbance, or carbon sequestration.
The Peer-to-Peer Human-Robot Interaction Project

The Peer-to-Peer Human-Robot Interaction (P2P-HRI) project is developing techniques to improve task coordination and collaboration between human and robot partners. Our hypothesis is that peer-to-peer interaction can enable robots to collaborate in a competent, non-disruptive (i.e., natural) manner with users who have limited training, experience, or knowledge of robotics. Specifically, we believe that failures and limitations of autonomy (in planning, in execution, etc.) can be compensated for using human-robot interaction. In this paper, we present an overview of P2P-HRI, describe our development approach and discuss our evaluation methodology.
A gradient-based foreground detection technique for object tracking in a traffic monitoring system

Moving object detection is an important process in video surveillance and monitoring system. A conventional approach to moving object detection in video surveillance application is background subtraction. Information about background can be determined by various techniques. However, many existing background modelling techniques suffer in uncontrolled environments. This paper presents a new background modelling approach based on edge information. The proposed approach is robust to changes in outdoor environments where light can change randomly. Furthermore, edges require significantly less computing resource and result in faster processing time relative to intensity-based approaches. Experimental results show that, compared to intensity-based approaches in outdoor environments, high detection accuracy has been achieved with significantly less computing power.
Brain regions showing increased activation by threat-related words in panic disorder

Threat-related stimuli consistently activate the posterior cingulate cortex in normal subjects and have exaggerated effects on memory in patients with panic disorder. We hypothesized that panic patients would show increased response to threat-related stimuli in the posterior cingulate cortex. While undergoing fMRI, six panic patients and eight healthy volunteers made valence judgements of threat-related and neutral words. Both groups showed threat-related activation in the left posterior cingulate and left middle frontal cortices, but the activation was significantly greater in panic patients. Panic patients also had more right>left asymmetry of activation in the mid-parahippocampal region. The increased responsivity observed in the posterior cingulate and dorsolateral prefrontal cortices is consistent with the hypothesis that panic disorder patients engage in more extensive memory processing of threat-related stimuli.
Strathprints Institutional Repository Mechanical Stimulation of the Foot Sole in a Supine Position for Ground Reaction Force Simulation

(2014) Mechanical stimulation of the foot sole in a supine position for ground reaction force simulation. Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (http://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Abstract Background
ENERGY CONSERVATION THROUGH ENERGY EFFICIENT TECHNOLOGIES AT THERMAL POWER PLANT

Energy audit and conservation is the burning issue nowadays due to the tremendous scarcity of electricity across the county. It is specifically quantifies as optimum use of electricity available. Normally it is extended to all the sectors viz, Industrial, commercial, residential as well as agriculture. Energy efficiency is a technique which needs to be adopted seriously and religiously for effective energy conservation. Energy saved by efficient use of energy of any electrically operated device not only leads to monetary saving but is extra energy generated for the use elsewhere. Indian power sector is caught between the pressure of adding new generating capacities to match the rapid growing demand of power to achieve economic and social development and the environmental challenges which is why our per capita power consumption is still 778 KWh only. Therefore by implementing Energy conservation in thermal power plant we try to recover the losses which go waste. By energy management, one can draw a line between the avoidable and un-avoidable losses and plan to minimize the same. So measurement Energy is a must within a plant. This paper describes basic understanding and awareness about energy conservation around the thermal power plant and also explains the energy saving measures at thermal power plant so as to minimize the auxiliary power consumption. Keywords— Energy efficiency, Auxiliary Power Consumption, Thermal Power Plant, Energy Management.
Time-Variant, Frequency-Selective, Linear and Nonlinear Analysis of Heart Rate Variability in Children With Temporal Lobe Epilepsy

The major aim of our study is to demonstrate that a concerted combination of time-variant, frequency-selective, linear and nonlinear analysis approaches can be beneficially used for the analysis of heart rate variability (HRV) in epileptic patients to reveal premonitory information regarding an imminent seizure and to provide more information on the mechanisms leading to changes of the autonomic nervous system. The quest is to demonstrate that the combined approach gains new insights into specific short-term patterns in HRV during preictal, ictal, and postictal periods in epileptic children. The continuous Morlet-wavelet transform was used to explore the time-frequency characteristics of the HRV using spectrogram, phase-locking, band-power and quadratic phase coupling analyses. These results are completed by time-variant characteristics derived from a signal-adaptive approach. Advanced empirical mode decomposition was utilized to separate out certain HRV components, in particular blood-pressure-related Mayer waves (≈0.1 Hz) and respiratory sinus arrhythmia (≈0.3 Hz). Their time-variant nonlinear predictability was analyzed using local estimations of the largest Lyapunov exponent (point prediction error). Approximately 80-100 s before the seizure onset timing and coordination of both HRV components can be observed. A higher degree of synchronization is found and with it a higher predictability of the HRV. All investigated linear and nonlinear analyses contribute with a specific importance to these results.
Time-dependent Changes of Collagen Crosslinks in the Socket after Tooth Extraction in Rabbits

Time-dependent changes of the reducible collagen crosslinks in the healing tissue of rabbit tooth extraction wounds were analyzed chromatographically. The ratio of dihydroxylysinonorleucine to hydroxylysinonorleucine in the collagen from normal alveolar bone was 4.4. This value increased about four times, on the 10th day after tooth extraction, coinciding with the phase of active woven bone formation, and then decreased rapidly toward a normal value on the 14th day after tooth extraction. The data suggest that active biosynthesis and fibrillogenesis of bone collagen precede the morphological completion of lamellar bone formation.
Non-Linear Symmetry-Preserving Observers on Lie Groups

In this technical note, we give a geometrical framework for the design of observers on finite-dimensional Lie groups for systems which possess some specific symmetries. The design and the error (between true and estimated state) equation are explicit and intrinsic. We consider also a particular case: left-invariant systems on Lie groups with right equivariant output. The theory yields a class of observers such that the error equation is autonomous. The observers converge locally around any trajectory, and the global behavior is independent from the trajectory, which is reminiscent of the linear stationary case.
Learning an Intermittent Control Strategy for Postural Balancing Using an EMG-Based Human-Computer Interface

It has been considered that the brain stabilizes unstable body dynamics by regulating co-activation levels of antagonist muscles. Here we critically reexamined this established theory of impedance control in a postural balancing task using a novel EMG-based human-computer interface, in which subjects were asked to balance a virtual inverted pendulum using visual feedback information on the pendulum's position. The pendulum was actuated by a pair of antagonist joint torques determined in real-time by activations of the corresponding pair of antagonist ankle muscles of subjects standing upright. This motor-task raises a frustrated environment; a large feedback time delay in the sensorimotor loop, as a source of instability, might favor adopting the non-reactive, preprogrammed impedance control, but the ankle muscles are relatively hard to co-activate, which hinders subjects from adopting the impedance control. This study aimed at discovering how experimental subjects resolved this frustrated environment through motor learning. One third of subjects adapted to the balancing task in a way of the impedance-like control. It was remarkable, however, that the majority of subjects did not adopt the impedance control. Instead, they acquired a smart and energetically efficient strategy, in which two muscles were inactivated simultaneously at a sequence of optimal timings, leading to intermittent appearance of periods of time during which the pendulum was not actively actuated. Characterizations of muscle inactivations and the pendulum¡Çs sway showed that the strategy adopted by those subjects was a type of intermittent control that utilizes a stable manifold of saddle-type unstable upright equilibrium that appeared in the state space of the pendulum when the active actuation was turned off.
Factors influencing hospital admissions and emergency department visits among children with complex chronic conditions: a qualitative study of parents’ and providers’ perspectives

Children with complex chronic conditions (CCC) have greater health care needs and use rates than children in general. Elevated health care use includes more frequent hospital admissions, longer hospital stays, and greater health care expenses. Prior studies have examined population characteristics associated with increased hospital admissions, emergency department (ED) use, and general healthcare use, yet few studies have investigated these events from the parents’ or health care providers’ point of view. The purpose of this study was to explore parents/caregivers’ and health care providers’ perceptions of the factors placing infants and young children with CCC at risk for or protecting them against hospital admissions and ED visits. Parents or primary caregivers participated in interviews, and health care providers in pediatric acute care, pediatric primary care, and emergency care participated in focus groups. Interview and focus group data were analyzed using directed content analysis and an ecological risk and protective factors model. The analysis revealed that parents/caregivers and health care providers described risk factors and protective factors on multiple ecological levels surrounding the child with CCC. This article presents these findings, which add to current knowledge of factors influencing hospital admissions and ED visits and may be used to inform interventions addressing high health care utilization in this population. This article concludes with the implications of the findings for future research and nursing practice.
A two-stage text mining model for information filtering

Mismatch and overload are the two fundamental issues regarding the effectiveness of information filtering. Both term-based and pattern (phrase) based approaches have been employed to address these issues. However, they all suffer from some limitations with regard to effectiveness. This paper proposes a novel solution that includes two stages: an initial topic filtering stage followed by a stage involving pattern taxonomy mining. The objective of the first stage is to address mismatch by quickly filtering out probable irrelevant documents. The threshold used in the first stage is motivated theoretically. The objective of the second stage is to address overload by apply pattern mining techniques to rationalize the data relevance of the reduced document set after the first stage. Substantial experiments on RCV1 show that the proposed solution achieves encouraging performance.
Effective materials properties: Determination and application in mechanical design and optimization

Abstract A finite element procedure for determination of effective mechanical, thermal and thermomechanical properties of multi-phase materials is developed in this paper. To account for the variation in phases contiguity with the change in volume fractions, a computer-based procedure is proposed for generation of the materials microstructure. The procedure is applied to the cobalt-tungsten carbide (Co-WC) two-phase material. The results show significantly better agreement with their experimental counterparts compared with analytical methods of predicting effective materials properties. Finally, the case of flywheel optimization is used to demonstrate that accurate determination of effective materials properties can be very critical in heterogeneous-materials design problems.
Parameter Identification by Inverse Analysis Coupled with a Finite Pointset Method for Polyurethane Foam Expansion

This paper deals with the parameter identification for polyurethane foaming process simulation by using an inverse analysis coupled with a Finite Pointset Method. Simultaneous measurements of the foam height rise, the reaction temperature and the viscosity on a cylindrical cardboard test tube are obtained by using the foam measurement system (FOAMAT). The simulation of the foam expansion is obtained by solving unsteady Navier-Stokes equations coupled with the energy equation, the curing reaction (reaction of isocyanate with polyol) and the foaming reaction (reaction of isocyanate with water to emit the CO2 gas) by using a mesh-free method. The inverse identification method consists in determining the parameters by comparing the computed quantities (height rise, reaction temperature and viscosity) computed by the finite pointset method to those measured experimentally.
Validation of Underwater Sensor Package Using Feature Based SLAM

Robotic vehicles working in new, unexplored environments must be able to locate themselves in the environment while constructing a picture of the objects in the environment that could act as obstacles that would prevent the vehicles from completing their desired tasks. In enclosed environments, underwater range sensors based off of acoustics suffer performance issues due to reflections. Additionally, their relatively high cost make them less than ideal for usage on low cost vehicles designed to be used underwater. In this paper we propose a sensor package composed of a downward facing camera, which is used to perform feature tracking based visual odometry, and a custom vision-based two dimensional rangefinder that can be used on low cost underwater unmanned vehicles. In order to examine the performance of this sensor package in a SLAM framework, experimental tests are performed using an unmanned ground vehicle and two feature based SLAM algorithms, the extended Kalman filter based approach and the Rao-Blackwellized, particle filter based approach, to validate the sensor package.
Optimizing Adaptive Synchronization in Parallel Simulators for Large-scale Parallel Systems and Applications

This paper addresses the optimization of parallel simulators for large-scale parallel systems and applications. Such simulators are often based on parallel discrete event simulation with conservative or optimistic protocols to synchronize the simulating processes. The paper considers how available future information about events and application behaviors can be efficiently extracted and further exploited to improve the performance of adaptive optimistic protocols. First, we extract information about future events and their dependencies in application traces to guide adaptive adjustments of time window in trace-driven parallel simulation. Second, we use information about application behaviors, specifically the iterative behavior found in many applications, to avoid the unnecessary adjustments of time window. These techniques are implemented in the BigSim simulator and tested by real-world and standard benchmark applications including Jacobi3D and HPL. The results show that our optimization approaches can reduce the execution times of simulation ranging from 11% up to 32%. Moreover, our methods are easy to implement and don’t need to augment compilers or even modify the core codes of parallel simulators.
One-Class Support Vector Machine for Sentiment Analysis of Movie Review Documents

 Abstract — Sentiment analysis means to classify a given review document into positive or negative polar document. Sentiment analysis research has been increased tremendously in recent times due to its large number of applications in the industry and academia. Sentiment analysis models can be used to determine the opinion of the user towards any entity or product. E-commerce companies can use sentiment analysis model to improve their products on the basis of users’ opinion. In this paper, we propose a new One-class Support Vector Machine (One-class SVM) based sentiment analysis model for movie review documents. In the proposed approach, we initially extract features from one class of documents, and further test the given documents with the one-class SVM model if a given new test document lies in the model or it is an outlier. Experimental results show the effectiveness of the proposed sentiment analysis model.