Miracle-dz/UniFAR-FTUs huggingface dataset:
Assistive technology for complex support of children rehabilitation with autism spectrum disorder

This paper is devoted to a problem of complex computer support of children rehabilitation with autism spectrum disorder. This goal is achieved due to the development of computer assistive technology which includes web system for online diagnostics, a mobile application for support of communications using PECS and interactive visual timetable. The proposed assistive technology was design and implemented in accordance with the requirements of specialists from Regional rehabilitation center for children with disabilities “Nadezhda” in Volzhsky city (Russia). At present, the developed web services and mobile applications are tested and evaluated in the rehabilitation center.
A simpli fi ed version of the weight-bearing ankle lunge test : Description and test e retest reliability

The purpose of this study was twofold: (1) to describe a new version of the weight-bearing ankle lunge test (WBLT) that is simple to administer, that allows clinicians and sports medicine practitioners to directly assess (in degrees) the ankle dorsiflexion range of motion in a very short period of time while adopting a comfortable testing position; as well as (2) to determine the testeretest reliability of the ankle dorsiflexion range of motion measure obtained from the new version of the WBLT. A total of 50 active adults completed this study. All participants performed the new version of the WBLT on three different occasions, with a two-week interval between testing sessions. Reliability was examined through the change in the mean between consecutive pairs of testing sessions (ChM), standard error of measurement (SEM), minimal detectable change at 95% confidence interval (MDC95), and intraclass correlation coefficient (ICC2,k). The findings showed negligible or trivial ChM values for all the flexibility measures analysed (<1 ). Furthermore, the SEM and MDC95 scores for the ankle dorsiflexion measure were 1.3 and 3.8 respectively, and the ICC2k was 0.95. Therefore, this study demonstrated that the ankle dorsiflexion measure obtained from the new version of the WBLT has excellent testeretest reliability scores. Thus, an observed change larger than 3.8 from baseline scores after performing a treatment would indicate that a real change in ankle dorsiflexion range of motion was likely. 2014 Elsevier Ltd. All rights reserved.
On the Reconstruction Performance of Compressed Orthogonal Moments

In this paper, a wavelet-based technique is applied to three moment feature vectors corresponding to three different families of orthogonal moments. The resulted compressed vectors are studied experimentally, in order to extract useful information about their behaviour to a reconstruction procedure. The reconstruction performance of these moments is identical to the amount of image information that they contain to certain moment orders. Since the moment vectors are imposed to compression at the high frequency components, a conclusion about their information redundancy can be also determined. The most efficient moment family, by means of the reconstruction error, will form feature vectors with low dimension, yet with high information content and thus will be very useful for pattern recognition applications, guarantying high recognition rates.
OPTIMISATION OF EDM FAST HOLE-DRILLING THROUGH EVALUATION OF DIELECTRIC AND ELECTRODE MATERIALS

Electrical discharge machining (EDM) fast hole-drilling is a highly developed technology used for making holes in parts such as turbine blades, fuel injectors, cutting tool coolants, hardened punch ejectors and plastic-mould vents. Hole sizes generally vary between 0.3 and 3mm, with a length to diameter ratio of over 150:1. EDM fast hole- drilling is a key manufacturing technology for gas turbine components, and Rolls-Royce currently operates over fifty machines using brass electrodes and deionised water dielectric in a range of factories. The process, however, leads to high production costs, mainly due to the high consumption of electrodes. Using fractional factorial and response surface design, a series of studies have been carried out with the purpose of optimising the drilling process through the evaluation of a water-based dielectric and an electrode material different from the standard materials of deionised water/brass, through analysis of drilling time, electrode wear, surface integrity and dimensional accuracy. The results showed that it is possible to obtain good drilling rates and achieve a reduction in electrode wear of nearly fifty percent.
An arbitrary order scheme on generic meshes for miscible displacements in porous media

We design, analyse and implement an arbitrary order scheme applicable to generic meshes for a coupled elliptic-parabolic PDE system describing miscible displacement in porous media. The discretisation is based on several adaptations of the Hybrid-High-Order (HHO) method due to Di Pietro et al. [Computational Methods in Applied Mathematics, 14(4), (2014)]. The equation governing the pressure is discretised using an adaptation of the HHO method for variable diffusion, while the discrete concentration equation is based on the HHO method for advection-diffusion-reaction problems combined with numerically stable flux reconstructions for the advective velocity that we have derived using the results of Cockburn et al. [ESAIM: Mathematical Modelling and Numerical Analysis, 50(3), (2016)]. We perform some rigorous analysis of the method to demonstrate its $L^2$ stability under the irregular data often presented by reservoir engineering problems and present several numerical tests to demonstrate the quality of the results that are produced by the proposed scheme.
Robust strict positive realness: characterization and construction

Let P be a convex set of real polynomials. Consideration is given to the question of when there exists a real polynomial b(s), or, more generally, a real transfer function b(s), such that p(s)/b(s) is strictly positive real for all p(s) in P. Necessary and sufficient conditions are found for the transfer function case, and when the degree of the polynomials in P is restricted, such conditions are also found for the polynomial b(s) case. Closely related results are also obtained for a z-transform version of the problem. The results have application in adaptive systems. <<ETX>>
CELL AGGREGATION OF THE CYANOBACTERIUM SYNECHOCOCCUS ELONGATUS: ROLE OF THE ELECTRON TRANSPORT CHAIN

Cell aggregation, the formation of irregular clusters of individual cells or filaments, is frequently observed in many cyanobacterial species. The mechanism(s) and potential causes of cell aggregation were studied in a thermophilic strain of the unicellular cyanobacterium Synechococcus elongatus Näg. We found that cell aggregation occured as the natural response of a healthy, well‐growing culture to a sudden increase in irradiance. We propose that aggregation represents a fast (time scale in minutes), light‐adapting mechanism, affected by both light quality and the presence of substances altering photosynthetic electron transfer. Our data suggest an involvement of electron transfer downstream of PSI, with reactive oxygen species triggering the signal. Aggregation was an ATP‐independent process and did not require de novo protein synthesis. We suggest a specific role of glutathione in this process based on its ability to induce aggregation in the dark.
Estimating Aging Effects in Running Events

Abstract This paper uses world running records by age to estimate a biological frontier of decline rates. Two models are compared: a linear/ quadratic (LQ) model and a nonparametric model. Two estimation methods are used: (a) minimizing the squared difference between the observed records and the modeled biological frontier and (b) using extreme value theory to estimate the biological frontier that maximizes the probability of observing the existing world records by age. The results support the LQ model and suggest a linear percentage decline up to the late 70s and quadratic decline after that.
TopUp SERS Substrates with Integrated Internal Standard

Surface-enhanced Raman spectroscopy (SERS) is known as a molecular-specific and highly sensitive method. In order to enable the routine application of SERS, powerful SERS substrates are of great importance. Within this manuscript, a TopUp SERS substrate is introduced which is fabricated by a top-down process based on microstructuring as well as a bottom-up generation of silver nanostructures. The Raman signal of the support material acts as an internal standard in order to improve the quantification capabilities. The analyte molecule coverage of sulfamethoxazole on the surface of the nanostructures is characterized by the SERS signal evolution fitted by a Langmuir–Freundlich isotherm.
Robust input disturbance suppression for nonlinear systems based on multiple model adaptive control

In this paper, we make initial steps in the direction of nonlinear multiple model adaptive control by focusing on a contrived example in which an unknown parameter has a nominal value in one of the two intervals [-0.3, -0.1] and [0.1, 0.3] and can switch between them. The design should suppress a constant input disturbance. We discuss the use of a multi-estimator and multi-controller to achieve the goal, with the construction of the stable multi-estimator being based on stable kernel representation of the plant. Simulation results indicate that satisfactory performance is achieved.
Fuzzy logic controller for autonomous vehicle path tracking

This work deals with the problem of path tracking for an electric vehicle which has four electromechanical wheel Systems. A controller based on fuzzy logic sends the speed and the steering instructions to the robot to ensure that the robot follows the trajectory, during the design of this controller the effort was concentrated on the simplicity and efficiency. The processing and response time are very essential factors in the process control, which is what motivated the choices of the structure and the parameters of the different parts of the fuzzy controller. The effectiveness of the proposed trajectory control method are confirmed by numerical simulations.
Mobility knowledge discovery to generate activity pattern trajectories

Geographic mining of mobility behavior of individuals and analysis of multi-dimensional correlations between travelers, locations, and activities is crucial in the design and operation of urban infrastructures. The work presented here develops methods to learn mobility behavior of travelers through a mobility knowledge discovery process and to infer the spatial-temporal attributes of activity trajectories based on the demographics of travelers and the features of urban forms. The proposed mobility knowledge discovery process is comprised of two sections: (a) modeling the transactions between activity state vectors (type, location, duration and time), and (b) modeling the correlations between travelers and locations. After activity trajectories are generated, the inferred patterns are assigned to census tracts using an OD-based probabilistic forward-backward method. The proposed methods are validated by applying the model on household travel survey data collected for New York City.
Cooperative Secure Communication in Two-Hop Buffer-Aided Networks

We propose two cooperative secure transmission schemes to protect a two-hop buffer-aided network assisted by an energy harvesting relay. In the first scheme, we assume that the knowledge of the energy harvesting and fading channels states is known in a non-causal manner (offline). In the second scheme, we assume that this knowledge is known in a causal manner (online). For both schemes, we first design an effective link selection policy by taking into account of the transmission efficiency and information security requirements. We then optimally allocate the harvested power at the relay node. For the offline scheme, we maximize the average secrecy rate under the stability constraints of the data queue and the energy queue according to the proposed link selection policy, and design a two-stage iterative algorithm to select the transmission link and allocate relay’s transmit power. In the online scheme, we first model the average secrecy rate maximum problem as a Markov decision process, and then utilize the causal knowledge to select the best transmission link and optimally allocate relay’s transmit power. In addition, the exact and asymptotic closed-form expressions are derived for the ergodic secrecy rate. Numerical results are presented to validate our analysis and demonstrate that the proposed schemes outperform the other buffered-aided secure transmission schemes assisted by the energy harvesting relay in terms of average secrecy rate.
Predisposition of six well-characterized microRNAs to syndesmophytes among Chinese patients with ankylosing spondylitis

Abstract Objectives: We quantified the expression of six well-characterized microRNAs (miRNAs) in peripheral blood mononuclear cells to see whether they can predispose to syndesmophytes in ankylosing spondylitis (AS) patients. Methods: This is a cross-sectional study involving 46 AS patients (23/23 with/without syndesmophytes) and 22 healthy controls. miRNAs expression was quantified by real-time PCR. Results: Six examined miRNAs were comparably expressed between AS patients without syndesmophytes and healthy controls (p > .05). Relative to AS patients without syndesmophytes, patients with syndesmophytes had significantly higher levels of miR-29a, miR-335-5p, miR-27a and let-7i (p = .001, .002, .013 and .029, respectively). Nine significant contributors associated with syndesmophytes in AS, including smoking, AS duration, human leukocyte antigen B27, erythrocyte sedimentation rate, C-reactive protein, miR-335-5p, miR-27a, miR-218 and sacroiliitis, were identified. The addition of miR-335-5p, miR-27a and miR-218 can significantly improve the accuracy of baseline risk factors. Based on the nine significant contributors, a nomogram was constructed, with good prediction accuracy (C-index: 0.86, p < .001). Conclusion: We provide evidence for the predisposition of miR-335-5p, miR-27a and miR-218 to syndesmophytes in AS patients, indicating a contributory role of miRNAs in the pathogenesis of syndesmophytes. Further validation is warranted.
Electrical activity signal spectrum of the artificial neural net on the base of pulsed neurons and memristors

Theoretical and experimental research results of the ANN (Artificial Neural Net) realized on the base of the superposed hybrid technology of a FPGA (Field Programmable Gate Array) and synaptic elements with built in analog memory are presented. In order not to limit possibility of structural modification during self-organization of critical states in ANN due to local interactions between neurons we have used single layer fully connected neural network of pulsed neurons. Pulsed neuron with resonance properties is characterized by spatial-temporal summation of the input signals in the under threshold region, possibility to process bipolar data, perform frequency selection in a wide dynamic range. The designed ANN has been oriented by self-organization for real time speech analysis. In the active state developed electronic structures exhibit fractal properties, have noise spectrum 1/fα (α = 0.8 ∼ 1.2), certain spectrum rise in the frequency bands 8 ∼ 14 Hz (α — rhythm), 14 ∼ 33 Hz (β — rhythm). Low spectrum level in the frequency band below 1 Hz is explained by limited size of the ANN. Practical realization aspects of the ANN with increased size and “intellectual” properties are analyzed.
Simulated Annealing on Cubic Splines for Vectorizing Outlines of Generic Shapes

A new technique has been proposed for the outline capture of generic shapes. The overall technique has various phases including extracting outlines of images, detecting corner points from the detected outline, curve fitting, and addition of extra knot points if needed. The idea of simulated annealing has been used to optimize the shape parameters in the description of the generalized cubic spline introduced.
Generalised Structural CNNs (SCNNs) for time series data with arbitrary graph topology

Deep Learning methods, specifically convolutional neural networks (CNNs), have seen a lot of success in the domain of image-based data, where the data offers a clearly structured topology in the regular lattice of pixels. This 4-neighbourhood topological simplicity makes the application of convolutional masks straightforward for time series data, such as video applications, but many high-dimensional time series data are not organised in regular lattices, and instead values may have adjacency relationships with non-trivial topologies, such as small-world networks or trees. In our application case, human kinematics, it is currently unclear how to generalise convolutional kernels in a principled manner. Therefore we define and implement here a framework for general graph-structured CNNs for time series analysis. Our algorithm automatically builds convolutional layers using the specified adjacency matrix of the data dimensions and convolutional masks that scale with the hop distance. In the limit of a lattice-topology our method produces the well-known image convolutional masks. We test our method first on synthetic data of arbitrarily-connected graphs and human hand motion capture data, where the hand is represented by a tree capturing the mechanical dependencies of the joints. We are able to demonstrate, amongst other things, that inclusion of the graph structure of the data dimensions improves model prediction significantly, when compared against a benchmark CNN model with only time convolution layers.
Derivation of the respiratory rate from directly and indirectly measured respiratory signals using autocorrelation

Abstract The estimation of respiratory rates from contineous respiratory signals is commonly done using either fourier transformation or the zero-crossing method. This paper introduces another method which is based on the autocorrelation function of the respiratory signal. The respiratory signals can be measured either directly using a flow sensor or chest strap or indirectly on the basis of the electrocardiogram (ECG). We compare our method against other established methods on the basis of real-world ECG signals and use a respiration-based breathing frequency as a reference. Our method achieved the best agreement between respiration rates derived from directly and indirectly measured respiratory signals.
Learning Object States from Videos

In this work, we borrow the idea of states in automata to define states for objects that are common in our daily activities. When human interact with objects, the objects are likely to go through a series of state changes, altering their appearance and functionality from time to time. The goal of this project is to take an initial step toward enabling robots to obtain knowledge of unseen objects either by watching users‘ demonstrations or by playing around with the object’ by itself. We collected a small dataset that covers a wide range of objects with diverse property and functionality. Our result pointed out that detecting state changes is a non-trivial task and requires higher level of understanding and reasoning than simply detecting change of frame appearance.
Camera Model Identification With Unknown Models

Feature based camera model identification plays an important role for forensics investigations on images. The conventional feature based identification schemes suffer from the problem of unknown models, that is, some images are captured by the camera models previously unknown to the identification system. To address this problem, we propose a new scheme: Source Camera Identification with Unknown models (SCIU). It has the capability of identifying images of the unknown models as well as distinguishing images of the known models. The new SCIU scheme consists of three stages: 1) unknown detection; 2) unknown expansion; and 3) (K+1 )-class classification. Unknown detection applies a k -nearest neighbours method to recognize a few sample images of unknown models from the unlabeled images. Unknown expansion further extends the set of unknown sample images using a self-training strategy. Then, we address a specific (K+1)-class classification, in which the sample images of unknown (1-class) and known models (K-class) are combined to train a classifier. In addition, we develop a parameter optimization method for unknown detection, and investigate the stopping criterion for unknown expansion. The experiments carried out on the Dresden image collection confirm the effectiveness of the proposed SCIU scheme. When unknown models present, the identification accuracy of SCIU is significantly better than the four state-of-art methods: 1) multi-class Support Vector Machine (SVM); 2) binary SVM; 3) combined classification framework; and 4) decision boundary carving.
Ionotropic Chemosensory Receptors Mediate the Taste and Smell of Polyamines

The ability to find and consume nutrient-rich diets for successful reproduction and survival is fundamental to animal life. Among the nutrients important for all animals are polyamines, a class of pungent smelling compounds required in numerous cellular and organismic processes. Polyamine deficiency or excess has detrimental effects on health, cognitive function, reproduction, and lifespan. Here, we show that a diet high in polyamine is beneficial and increases reproductive success of flies, and we unravel the sensory mechanisms that attract Drosophila to polyamine-rich food and egg-laying substrates. Using a combination of behavioral genetics and in vivo calcium imaging, we demonstrate that Drosophila uses multisensory detection to find and evaluate polyamines present in overripe and fermenting fruit, their favored feeding and egg-laying substrate. In the olfactory system, two coexpressed ionotropic receptors (IRs), IR76b and IR41a, mediate the long-range attraction to the odor. In the gustatory system, multimodal taste sensation by IR76b receptor and GR66a bitter receptor neurons is used to evaluate quality and valence of the polyamine providing a mechanism for the fly’s high attraction to polyamine-rich and sweet decaying fruit. Given their universal and highly conserved biological roles, we propose that the ability to evaluate food for polyamine content may impact health and reproductive success also of other animals including humans.