Miracle-dz/UniFAR-FTUs huggingface dataset:
Static Force Model-Based Stiffness Model for Pneumatic Muscle Actuators

Pneumatic muscle actuators (PMAs) differ from general pneumatic systems as they have no inner moved parts and there is no sliding on the surfaces. During action they reach high velocities, while the power/weight and power/volume rations reach high levels. The main drawbacks of PMAs are limited contraction (relative displacement), nonlinear and time variable behaviour, existence of hysteresis and step-jump pressure (to start radial diaphragm deformation) and also antagonistic connection of PMAs to generate two-direction motion. These make PMAs difficult to modelling and control. In this paper a new stiffness model and the variable-stiffness spring-like characteristics are described and tested using two Fluidic Muscles made by Festo Company. The muscles have the same diameter, but different length.
Modeling biochemical reactions and gene networks with memristors

This paper investigates qualitative and quantitative analogies between biochemical reactions and memristive devices. It shows that memristors can mimic biochemical reactions and gene networks efficiently, and capture both deterministic and stochastic dynamics at the nanoscale level. We present different abstraction models and memristor-based circuits that inherently model the activity of genetic circuits with low signal-to-noise ratio (SNR). These findings constitute a promising step towards noise-tolerant and energy-efficient electronic circuit design, which can provide a fast and simple emulative framework for large-scale synthetic molecular system design in cell biology.
Video game playing increases food intake in adolescents: a randomized crossover study.

BACKGROUND Video game playing has been linked to obesity in many observational studies. However, the influence of this sedentary activity on food intake is unknown. OBJECTIVE The objective was to examine the acute effects of sedentary video game play on various components of energy balance. DESIGN With the use of a randomized crossover design, 22 healthy, normal-weight, male adolescents (mean ± SD age: 16.7 ± 1.1 y) completed two 1-h experimental conditions, namely video game play and rest in a sitting position, followed by an ad libitum lunch. The endpoints were spontaneous food intake, energy expenditure, stress markers, appetite sensations, and profiles of appetite-related hormones. RESULTS Heart rate, systolic and diastolic blood pressures, sympathetic tone, and mental workload were significantly higher during the video game play condition than during the resting condition (P < 0.05). Although energy expenditure was significantly higher during video game play than during rest (mean increase over resting: 89 kJ; P < 0.01), ad libitum energy intake after video game play exceeded that measured after rest by 335 kJ (P < 0.05). A daily energy surplus of 682 kJ (163 kcal) over resting (P < 0.01) was observed in the video game play condition. The increase in food intake associated with video game play was observed without increased sensations of hunger and was not compensated for during the rest of the day. Finally, the profiles of glucose, insulin, cortisol, and ghrelin did not suggest an up-regulation of appetite during the video game play condition. CONCLUSION A single session of video game play in healthy male adolescents is associated with an increased food intake, regardless of appetite sensations. The trial was registered at clinicaltrials.gov as NCT01013246.
DUO: Exposing On-Chip Redundancy to Rank-Level ECC for High Reliability

DRAM row and column sparing cannot efficiently tolerate the increasing inherent fault rate caused by continued process scaling. In-DRAM ECC (IECC), an appealing alternative to sparing, can resolve inherent faults without significant changes to DRAM, but it is inefficient for highly-reliable systems where rank-level ECC (RECC) is already used against operational faults. In addition, DRAM design in the near future (possibly as early as DDR5) may transfer data in longer bursts, which complicates high-reliability RECC due to fewer devices being used per rank and increased fault granularity. We propose dual use of on-chip redundancy (DUO), a mech- anism that bypasses the IECC module and transfers on-chip redundancy to be used directly for RECC. Due to its increased redundancy budget, DUO enables a strong and novel RECC for highly-reliable systems, called DUO SDDC. The long codewords of DUO SDDC provide fundamentally higher detection and correction capabilities, and several novel secondary-correction techniques integrate together to further expand its correction capability. According to our evaluation results, DUO shows performance degradation on par with or better than IECC (average 2–3%), while consuming less DRAM energy than IECC (average 4–14% overheads). DUO provides higher reliability than either IECC or the state-of-the-art ECC technique. We show the robust reliability of DUO SDDC by comparing it to other ECC schemes using two different inherent fault-error models.
Queue occupancy estimation technique for adaptive threshold based RED

The proper estimation of queue occupancy in gateways is a key for effectiveness of actions taken based on its value. Adaptive threshold based random early detection (RED) uses queue occupancy status (e.g. low, medium and high) to make threshold adjustment decisions. Normally, current value of average queue size is used to determine occupancy. In this paper, we propose a method that takes into account instantaneous and average queue size to estimate queue occupancy. Both present and few past values are analyzed to estimate queue occupancy. The situation of under utilization of link and buffer overflow can be avoided with this method. Simulation results of throughput shows that our method keeps high throughput and low packet drop rate. It also tends to reduce peaks in queue and keeps minimum values higher.
A portable system for real-time non-contact blood oxygen saturation measurements

A portable low cost system for blood oxygen saturation (SpO2) measurement is developed in this paper. It uses the principles of polarization based optical sectioning of the light reflecting from bodyparts to measure the oxygen saturation. In contrast to the existing devices like oximeter, the proposed system does not require any body contact for the measurements. The proposed system is designed using commercial webcams and well known mini computer device, Raspberry-pi. A linear relationship between the SpO2 and ratio of the partially polarized and polarized light components is observed. The observed results of the SpO2 measurements are consistent with the reference.
Minority Oversampling in Kernel Adaptive Subspaces for Class Imbalanced Datasets

The class imbalance problem in machine learning occurs when certain classes are underrepresented relative to the others, leading to a learning bias toward the majority classes. To cope with the skewed class distribution, many learning methods featuring minority oversampling have been proposed, which are proved to be effective. To reduce information loss during feature space projection, this study proposes a novel oversampling algorithm, named minority oversampling in kernel adaptive subspaces (MOKAS), which exploits the invariant feature extraction capability of a kernel version of the adaptive subspace self-organizing maps. The synthetic instances are generated from well-trained subspaces and then their pre-images are reconstructed in the input space. Additionally, these instances characterize nonlinear structures present in the minority class data distribution and help the learning algorithms to counterbalance the skewed class distribution in a desirable manner. Experimental results on both real and synthetic data show that the proposed MOKAS is capable of modeling complex data distribution and outperforms a set of state-of-the-art oversampling algorithms.
DataSampling Schemes forMicrostructureDesignwithVibrational Tuning Constraints

Microstructures significantly impact the performance of sensitively engineered components, such as wireless impact detectors used in military vehicles or sensors used in aircrafts. These components can operate safely only within a certain range of frequencies, and frequencies outside that range can lead to instability because of resonance. This paper addresses optimization of the microstructure design to maximize the yield stress of a galfenol beam under vibration tuning constraints defined for the first torsional and bending natural frequencies by using a data-driven solution scheme. In this study, two carefully designed algorithms are used to sample the entire microstructure space. Classical optimization techniques often lead to a unique microstructural solution rather than yielding the complete space of optimal microstructures. Multiple optimal solutions are imperative for the practicality of design because conventional low-cost manufacturing processes can generate only a limited set of microstructures. The current data sampling-based methodology outperforms or is on par with other optimization techniques but also provides numerous near-optimal solutions, which is two to three orders of magnitude more than previous methods. Consequently, the proposed framework delivers a spectrum of optimal solutions in themicrostructure space that can accelerate material development and reduce manufacturing costs.
Distribution of the Related Weevil Species Sitophilus oryzae and S. zeamais (Coleoptera: Curculionidae) in Farmer Stored Grains of China

Abstract Sitophilus oryzae (Coleoptera: Curculionidae) and Sitophilus zeamais (Coleoptera: Curculionidae) are major insect pests of farm-stored grains in China. Moreover, their respective distribution and prevalence are not yet assessed for grain storage facilities in China. The two species are often difficult to identify by morphology because they are immature or their presence is only evident from fragments. Species-specific primers were, therefore, designed based on the cytochrome c oxidase subunit I (COI) of 34 populations found throughout China and three foreign populations. Following the validation of this molecular-based approach for species identification, the distribution of the two species in China was determined from 68 different grain storage facilities. The results indicate that S. zeamais is prevalent throughout the country whereas S. oryzae is mainly present in the south and the center of China. It is believed that this distribution pattern is in function of ecological adaptation, mostly determined by temperature and the grain species. This is the first report of its kind, demonstrating the distribution of S. zeamais and S. oryzae in grain storage facilities throughout China and analyzed by species-specific primers of COI.
Salicylic acid reduces the production of several potential virulence factors of Pseudomonas aeruginosa associated with microbial keratitis.

PURPOSE Pseudomonas aeruginosa is a common cause of contact-lens-related microbial keratitis. This bacterium is becoming increasingly resistant to antibiotics, and even if the infection can be treated with antibiotics, damage to the cornea resulting from the combined effect of bacteria and host factors can lead to loss of vision. The purpose of this study was to test the effect of salicylic acid on the production of potential virulence factors during the growth of P. aeruginosa. METHODS Bacterial cells were grown in a subinhibitory concentration of salicylic acid, and supernatants were collected and analyzed for presence of proteases by using zymography and hydrolysis of chromogenic substrates. The supernatants were also analyzed for the amount of acetylated homoserine lactones by using bacterial reporter strains. Pseudomonas cells from salicylic acid cultures were analyzed for their twitching and swimming motility as well as their ability to invade or cause the death of corneal epithelial cells. RESULTS Growth in a subinhibitory concentration of salicylic acid resulted in a significant reduction in the number of bacterial cells and a reduction in the rate of the number of bacteria increasing during logarithmic growth, but the time to reach the stationary phase of growth was unchanged. These changes in growth pattern affected the amount of acylated homoserine lactones produced by P. aeruginosa 6294. Also affected by growth in salicylic acid was the ability of strain 6294 to show twitching or swimming motility. Salicylic acid also reduced the invasion of strain 6294 into corneal epithelial cells and the epithelial cell death caused by strain 6206. Furthermore, production of proteases by P. aeruginosa was significantly reduced by growth in salicylic acid. CONCLUSIONS The results of this study clearly demonstrate that salicylic acid has a significant impact on several potential virulence factors of P. aeruginosa that may be involved in the production of microbial keratitis. These effects were probably mediated by reduction in the cell density and concomitant reduction in the quorum-sensing signaling molecules, the acylated homoserine lactones, produced by P. aeruginosa.
An Optimal Low Dynamic Range Image Generation Method Using a Neural Network

This paper presents a neural network-based method to generate multiple images with different exposures from a single input low dynamic range (LDR) image for improved high dynamic range (HDR) imaging. The proposed algorithm consists of three steps: 1) 2-D histogram estimation; 2) neural network-based LDR images estimation; and 3) generation of an optimal set of differently exposed images. The proposed method first generates image features by estimating a patched-based 2-D histogram. The extracted features are used in an input layer of the neural network, which plays a role to select an optimal set of LDR images. A set of LDR images is generated using a curvature-based contrast enhancement method. Experimental results show that the proposed method can generate an optimal set of LDR images using neural network and provide improved HDR images. In addition, the proposed method can be implemented as a preprocessing step in most existing HDR frameworks. The proposed HDR approach is considered as a single-input method that gives almost the same performance to multiple image-based HDR method.
Exploiting Three-Dimensional Gaze Tracking for Action Recognition During Bimanual Manipulation to Enhance Human–Robot Collaboration

Human–robot collaboration could be advanced by facilitating the intuitive, gaze-based control of robots, and enabling robots to recognize human actions, infer human intent, and plan actions that support human goals. Traditionally, gaze tracking approaches to action recognition have relied upon computer vision-based analyses of two-dimensional egocentric camera videos. The objective of this study was to identify useful features that can be extracted from three-dimensional (3D) gaze behavior and used as inputs to machine learning algorithms for human action recognition. We investigated human gaze behavior and gaze–object interactions in 3D during the performance of a bimanual, instrumental activity of daily living: the preparation of a powdered drink. A marker-based motion capture system and binocular eye tracker were used to reconstruct 3D gaze vectors and their intersection with 3D point clouds of objects being manipulated. Statistical analyses of gaze fixation duration and saccade size suggested that some actions (pouring and stirring) may require more visual attention than other actions (reach, pick up, set down, and move). 3D gaze saliency maps, generated with high spatial resolution for six subtasks, appeared to encode action-relevant information. The “gaze object sequence” was used to capture information about the identity of objects in concert with the temporal sequence in which the objects were visually regarded. Dynamic time warping barycentric averaging was used to create a population-based set of characteristic gaze object sequences that accounted for intra- and inter-subject variability. The gaze object sequence was used to demonstrate the feasibility of a simple action recognition algorithm that utilized a dynamic time warping Euclidean distance metric. Averaged over the six subtasks, the action recognition algorithm yielded an accuracy of 96.4%, precision of 89.5%, and recall of 89.2%. This level of performance suggests that the gaze object sequence is a promising feature for action recognition whose impact could be enhanced through the use of sophisticated machine learning classifiers and algorithmic improvements for real-time implementation. Robots capable of robust, real-time recognition of human actions during manipulation tasks could be used to improve quality of life in the home and quality of work in industrial environments.
Cognitive assessment of patients with nonalcoholic fatty liver disease

Objective The aim of this study was to investigate cognitive performance for the first time in participants with nonalcoholic fatty liver disease (NAFLD) using the Montreal Cognitive Assessment (MoCA). Participants and methods In total, 70 participants with NAFLD and 73 age-matched and sex-matched healthy participants were enrolled in this prospective cross-sectional study. The diagnosis of NAFLD was made on the basis of abdominal ultrasonography findings. Anthropometric indices were calculated, and routine laboratory analyses were carried out for each participant. All participants provided sociodemographic data and completed the Beck Depression Inventory-II. Cognitive functions were evaluated using the Turkish version of the MoCA, with a cut-off score for mild cognitive impairment of less than 21 points. Results The MoCA scores were significantly lower in participants with NAFLD than in the healthy group (P<0.05). In addition, more NAFLD participants than healthy participants presented with deficits in the visuospatial (P<0.05) and executive function domains (P<0.05). In the multivariate model, education level [2.79 (1.12–6.96); P<0.05] and area of residence [5.68 (2.24–14.38); P<0.001] were associated independently with cognitive dysfunction in both the NAFLD and the healthy groups. The MoCA scores were correlated negatively with fibrosis 4 scores in NAFLD participants (r=−0.359; P<0.05). However, hepatosteatosis grade and the presence of metabolic syndrome were not correlated with MoCA scores in the NAFLD group (P>0.05). Conclusion Our results show that NAFLD patients may have early or subtle cognitive dysfunction, including in the visuospatial and executive function domains, as indexed by scores on the MoCA test. Further targeted psychometric testing will be required to confirm the presence of cognitive impairment in this population.
Exploring Microsoft Hololens for Interactive Visualization of UML Diagrams

Large diagrams of Unified Modeling Language (UML) are complex. In this paper we propose a novel approach to reducing this complexity by not focusing on UML itself but how we interact with it. We lift UML into an interactive 3D space and apply concepts and design guidelines of Natural User Interfaces (NUI). The goal is to present UML in a way that is easy to understand and that scales well regardless of the size and complexity of a software architecture. This research is aimed at a business landscape and collaboration between several architects. We start by describing the interactive system with a small introduction to the technologies in use and then look at the current state of UML and different interpretations of virtual realities. We follow up with our design rationale for the 3DML system and then evaluate it based on established design guidelines for NUI. We finally elaborate on valuable outcomes in the short and long term as well as a conclusion on the 3DML project in general.
Cooperative Localization in Visible Light Networks: Theoretical Limits and Distributed Algorithms

Light emitting diode (LED) based visible light positioning (VLP) networks can provide accurate location information in indoor environments. In this manuscript, we propose to employ cooperative localization for visible light networks by designing a VLP system configuration that involves multiple LED transmitters with known locations (e.g., on the ceiling) and visible light communication (VLC) units equipped with both LEDs and photodetectors (PDs) for the purpose of cooperation. First, we derive the Cram\'er-Rao lower bound (CRLB) and the maximum likelihood estimator (MLE) for the localization of VLC units in the proposed cooperative scenario. To tackle the nonconvex structure of the MLE, we adopt a set-theoretic approach by formulating the problem of cooperative localization as a quasiconvex feasibility problem, where the aim is to find a point inside the intersection of convex constraint sets constructed as the sublevel sets of quasiconvex functions resulting from the Lambertian formula. Next, we devise two feasibility-seeking algorithms based on iterative gradient projections to solve the feasibility problem. Both algorithms are amenable to distributed implementation, thereby avoiding high-complexity centralized approaches. Capitalizing on the concept of quasi-Fej\'er convergent sequences, we carry out a formal convergence analysis to prove that the proposed algorithms converge to a solution of the feasibility problem in the consistent case. Numerical examples illustrate the improvements in localization performance achieved via cooperation among VLC units and evidence the convergence of the proposed algorithms to true VLC unit locations in both the consistent and inconsistent cases.
Speckle-Tracking Imaging, Principles and Clinical Applications: A Review for Clinical Cardiologists

Evaluation of myocardial mechanics, although complex, has now entered the clinical arena, thanks to the introduction of bedside imaging techniques, such as speckle-tracking echocardiography. Overcoming the limitations of previous techniques, such as tissue Doppler Imaging (TDI), bi-dimensional (2D) and, only recently, three-dimensional (3D) speckle tracking, allows a fast, reproducible, and semi-automated description of myocardial deformation parameters, including strain, strain rate, velocity, displacement, torsion, and timing of contraction/relaxation. From research tool, speckle tracking has become a great help for clinicians, validated with respect to more complex, time-consuming, and expensive techniques. Nowadays, further development in technology and image processing draws the attention of the cardiology community. This review intends to describe the fundamental aspects of the imaging technique, together with some recent innovations and clinical applications in this field.
Future Generation Computer Systems

the spatial, temporal and content features to exploit these similarities; afterwards, we employ the machine learning method to match user accounts, and conduct the experiments on three ground truth datasets. The results show that the proposed method has given excellent performance with F1 values reaching 89.79%, 86.78% and 86.24% on three ground truth datasets, respectively. This work presents the possibility of matching user accounts with high accessible online data. © 2018 Elsevier B.V. All rights reserved.