Miracle-dz/UniFAR-FTUs huggingface dataset:
3D-Printed Soft Pneumatic Robotic Digit Based on Parametric Kinematic Model for Finger Action Mimicking

A robotic digit with shape modulation, allowing personalized and adaptable finger motions, can be used to restore finger functions after finger trauma or neurological impairment. A soft pneumatic robotic digit consisting of pneumatic bellows actuators as biomimetic artificial joints is proposed in this study to achieve specific finger motions. A parametric kinematic model is employed to describe the tip motion trajectory of the soft pneumatic robotic digit and guide the actuator parameter design (i.e., the pressure supply, actuator material properties, and structure requirements of the adopted pneumatic bellows actuators). The direct 3D printing technique is adopted in the fabrication process of the soft pneumatic robotic digit using the smart material of thermoplastic polyurethane. Each digit joint achieves different ranges of motion (ROM; bending angles of distal, proximal, and metacarpal joint are 107°, 101°, and 97°, respectively) under a low pressure of 30 kPa, which are consistent with the functional ROM of a human finger for performing daily activities. Theoretical model analysis and experiment tests are performed to validate the effectiveness of the digit parametric kinematic model, thereby providing evidence-based technical parameters for the precise control of dynamic pressure dosages to achieve the required motions.
Error-Based Feedforward Control for a Charge-Coupled Device Tracking System

High control bandwidth is usually restricted in a finite sampling rate of a charge-coupled device (CCD)-based tracking loop, which hinders a good closed-loop performance. Conventionally, a rate feedforward controller is usually used to improve the control performance; however, it is restricted by the line-of-sight (LOS) rate, which is required to be estimated due to only the LOS error available in the CCD-based tracking control system. Besides that, it is also affected by the inverse of the control model. A new error-based feedforward (EBF) control mode is proposed to overcome these difficulties. Due to being plugged into an existing feedback control loop, the closed-loop performance of the CCD-based control system can be minimized by optimizing a Q-filter. The closed-loop stability and robustness with this EBF control system are investigated on the condition of the gain margin and the phase margin of the open-loop transfer function. Furthermore, this paper proposes a new criterion to enhance the robustness of the EBF control system and reveals the sensitivity function property. Because of not being restricted by an accurate model, this new feedforward controller cannot lead to a compromise between error attenuation and noise propagation in the control loop. Simulations and experiments are both used to testify the effectiveness of the improved control mode.
The Effect of Popular Music on Female Students� Fitness in Physical Education Courses

Background. Music has been seen as a motivator to help individuals who are not persistent enough to do a full set of exercises. However, the effect of long-term using music has not been determined, especially for amateur students who participated in Physical Education courses. Objectives: The purpose of the study was to find out the effect of popular music on female physical fitness students when participating in physical education courses. Methods. Seventy-three healthy female students were chosen and divided into three groups, of which 24 female students were in the 15-week experimental group, another 24 female students were in the 7-week experimental group, and the 25 female students remaining were in 15-week training without music. The music tempos were classified into three parts of a training session such as the warm-up (90-120 bpm), training (120-150 bpm), and cool-down (60-90 bpm). Results. The results indicated that the application of popular music improved speed (F (2,68) =13.35, p=0.000), agility (F (2,28) =11.93, p=0.000), maximum aerobic speed (F (2,68) =4.93, p=0.01), and decreased rating perceived exertion value when compared between the use of music groups and no music in all three sessions (warm-up, training, and cool-down). However, it showed unchanged in the core strength (F (2,68) =0.006, p=0.994), power of the legs (F (2,68) =0.034, p=0.967), and strength of the hand (F (2,68) =0.229, p=0.796). Besides, there was no difference in the effect of music on differences in long-term experimental times between 7-week and 15-week groups in all tests and the rating perceived exertion index, too. Conclusion. Long-term using popular music (7e vs. 15e) did not affect female students’ fitness in Physical Education courses. However, it can be said that popular music has been a beneficial tool to improve physical fitness and create a dynamic practice environment in physical education courses.
Hybrid approach for ECG signal enhancement using dictionary learning‐based sparse representation

Electrocardiogram (ECG) signal quality enhancement is a crucial task in the computer-based automated processing system. In this study, a dictionary learning (DL)-based sparse representation framework is presented for ECG signal enhancement through denoising. Unlike, traditional filtering techniques, the proposed method removes both low- and high-frequency noises for complete enhancement of the signal quality. The frequency localised DL-based sparse representation approach is applied to remove both baseline wander and power-line interference. The time-localised and signal characteristics based DL-based sparse representation scheme is employed for extraction of clean ECG components from white Gaussian noise and muscle artefact added noisy ECG record. Both qualitative and quantitative performance analyses are carried out using the Massachusetts Institute of Technology - Beth Israel Hospital (MIT-BIH) database, QT database and synthetic ECG signals. The efficacy of the proposed method is compared with the existing ECG denoising approaches using standard performance metrics: signal-to-noise ratio, root-mean-square error and percentage RMS difference. It is observed through the detailed study and analysis that the proposed approach outperforms the existing ones and can be served to further enhance the overall quality of ECG in a computer-based automated medical system.
Role of Homoclinic Breathers in the Interpretation of Experimental Measurements, With Emphasis on the Peregrine Breather

A class of generalized homoclinic solutions of the nonlinear Schrödinger (NLS) equation in 1+1 dimensions is studied. These are homoclinic breathers that are shown to be derivable from the ratio of Riemann theta functions for the genus-2 solutions of the nonlinear Schrödinger equation. We discuss how these solutions behave in the homoclinic limit for which a fundamental parameter ε goes to zero, ε → 0 (such that two points of simple spectrum converge to double points at some particular lambda-plane eigenvalue). The homoclinic solutions cover the entire lambda plane (the Riemann surface of the NLS equation) and are given in terms of simple trigonometric functions. When the spectral eigenvalues converge to the carrier amplitude in the lambda plane we have the Peregrine breather. While the Peregrine solution is often called a soliton, it is in reality a breather, albeit occurring at the “singular point” corresponding to the carrier eigenvalue in the lambda plane and consequently “breathes” only once in its lifetime. The Peregrine breather separates small-amplitude modulations below the carrier from large amplitude modulation above the carrier. This fact means that the Peregrine breather has a “central” role in the lambda plane characterization of the NLS nonlinear spectrum. The Akhmediev breather occurs somewhat below the carrier (and is therefore a small-amplitude modulation) and the Kuznetsov-Ma breather occurs above the carrier (and is therefore a large-amplitude modulation). The general homoclinic solutions can be constructed everywhere in the lambda plane and are shown to be a useful tool to interpret the nonlinear Fourier spectrum of space and time series recorded in the laboratory and ocean environment. Nonlinear filtering is suggested as a way to extract breather trains from experimental time series. The generalized homoclinic breathers can be thought of as “extreme wave packets” or “rogue wave” solutions of water waves for scientific and engineering applications in various fields of physics including physical oceanography and nonlinear optics.
A Pedagogical Paradigm to Support Infrastructure Projects through Coupling Front-End Planning Techniques with Sustainability Practices

ABSTRACT Effective project management of sustainable infrastructure projects is critical for successful project delivery and improved project performance of construction projects. Coupling and integrating Front-End Planning (FEP) techniques and sustainability rating systems such as the EnvisionTM rating system in the curricula can equip our construction workforces with advanced skills in planning and managing such projects. Therefore, this study investigates the existing synergies between FEP and EnvisionTM and establishes the necessity of incorporating these concepts into the Construction Management (CM) curricula. The research methodology included (1) surveying infrastructure experts to investigate the nexus between EnvisionTM credits and FEP; (2) investigating the use of pre-project planning tools in the industry; and (3) implementing a Problem Based Learning (PBL) activity to enhance construction students’ knowledge of FEP and sustainability. The results highlighted that sustainability practices greatly align with FEP. Moreover, the knowledge improvement through the PBL activity demonstrates that such an active learning method can be a strong candidate for effective pedagogical design to teach these concepts. The novelty of this study encompasses highlighting the necessity to link and incorporate EnvisionTM with FEP in construction curricula to support CM students to become adept in both planning and delivering sustainable infrastructure projects.
Arterial Wave Reflection and Aortic Valve Stenosis: Diagnostic Challenges and Prognostic Significance

Introduction Arterial wave reflection is an important component of the left ventricular afterload, affecting both pressure and flow to the aorta. The aim of the present study was to evaluate the impact of wave reflection on transvalvular pressure gradients (TPG), a key parameter for the evaluation of aortic valve stenosis (AS), as well as its prognostic significance in patients with AS undergoing a transcatheter aortic valve replacement (TAVR). Materials and Methods The study population consisted of 351 patients with AS (mean age 84 ± 6 years, 43% males) who underwent a complete hemodynamic evaluation before the TAVR. The baseline assessment included right and left heart catheterization, transthoracic echocardiography, and a thorough evaluation of the left ventricular afterload by means of wave separation analysis. The cohort was divided into quartiles according to the transit time of the backward pressure wave (BWTT). Primary endpoint was all-cause mortality at 1 year. Results Early arrival of the backward pressure wave was related to lower cardiac output (Q1: 3.7 ± 0.9 lt/min vs Q4: 4.4 ± 1.0 lt/min, p < 0.001) and higher aortic systolic blood pressure (Q1: 132 ± 26 mmHg vs Q4: 117 ± 26 mmHg, p < 0.001). TPG was significantly related to the BWTT, patients in the arrival group exhibiting the lowest TPG (mean TPG, Q1: 37.6 ± 12.7 mmHg vs Q4: 44.8 ± 14.7 mmHg, p = 0.005) for the same aortic valve area (AVA) (Q1: 0.58 ± 0.35 cm2 vs 0.61 ± 0.22 cm2, p = 0.303). In multivariate analysis, BWTT remained an independent determinant of mean TPG (beta 0.3, p = 0.002). Moreover, the prevalence of low-flow, low-gradient AS with preserved ejection fraction was higher in patients with early arterial reflection arrival (Q1: 33.3% vs Q4: 14.9%, p = 0.033). Finally, patients with early arrival of the reflected wave (Q1) exhibited higher all-cause mortality at 1 year after the TAVR (unadjusted HR: 2.33, 95% CI: 1.17–4.65, p = 0.016). Conclusion Early reflected wave arrival to the aortic root is associated with poor prognosis and significant aortic hemodynamic alterations in patients undergoing a TAVR for AS. This is related to a significant decrease in TPG for a given AVA, leading to a possible underestimation of the AS severity.
The Long-Term Association between Physical Activity and Weight Regain, Metabolic Risk Factors, Quality of Life and Sleep after Bariatric Surgery

Bariatric surgery is currently regarded as a safe and effective long-term procedure for the treatment of obesity and related comorbidities. We analyzed the association between physical activity (PA), weight regain, metabolic risk factors and quality of life in patients submitted to bariatric surgery. This study also aimed to preliminarily assess how physical activity and weight regain may be associated with sleep quality and sedentary behavior. This was an observational study, with retrospective data collection and a cross-sectional survey. Retrospective clinical data were collected from a sample of 84 individuals who had undergone bariatric gastric bypass surgery at least five years prior to the study period in an Integrated Responsibility Center for Obesity and Metabolic Diseases Surgery. The survey, developed from validated questionnaires and applied in telephone interviews, focused on health data, associated comorbidities, quality of life, physical activity, sedentary behavior and sleep. Descriptive and comparative statistics were performed with a 95% confidence level. Bariatric surgery induced a significant weight loss in the first year after surgery. Our analysis also revealed that lower levels of PA were associated with weight regain. Quality of life as well as sleep quality were inversely related to weight regain, as well as sedentary behavior in general. Primary and secondary outcomes of bariatric surgery can be better achieved if the practice of PA could be maintained for consecutive years.
Divergence of Chemerin Reduction by an ATS9R Nanoparticle Targeting Adipose Tissue In Vitro vs. In Vivo in the Rat

Nanoparticles (NPs) can enable delivery of a drug to a targeted tissue. Previous studies have shown that an NP utilizing an adipose targeting sequence (ATS) peptide in conjunction with a drug can selectively deliver the drug to mouse adipose tissues, using the prohibitin protein expressed in adipose tissue as the target of the ATS. Adipose tissue is a major source of the adipokine chemerin, a prohypertensive protein. Liver-derived chemerin, the largest source of circulating chemerin, is biologically inactive in blood pressure regulation. Our goal is to understand if chemerin produced in adipose tissue contributes to blood pressure/hypertension. We hypothesize the ATS drug delivery system could be used specifically to reduce the levels of adipose tissue-derived chemerin. We created an NP consisting of an antisense oligonucleotide (ASO) against chemerin and a FITC-labeled ATS with a nine arginine sequence (ATS9R). In vitro studies showed that the ASO is functional when incorporated into an NP with ATS9R as it reduced chemerin mRNA expression in isolated epidydimal (Epi) and retroperitoneal (RP) fat adipocytes from Dahl SS rats. This same NP reduced chemerin in isolated whole fats. However, this NP was unable to selectively deliver the ASO to adipose tissue in vivo; liver delivery was dominant. Varying NP doses, administration route, and the concentration of components constituting the NP showed no improvement in ASO delivery to fats vs. the liver. Further studies are therefore needed to develop the ATS9R system to deliver an ASO to adipose beds in rats.
Mapping near field electromagnetic responses to sub-wavelength structures

When light interacts with an object, some of the wave is converted into the decaying mode, also known as the evanescent wave, which only exists at the surface of the object or the near field. Evanescent wave contains information of sub-wavelength structures. Conventionally, it’s hard to interpret the complex near field and extract information about the object from it. Here, I train several deep learning network models to predict sub-wavelength structures’ refractive index based on the input of the electric field at the surface of the object. The convolutional neural network (CNN) model could achieve a high prediction accuracy of 98% . The saliency maps analysis also confirms that sub-wavelength information concentrates at the surface of the object.
Recurrent GlimpseNet

Current approaches to image classification ’brute force’ the problem by passing convolution filters over every nook and cranny of an image. Humans and other animals, how-ever, take a much more parsimonious approach by intelli-gently directing their gaze towards regions of interest. Our work is built on an existing implementation of this concept: MGNet introduced by Tan et al. [4]. We explore the capa-bilities and limitations of MGNet and shed some light on the root cause of these limitations through model inspection and visualization. Additionally, we show a minor improvement in performance by replacing their spatial clue mechanism with a 2d positional encoding.
AIS Electronic Library (AISeL)

An appropriate pricing strategy is inevitable in achieving competitive advantage; however, firms have to be aware of the key resources needed for successful pricing and invest in developing the firm’s pricing ability. Pricing of digital goods and services is especially challenging due to the high-volatility environments and the special characteristics that these offerings have as compared to other economic goods. Despite of its importance, the literature, to date, did not investigate the pricing of digital goods and services from the resource-based perspective (RBW) and the capability-based view (CBV). After conducting a multi-case study of five firms, this research provides an in-depth view on the pricing practices of digital goods and services through the theoretical lenses of RBV and CBV and proposes a model that captures the key activities and the key resources needed for pricing of digital goods and services.
Using a convolutional neural network to super-resolve airflow around a building

While small-scale structures can have a large effect on the full flow field and important engineering quantities of interest, they are difficult to resolve in fluid simulations. Increasing spatial resolution makes numerically solving the underlying Navier-Stokes equation increasingly expensive. This, coupled with the success of recent super-resolution techniques for natural-images, has sparked interest in applying deep neural network to super-resolve flow fields from lower resolution data. However, many techniques balance the expressivity of state-of-the-art super-resolution networks with limitations to enforce physical constraints or curtail unexplainable results. To investigate this trade-off, we compare the enhanced deep super-resolution network (EDSR) [13], developed for natural-image super-resolution, with the hybrid downsampled skip-connection/multi-scale (DSC/MS) model, built specifically for super-resolving flow fields. We also explore the effect of pretraining the EDSR model on natural images. The pre-trained model gives the best performance, while the EDSR model in general performs better than the base DSC/MS. While the EDSR model is able to reconstruct test data omitted from the training time-series, all models perform much worse with test data taken as the simulation advances in time away from the time steps used for training
Machine comprehension using semantic graphs

We present a novel method for recognizing entailment using semantic graphs and apply it to the 2018 Semeval task on Machine Comprehension (MC) . Concept graphs are built automatically from MC texts, questions, and answers, using the 4lang system [1]. A strong baseline method using only these graphs is presented, followed by an enhancement of a state-of-the-art system [2]. Preliminary results suggest that these features achieve a . 5 percentage point improvement over the original system [3].
Improving Image Classifiers with IQ Loss functions: Non-adversarial f -Divergence Minimization

Image classification is an important and broadly re-searched topic in computer vision, and to perform this task, classifiers utilize loss functions to distinguish the models predictions and the ground-truth labels. These loss functions guide the training of the classifier. However, concerns have emerged regarding the miscalibration of deep neural networks for multi-class classification tasks. Particularly worrisome is the misestimation of class probabilities that manifests as overconfidence in the model’s final prediction, which has significant downstream effects. Moreover, existing loss functions lack robustness to distributional shifts. Existing literature suggests that viewing classifiers as energy-based models can improve classification by providing superior calibration and robustness. In this paper, we present IQ Loss , a loss function that can minimize f-divergences while avoiding adversarial training and thereby achieve superior calibration and robustness to distributional shifts to existing loss functions, including focal loss and cross-entropy. We empirically validate this approach by training standard computer vision architectures using IQ Loss on the CIFAR-10 and the CIFAR-100 datasets. We then assess the performance and calibration of these models and demonstrate that IQ Loss outperforms both focal loss and cross entropy on these vital classification tasks.
FAA Aerospace Forecast Fiscal Years 2022–2042

The forecasts in this document are forecasts of aviation demand, driven by models built on forecasts of economic activity. There are many assumptions in both the economic forecasts and in the FAA models that could affect the degree to which these forecasts are realized. This year’s forecast is driven, at least in the near-term, by the pace of recovery from the impacts to the U.S. and global economies and the aviation industry resulting from the novel coronavirus (COVID19). It does go without saying that terrorism remains among the greatest world-wide risks to aviation growth. Any terrorist incident aimed at aviation could have an immediate and significant impact on the demand for aviation services that could be greater than its impact on overall economic activity.
Simulating Deployment of a Multi-{ Modal, Task, Site } Chest X-Ray Model

With access to a multi-task chest X-ray (CXR) model deployed in the Intermountain healthcare system, we iterate on this model with new data and a multi-step pretraining method (multimodal contrastive self-supervised, followed by supervised). Our model achieves equal or better performance than supervised and image-only contrastive pre-training methods on three tasks: pneumonia (0.880 AU-ROC), multilobar pneumonia (0.883 AUROC), and pleural effusion (0.959 AUROC) in a set of real-world CXRs from multiple sites across the Intermountain healthcare system. We build a framework for retraining our model on a regular monthly cadence and, for a model ”deployed” in 2020, simulate this retraining through 2021. We see meaningful improvement in model performance due to retraining, including an increase of 0.024 (from 0.881 to 0.905) for pneumonia AUROC on a 2021 test set. Finally, we analyze performance of our model using class activation maps (CAM) and confusion matrices.
EatRight: Calorie Counter

In this project we attempted to simplify the task of finding the calories present in the food being consumed using Deep Learning. We experimented with different architectures such as Residual Network design and Mask-RCNN on a smaller subset of very large dataset Food-101. Mask-RCNN can successfully perform instance segmentation and has capability to record several detections of more than one type of food item present in an image. The detected labels are used to calculate total calories in the food image using standard serving size and calories associated with them.