Miracle-dz/UniFAR-FTUs huggingface dataset:
Controlling 3D Model of Human Hand Exploiting Synergistic Activation of The Upper Limb Muscles

Surface electromyography (EMG) based control of hand exoskeleton has been developed in numerous methods, but a proportional control of multiple degrees of freedom (DOF) exoskeleton is still rarely seen in recent years. This paper proposes an alternative to the most recent solutions for proportionally estimating multiple DOFs of the hand. High-density electromyography (HD-EMG) was used to estimate the joint angle of five fingers in sophisticated gestures. The gestures were the ten alphabets of American Sign Language (A B C D F I K L O W). The association between HD-EMG and kinematics was mapped by using a neural network (NN) and k-Nearest Neighbour (kNN). The estimation accuracy was around 70-95 % (R index) for the eleven DOFs in four normal-bodied subjects’ hands. Furthermore, kNN performed better than NN, even in the case of input feature reduction. The regression results also were able to distinguish 9 of 10 alphabets (except O), with the false interpretation because of the identical muscle activity and kinematics between O and C. The approach described in this paper provides a practical solution to proportionally and simultaneously control multiple DOFs for the hand exoskeleton.
Heister mouth gag aided endotracheal intubation in patients with maxillofacial trauma: A case report

Securing the airway in patients with maxillofacial trauma is challenging for the anesthesiologist. Pain and facial deformities limit mouth opening and hence direct laryngoscopy. Fractured bone segments, blood, oral secretions, and tissue edema preclude the use of fiber-optic bronchoscopes for intubation of the trachea. We report a successful attempt of orotracheal intubation with a Macintosh blade in a 25-year-old patient with restricted mouth opening with the use of a Heister mouth gag.
Triggers and Tweets: Implicit Aspect-Based Sentiment and Emotion Analysis of Community Chatter Relevant to Education Post-COVID-19

This research proposes a well-being analytical framework using social media chatter data. The proposed framework infers analytics and provides insights into the public’s well-being relevant to education throughout and post the COVID-19 pandemic through a comprehensive Emotion and Aspect-based Sentiment Analysis (ABSA). Moreover, this research aims to examine the variability in emotions of students, parents, and faculty toward the e-learning process over time and across different locations. The proposed framework curates Twitter chatter data relevant to the education sector, identifies tweets with the sentiment, and then identifies the exact emotion and emotional triggers associated with those feelings through implicit ABSA. The produced analytics are then factored by location and time to provide more comprehensive insights that aim to assist the decision-makers and personnel in the educational sector enhance and adapt the educational process during and following the pandemic and looking toward the future. The experimental results for emotion classification show that the Linear Support Vector Classifier (SVC) outperformed other classifiers in terms of overall accuracy, precision, recall, and F-measure of 91%. Moreover, the Logistic Regression classifier outperformed all other classifiers in terms of overall accuracy, recall, an F-measure of 81%, and precision of 83% for aspect classification. In online experiments using UAE COVID-19 education-related data, the analytics show high relevance with the public concerns around the education process that were reported during the experiment’s timeframe.
Solving parameterized generalized inverse eigenvalue problems via Golub-Kahan bidiagonalization

In this study, we present two two-step methods to solve parameterized generalized inverse eigenvalue problems that appear in diverse areas of computation and engineering applications. At the (cid:12)rst step, we transfer the inverse eigenvalue problem into a system of nonlinear equations by using of the Golub-Kahan bidiagonalization. At the second step, we use Newton’s and Quasi-Newton’s methods for the numerical solution of system of nonlinear equations. Finally, we present some numerical examples which show that our methods are applicable for solving the parameterized inverse eigenvalue problems. Copyright c ⃝ 2022 Shahid Beheshti University.
Emotion Recognition Based on Facial Expression by Exploring Batch Normalization Convolutional Neural Network

Emotions are intense and complex feelings directed at someone or something as a reaction to something. Emotions are generally displayed subconsciously in a variety of ways, and one of the easiest ways to see them is through facial expressions. Convolutional neural networks are a class of deep neural networks that are useful for analyzing visual images in deep learning, but there are no set guidelines for how to construct the best model or how many layers are required. Batch normalization is another feature of deep learning that serves as a regularizer, enabling much higher learning rates and laxer initialization requirements. This paper presents an emotion recognition system based on human facial expressions. Exploring batch normalization and other layers in the algorithm provides improved recognition accuracy. This study uses the KDEF dataset as a training dataset and partly as a test dataset. Several architectures are built to compare accuracy, and the results are quite good. Compared to other case architecture models, the model that gets the best accuracy, consists of 6 convolutional layers, 3 Batch Normalization, 2 Fully Connected, an Average Pooling, and a dropout. It got 98.51% training accuracy compared to other models.
Controllability and Observability of Multi-Spin Systems: Constraints by Symmetry and by Relaxation

We investigate the universality of multi-spin systems in architectures of various symmetries of coupling type and topology. Explicit reachability sets under symmetry constraints are provided. Thus for a given (possibly symmetric) experimental coupling architecture several decision problems can be solved in a unified way: (i) can a target Hamiltonian be simulated? (ii) can a target gate be synthesised? (iii) to which extent is the system observable by a given set of detection operators? and, as a special case of the latter, (iv) can an underlying system Hamiltonian be identified with a given set of detection operators? Finally, in turn, lack of symmetry provides a convenient necessary condition for full controllability. Though often easier to assess than the well-established Lie-algebra rank condition, this is not sufficient unless the candidate dynamic simple Lie algebra can be pre-identified uniquely, which is fortunately less complicated than expected.
Phase Retrieval and Zero Crossings: Mathematical Methods in Image Reconstruction

1 Introduction. - 2 Polynomials: A Review. - 3 Entire Functions and Signal Recovery. - 4 Homometric Distributions. - 5 Analytic Signals and Signal Recovery from Zero Crossings. - 6 Signal Representation by Fourier Phase and Magnitude in One Dimension. - 7 Recovery of Distorted Band-Limited Signals. - 8 Compact Operators, Singular Value Analysis and Reproducing Kernel Hilbert Spaces. - 9 Kaczmarz Method, Landweber Iteration, Gerchberg-Papoulis and Regularization. - 10 Two Dimensional Signal Recovery Problems. - 11 Reconstruction Algorithms in Two Dimensions. - 12 Nonexpansive Maps and Signal Recovery. - 13 Projections on Convex Sets in Signal Recovery. - 14 Method of Generalized Projections and Steepest Descent. - 15 Closed Form Reconstruction of the Support and the Object. - 16 Fienup's Input-Output Algorithms and Variations on this Theme. - 17 Topics and Applications of Signal Recovery. - A The Geometry of Projections on Convex Sets. - B Reference Summary. - C References.
An intelligent intrusion detection system for distributed denial of service attacks: A support vector machine with hybrid optimization algorithm based approach

Cloud computing offers comfortable service to business sectors as they can concentrate on their products. Over the internet, cloud computing is liable to various security threats and attacks which is a primary obstacle to the growth of cloud computing services. Distributed denial of service (DDoS) is one such attack that exploits cloud computing services using compromised machines; hence, its detection is a significant field of research. Several DDoS detection schemes have been proposed in the past, but they fail to detect real‐time active DDoS attacks because of their growth in severity and volume. Machine learning (ML) techniques are efficient in making predictions; hence, in this study, a hybrid ML intrusion detection system (IDS) model is proposed. The performance of the proposed IDS model is improved by employing a 10‐fold cross‐validation technique to perform feature selection, reducing data dimensions on the publicly available benchmark NSL‐KDD dataset. Performance validation of the proposed hybrid IDS model is done using the confusion matrix. Support vector machine (SVM) parameters are fine‐tuned using hybrid Harris Hawks optimization (HHO) and particle swarm optimization (PSO) algorithms. The performance of these hybrid algorithms is compared with other classical algorithms such as C4.5, K‐nearest neighbor, and SVM using performance metrics such as precision, sensitivity, specificity, F1 score, and accuracy. From these comparisons, it can be inferred that the proposed SVM with hybrid optimization HHO‐PSO machine learning IDS model performs better DDoS detection with good performance metric values.
Design and Simulation of a Ring Transducer Array for Ultrasound Retinal Stimulation

Argus II retinal prosthesis is the US Food and Drug Administration (FDA) approved medical device intended to restore sight to a patient’s blind secondary to retinal degeneration (i.e., retinitis pigmentosa). However, Argus II and most reported retinal prostheses require invasive surgery to implant electrodes in the eye. Recent studies have shown that focused ultrasound can be developed into a non-invasive retinal prosthesis technology. Ultrasound energy focused on retinal neurons can trigger the activities of retinal neurons with high spatial-temporal resolution. This paper introduces a novel design and simulation of a ring array transducer that could be used as non-invasive ultrasonic retinal stimulation. The array transducer is designed in the shape of a racing ring with a hemisphere surface that mimics a contact lens to acoustically couple with the eye via the tear film and directs the ultrasound to avoid the high acoustic absorption from the crystalline lens. We will describe the design methods and simulation of the two-dimensional pattern stimulation. Finally, compared with other existing retinal prostheses, we show that the ultrasound ring array is practical and safe and could be potentially used as a non-invasive retinal prosthesis.
Physicochemical, Morphological, Thermal, and Rheological Properties of Native Starches Isolated from Four Cultivars of Anchote (Coccinia abyssinica (Lam.) Cogn.) Tuber

Anchote (Coccinia abyssinica (Lam.) Cogn) is a potentially important source of starch and an underutilized root and tuber crop indigenous to Ethiopia. In this study, the physicochemical, morphological, thermal, and rheological properties of native starches isolated from four cultivars of anchote tubers were studied and compared to potato and cassava starches, which were considered as references. The amylose content of anchote starches varied from 15.8–22.3%. The anchote cultivars showed different granule sizes, but all revealed a B-type crystalline structure, identical to potato starch. The phosphorus content of anchote starches ranged from 82–93 mg/100 g and was much higher than that of potato and cassava (60.3 and 5.8 mg/100 g, respectively). This characteristic could govern several functional properties of anchote starches, making them suitable for applications in different types of noodles, glucose syrups, and viscous products. The gelatinization temperature and enthalpy of anchote starches, which ranged from 60.97 °C to 69.33 °C and 16.87 to 18.38 J/g, respectively, were considerably different compared to potato and cassava starches. Significant variations were also observed among the pasting properties of starches from anchote cultivars. They showed a higher stability to heating and shearing, having higher TV (2046 to 2280 mPa·s) and lower BV (248 to 487 mPa·s) values, and a higher final viscosity (3409 to 3686 mPa·s) than potato and cassava, which are important characteristics in food processing and when high gel viscosity is required after cooling. Anchote starch gels exhibited rheological characteristics of true gels, showing much lower (tan δ)1 values and significantly higher viscoelastic moduli than those found in cassava and potato gels. The present study revealed significant differences among the physicochemical properties of anchote starches, depending on the cultivar, and demonstrated their promising potential in food product development and other industrial applications.
Hyperspectral Image Classification Based on Novel Hybridization of Spatial-Spectral-Superpixelwise Principal Component Analysis and Dense 2D-3D Convolutional Neural Network Fusion Architecture

Abstract We propose a hybridized technique named Spatial-Spectral-Superpixelwise PCA-based Dense 2D-3D CNN Fusion Architecture (3SPCA-D-2D-3D-CNN), that deals with the detailed and complex study of dimensionality reduction and classification of Hyperspectal images (HSI). Our work is 2-fold: At first (1), 3SPCA is applied on raw HSI that adopts superpixels-based local reconstruction to filter the images, whereas PCA-based supplementary global features acquire the relevant and low-dimensional local features. Every HSI pixel is reconstituted by the pixels of its closest neighbors in the same superpixel block to reduce noise and improve spatial information. Next, PCA is conducted on every zone and the full HSI to get local and global features. The local-global and spatial-spectral properties are then concatenated. Secondly (2), the D-2D-3D-CNN fusion architecture is made up of three 3D convolution blocks, two 2D convolution blocks with varied kernel sizes and filters, and four fully connected (FC) dense layers, totaling nine distinguishing and information-enriched features. These features can generate precise class labels and apply them to the appropriate landcovers. The proposed method has been applied to three publicly available HSI landcover datasets, the Indian Pines, the Salinas Valley, and the Pavia University. It achieved respectively 98.33%, 99.99%, and 98.73% average accuracy scores. Due to its improved Feature Extraction capacity from a limited number of training samples and its classification performance with fewer epochs, this method outperforms other relevant state-of-the-art CNN-based methods.
Factors Controlling the Flow and Connectivity in Fracture Networks in Naturally Fractured Geothermal Formations

The productivity and injectivity of hydraulically fractured geothermal wells in naturally fractured formations depend on the connectivity of fracture networks created by the interaction of hydraulic fractures with natural fractures. The primary objectives of this paper are (a) to define quantitatively the connectivity of the created fracture network, (b) to determine the factors that control the connectivity of fracture networks bounded by wells, and (c) to propose ways in which the flow capacity and fracture connectivity can be improved by changes to the hydraulic fracture design. A fully 3D hydraulic fracturing simulator has been developed that considers the interaction of hydraulic fractures with natural fractures by solving for the stresses, fluid flow, heat transfer, fracture growth, and intersection. These propagated fractures, which include hydraulic fractures and reactivated natural fractures, are divided into backbone, dead-end, and isolated fractures. Different well patterns that aim to optimize the connectivity of the injector to the producer (optimize the area of the backbone fractures) are simulated. A sensitivity analysis is conducted to investigate the effect of various parameters on the connectivity of wells through fractures. An optimal well pattern is needed to maximize the connected fracture area that provides a conductive path for heat extraction from naturally fractured geothermal reservoirs. Our results show that the connectivity of fracture networks is dramatically impacted by the degree of deflection, crossing, and merging of hydraulic fractures with natural fractures. An example is used to investigate the effect of backbone and dead-end fractures on heat extraction from an enhanced geothermal system (EGS). The detailed parametric study helps us better understand the factors that influence the geometry and connectivity of fracture networks and guide us in hydraulic fracture design and well spacing optimization in naturally fractured geothermal reservoirs.
A Program to Reduce Post-Operative Opioid Prescribing at a Veteran’s Affairs Hospital

Variability in surgeon prescribing patterns is common in the post-operative period and can be the nidus for dependence and addiction. This project aims to reduce opioid overprescribing at the Veteran’s Affairs Pittsburgh Healthcare System (VAPHS). The VAPHS Opioid Stewardship Committee collaborated to create prescribing guidelines for inpatient and outpatient general, thoracic, and vascular surgery procedures. We incorporated bundled order sets into the provider workflow in the electronic medical system and performed a retrospective cohort study comparing opioid prescription patterns for Veterans who underwent any surgical procedure for a three-month period pre- and post- guideline implementation. After implementation of opioid prescribing guidelines, morphine milligram equivalents (MME), quantity of pills prescribed, and days prescribed were statistically significantly reduced for procedures with associated guidelines, including cholecystectomy (MME 140.8 vs. 57.5, p = 0.002; quantity 18.8 vs. 8, p = 0.002; days 5.1 vs. 2.8, p = 0.021), inguinal hernia repair (MME 129.9 vs. 45.3, p = 0.002; quantity 17.3 vs. 6.1, p = 0.002; days 5.0 vs. 2.4, p = 0.002), and umbilical hernia repair (MME 128.8 vs. 53.8, p = 0.002; quantity 17.1 vs. 7.8, p = 0.002; days 5.1 vs. 2.5, p = 0.022). Procedures without associated recommendations also preceded a decrease in overall opioid prescribing. Post-operative opioid prescribing guidelines can steer clinicians toward more conscientious opioid disbursement. There may also be reductions in prescribing opioids for procedures without guidelines as an indirect effect of practice change.
BER Aided Energy and Spectral Efficiency Estimation in a Heterogeneous Network

In this work, we adopt the analysis of a heterogeneous cellular network by means of stochastic geometry, to estimate energy and spectral network efficiency. More specifically, it has been the widely spread experience that practical field assessment of the Signal-to-Noise and Interference Ratio (SINR), being the key physical-layer performance indicator, involves quite sophisticated test instrumentation that is not always available outside the lab environment. So, in this regard, we present here a simpler test model coming out of the much easier-to-measure Bit Error Rate (BER), as the latter can deteriorate due to various impairments regarded here as equivalent with additive white Gaussian noise (AWGN) abstracting (in terms of equal BER degradation) any actual non-AWGN impairment. We validated the derived analytical model for heterogeneous two-tier networks by means of an ns3 simulator, as it provided the test results that fit well to the analytically estimated corresponding ones, both indicating that small cells enable better energy and spectral efficiencies than the larger-cell networks.
Measurements of Flow in the Boundary Layer of a 1/40-Scale Model of the U. S. Airship "Akron."

This report presents the results of measurements of flow in the boundary layer of a 1/40-scale model of the U. S. Airship "Akron" (ZRS-4) made with the object of determining the boundary-layer thickness, the point of transition from laminar to the turbulent flow, and the velocity distribution in the boundary layer.
Downregulation of Mannose-6-Phosphate Receptors in Fabry Disease Cardiomyopathy: A Potential Target for Enzyme Therapy Enhancement

Background: The efficacy of enzyme replacement therapy (ERT) in mobilizing globotryaosylceramide (GB-3) from Fabry cardiomyocytes is limited. The mechanism involved is still obscure. Methods: Assessment of M6Pr, M6Pr-mRNA, and Ubiquitin has been obtained by Western blot analysis and real-time PCR of frozen endomyocardial biopsy samples, from 17 pts with FD, various degree of left ventricular hypertrophy, and maximal wall thickening (MWT) from 11.5 and 20 mm. The diagnosis and severity of FDCM followed definitions of GLA mutation, α-galactosidase A enzyme activity, cardiac magnetic resonance, and left ventricular endomyocardial biopsy with the quantification of myocyte hypertrophy and the extent of Gb-3 accumulation. All patients have received alpha or beta agalsidase for ≥3 years without a reduction in LV mass nor an increase in T1 mapping at CMR. Controls were surgical biopsies from 15 patients undergoing mitral valve replacement. Results: Protein analysis showed mean M6Pr in FDCM to be 5.4-fold lower than in a normal heart (4289 ± 6595 vs. 23,581 ± 4074, p = 0.0996) (p < 0.001): specifically, 9-fold lower in males, p = 0.009, (p < 0.001) and 3-fold lower in females, p = 0.5799, (p < 0.001) showing, at histology, a mosaic of normal and diseased cells. M6Pr-mRNA expression was normal, while ubiquitin showed an increase of 4.6 fold vs. controls (13,284 ± 1723 vs. 2870 ± 690, p = 0.001) suggesting that ubiquitin-dependent post-translational degradation is likely responsible for the reduction of M6Pr in FDCM. Conclusion: M6Pr expression is remarkably reduced in FDCM as a likely result of post-translational degradation. This may explain the reduced efficacy of ERT and be a therapeutic target for the enhancement of ERT activity.
Cross-Example Patch Fusion for Face Anti-Spoofing

In this paper, we proposed a novel pipeline for face anti-spoofing (FAS) tasks with re-structure face images based on the cross-example patch fusion (CEPF) mechanism. The effective end-to-end pipeline mainly includes three modules: a face patches mixer, a spoof cue encoder, and a semantic map predictor. Firstly, we generate re-structure face images from the mixer by using multi-scale patches of one sample selected randomly from a mini-batch dataset to overlap corresponding spatial patches of the input example. Then, we capture spoofing cues via an encoder, a designed Convolutional neural network. Last, we achieve the result of anti-spoofing with a semantic map generated from the predictor. Our approach outperforms the existing methods in performance and speed under the same scale architecture and reveals valuable facts on learning spoofing cues. Using random multi-scale CEPF face images can extract the intrinsic spoofing cues more effectively than the original spatial information of none CEPF faces by a simple encoder, which provides inspirable insights for future supervision design.