Miracle-dz/UniFAR-FTUs huggingface dataset:
Modern Control Methods for First Order Hyperbolic Partial Differential Equations

This work is focused on two control methods for first order hyperbolic partial differential equations (PDE). The first method investigated is output regulation by employing the internal model control (IMC) principle where the controller generates the internal dynamic of the exosystem in order to asymptotically track the trajectory and reject the disturbances. This method is implemented on both systems with in-domain and boundary actuation. The actuator is distributed throughout the system in the first approach, while it is limited at the boundary in the second one.The second method revolves around designing full state feed back controller using so-called backstepping method. Backstepping converts the unstable system to a stable target plant by an integral transformation. This method is assessed for a general first order hyperbolic PDE. Finally, the backstepping method is expanded to a special case in which the boundaries are coupled. This case represents tubular reactors with recycle and exhibits interesting response under controller implementation. Results show that both methods are able to successfully stabilize first order hyperbolic PDEs at the origin. Results are illustrated with simulations.
Hemoglobin Estimation from Smartphone-Based Photoplethysmography with Small Data

Photoplethysmography (PPG) is a well-known technique to estimate blood pressure, oxygen saturation, and heart frequency. Recent efforts aim to obtain PPG from wearable and mobile devices, allowing more democratic access. This paper explores the potential of using a smartphone camera as a PPG sensor, getting a time series based on the RGB values of video recordings of patients' fingertips. Through this PPG, we apply machine learning for the non-invasive estimation of hemoglobin levels. We assume a realistic scenario where the data has a low volume and potentially a low quality. The generalization capacity of the models built on these scenarios usually achieves undesirable performance. This paper presents a novel dataset that comprises real-world mobile phone-based PPG and a comprehensive ex-periment on how different techniques may improve hemoglobin estimation using deep neural architectures. In general, cleaning, augmentation, and ensemble positively affect the results. In some cases, these techniques reduced the mean absolute error by more than thirty percent.
Materials and techniques for effective at-home rehabilitation for hand mobility restoration based on the Virtual Glove

Telerehabilitation has many proven advantages to present for hand mobility restoration. To produce a way for the patient to benefit from it without losing the continuous support of the therapist that traditional rehabilitation offers, we built a complete system of controlled rehabilitation based on touchless hand tracking and developed the framework for its use from therapists and patients. In this paper, we present the devices it uses together with user-friendly applications developed keeping in mind the patients and therapists. Furthermore, we present the methods that can be used for analyzing hand tracking data in order to acquire information for joint-wise mobility improvement.
Multiple vehicle tracking in aerial video sequence using driver behavior analysis and improved deterministic data association

Abstract. Along with the rapid development of the unmanned aerial vehicle technology, multiple vehicle tracking (MVT) in aerial video sequence has received widespread interest for providing the required traffic information. Due to the camera motion and complex background, MVT in aerial video sequence poses unique challenges. We propose an efficient MVT algorithm via driver behavior-based Kalman filter (DBKF) and an improved deterministic data association (IDDA) method. First, a hierarchical image registration method is put forward to compensate the camera motion. Afterward, to improve the accuracy of the state estimation, we propose the DBKF module by incorporating the driver behavior into the Kalman filter, where artificial potential field is introduced to reflect the driver behavior. Then, to implement the data association, a local optimization method is designed instead of global optimization. By introducing the adaptive operating strategy, the proposed IDDA method can also deal with the situation in which the vehicles suddenly appear or disappear. Finally, comprehensive experiments on the DARPA VIVID data set and KIT AIS data set demonstrate that the proposed algorithm can generate satisfactory and superior results.
Systems-of-Systems and Digital Twins: A Survey and Analysis of the Current Knowledge

Understanding the needs and constraints of systems in general and a system-of-systems in particular can be challenging, yet crucial. Relying only on upfront activities will not be sufficient. Important information can be gathered around the performance and behavior of the system as well as stakeholder needs in operation. A digital twin is a way to model and understand the operation of a system. To understand the challenges and enablers related to digital twins in a system-of-systems context, we performed a literature study. In total, only 10 papers were identified that explicitly address this topic, all from the last five years, indicating that this is an active field of research. The papers revealed that definitions and terminology are unclear and that similar challenges as for systems-of-systems also exist for systems-of-digital twins. The complexity and dynamic nature of systems-of-systems motivate further study of digital twins to understand needs and constraints. However, key challenges such as concepts and principles of digital twins for systems-of-systems, cost and benefits, and evolution needs to be better understood.
The Effect of Expert Patient Simulation on Clinical Judgment: A Quasi-Experimental Study

Background Worldwide, quality education is one of the important tools to improve healthcare quality. Healthcare practitioners must be competent in their clinical judgement to meet clients’ need. However, poor clinical judgment skill accounts for almost one-third of all patient problems in health care. Expert patient simulation has been used as a training method for clinical judgement skill. However, according to empirical studies, using expert patients to develop clinical judgement skill is unclear. The method is effective in one situation but not in another. Objective To examine the effect of expert patient simulation on the clinical judgment skill of health science students of Mizan-Tepi University. Methods A pre-test/post-test quasi-experimental design was used on 92 randomly selected samples from the graduating cohort of midwifery students. The research subjects who took part in the experiment were picked at random. Tools included the Creighton Competency Evaluation Instrument (C-CEI®), the Learning Satisfaction and Self-Confidence Questionnaire, and the Kolb Learning Style Inventory (LSI). The Wilcoxon-signed rank test was utilized to compare the self-confidence scores among intervention and control group of students, and the paired sample test was used to compare clinical judgment scores. Cohen’s d was used to assess the effect size, and Spearman correlation was used to explore the association. Results Clinical decision-making ability and self-confidence measures revealed statistical and practical differences between before and after simulation. There was a mean difference of 2.28 (95% CI, 1.78, 2.79), t (45)=9.13, p 0.001, and an effect size of 1.3, p 0.001. A pre-and post-simulation self-confidence measure showed statistically significant improvement after simulation (W = 1, Z = −3.57, P 0.001). A moderately significant positive connection (r = 0.419, p 0.004) was also discovered. Conclusion The study found that human expert patient simulation is a tremendous clinical training technique for improving students’ clinical decision-making skill competency and self-confidence.
Source Localization Using RSS Measurements with Sensor Position Uncertainty

Received signal strength- (RSS-) based localization has attracted considerable attention for its low cost and easy implementation. In plenty of existing work, sensor positions, which play an important role in source localization, are usually assumed perfectly known. Unfortunately, they are often subject to uncertainties, which directly leads to effect on localization result. To tackle this problem, we study the RSS-based source localization with sensor position uncertainty. Sensor position uncertainty will be modeled as two types: Gaussian random variable and unknown nonrandom variable. For either of the models, two semidefinite programming (SDP) methods are proposed, i.e., SDP-1 and SDP-2. The SDP-1 method proceeds from the nonconvex problem with respect to the maximum likelihood (ML) estimation and then obtains an SDP problem using proper approximation and relaxation. The SDP-2 method first transfers the sensor position uncertainties to the source position and then obtains an SDP formulation following a similar idea as in SDP-1 method. Numerical examples demonstrate the performance superiority of the proposed methods, compared to some existing methods assuming perfect sensor position information.
THE EFFECT OF LEFT VENTRICULAR GEOMETRY ON MYOCARDIAL PERFORMANCE INDEX IN HYPERTENSIVE PATIENTS.

Tarek M. Elmasry, MSc, Mohammed F. Abd Elbary, MD, Kamal A. Marghany, MD and Mohammed S. AboElmagd, MD. Cardiology Department, Faculty of Medicine, Al-Azhar University, cairo . Manuscript Info Abstract . Manuscript History
Word Embeddings for Domain Specific Semantic Relatedness

Word embeddings are becoming pervasive in natural language processing (NLP), with one of their main strengths being their ability to capture semantic relationships between words. Rather than training their own embeddings many NLP practitioners elect to use pre-trained word embeddings. These pre-trained embeddings are typically created and evaluated using general corpora. Thus, there is a deficiency in the understanding of their performance within a technical domain. In this thesis, we explore how the nature of the data used to train embeddings can affect their performance when computing semantic relatedness within different domains. The three main contributions are as follows. Firstly, we find that the performance of general pre-trained embeddings is lacking in the biomedical domain. Secondly, we provide key insights that should be considered when working with word embeddings for any semantic task. Finally, we develop new biomedical word embeddings and provide them as publicly available for use by others.
Non-Isolated PFC SEPIC Rectifier in IPOP Connection With Current Self-Sharing Capability

This article proposes a method for increasing the nonisolated rectifiers’ power capability by adding more modules in parallel without the drawback of increasing current control complexity. The article applies the method to conceive a SEPIC rectifier from input- and output-parallel connection (IPOP) of modular nonisolated conventional SEPIC rectifiers in discontinuous conduction mode (DCM) operating. The main attribute of the proposed structure is to provide a current self-sharing capability in IPOP connection, with no extra current control. The current-balancing mechanism approaches herein for the SEPIC topology are applicable to all nonisolated rectifiers. The static and dynamic analyses are presented in the article, as well as the output characteristic analysis to show the self-sharing capability when the rectifier operates in DCM. A single voltage-control system is proposed for the modular connection, and there is no need for output-current control per module to guarantee a balanced power and input-current control to guarantee a high-power factor. A 1500 W prototype with three modules is designed, built, and tested in the laboratory to verify the theoretical analysis. The peak of efficiency was 93%, and the input current harmonic distortion was 2.85%, leading to a power factor of 0.999.
Acute Investigation of Maximal Strength, Power and Rapid Strength Production on Lower Compartment Circuit Resistance Training of International Female Wrestling Athletes

The circuit resistance training is high intense and high load repetition training method completed on different sport modalities for wrestling women. A total of 10 wrestling women age mean 17 yr, height 1.63 m, body mass 54 kg, training experience 10 yrs participated in this study. Equipment and methods: To circuit resistance training determined on 1RM maximal test, gradually increase rapid strength production and lower leg power to CMJ and SJ actualized on six resistance exercises; a) hip flexion (HP), b) ankle inversion (AI), c) squat (SQ), d) hip extension (HE), e) deadlift (DE), and f) ankle eversion (AE) for wrestling women athletes on team professional sport performance. The lower compartment circuit resistance training was provided on advanced maximal load and power performance by 85% of 1RM (6 rep) high training load and (10 repetition – 20 s interval) intensity. Compared to Pre-test and Post-test that lower compartment resistance training concluded increased maximal strength for acute training load repetition and rapid strength load lifting performance and lower leg power resulted to CMJ decreased for wrestling and SJ increased for wrestling, circuit resistance training promoted high resistance load variability and minimum short time recovery on the one session non-periodic exercises performance with strength and power development. Other, experimental results for different sport modalities may be performing on maximal strength and power performance change in short recovery training phase for minimum effort rapid strength performance.
A New Strategy for the Old Challenge of Thalidomide: Systems Biology Prioritization of Potential Immunomodulatory Drug (IMiD)-Targeted Transcription Factors

Several molecular mechanisms of thalidomide embryopathy (TE) have been investigated, from anti-angiogenesis to oxidative stress to cereblon binding. Recently, it was discovered that thalidomide and its analogs, named immunomodulatory drugs (IMiDs), induced the degradation of C2H2 transcription factors (TFs). This mechanism might impact the strict transcriptional regulation of the developing embryo. Hence, this study aims to evaluate the TFs altered by IMiDs, prioritizing the ones associated with embryogenesis through transcriptome and systems biology-allied analyses. This study comprises only the experimental data accessed through bioinformatics databases. First, proteins and genes reported in the literature as altered/affected by the IMiDs were annotated. A protein systems biology network was evaluated. TFs beta-catenin (CTNNB1) and SP1 play more central roles: beta-catenin is an essential protein in the network, while SP1 is a putative C2H2 candidate for IMiD-induced degradation. Separately, the differential expressions of the annotated genes were analyzed through 23 publicly available transcriptomes, presenting 8624 differentially expressed genes (2947 in two or more datasets). Seventeen C2H2 TFs were identified as related to embryonic development but not studied for IMiD exposure; these TFs are potential IMiDs degradation neosubstrates. This is the first study to suggest an integration of IMiD molecular mechanisms through C2H2 TF degradation.
A Qualitative Study of the Values, Needs, and Preferences of Patients Regarding Stroke Care: The ValueCare Study

Introduction: An in-depth understanding of patient perspectives contributes to high-quality, value-based health care. The aim of this study was to explore the values, needs, and preferences of stroke patients across the continuum of care. Methods: We performed a qualitative study, as part of the larger ValueCare study, involving 36 patients who have had ischemic stroke within the past 18 months at the time of recruitment. Data were collected between December 2020 and April 2021 via one-to-one telephone interviews. All interviews were audio-taped and transcribed verbatim. The interview data were analysed using a thematic approach. Results: The analysis resulted in five themes: (1) patients’ values about health care, (2) information and education, (3) psychological support, (4) follow-up care, and (5) continuity and coordination of care. Patients valued a compassionate professional who is responsive to their needs. Furthermore, patients indicated a need for tailored health information, psychosocial services, pro-active follow-up care and improved coordination of care. Discussion and conclusion: Stroke patients emphasised the need for tailored information, psychological support, pro-active follow-up, and improved coordination of care. It is advocated for professionals to use a value-based care approach in order to satisfy the individual needs of patients with regard to information, communication, and follow-up care.
Stroke-GFCN: ischemic stroke lesion prediction with a fully convolutional graph network

Abstract. Purpose The interpretation of image data plays a critical role during acute brain stroke diagnosis, and promptly defining the requirement of a surgical intervention will drastically impact the patient’s outcome. However, determining stroke lesions purely from images can be a daunting task. Many studies proposed automatic segmentation methods for brain stroke lesions from medical images in different modalities, though heretofore results do not satisfy the requirements to be clinically reliable. We investigate the segmentation of brain stroke lesions using a geometric deep learning model that takes advantage of the intrinsic interconnected diffusion features in a set of multi-modal inputs consisting of computer tomography (CT) perfusion parameters. Approach We propose a geometric deep learning model for the segmentation of ischemic stroke brain lesions that employs spline convolutions and unpooling/pooling operators on graphs to excerpt graph-structured features in a fully convolutional network architecture. In addition, we seek to understand the underlying principles governing the different components of our model. Accordingly, we structure the experiments in two parts: an evaluation of different architecture hyperparameters and a comparison with state-of-the-art methods. Results The ablation study shows that deeper layers obtain a higher Dice coefficient score (DCS) of up to 0.3654. Comparing different pooling and unpooling methods shows that the best performing unpooling method is the proportional approach, yet it often smooths the segmentation border. Unpooling achieves segmentation results more adapted to the lesion boundary corroborated with systematic lower values of Hausdorff distance. The model performs at the level of state-of-the-art models without optimized training methods, such as augmentation or patches, with a DCS of 0.4553 ± 0.0031. Conclusions We proposed and evaluated an end-to-end trainable fully convolutional graph network architecture using spline convolutional layers for the ischemic stroke lesion prediction. We propose a model that employs graph-based operations to predict acute stroke brain lesions from CT perfusion parameters. Our results prove the feasibility of using geometric deep learning to solve segmentation problems, and our model shows a better performance than other models evaluated. The proposed model achieves improved metric values for the DCS metric, ranging from 8.61% to 69.05%, compared with other models trained under the same conditions. Next, we compare different pooling and unpooling operations in relation to their segmentation results, and we show that the model can produce segmentation outputs that adapt to irregular segmentation boundaries when using simple heuristic unpooling operations.
Comparison between Cultivated Oral Mucosa and Ocular Surface Epithelia for COMET Patients Follow-Up

Total bilateral Limbal Stem Cell Deficiency is a pathologic condition of the ocular surface due to the loss of corneal stem cells. Cultivated oral mucosa epithelial transplantation (COMET) is the only autologous successful treatment for this pathology in clinical application, although abnormal peripheric corneal vascularization often occurs. Properly characterizing the regenerated ocular surface is needed for a reliable follow-up. So far, the univocal identification of transplanted oral mucosa has been challenging. Previously proposed markers were shown to be co-expressed by different ocular surface epithelia in a homeostatic or perturbated environment. In this study, we compared the transcriptome profile of human oral mucosa, limbal and conjunctival cultured holoclones, identifying Paired Like Homeodomain 2 (PITX2) as a new marker that univocally distinguishes the transplanted oral tissue from the other epithelia. We validated PITX2 at RNA and protein levels to investigate 10-year follow-up corneal samples derived from a COMET-treated aniridic patient. Moreover, we found novel angiogenesis-related factors that were differentially expressed in the three epithelia and instrumental in explaining the neovascularization in COMET-treated patients. These results will support the follow-up analysis of patients transplanted with oral mucosa and provide new tools to understand the regeneration mechanism of transplanted corneas.
Outsourcing Rework of Imperfect Items in the Economic Production Quantity (EPQ) Inventory Model With Backordered Demand

This paper deals with an economic production quantity (EPQ) model with imperfect items and backordered demand. The manufacturing system is imperfect and generates a fraction of imperfect items, which is a random variable with a distribution function known. All manufactured items are screened and the imperfect items are identified. The rework of imperfect items cannot be done in the same manufacturing system. The company decides to outsource the rework in a repair store. Therefore, after the screen period all imperfect items are withdrawn and sent to the repair store; and at the end of the reparation process all the repaired items come back to the company and are received by the inventory. There are three cases that can occur depending on when the repaired items enter the inventory. Numerical examples and sensitivity analysis are presented to show the applicability of the three cases of the proposed inventory model.
A compact dual band MIMO PIFA for 5G applications

5G applications support operations in 28, 37, 60 and 73GHz bands and is expected to support 1GHz bandwidth. In the present paper, planar inverted F antenna for 28GHz operation has been proposed for 5G applications for which a return loss of -17.46dB and a gain of 9.30dB have been observed. In addition, the design has been extended for dual band operation at 28 and 37GHz by implementing an L slot in the patch. An excellent return loss of -32.54dB and -18.57dB with a gain of 8.62dB has been observed. Moreover, a feasible bandwidth of 1.02GHz has been obtained in former design, while an enhanced bandwidth of 1.3GHz has been obtained at both bands in case of latter design. However, for better gain & data rate considerations, the previous design has been extended as a MIMO configuration with 2 antenna elements (2x1) and corresponding performance parameters have been evaluated.