Miracle-dz/UniFAR-FTUs huggingface dataset:
SIMULATION OF THE STATIC INFLATION OF THE PASSIVE LEFT VENTRICLE TO AN END-DIASTOLIC STATE

To initiate our simulations of canine left ventricle (LV) mechanics, we needed to specify an initial geometry and an initial wall stress distribution. Although there are sufficient measurements of LV geometries, there are no assessments of stresses under any conditions. To estimate a physiologically plausible range of stresses at end diastole, we have inflated an unloaded reference geometry using static pressure loads. The LV was modelled as a six-layered truncated prolate ellipsoid. The myocardium was defined as a slightly compressible, transversely isotropic, hyperelastic material. The reference LV was inflated statically by gradually increasing the pressure on its inner surface until an end-diastolic state was reached. The calculated dependence of normalized LV volume changes on the applied pressure was in good agreement with previous experimental results. Our calculated geometry was found to be comparable to previous measurements. The end-diastolic stresses were found to have complex variations, which.
The Mediator Role of Task Performance in the Effect of Digital Literacy on Firm Performance

Digital technologies, which have made significant progress in the last two decades, have paved the way for the emergence of many new-generation devices, platforms and applications. The increase in the use of these technologies has transformed many activities in daily life and significantly changed the business world. The concept of digital transformation, which has become a popular motto for many companies today, has improved the interaction between companies and consumers and changed how companies do business, making the transformation necessary. Digital transformation in businesses can be partial (such as establishing new departments or marketing channels) or major (such as changing the entire business model). In any case, digital transformation is a necessity of the current age. Human capital is vital in increasing the firm performance of companies and gaining a competitive advantage against their competitors. Today, one factor that can improve employees’ task performance in digital economies is digital competencies. Therefore, having a certain level of digital literacy among employees is crucial for companies to achieve adequate performance in digitalization and beyond. From this perspective, this research aims to determine the effect of employees’ digital literacy on their task performance and firm performance. Investigation of this topic in the paper is carried out in the following logical sequence: First of all, the research presents the conceptual framework for digital literacy, task performance, and firm performance. The results of studies in the literature are presented, and the hypothesis development process is based on the research results. The subsequent section provides information about the study’s methodology and findings. Finally, the research concludes with the results and discussion section. Within the scope of the study, data were collected from 222 white-collar employees in Istanbul through online questionnaires. A convenience sampling technique was used to determine the sample. SPSS 25 and SPSS Process 2.13 package programs were used to analyse the data. The research results show a medium-level relationship between digital literacy and task performance, a medium-level relationship between digital literacy and firm performance, and a high and positive relationship between firm performance and task performance. According to the mediation analysis results, employees’ digital literacy positively affects task performance and firm performance. In addition, it has been determined that task performance plays a mediating role in the effect of employees’ digital literacy on firm performance. It appears that company managers should prioritize the focus of the «Reskilling Revolution Initiative», which emphasizes the transformation of employees’ skills to attain sustainable competitive advantage and enable digital transformation.
Investigating Effective Spectral Bands for Tissue Visualization in Oral Mucosa

Detection of malignancies in the gastrointestinal tract through endoscopic investigations is crucial for managing the disease and improving cancer prognosis. However, current standard endoscopic procedures using white light sources have limitations in providing comprehensive tissue information. To address this issue, spectral imaging technology has been developed, which allows light to penetrate different tissue layers to capture the true spectral response of a tissue. The study discussed here focuses on using spectral imaging to image the sublingual region of the oral mucosa using a broadband light source and five different individual light illuminations to understand the characteristics of vascular structures under different wavelengths. This approach has the potential to provide valuable insights into the structure and function of different tissues and could lead to improvements in the detection and diagnosis of a wide range of diseases, including oral cancer. This study focuses on imaging the sublingual region of the oral mucosa using the broadband light source and five different individual light illuminations to understand the characteristics of vascular structures under different wavelengths. The study using Weber’s contrast, reveals that superficial blood vessels (SBV) have high contrast values at low wavelengths, while deep-seated blood vessels (DSBV) have high contrast values at higher wavelengths. This study confirms the findings of our previous study in a different system. The paper emphasizes the importance of using spectral imaging to capture comprehensive tissue information for the early detection of cancer and improved prognosis. The study highlights the need for an appropriate light source and the usage of individual LEDs for building a standalone system to effectively visualize vascular features. Further research on a larger scale is necessary to translate these findings into practical use for real-time endoscopic investigations.
Variational integrators for reduced field equations

In the reduction of field theories in principal $G$-bundles, when a subgroup $H\subset G$ acts by symmetries of the Lagrangian, each of the $H$-reduced unknown fields decomposes as a flat principal connection and a parallel H-structure. A suitable variational principle with differential constraints on such fields leads to necessary criticality conditions known as Euler-Poincare equations. We model constrained discrete variational theories on a simplicial complex and generate from the smooth theory, in a covariant way, a discrete variational formulation of $H$-reduced field theories. Critical fields in this formulation are characterized by a corresponding discrete version of Euler-Poincare equations. We present a numerical integration algorithm for discrete Euler-Poincare equations, that extends integration algorithms for Euler-Poincare equations in mechanics to the case of field theories and, also, extends integration algorithms for Euler-Lagrange equations in discrete field theories to the case of variational principles with constraints. For regular reduced discrete Lagrangians, this algorithm allows to univocally propagate initial condition data, on an initial condition band, into a solution of the corresponding equations for the discrete variational principle.
Motorized and Functional Electrical Stimulation Induced Cycling via Switched Repetitive Learning Control

Cycling induced by functional electrical stimulation (FES) coupled with motorized assistance is a promising rehabilitative strategy. A switching controller that activates lower limb muscles alongside an electric motor based on the crank angle is developed to facilitate cycling. Due to the periodic nature of cadence tracking in cycling, a repetitive learning controller (RLC) is developed to track a desired cadence trajectory with a known period. The RLC is developed for an uncertain, nonlinear cycle-rider system with autonomous state-dependent switching. Electrical stimulation switches across multiple lower limb muscle groups based on the torque effectiveness throughout the crank cycle. The electric motor provides assistance when the muscle groups yield low torque production. A Lyapunov-based stability analysis that invokes a recently developed LaSalle–Yoshizawa corollary for nonsmooth systems is used to guarantee asymptotic tracking. The developed controller was tested during FES-cycling experiments in five able-bodied individuals and three participants with neurological conditions. The added value of the RLC in cadence tracking is illustrated by comparing the results of two trials with and without the learning feedforward term. The results indicate that the RLC yields a lower mean root-mean-squared cadence tracking error.
Comparative Spectral Analysis of Flexible Structure Models: the Euler-Bernoulli Beam model, the Rayleigh Beam model, and the Timoshenko Beam Model

COMPARATIVE SPECTRAL ANALYSIS OF FLEXIBLE STRUCTURE MODELS: THE EULER-BERNOULLI BEAM MODEL, THE RAYLEIGH BEAM MODEL, AND THE TIMOSHENKO BEAM MODEL By Anhhong Rose Nguyen University of New Hampshire, December, 2017 We derive herein approximate spectra for three different models of transversely vibrating beams. Each model consists of a system of partial differential equations (PDEs) with various boundary conditions. The three models that we consider are the Euler-Bernoulli model, the Rayleigh model, and the Timoshenko model. We first discuss a brief history of the models before delving into obtaining the spectral equations for each beam model under different boundary conditions. Lastly, we present asymptotic approximations of some of the various spectral equations we found from each model.
Detection and dynamics of satellite exospheres

Je presente une analyse multidisciplinaire du comportement d’une exosphere d’un satellite dont la limite inferieure est une surface solide. Une exosphere par definition n’a pas de limite superieure, est constituee d’un gaz dont la dynamique n’est pas regie par des collisions et correspond a la region d’interaction entre un objet planetaire et sa planete mere ou etoile. Dans cette these, je montrerai qu’une population exospherique d’un satellite qui serait volatile et dont la dynamique serait fortement dependante de la temperature de surface, aura une evolution orbitale synchrone avec le cycle diurne. Par exemple, l’oxygene moleculaire autour d’Europa et de Ganymede, satellites de Jupiter,devrait suivre une telle evolution. Je m’attacherai donc a comparer les resultats d’un modele 3D Monte Carlo reconstruisant l’evolution de cette exosphere et les observations d’emissions aurorales par le telescope Hubble pour souligner la persistance d’une asymetrie matin/soir caracteristique de ce cycle diurne. Par ailleurs,une analyse plus theorique de l’origine de cette asymetrie nous suggere qu’un reservoird’O2 sous forme gazeuse dans le regolite pourrait etre a l’origine de la formation de cette exosphere. En plus de la description de l’O2 exospherique autour d’Europa, je soulignerai les differences notables avec l’H2O et ses produits. Enfin, j’ai egalement travaille a la caracterisation d’une nouvelle technologie pour une source d’ionisation basee sur l’utilisation de nano-tubes de carbone. Cet emetteur d’electron utilise pour la spectrometrie de masse neutre s’avere nettement plus efficace que les emetteurs classiquement utilises dans le spatial et devrait donc nous aider a explorer ces exospheres.
Beyond the Complement Cascade: Insights into Systemic Immunosenescence and Inflammaging in Age-Related Macular Degeneration and Current Barriers to Treatment

Landmark genetic studies have revealed the effect of complement biology and its regulation on the pathogenesis of age-related macular degeneration (AMD). Limited phase 3 clinical trial data showing a benefit of complement inhibition in AMD raises the prospect of more complex mediators at play. Substantial evidence supports the role of para-inflammation in maintaining homeostasis in the retina and choroid. With increasing age, a decline in immune system regulation, known as immunosenescence, has been shown to alter the equilibrium maintained by para-inflammation. The altered equilibrium results in chronic, sterile inflammation with aging, termed ‘inflammaging’, including in the retina and choroid. The chronic inflammatory state in AMD is complex, with contributions from cells of the innate and adaptive branches of the immune system, sometimes with overlapping features, and the interaction of their secretory products with retinal cells such as microglia and retinal pigment epithelium (RPE), extracellular matrix and choroidal vascular endothelial cells. In this review, the chronic inflammatory state in AMD will be explored by immune cell type, with a discussion of factors that will need to be overcome in the development of curative therapies.
Carbapenem-Resistant Acinetobacter spp Infection in Critically Ill Patients With Limited Treatment Options: A Descriptive Study of Cefiderocol Therapy During the COVID-19 Pandemic

Abstract Background Carbapenem-resistant Acinetobacter baumannii infections are difficult to treat and are a significant public health threat due to intrinsic/acquired resistance and limited treatment options. Methods A retrospective, observational cohort study in patients receiving cefiderocol via Shionogi's early access program for Acinetobacter spp infections (1 April 2020–30 April 2021; 27 sites; Italy, Spain, Germany, France). Primary outcome was clinical success, defined as clinical resolution of infection at day 14 or day 28 survival. Results Overall, 147 patients were included. Primary infection sites were respiratory (65.3%) and bloodstream (unknown source [15.6%]; catheter-related [10.9%]); 24.5% of patients had polymicrobial infection. Of 136 patients in intensive care (92.5%), 85.3% (116/136) received mechanical ventilation. Septic shock (55.6% [70/126]) and coronavirus disease 2019 (COVID-19) (81.6%) were prevalent. Prior to cefiderocol, 85.0% of patients received gram-negative treatment, 61.2% received ≥2 antimicrobials, and most received colistin (58.5%; median duration, 11.5 days). Cefiderocol monotherapy was used in 30.6% of patients. Clinical success rate was 53.1% and was higher in patients without septic shock (62.5%), without COVID-19 (77.8%), and with lower Sequential Organ Failure Assessment (SOFA) scores (quartile 1 [median, 3; range, 0–5]: 82.9%). Day 28 survival was 44.9% and was higher in patients without septic shock (60.7%), without COVID-19 (59.3%), with lower SOFA score (quartile 1: 82.9%), and receiving first-line cefiderocol (68.2% [15/22]). Resolution of infection at day 14 occurred in 39.5% of patients. Conclusions Despite use in complex patients with limited treatment options and high septic shock/COVID-19 rates, cefiderocol treatment was associated with an overall clinical success rate of 53%.
Principles in Sasak Local Wisdom as Character Education Values for Children

Sasak people’s life is strongly embedded by the philosophical values in each tradition that can lead them to have better perspectives. This study aimed to describe the values of character education in the local wisdom of the Sasak tribe, and the process of instilling character education values in the Sasak family. An ethnographic qualitative approach was used to obtain the information based on the research objectives. The data were collected through observations, interviews, documentations, and FGD techniques.The data were obtained from the families, traditional leaders, and communities. The results of the study revealed that the character values as the identity of the Sasak people were found in four principles, namely: wirame principle, wirage principle, wirase principle, and wibusane principle. The process of transmitting character values to children was done by giving knowledge, doing habituation, and imitation of the parents’ good habits and behaviors. The value of local wisdom in the traditions could be used as an insight for parents and society as educational references to build children’s characters
Methods of Constructing One‐Way and Factorial Designs Balanced for Trend

This paper is a sequel to a previous one on magic squares (Phillips, 1964) and illustrates the use of other magic figures (rectangles, parallelepipeda and similar higher dimensional constructions) for balancing‐assessing linear and, occasionally, quadratic trend in certain general classes of one‐way and factorial designs. Constructional methods from the literature are given for the cases of: one‐way designs with either an even number of observations per level, or the numbers of observations and levels both odd; factorial designs having more than one observation per cell, with analogous conditions on the numbers of cells and observations; and factorial designs with one observation per cell, the numbers of levels of the variables either all even or all odd. The underlying assumptions of the designs, and their selection and randomization, are briefly discussed.
Towards Finding a Minimal Set of Features for Predicting Students' Performance Using Educational Data Mining

: An early prediction of students' academic performance helps to identify at-risk students and enables management to take corrective actions to prevent them from going astray. Most of the research works in this field have used supervised machine learning approaches to their crafted datasets having numerous attributes or features. Since these datasets are not publicly available, it is hard to understand and compare the significance of the chosen features and the efficacy of the different machine learning models employed in the classification task. In this work, we analyzed 27 research papers published in the last ten tears (2011-2021) that used machine learning models for predicting students' performance. We identify the most frequently used features in the private datasets, their interrelationships, and abstraction levels. We also explored three popular public datasets and performed statistical analysis like the Chi-square test and Person's correlation on its features. A minimal set of essential features is prepared by fusing the frequent features and the statistically significant features. We propose an algorithm for selecting a minimal set of features from any dataset with a given set of features. We compared the performance of different machine learning models on the three public datasets in two experimental setups-one with the complete feature set and the other with a minimal set of features. Compared to using the complete feature set, it is observed that most supervised models perform nearly identically and, in some cases, even better with the reduced feature set. The proposed method is capable of identifying the most essential feature set from any new dataset for predicting students' performance.
Exploring the Epidemiology and Survival Trends in Pediatric Major Salivary Gland Malignancies: Insights from the National Cancer Database

Objective: To investigate the clinicopathological, therapeutic, and survival data on pediatric major salivary gland cancers. Materials and Methods: National Cancer Database (NCDB) query from 2004 to 2018. Results: In total, 967 cases of individuals under the age of 21 were identified. Most cancers affected the parotid gland (86%). Mucoepidermoid carcinoma (41.3%) and acinic cell adenocarcinoma (33.6%) were the most common. Tumors occurred more often from age 11 to 21, and females were more affected. Histology varied by age, gender, and race. In the 0–5 age group, mucoepidermoid carcinoma and myoepithelial carcinoma/sarcoma/rhabdomyosarcoma were the most common pathologies. In patients over 5 years old, mucoepidermoid carcinoma was the most frequent tumor in boys, while acinic cell adenocarcinoma was more common in girls. African American patients had a higher incidence of mucoepidermoid carcinoma, while White patients in the 0–5 age group had a higher incidence of myoepithelial carcinoma/sarcoma/rhabdomyosarcoma tumors. Low-grade tumors were commonly diagnosed at stage I, but the 0–5 age group had a high frequency of stage IV tumors. The overall 5-year survival rate was 94.9%, with 90% for the 0–5 years age group and 96% for the 11–15 years age group. Negative margins were associated with higher 5-year survival rates in high-stage tumors (93%) compared to positive margins (80%). Submandibular malignancies had worse 5-year survival rates across all age groups. Conclusions: Major salivary gland malignancies in pediatric patients exhibit variations in histopathologic characteristics by age, gender, and race. Negative margins impact 5-year survival rates, especially in high-stage tumors.
A Qualitative Analysis of the Experiences of Young Patients and Caregivers Confronting Pediatric and Adolescent Oncology Diagnosis

Epidemiological studies show that new cases of young oncology patients are increasing by 400,000 every year. Psychological literature has shown that receiving an oncological diagnosis can cause significant psychological stress and discomfort. However, the experiences of young patients and their caregivers as they confront this challenge are not yet fully understood. This paper adopts a qualitative methodological approach to explore how young patients with an oncological diagnosis and their parents make sense of the experiential challenges they face. Thus, the research realized 18 semi-structured interviews, 11 of which were with pediatric and adolescent oncology patients, and 7 of which were with 6 mothers and 1 father. The qualitative thematic analysis revealed that the oncological diagnosis triggers different emotions taking the scene in the attempt to cope with the threats of meaning that the diagnosis poses. However, such intense experience promotes transformative feelings in parents and young patients, leading to important personal growth. Lastly, this article discusses the need to improve palliative psychological care competences in pediatric oncology. By providing comprehensive psychological care to young oncology patients and their families, healthcare providers can mitigate the psychological stress and pain associated with the diagnosis and treatment of cancer.
Multiscale Representation of Radar Echo Data Retrieved through Deep Learning from Numerical Model Simulations and Satellite Images

Radar reflectivity data snapshot fine-grained atmospheric variations that cannot be represented well by numerical weather prediction models or satellites, which poses a limit for nowcasts based on model–data fusion techniques. Here, we reveal a multiscale representation (MSR) of the atmosphere by reconstructing the radar echoes from the Weather Research and Forecasting (WRF) model simulations and the Himawari-8 satellite products using U-Net deep networks. Our reconstructions generated the echoes well in terms of patterns, locations, and intensities with a root mean square error (RMSE) of 5.38 dBZ. We find stratified features in this MSR, with small-scale patterns such as echo intensities sensitive to the WRF-simulated dynamic and thermodynamic variables and with larger-scale information about shapes and locations mainly captured from satellite images. Such MSRs with physical interpretations may inspire innovative model–data fusion methods that could overcome the conventional limits of nowcasting.