Miracle-dz/UniFAR-FTUs huggingface dataset:
A Validation Study for the Computation of Nonlinear Modal Frequency using a Hamiltonian Reduced Order Model

A common structural design verification is to conduct modal analysis and to ensure that the vibration modes of the structure do not get harmonically excited during operation. Since the modal frequencies are dependent on the mass and stiffness properties of the structure, they are an influencing factor in the design optimization. Modal frequencies are obtainable using eigenvalue analysis after linearization approximations in the restoring force terms of the governing equation of motion. While this approximation is valid for lower loads, it is not acceptable for strong nonlinear vibrations. A measurable shift in the modal frequency is observable when the amplitude of vibrations is in the order of the thickness of the vibrating structure. This phenomenon is more pronounced in thin walled and flexible structures where the vibration amplitudes can exceed the linear threshold relatively more easily. A credible approach in computation of nonlinear modes is the utilization of parametric continuation schemes for generating nonlinear frequency-amplitude response. However, utilization of this scheme with a large degree of freedom model is a computationally intensive approach which necessitate the development of reduced order models. A model reduction method in the finite element framework, termed as Hamiltonian Reduced Order Model (ROM), has been recently developed at Delft University of Technology. The present study is conducted for the experimental validation of the ROM. Governing equations of motion of a stiffened plate have been derived using the Hamiltonian ROM and the nonlinear frequency response has been generated using a continuation scheme. For validation, experiments have been conducted using the Laser Doppler Vibrometer to obtain similar frequency response curves. A comparison of the numerical and the experimental results shows excellent agreement. Furthermore, accurate numerical response is obtainable using only a single degree of freedom in the reduced order model which proves the effectiveness of the Hamiltonian ROM. The results also demonstrate the necessity of a nonlinear damping model for obtaining comparable results.
Cut Elimination Theorem for Non-Commutative Hypersequent Calculus

Hypersequent calculi (HC) can formalize various non-classical logics. In [9] we presented a non-commutative variant of HC for the weakest temporal logic of linear frames Kt4.3 and some its extensions for dense and serial ﬂow of time. The system was proved to be cut-free HC formalization of respective temporal logics by means of Sch¨utte/Hintikka-style semantical argument using models built from saturated hypersequents. In this paper we present a variant of this calculus for Kt4.3 with a constructive syntactical proof of cut elimination.
Edinburgh Research Explorer Associations between executive attention and objectively measured physical activity in adolescence:

Studies of relationships between physical activity and children ’ s attention skills are often constrained by small samples, lack of objective measurements and lack of control for confounders. The present study explores the relationship using objective measures of physical activity from a large birth cohort which permits both longitudinal and cross-sectional analyses. Data from 4755 participants (45% male) with valid measurement of physical activity (total volume and intensity) by accelerometry at age 11 from the Avon Longitudinal Study of Parents and Children (UK) were analysed. Attention was evaluated by the Test of Everyday Attention for Children (TEA-Ch) at 11 years and by the Cognitive Drug Research (CDR) computerised cognitive assessment system at 13 years. Males engaged in an average of 29 min (SD 17) of daily moderate-to-vigorous physical activity (MVPA) at age 11 years compared with 18 min (SD 12) among females. In unadjusted models, higher total volume of physical activity was associated with lower performance across attention tasks. When total volume of physical activity and potential confounding variables were controlled for, higher MVPA was associated with better performance at both 11 and 13 years. Correction for regression dilution approximately doubled the standardised b coef ﬁ cients. We observed complex associations but results suggest that MVPA may be bene ﬁ cial for attention processes in adolescence, especially in males. This has implications for interventions aimed at improving executive attention but may also be supportive of the bene ﬁ ts of physical activity for educational and mental health
Edinburgh Research Explorer Associations between short/medium-term variations in black smoke air pollution and mortality in the Glasgow conurbation, UK

Objectives: To examine associations between short/medium-term variations in black smoke air pollution and mortality in the population of Glasgow and the adjacent towns of Renfrew and Paisley over a 25-year period at different time lags (0-30 days). Methods: Generalised linear (Poisson) models were used to investigate the relationship between lagged black smoke concentrations and daily mortality, with allowance for confounding by cold temperature, between 1974 and 1998. Results: When a range of lag periods were investigated significant associations were noted between temperature-adjusted black smoke exposure and all-cause mortality at lag periods of 13-18 and 19-24 days, and respiratory mortality at lag periods of 1-6, 7-12, and 13-18 days. Significant associations between cardiovascular mortality and temperature-adjusted black smoke were not observed. After adjusting for the effects of temperature a 10 µg m -3 increase in black smoke concentration on a given day was associated with a 0.9% [95% Confidence Interval (CI): 0.3-1.5%] increase in all cause mortality and a 3.1% [95% CI: 1.4-4.9%] increase in respiratory mortality over the ensuing 30-day period. In contrast for a 10 µg m -3 increase in black smoke concentration over 0-3 day lag period, the temperature adjusted exposure mortality associations were substantially lower (0.2 % [95% CI: -0.0-0.4%] and 0.3 % [95% CI: -0.2-0.8%] increases for all-cause and respiratory mortality respectively). Conclusions: This study has provided evidence of association between black smoke exposure and mortality at longer lag periods than have been investigated in the majority of time series analyses.
CD114: A New Member of the Neural Crest‐Derived Cancer Stem Cell Marker Family

The neural crest is a population of cells in the vertebrate embryo that gives rise to a wide range of tissues and cell types, including components of the peripheral nervous system and the craniofacial skeleton as well as melanocytes and the adrenal medulla. Aberrations in neural crest development can lead to numerous diseases, including cancers such as melanoma and neuroblastoma. Cancer stem cells (CSCs) have been identified in these neural crest‐derived tumors, and these CSCs demonstrate resistance to treatment and are likely key contributors to disease relapse. Patients with neural crest‐derived tumors often have poor outcomes due to frequent relapses, likely due to the continued presence of residual treatment‐resistant CSCs, and therapies directed against these CSCs are likely to improve patient outcomes. CSCs share many of the same genetic and biologic features of primordial neural crest cells, and therefore a better understanding of neural crest development will likely lead to the development of effective therapies directed against these CSCs. Signaling through STAT3 has been shown to be required for neural crest development, and granulocyte colony stimulating factor (GCSF)‐mediated activation of STAT3 has been shown to play a role in the pathogenesis of neural crest‐derived tumors. Expression of the cell surface marker CD114 (the receptor for GCSF) has been identified as a potential marker for CSCs in neural crest‐derived tumors, suggesting that CD114 expression and function may contribute to disease relapse and poor patient outcomes. Here we review the processes of neural crest development and tumorigenesis and we discuss the previously identified markers for CSC subpopulations identified in neural crest tumors and their role in neural crest tumor biology. We also discuss the potential for CD114 and downstream intracellular signaling pathways as potential targets for CSC‐directed therapy. J. Cell. Biochem. 118: 221–231, 2017. © 2016 Wiley Periodicals, Inc.
Object-based classification of wetland vegetation using very high-resolution unmanned air system imagery

ABSTRACT The purpose of this study is to examine the use of multi-resolution object-based classification methods for the classification of Unmanned Aircraft Systems (UAS) images of wetland vegetation and to compare its performance with pixel-based classification approaches. Three types of classifiers (Support Vector Machine, Artificial Neural Network and Maximum Likelihood) were utilized to classify the object-based images, the original 8-cm UAS images and the down-sampled (30 cm) version of the image. The results of the object-based and two pixel-based classifications were evaluated and compared. Object-based classification produced higher accuracy than pixel-based classifications if the same type of classifier is used. Our results also showed that under the same classification scheme (i.e. object or pixel), the Support Vector Machine classifier performed slightly better than Artificial Neural Network, which often yielded better results than Maximum Likelihood. With an overall accuracy of 70.78%, object-based classification using Support Vector Machine showed the best performance. This study also concludes that while UAS has the potential to provide flexible and feasible solutions for wetland mapping, some issues related to image quality still need to be addressed in order to improve the classification performance.
Stereotactic Radiation Therapy of Single Brain Metastases: A Literature Review of Dosimetric Studies

Simple Summary Solid tumors often lead to a significant occurrence of brain metastases (BMs), which can have severe consequences for affected patients. Stereotactic radiotherapy (SRT) plays a major role in treating BMs. We present a systematic review adhering to PRISMA guidelines, summarizing the relevant literature on dosimetric studies of SRT for the treatment of single BMs. The variations in factors such as PTV margins and volumes, total dose, number of fractions, and isodose prescription have made it difficult to directly compare the eleven studies included. Considering the prolonged survival of patients and the increasing occurrence of reirradiation, it is imperative to consider dosimetric parameters such as conformity and gradient indexes, while also ensuring the preservation of healthy tissue. Based on our review, future studies with robust designs are required to develop guidelines that aid in the selection of the most suitable SRT technique based on the characteristics of the treated BMs. Abstract Stereotactic radiotherapy (SRT) plays a major role in treating brain metastases (BMs) and can be delivered using various equipment and techniques. This review aims to identify the dosimetric factors of each technique to determine whether one should be preferred over another for single BMs treatment. A systematic literature review on articles published between January 2015 and January 2022 was conducted using the MEDLINE and ScienceDirect databases, following the PRISMA methodology, using the keywords “dosimetric comparison” and “brain metastases”. The included articles compared two or more SRT techniques for treating single BM and considered at least two parameters among: conformity (CI), homogeneity (HI) and gradient (GI) indexes, delivery treatment time, and dose-volume of normal brain tissue. Eleven studies were analyzed. The heterogeneous lesions along with the different definitions of dosimetric indexes rendered the studied comparison almost unattainable. Gamma Knife (GK) and volumetric modulated arc therapy (VMAT) provide better CI and GI and ensure the sparing of healthy tissue. To conclude, it is crucial to optimize dosimetric indexes to minimize radiation exposure to healthy tissue, particularly in cases of reirradiation. Consequently, there is a need for future well-designed studies to establish guidelines for selecting the appropriate SRT technique based on the treated BMs’ characteristics.
The potential for mitochondrial therapeutics in the treatment of primary open-angle glaucoma: a review

Glaucoma, an age-related neurodegenerative disease, is characterized by the death of retinal ganglion cells (RGCs) and the corresponding loss of visual fields. This disease is the leading cause of irreversible blindness worldwide, making early diagnosis and effective treatment paramount. The pathophysiology of primary open-angle glaucoma (POAG), the most common form of the disease, remains poorly understood. Current available treatments, which target elevated intraocular pressure (IOP), are not effective at slowing disease progression in approximately 30% of patients. There is a great need to identify and study treatment options that target other disease mechanisms and aid in neuroprotection for POAG. Increasingly, the role of mitochondrial injury in the development of POAG has become an emphasized area of research interest. Disruption in the function of mitochondria has been linked to problems with neurodevelopment and systemic diseases. Recent studies have shown an association between RGC death and damage to the cells’ mitochondria. In particular, oxidative stress and disrupted oxidative phosphorylation dynamics have been linked to increased susceptibility of RGC mitochondria to secondary mechanical injury. Several mitochondria-targeted treatments for POAG have been suggested, including physical exercise, diet and nutrition, antioxidant supplementation, stem cell therapy, hypoxia exposure, gene therapy, mitochondrial transplantation, and light therapy. Studies have shown that mitochondrial therapeutics may have the potential to slow the progression of POAG by protecting against mitochondrial decline associated with age, genetic susceptibility, and other pathology. Further, these therapeutics may potentially target already present neuronal damage and symptom manifestations. In this review, the authors outline potential mitochondria-targeted treatment strategies and discuss their utility for use in POAG.
Investigating Students' Motivation to Speak English in EFL Classroom

Owning a motivation to learn the English language can act as a catalyst to success in the student’s professional career. Nonetheless, the student’s English proficiency and motivation to learn English cannot be attained unless the teacher implemented some strategies in the classroom. This prompted the researcher to investigate students’ motivation despite their shortcomings in the EFL classroom. The study aims to investigate students’ motivation and explore the teacher’s strategies to motivate students. The study was conducted by a case-study design using secondary students as participants. The findings indicated that the students were equally motivated to learn English, either intrinsic or extrinsic.
ADMM for Penalized Quantile Regression in Big Data

Traditional linear programming algorithms for quantile regression, for example, the simplex method and the interior point method, work well for data of small to moderate sizes. However, these methods are difficult to generalize to high‐dimensional big data for which penalization is usually necessary. Further, the massive size of contemporary big data calls for the development of large‐scale algorithms on distributed computing platforms. The traditional linear programming algorithms are intrinsically sequential and not suitable for such frameworks. In this paper, we discuss how to use the popular ADMM algorithm to solve large‐scale penalized quantile regression problems. The ADMM algorithm can be easily parallelized and implemented in modern distributed frameworks. Simulation results demonstrate that the ADMM is as accurate as traditional LP algorithms while faster even in the nonparallel case.
The Future Impacts of Autonomous Aid on Disaster Relief Efforts

Natural disasters and other catastrophes have significantly increased in recent years (Oishi & Komiya, 2017). Currently, resources available to succor in response to cataclysms are limited. Humans save lives, while ultimately risking their own. In order to bypass this risk, autonomous robot programming is essential. Research and advocacy regarding autonomous aid in the scientific community has yet to be fully addressed (Oishi & Komiya, 2017). Because of this, first responders, firefighters, and policemen are perpetually endangered. Furthermore, technological contributions would also eliminate human error, promote productivity, and stimulate collaboration on matters unable to be solved autonomously. A robot prototype, designed to retrieve 6x6х6 inch cubes, was programmed to a controller, but also operated autonomously. Despite the controller being beneficial for specific functions, autonomous programming proved to be advantageous when used applicably. While the initial task was direct, the knowledge acquired from the project possesses the potential to enhance future ventures seeking to aid disaster relief.
Apoptotic cells for treatment of acute respiratory distress syndrome associated with COVID-19

Background Hyper-inflammatory immune response, a hallmark of severe COVID-19, is associated with increased mortality. Acute respiratory distress syndrome (ARDS) is a common manifestation. We undertook two phase I/II studies in five and then 16 subjects with severe/critical COVID-19 to assess the safety and preliminary efficacy of apoptotic cells (Allocetra™-OTS, Enlivex Therapeutics), a cellular immunomodulatory therapy that reprograms macrophages to reduce hyper-inflammatory response severity. Methods Eligible patients presenting to the Emergency Room with severe COVID-19 and respiratory dysfunction received one intravenous administration of Allocetra™-OTS and were monitored for adverse events (AEs) for 28 days. The primary aim was to determine the safety profile of treatment; secondary aims were recovery from ARDS, intensive care unit (ICU) and hospital length-of-stay, and mortality. Immune modulator markers were measured to elucidate the mechanism of action of Allocetra™-OTS. Results 21 patients with severe-critical COVID-19 of Gamma, Alpha and Delta variants, were treated with a single dose of apoptotic cells. 19/21 patients had mild-to-severe ARDS at presentation. Median age was 53 years, 16/21 were males, 16/21 were overweight/obese. No serious related adverse events (SAEs) were reported. All 21 study subjects survived to day 28 (end of study); 19/21 recovered completely. Comparable mortality rates at the hospital were 3.8%−8.9% for age- and gender-matched patients, and 39%−55% for critical patients. Recovering patients exhibited rapid ARDS resolution and parallel resolution of inflammation markers and elevated cytokines/chemokines. Conclusion In patients with severe/critical COVID-19 associated with ARDS, Allocetra™-OTS was safe, well-tolerated, and showed promising results for resolution of respiratory failure and inflammation. Trial registration https://clinicaltrials.gov/ct2/show/study/NCT04513470, https://clinicaltrials.gov/ct2/show/study/NCT04590053, Identifiers NCT04513470, NCT04590053.
Anxiety is not the right choice! Individual differences in trait anxiety modulate biases in pseudoneglect

Pseudoneglect, the tendency to display a leftward perceptual bias, is consistently observed in line bisection tasks. Some studies have shown that pseudoneglect is sensitive to emotions. This emotion-related modulation is likely related to valence-dependent hemispheric lateralization, although the results do not converge. A possible explanation for these inconsistencies could be individual differences in emotional tone. Considering that negative and positive emotions produce different basic activations of the two hemispheres, emotional characteristics of the subjects, such as trait anxiety, could in fact modulate the pseudoneglect phenomenon. To verify this, high- and low-anxiety participants were asked to centrally bisect horizontal lines delimited by neutral or emotional (happy and sad) faces. In line with previous studies, results here showed a decrease in the leftward bisection error in the presence of happy faces, indicating a greater involvement of the left hemisphere in processing positive emotional stimuli. In addition, trait anxiety influenced the magnitude of the visual bias. High-anxiety subjects, compared to low-anxiety subjects, showed a general bias in visual attention toward the left space as a function of emotional valence. Results are discussed within the framework of valence-dependent hemispheric specialization and the relative degree of activation. In sum, our data highlight the relevance of considering emotional individual differences in studying the pseudoneglect phenomenon.
Retinal electrophysiology in central nervous system disorders. A review of human and mouse studies

The retina and brain share similar neurochemistry and neurodevelopmental origins, with the retina, often viewed as a “window to the brain. ” With retinal measures of structure and function becoming easier to obtain in clinical populations there is a growing interest in using retinal findings as potential biomarkers for disorders affecting the central nervous system. Functional retinal biomarkers, such as the electroretinogram, show promise in neurological disorders, despite having limitations imposed by the existence of overlapping genetic markers, clinical traits or the effects of medications that may reduce their specificity in some conditions. This narrative review summarizes the principal functional retinal findings in central nervous system disorders and related mouse models and provides a background to the main excitatory and inhibitory retinal neurotransmitters that have been implicated to explain the visual electrophysiological findings. These changes in retinal neurochemistry may contribute to our understanding of these conditions based on the findings of retinal electrophysiological tests such as the flash, pattern, multifocal electroretinograms, and electro-oculogram. It is likely that future applications of signal analysis and machine learning algorithms will offer new insights into the pathophysiology, classification, and progression of these clinical disorders including autism, attention deficit/hyperactivity disorder, bipolar disorder, schizophrenia, depression, Parkinson’s, and Alzheimer’s disease. New clinical applications of visual electrophysiology to this field may lead to earlier, more accurate diagnoses and better targeted therapeutic interventions benefiting individual patients and clinicians managing these individuals and their families.