Miracle-dz/UniFAR-FTUs huggingface dataset:
FPGA IMPLEMENTATION OF OPTIMAL STEP SIZE NLMS ALGORITHM AND ITS PERFORMANCE ANALYSIS

The Normalized Least Mean Square error (NLMS) algorithm is most popular due to its simplicity. The conflicts of fast convergence and low excess mean square error associated with a fixed step size NLMS are solved by using an optimal step size NLMS algorithm. The main objective of this paper is to derive a new nonparametric algorithm to control the step size and also the theoretical performance analysis of the steady state behavior is presented in the paper. The simulation experiments are performed in Matlab. The simulation results show that the proposed algorithm as superior performance in Fast convergence rate, low error rate, and has superior performance in noise cancellation. Index Terms: Least Mean square algorithm (LMS), Normalized least mean square algorithm (NLMS)
Development and preliminary validation of Cancer-related Psychological Flexibility Questionnaire

The Cancer-related Psychological Flexibility Questionnaire (CPFQ) was developed and validated for assessing cancer patients’ psychological flexibility, including attitudes and behavior toward cancer. In a systematic process, the CPFQ identified four factors through principal component analysis and confirmatory factor analysis: Cancer Acceptance, Cancer Avoidance, Activity Engagement, and Valued Action. The results of this study reveal that the CPFQ has a clear factor structure and good psychometric properties. The specific nature of cancer and the need for a specific measure of cancer patient psychological flexibility make this questionnaire valuable for research on psychological flexibility in cancer patients.
Physics-Informed Fully Connected and Recurrent Neural Networks for Cardiac Electrophysiology Modelling

Cardiovascular diseases, the leading cause of death and disability, are often underlined by cardiac arrhythmias. Cardiac electrophysiology models play an increasingly important role in dissecting arrhythmogenic mechanisms and improving treatments, but high computational costs hinder their application. We develop and compare four novel deep learning (DL) models to solve the Fitzhugh-Nagumo (FN) electrophysiology equations efficiently in OD, 1D and 2D. The training datasets were created using numerical solutions of FN equations. Physics-informed neural network (PINN) was based on the incorporation of FN equations into the Loss function, allowing the optimal combination of training data with physical constraints. Another recurrent NN (RNN) with a mean squared error (MSE) loss function was developed as a baseline. The DL models were evaluated using the MSE score. In OD and 1D, similar performances were achieved for all DL models, with a typical MSE of 10−2. In 2D, PINN and RNN succeeded in simulating plane and spiral waves with similar MSE of 10−2. Hence, PINNs can provide an efficient tool for cardiac electrophysiology simulations.
Validity and Reliability of Short-Term Heart Rate Variability Parameters in Older People in Response to Physical Exercise

Background: Currently, and to the best of our knowledge, there is no standardized protocol to measure the effect of low- to moderate-intensity physical exercise on autonomic modulation focused in older people. Aim: Validate a test–retest short-term exercise protocol for measuring the autonomic response through HRV in older people. Methods: A test–retest study design was used. The participants were selected through intentional non-probabilistic sampling. A total of 105 older people (male: 21.9%; female: 78.1%) were recruited from a local community. The assessment protocol evaluated HRV before and immediately after the 2-min step test. It was performed twice on the same day, considering a time of three chronological hours between the two measurements. Results: The posterior distribution of estimated responses in the Bayesian framework suggests moderate to strong evidence favoring a null effect between measurements. In addition, there was moderate to robust agreement between heart rate variability (HRV) indices and assessments, except for low frequency and very low frequency, which showed weak agreement. Conclusions: Our results provide moderate to strong evidence for using HRV to measure cardiac autonomic response to moderate exercise, suggesting that it is sufficiently reliable to show similar results to those shown in this test–retest protocol.
BIPROJECTIVE BANACH ALGEBRAS

This paper studies the structure of biprojective semisimple Banach algebras having the approximation property. It is shown that every such algebra can be represented as the completion of a direct sum of topologically simple biprojective Banach algebras. The component parts of this sum are described as algebras of nuclear operators generated by a dual pair. Furthermore, the closed ideals of such algebras are studied. Bibliography: 16 titles.
Automatic detection of beacon towers in historical aerial images using improved FCOS

ABSTRACT Beacon towers are an important infrastructure responsible for transmitting military information in ancient times. However, many of the beacons have disappeared due to natural erosion and man-made vandalism. Historical U2 aerial images provide heritage and geographical information over the last century to research the beacon tower system. However, the fragmented distribution of beacons and the greyscale colouring of the aerial images make it difficult to manually identify small-sized beacons in a wide range of aerials. This study introduced deep learning to automatically detect beacons in U2 images. Three improvements were added to the standard Fully Convolutional One-Stage Object Detection (FCOS) network: 1) The structure of the Feature Pyramid Network (FPN) was adjusted to enhance the small objects feature at lower layers; 2) The standard convolutional kernel in backbone network was replaced with DCNv2 to account for irregular towers; 3) NMS was replaced with Soft-NMS to improve the accuracy of the detection box prediction. Our results demonstrate that more than 60% average precision (AP) can be obtained using our improved FCOS. After testing, the results showed that the three-part methodology can automatically detect most beacons in historical U2 aerial images, reduce the manual miss rate, and improve efficiency. The results of the test were the first to successfully identify destroyed beacons, recreate the beacon route, and retrace the beacon siting strategy. Our method helps to speed up the efficiency of heritage excavation in historical aerial images, and it may provide a convenient means of processing in other architectural heritage restoration studies.
High speed resistive switching in Pt∕TiO2∕TiN film for nonvolatile memory application

We have fabricated and investigated the bipolar resistive switching characteristics of Pt/rutile-TiO2∕TiN devices for resistance memory applications. Data writing for five-level resistance states has been demonstrated by varying the amplitude of 5ns voltage pulses. In addition, data retention of more than 256h at 85°C and an excellent endurance of over 2×106cycles have been confirmed. These results indicate that Pt∕TiO2∕TiN devices have a potential for nonvolatile multiple-valued memory devices.
The central cluster and X-ray emission from Sgr A*

Abstract At the centre of the Milky Way is Sgr A*, a putative 3 million solar mass black hole with an observed luminosity that is orders of magnitude smaller than that expected from simple accretion theories. The number density of early-type stars is quite high near Sgr A*, so the ensemble of their stellar winds has a significant impact on the black hole's environment. We present results of 3D hydrodynamic simulations of the accretion of stellar winds onto Sgr A*. Using the LANL/SAIC code, RAGE, we model the central arc-second of the Galaxy, including the central cluster stars (the S-stars) with orbits and wind parameters that match observations. A significant fraction of the winds from the S stars becomes gravitationally bound to the black hole and thus could provide enough hot gas to produce the X-ray emission seen by Chandra. We perform radiative transfer calculations on the 3D hydrodynamic data cubes and present the resulting synthetic X-ray spectrum.
Correction of ozone influence on TOA radiance

An accurate atmospheric correction (AC) of Earth remote-sensing data in the spectral region 450–800 nm has to account for the ozone gas absorption influence. Usual operational AC codes employ a fixed ozone concentration corresponding to a climatologic average for a certain region and season, e.g. the mid-latitude summer atmosphere of the Moderate Resolution Atmospheric Transmission (MODTRAN) code. The reasons for a fixed ozone column are that ozone does not vary rapidly on a spatial and temporal scale, and additionally, the look-up table (LUT) size for AC is already big. This means that another degree of freedom for the ozone parameter would dramatically increase the size of the LUT database and the time required for LUT interpolation. In order to account for this effect, we use already existing LUTs that were calculated for a certain ozone reference level, e.g. an ozone column of g = 330 Dobson Units (DU) for MODTRAN’s mid-latitude summer atmosphere. Then the deviation of the top-of-atmosphere (TOA) radiance ΔL(g) from the reference level L(g = 330) is calculated as a function of solar and view geometries. The calculation is performed for a set of 36 wavelengths in the ozone-sensitive spectrum (450–800 nm) and five ozone columns. The last step computes the linear regression coefficients for each wavelength and geometry. The results are stored in a small table (11 kB). It is shown that the ozone influence is accurately accounted for by multiplying the modelled radiance L(g = 330) with a factor depending on g and wavelength yielding TOA radiance relative errors smaller than 0.5% for a wide range of ozone concentrations between 180 and 500 DU. Selected examples of a sensitivity study of the ozone effect on the retrieval of water constituents demonstrate the need to account for ozone in the AC step.
RCzechia: Spatial Objects of the Czech Republic

The history of spatial data analysis in R is long and respectable Bivand (2021). The first packages focusing specifically on providing spatial data originate from the S days Becker & Wilks (1993), with maps Deckmyn (2022) being one of the oldest packages in continuous use on CRAN (since 2003). The early packages used pattern of storing spatial data internally, which created a hard limit on volume and level of detail stored.
Listening Effort: Cardiovascular Investigation Through the Point Process

In the context of quantifying listening effort, traditional hearing tests do not provide any information about the stress experienced during listening tasks. Although there have been attempts to quantify memory allocation during acoustics tests, there is no agreement in the literature regarding the role of physiological indices in characterizing different listening effort levels. To this extent, the aim of our study is to ascertain if cardiovascular measurements continuously recorded during the task can help in quantifying listening effort. In the presented protocol, 21 normal young hearing subjects performed a validated speech-in-noise test at two fixed effort levels, while electrocardiogram (ECG) and blood volume pulse (BVP) were continuously recorded. From these time series, the RR series, the amplitude difference between each systole and diastole and the pulse arrival time were extracted. In addition, the ECG-derived RR series were modelled trough a point process framework, yielding instantaneous cardiovascular and autonomic indexes to be considered in our statistical analysis. Overall, the average modelled RR intervals and the pulse arrival time were found effective in distinguishing the two different effort levels $(p=0.031$ and $p=0.016)$. In addition, the amplitude difference between each systole and diastole was able to significantly separate high effort from both low effort and the initial resting period $(p < e-3)$.
Exohedral and endohedral adsorption of nitrogen on the sidewall of single-walled carbon nanotubes

Different configurations of a single nitrogen atom adsorbed at different sites on the sidewall of single-walled carbon nanotubes (SWNT's) are studied through first-principles calculations. It is found that the outer surface of the tube wall is reactive to the nitrogen atom, and a nitrogen atom can be chemically adsorbed on it forming an exohedral complex with a binding energy of $\ensuremath{\sim}3.86\mathrm{eV}$ for a (5,5) SWNT. Although the inner surface of the tube wall is less reactive than the outer surface, a nitrogen atom can still be adsorbed on the inner wall of the (5,5) tube forming an endohedral complex with the binding energy on the order of $\ensuremath{\sim}1.78\mathrm{eV}. $ The local structure of the SWNT around the adsorption sites is substantially changed. From studying the binding energies of a nitrogen atom adsorbed on different armchair tubes $(n,n)$ with n ranging from 5 to 16, it is found that the binding energy for exohedral adsorption decreases significantly with increasing radius (or increasing $n)$ of the tube, while for endohedral adsorption the binding energy varies slightly. The influence of endohedral adsorption of a single nitrogen atom on the local density of states of a (5,5) SWNT is also discussed.
On risk charges and shadow account options in pension funds

This paper studies the economic implications of regulatory systems which allow equityholders of pension companies to not only charge a specific premium to compensate them for their higher risk (compared to policyholders), but also to accumulate these risk charges in a so-called shadow account in years when they are not immediately payable due to e.g. poor investment results. When surpluses are subsequently reestablished, clearance of the shadow account balance takes priority over bonus/participation transfers to policyholders. We see such a regulatory accounting rule as a valuable option to equityholders and our paper develops a model in which the influence of risk charges and shadow account options on stakeholders’ value can be quantified and studied. Our numerical results show that the value of shadow account options can be significant and thus come at the risk of expropriating policyholder wealth. However, our analysis also shows that this risk can be remedied if proper attention is given to the specific contract design and to the fixing of fair contract parameters at the outset.
Gender and Organizational Culture: Perceptions of Leadership Style and Effectiveness

The purpose of this paper was to demonstrate changes in the definition of effective leadership and present an argument for how the shift relates to evident changes in perceptions of women leaders. Perceptions of effectiveness estimations for female and male executives were compared, while taking into consideration organizational culture. Scenario methodology was used to manipulate gender, leadership styles, and organizational culture through an experimental online survey looking for differences in perceptions of effectiveness. Findings, in part 1, show that female executives were viewed as more transformational than male executives with no significant differences in perceptions of leadership effectiveness. Leadership style mediated relations between leader gender and perceptions of leadership effectiveness. In part 2, organizational culture was shown to increase the associations of gender and leadership style with expectations of effectiveness. Female leaders may over time be perceived as effective as male leaders, if the present shift in definitions of effective leadership styles continue to align with female gender roles and organizational cultures embrace collective practices. However, this paradigmatic change is slow and women continue to be stereotyped in leadership roles. This paper gives an explanation for why nil results are becoming more prevalent in the literature on gender in leadership.
Novel scFv against Notch Ligand JAG1 Suitable for Development of Cell Therapies toward JAG1-Positive Tumors

The Notch signaling ligand JAG1 is overexpressed in various aggressive tumors and is associated with poor clinical prognosis. Hence, therapies targeting oncogenic JAG1 hold great potential for the treatment of certain tumors. Here, we report the identification of specific anti-JAG1 single-chain variable fragments (scFvs), one of them endowing chimeric antigen receptor (CAR) T cells with cytotoxicity against JAG1-positive cells. Anti-JAG1 scFvs were identified from human phage display libraries, reformatted into full-length monoclonal antibodies (Abs), and produced in mammalian cells. The characterization of these Abs identified two specific anti-JAG1 Abs (J1.B5 and J1.F1) with nanomolar affinities. Cloning the respective scFv sequences in our second- and third-generation CAR backbones resulted in six anti-JAG1 CAR constructs, which were screened for JAG1-mediated T-cell activation in Jurkat T cells in coculture assays with JAG1-positive cell lines. Studies in primary T cells demonstrated that one CAR harboring the J1.B5 scFv significantly induced effective T-cell activation in the presence of JAG1-positive, but not in JAG1-knockout, cancer cells, and enabled specific killing of JAG1-positive cells. Thus, this new anti-JAG1 scFv represents a promising candidate for the development of cell therapies against JAG1-positive tumors.
Exploring the Mental Health Challenges and Coping Behaviour of Lesbian, Gay, and Bisexual Students at an Institution of Higher Learning

The South African university community is predominantly heterosexual, which fosters stigmatisation and discrimination against LGBTQI students despite the efforts to create conditions where LGBTQI students can succeed academically, socially, and personally. The study aimed to explore and describe the challenges experienced by LGBTQI students and their mental well-being as well as the coping behaviours adopted in a university in South Africa. This was accomplished using a descriptive phenomenological approach. A snowballing sampling method was used to select ten students who identified themselves as gay, lesbian, and bisexual (LGB). Semi-structured one-on-one interviews were conducted, and data were analysed thematically. The students perceived character defects stigma from fellow students and lecturers in and out of class. The mental health challenges experienced included a diminished sense of safety, lack of a sense of belonging, low self-esteem, and acting out of character. As a result, confrontation, passive withdrawal, and active dependent behaviour were utilised as different types of coping behaviour. The LGB students were subjected to stigma that negatively affected their mental health. Therefore, creating awareness about the rights of LGBTQI students to education, safety, and self-determination is recommended.
Emerging role of exosomes in vascular diseases

Exosomes are biological small spherical lipid bilayer vesicles secreted by most cells in the body. Their contents include nucleic acids, proteins, and lipids. Exosomes can transfer material molecules between cells and consequently have a variety of biological functions, participating in disease development while exhibiting potential value as biomarkers and therapeutics. Growing evidence suggests that exosomes are vital mediators of vascular remodeling. Endothelial cells (ECs), vascular smooth muscle cells (VSMCs), inflammatory cells, and adventitial fibroblasts (AFs) can communicate through exosomes; such communication is associated with inflammatory responses, cell migration and proliferation, and cell metabolism, leading to changes in vascular function and structure. Essential hypertension (EH), atherosclerosis (AS), and pulmonary arterial hypertension (PAH) are the most common vascular diseases and are associated with significant vascular remodeling. This paper reviews the latest research progress on the involvement of exosomes in vascular remodeling through intercellular information exchange and provides new ideas for understanding related diseases.
Serial Urinary C-C Motif Chemokine Ligand 14 and Risk of Persistent Severe Acute Kidney Injury

OBJECTIVES: To assess the added prognostic value of serial monitoring of urinary C-C motif chemokine ligand 14 (uCCL14) over that of single measurements, which have been shown to be prognostic for development of persistent severe acute kidney injury (AKI) in critically ill patients. DESIGN: Retrospective observational study. SETTING: Data derived from two multinational ICU studies (Ruby and Sapphire). PATIENTS: Critically ill patients with early stage 2–3 AKI. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We analyzed three consecutive uCCL14 measurements at 12-hour intervals after diagnosis of stage 2–3 AKI by Kidney Disease Improving Global Outcomes criteria. Primary outcome was persistent severe AKI, defined as 72 consecutive hours of stage 3 AKI, death, or receipt of dialysis prior to 72 hours. uCCL14 was measured using the NEPHROCLEAR uCCL14 Test on the Astute 140 Meter (Astute Medical, San Diego, CA). Based on predefined, validated cutoffs, we categorized uCCL14 as: low (≤ 1.3 ng/mL), medium (> 1.3 to ≤ 13 ng/mL), or high (> 13 ng/mL). Seventy-five of 417 patients with three consecutive uCCL14 measurements developed persistent severe AKI. Initial uCCL14 category strongly correlated with primary endpoint and, in most cases (66%), uCCL14 category was unchanged over the first 24 hours. Compared with no change and accounting for baseline category, decrease in category was associated with decreased odds of persistent severe AKI (odds ratio [OR], 0.20; 95% CI, 0.08–0.45; p < 0.001) and an increase in category with increased odds (OR, 4.04; 95% CI, 1.75–9.46; p = 0.001). CONCLUSIONS: In one-third of patients with moderate to severe AKI uCCL14 risk category altered over three serial measurements and such changes were associated with altered risk for persistent severe AKI. Serial CCL-14 measurement may detect progression or resolution of underlying kidney pathology and help refine AKI prognosis.
A QT Interval Inaccuracy Index (QTI) for Highly Automated TQT Studies

Automatic QT interval measurements during Thorough QT studies (TQT) are usually overread by experts and adjusted if necessary. Our study aimed to provide a QT Interval Inaccuracy index to automatically identify inaccurate automated QT interval estimations. 12-lead ECG recordings and their manual interval measurements were obtained from 2 TQT study databases in PhysioNet (ECGDMMLD and ECGRDVQ). We derived 268 single-lead features that might relate to the accuracy of automatic QT intervals computed by convolutional neural network (CNN) based QT estimators previously trained on our own ECG database. Classification of accurate and inaccurate automatic QT intervals was performed with a regularized logistic regression algorithm trained on ECGDMMLD. The QTI was then defined for each ECG recording as the average probability of the QT being inaccurate across the 12 leads. 41% inaccurate automatic QT intervals were identified in ECGRDVQ (QTI threshold = 0.5). Better estimates of the drug-induced QTc changes were obtained by correcting these QT intervals. The QTc prolongation obtained with this semi-automated method differed from the manual one by on average 2.98 ms (std = 1.82 ms) across the 4 drugs studied. The QTI would therefore allow a faster and more robust analysis of ECG signals from TQT studies.