Miracle-dz/UniFAR-FTUs huggingface dataset:
Dietary Iron Intake and Biomarkers of Iron Status in Slovenian Population: Results of SI.Menu/Nutrihealth Study

Inadequate iron intake and iron deficiency are recognised as a public health problem in the population at large, and particularly in specific subpopulations. Dietary iron intake was analysed using data of the national Slovenian food consumption study, SI.Menu (n = 1248 subjects; 10–74 years), while iron status was evaluated with laboratory analyses of blood haemoglobin, serum ferritin, and iron concentration in samples, collected in the Nutrihealth study (n = 280, adults). The estimated daily usual population-weighted mean iron intakes ranged from 16.0 mg in adults and the elderly to 16.7 in adolescents, and were lower in females for all three age groups. The main dietary iron sources in all the age groups were bread and bakery products, meat (products), fruit, and vegetables. The highest prevalence of haemoglobin anaemia was observed in females aged 51–64 years (6.7%). Critically depleted iron stores (ferritin concentration < 15 µg/L) were particularly found in premenopausal females (10.1%). Factors influencing low haemoglobin, ferritin, and iron intake were also investigated. We observed significant correlations between iron status with meat and fish intake, and with iron intake from meat and fish, but not with total iron intake. We can conclude that particularly premenopausal females are the most fragile population in terms of inadequate iron intake and iron deficiency, which should be considered in future research and public health strategies.
Incorporating Higher Order Constraints for Training Surrogate Models to Solve Inverse Problems

Inverse problems describe the task of recovering some underlying signal given some observables. Typically, the observables are related via some non-linear forward model applied to the underlying signal. Inverting the non-linear forward model can be computationally expensive, as it involves calculating the adjoint when computing a descent direction. Rather than inverting the non-linear model, we instead train a surrogate forward model and leverage modern auto-grad libraries to solve for SSPs within a classical optimization framework. Current methods to train surrogate models are done in a black box supervised machine learning fashion and don’t take advantage of any existing knowledge of the forward model. In this article, we propose a simple regularization method to enforce constraints on the gradients of the surrogate model in addition to the output to improve overall accuracy. We demonstrate the efficacy on an ocean acoustic tomography (OAT) example that aims to recover ocean sound speed profile (SSP) variations from acoustic observations (e.g. eigenray arrival times) within simulation of ocean dynamics in the Gulf of Mexico.
Variability and the nature of QSO optical-infrared continua

Preliminary results are presented of a program to study the optical and infrared continuum emission of quiescent QSOs using simultaneous UBVRIJHK photometry. The amplitude of variability in six of the seven sources discussed is greatest in the blue and ultraviolet and decreases toward longer wavelengths. Only 3C 273 exhibits variability at all wavelengths observed, changing by a similar amount in each spectral band.All of the other QSOs were nonvarying at 2.2 microns, despite variations in the visible and ultraviolet. This variability behavior and the optical-infrared spectral energy distribution in each object except 3C 273 strongly suggest that the infrared and variable optical continuum emission arise from distinct sources within the QSOs. Several possible models of the origin of the continua are examined. 29 references.
Non-spherical seeding 3D positioning from CCD images using Lorenz-Mie theory

This paper describes the imaging of spherical and non-spherical particle diffraction patterns using plane-wave illumination. A discussion is presented on both the theoretical analysis of the diffraction pattern of the particle on its image plane using the Lorenz–Mie theory and its applicability to non-spherical particles. The images obtained for both spherical and non-spherical particles are quantitatively compared to calculated results, and implications for particle position estimation of non-spherical particles are discussed.
Multi-objective Tree-structured Parzen Estimator Meets Meta-learning

Hyperparameter optimization (HPO) is essential for the better performance of deep learning, and practitioners often need to consider the trade-off between multiple metrics, such as error rate, latency, memory requirements, robustness, and algorithmic fairness. Due to this demand and the heavy computation of deep learning, the acceleration of multi-objective (MO) optimization becomes ever more important. Although meta-learning has been extensively studied to speedup HPO, existing methods are not applicable to the MO tree-structured parzen estimator (MO-TPE), a simple yet powerful MO HPO algorithm. In this paper, we extend TPE’s acquisition function to the meta-learning setting, using a task similarity deﬁned by the overlap in promising regions of each task. In a comprehensive set of experiments, we demonstrate that our method accelerates MO-TPE on tabular HPO benchmarks and yields state-of-the-art performance. Our method was also validated externally by winning the AutoML 2022 competition on “Multiobjective Hyperparameter Optimization for Transformers” .
Effective theory of black hole dynamics in the large D limit

We begin by exploring black hole type solutions to vacuum Einstein equations in higher dimensions and we study the implications of the large D limit, i.e. assuming the number of space dimensions is infinitely large. We study the metric fluctuations of these solutions, their stability and how the perturbations classify in the large D limit. We then expand the metric tensor in powers of 1/D and we solve Einstein equations up to its first higher order to obtain effective theories that describe linear and non linear dynamics of black holes in the large D limit. Numerical solving of the effective equations provides us with important results regarding black hole collisions, violation of Cosmic Censorship and the evolution of Gregory-Laflamme unstable black strings. We see that the final outcome of two black hole collisions depends only on the initial angular momentum per unit mass, giving a final outcome of either a single black hole, a black bar or more the one black hole violating Cosmic Censorship in the process. We also obtain that compactified black strings evolve into a stable non uniform configuration for some values of its length, despite having an initial uniform state with an infinitesimal perturbation. We’ve been able to reproduce these results from the effective equations in our own numerical calculations to confirm the conclusions that are already in the literature.
Disc actuators for turbulent drag reduction

The change in a turbulent channel flow subjected to the motion of flush-mounted disc actuators has been studied by means of direct numerical simulations. The Reynolds number is Rτ=180, based on the friction velocity of the stationary-wall case and the half-channel height. Variations to the original disc actuators of Ricco & Hahn (2013) are investigated, with the aim of improving their performance. This includes oscillating discs, annular and half-disc actuators, and freely-rotating discs. A maximum drag reduction of 26% arises from the half-disc actuators, while the maximum net power saving of 6% results from the oscillating discs. Through use of the Fukagata-Iwamoto-Kasagi identity it has been possible to show that the wall-friction reduction due to the disc actuators is due to two distinguished effects. One effect is linked to the direct shearing action of the near-wall oscillating-disc boundary layer on the wall turbulence, which causes the attenuation of the turbulent Reynolds stresses. The other effect is due to the additional disc-flow Reynolds stresses produced by the streamwise-elongated structures which form between discs and modulate slowly in time. The drag reduction due to turbulent Reynolds stress attenuation depends on the penetration thickness of the disc-flow boundary layer, while the contribution to drag reduction from the elongated structures scales linearly with a simple function of the maximum tip velocity and oscillation period. Through investigation of different wall arrangements of disc actuators it is found that the layout ot the discs has a complex and unexpected effect on the flow. For low disc-tip velocities, the drag reduction scales linearly with the percentage
A 0.7-V, 8.9- l A compact temperature-compensated CMOS subthreshold voltage reference with high reliability

This paper focuses on the study of a compact tem-perature-compensated CMOS voltage reference (VR) with high reliability. The temperature coefﬁcient (TC) of the gate-source voltage for a subthreshold NMOSFET has been derived and utilized to perform effective temperature compensation with a proportional to absolute temperature (PTAT) drain current. The desirable PTAT current is provided with reliable power supply rejection ratio (PSRR) based on low-voltage self-biased cascode subthrehold operation with enhanced negative feedback. The resulting reference voltage is less sensitive to the process variations of on-chip resistors and absolute currents as well as the TC and PSRR. In addition, the impact of the gate-source voltage variation is alleviated, thus ensuring high reliability of the proposed VR. The measurement results without trimming in 40-nm CMOS process demonstrate that the average of TC is 5.1 and 19.1 ppm = (cid:2) C in the temperature range of - 20 to - 80 (cid:2) C and - 40 to - 120 (cid:2) C, respectively, and the worst PSRR of - 55.0 dB at 300 kHz is achieved, while the line regulation is better than 0.32 mV/ V in the supply range of 0.9–1.6 V. The average current consumption is 8.9 l A at 0.7-V supply, with a die area of only 0.006 mm 2 .
A study Comparing Text-Based WhatsApp and Face-to-Face Interviews to Understand Early School Dropout

The majority of adolescents communicate via text-based messaging, particularly through WhatsApp, a widely used free communication application. Written content on WhatsApp has the methodological potential to provide rich qualitative interview data. This study compares data collected using text-based WhatsApp versus face-to-face interview techniques. Semi-structured interviews were conducted with a sample of early school leavers in South Africa, using WhatsApp (n = 9) and face-to-face (n = 27) followed by a focus group discussion with interviewers. Mann–Whitney U and chi-squared were used to assess associations. WhatsApp text-based interviews took significantly longer to complete but were comparable to face-to-face on the number of themes generated. Rapport, measured as the number of statements from the interviewer aimed at creating a sense of affinity, comfort, and distress reduction, differed between interview conditions. It may be methodologically appropriate for researchers to offer participants a choice of a preferred method of interviewing or consider pragmatic blended approaches of face-to-face and WhatsApp.
Laboratory Diagnosis of Novel Human Coronavirus (SARS-CoV-2) Infections-A Review

COVID-19 is a pandemic, highly contagious infectious disease caused by the Severe Acute Respiratory Syndrome Corona virus-2 (SARS-CoV-2). The World Health Organization has declared the ongoing outbreak a global public health emergency. This disease has spread rapidly and affected millions of people worldwide. Currently, there are no specific clinical signs or symptoms of SARS that can be used to differentiate it from other causes of community- or hospital-acquired viral pneumonia. Accurate diagnosis of cases holds the key to managing any pandemic through identification, isolation, and treatment of patients while defining the epidemiology of the pathogen. Because an increasing number of asymptomatic symptomatic individuals must be tested for COVID-19, a safe and efficient screening system is required. The diagnosis of suspected cases is presently confirmed by nucleic acid assays with real-time PCR using respiratory samples. On the other side, serological tests are comparatively easier to perform, but their utility may be limited by their ease of performance and the fact that antibodies appear later in the disease course. This review is aimed at summarizing the currently available information on different methods used for screening and diagnosing COVID-19 infections.
Acute Heart Failure in an Almost-Centenarian Patient With Symptomatic Severe Aortic Stenosis Treated With Ivabradine

We report the case of a 99-year-old woman with acute chest pain caused by myocardial ischemia due to severe aortic stenosis (AS) and severe anemia. Red blood cells were transfused; however, this increased the preload and worsened pulmonary congestion. Treatment with drugs and non-invasive positive pressure ventilation could not sufficiently improve the pulmonary congestion. Ivabradine was administered to control the resting heart rate, following which the pulmonary congestion significantly improved. Ivabradine is a promising drug for acute heart failure of patients with AS by improving cardiac output by prolonging the left ventricular diastolic filling time without a negative inotropic effect.
Establishing Normative Values for Amplitude of Accommodation and Accommodative Facility among University Students in Malawi

Aim: The study aimed to examine the distribution of Accommodative Facility (AF) and Amplitude of Accommodation (AA) and compare the findings with established guidelines. Place and Duration of Study: Mzuzu University, Malawi. Between May and July 2022. Methods: This cross-sectional study was conducted among students at Mzuzu university in Malawi. We recruited 77 students using a stratified random sampling technique. The participants' age ranged from 16 to 35 years of age. We measured AA using the push-up method while AF was measured using +/- 1.50 Diopters (D) flippers. Both techniques utilized black reading material on white background held at 40 centimeters (cm). Next, we measured the accommodation facility by counting the number of Cycles per Minute (c/m). We utilized the Pearson correlation test and the One-way ANOVA where appropriate. The value of p< 0.05 was considered statistically significant. Results: The participants comprised 44 (57.1%) males and 33 (42.9%) females. Monocular AA was 10.04D (SD=2.71) and Binocular AA was 10.51 (3.641). The difference was statistically significant (p=0.04). While Monocular AF and Binocular AF were 9 c/m (SD= SD=1.84) and 8.96 (SD=1.539) respectively but the difference was non-significant (p=0.868). AF and AA were not significantly different between males and females. All the parameters decreased with age. The measured AA was significantly higher than using Hofstetter’s formula. Conclusion: The study provides a cut-off value for practitioners diagnosing Accommodation anomalies. However, indices in the study differ from the well-established guidelines hence practitioners should endeavor to perform the clinical assessment instead of relying on equations.
The Effect of Discovery Learning Method on Students’ Self-Motivation Through Online Learning During The Covid-19 Pandemic

Introduction: The learning process at the university level, especially in nursing, has changed since the number of cases of COVID-19 has continued to increase. Various problems after one year of the pandemic include students complained of boredom and lack of focus on learning compared to conventional learning in the classroom. The aim of this study was to analyze the effect of discovery learning method on students’ self-motivation through online learning in the COVID-19 pandemic.Method: This study used a quasi-experimental with pre-post control group design and conducted in two areas, PSIK Sam Ratulangi University as an Intervention group and PSIK Stikes Jendral Achmad Yani Cimahi as control group. We recruited a total sample of 82 respondents in total. This study lasted for four weeks with six stages described as two t meetings a week. To measure students' self-motivation, the Motivated Strategies for Learning Questionnaire (MSLQ) instrument totaling 32 items was used. Data were analyzed using Wilcoxon test and Mann Whitney test.Results: The results of this study indicate that there is a significant effect of discovery learning methods with self-motivation in online learning in community nursing course [p=0.00; Z = -2.28] and emergency nursing course [p=0.00; Z = -3.43]. Conclusions: The results of the study are promising as the course can have a direct effect on the nursing education system, especially in improving the quality of learning methods. This method may be used as a way to improve student skill in the nursing courses especially community nursing and emergency nursing.
Comparison of Lagrangian and Eulerian Simulations of Slurry Flows in a Sudden Expansion

From a review of technical literature, it was not apparent if the Lagrangian or the Eulerian dispersed phase modeling approach was more valid to simulate dilute erosive slurry flow. In this study, both modeling approaches were employed and a comparative analysis of performances and accuracy between the two models was carried out. Due to an impossibility to define, for the Eulerian model already implemented in FLUENT , a set of boundary conditions consistent with the Lagrangian impulsive equations, an Eulerian dispersed phase model was integrated in the FLUENT code using subroutines and user-defined scalar equations. Numerical predictions obtained from the two different approaches for two-phase flow in a sudden expansion were compared with the measured data. Excellent agreement was attained between the predicted and observed fluid and particle velocity in the axial direction and for the kinetic energy. Erosion profiles in a sudden expansion computed using the Lagrangian scheme yielded good qualitative agreement with measured data and predicted a maximum impact angle of 29 deg at the fluid reattachment point. The Eulerian model was adversely affected by the reattachment of the fluid phase to the wall and the simulated erosion profiles were not in agreement with the Lagrangian or measured data. Furthermore, the Eulerian model under-predicted the Lagrangian impact angle at all locations except the reattachment point.
Great Saphenous Vein Insufficiency Evaluating the Impact of Reflux Pattern on Clinical Severity and Treatment Modality

Objectives: This research aimed to determine the impact of reflux patterns in patients with great saphenous vein (GSV) insufficiency on clinical severity and management. Methods: We evaluated 771 lower extremities of 452 patients having GSV insufficiency with Doppler Ultrasound. We characterized the reflux patterns like previously done in the literature: type 1, GSV reflux without the involvement of perimalleolar region or saphenofemoral junction (SFJ); type 2, GSV reflux involving perimalleolar region; type 3, GSV reflux involving SFJ; type 4, GSV reflux involving both perimalleolar region and SFJ
Impact of Uveitis Profile on Vision in Jordanian Adults at King Hussein Medical Center

Objective: to explore the severity and the causes of visual impairment related to uveitis and to determine the possible risk factors which contributed to severe visual deterioration. Method: This was a cross sectional study conducted at King Hussein Medical Centre (KHMC) between June 2017 and June 2018. All patients suffering from uveitis who attended to the ophthalmology clinic were enrolled in the study. After collecting data from the patients regarding age, gender and the past medical history the patients were subjected to detailed ophthalmic exam including best corrected visual acuity (BCVA). Anterior segment exam using the slit lamp, intra ocular pressure exam using Golmann applanation tonometry and posterior segment exam using 78 and 90 diopter Volk lenses were performed. Patients with other ocular diseases that affected visions not related to uveitis were excluded from the study. Results: 72 patients (120 eyes) with a mean age of 32±12 (range 18-48 years). 43 patients (60%) were males. 33 eyes (28%) had acute uveitis. Visual impairment was found in 43%of eyes with uveitis;14%,18% and 11% of eyes had mild, moderate and sever visual impairment respectively. 68% of eyes with posterior uveitis and 90% of eyes with pan-uveitis had moderate to severe impairment of vision. The most common complication encountered was cataract which was found in 43% of eyes with uveitis. Other complications like posterior synechiae, cystoid macular edema CME and glaucoma were reported in 28%,22% and 17% of eyes with uveitis respectively. Behcet’s disease (8%)and HLA B27 related uveitis (8%) were the most frequent known causes for uveitis. 69% of eyes with severe visual impairment had duration of uveitis for ≥ 4 years. 64% of eyes with severe visual impairment had more than 2 years duration before starting systemic treatment. Conclusion: Visual impairments are common among uveitis patients. Risk factors for severe visual impairment in uveitis include; chronic uveitis, pan and posterior uveitis, long duration of uveitis, Behcet’s disease, posterior segment complication involving the macula and the optic nerve, Delay in starting systemic treatment and late referrals.
The Risk Preferences Of Graduates Of Islamic Educational Institutions In Indonesia

Indonesia as a Muslim-majority country and the survey results show that the most Indonesian are religious. Religious education also gets facilities from the state as a form of support for people's needs for religion. Religiosity, and education are hypothesized to be determinants of risk preference. This study aims to uncover the relationship between risk preferences and Islamic faith-based education in the Indonesian context. This research tries to combine aspects of religiosity and educational aspects in one framework, namely Islamic faith-based education where previous studies do not unite aspects of religiosity and education. This research is expected to provide a more complete picture or map of the risk preferences of graduates of Islamic educational institutions.
Machine Learning-Assisted In Vitro Rooting Optimization in Passiflora caerulea

In vitro rooting as one of the most critical steps of micropropagation is affected by various extrinsic (e.g., medium composition, auxins) and intrinsic factors (e.g., species, explant). In Passiflora species, in vitro adventitious rooting is a difficult, complex, and non-linear process. Since in vitro rooting is a multivariable complex biological process, efficient and reliable computational approaches such as machine learning (ML) are required to model, predict, and optimize this non-linear biological process. Therefore, in the current study, a hybrid of generalized regression neural network (GRNN) and genetic algorithm (GA) was employed to predict in vitro rooting responses (rooting percentage, number of roots, and root length) of Passiflora caerulea based on the optimization of the level of auxins (indole-3-acetic acid (IAA), indolebutyric acid (IBA), and 1-naphthaleneacetic acid (NAA)) and the type of explant (microshoots derived from leaf, node, and internode). Based on the results, the GRNN model was accurate in predicting all in vitro rooting responses of P. caerulea (R2 > 0.92) in either training or testing sets. The result of the validation experiment also showed that there was a negligible difference between the predicted-optimized values and the validated results demonstrating the reliability of the developed GRNN-GA model. Generally, the results of the current study showed that GRNN-GA is a reliable and accurate model to predict and optimize in vitro rooting of P. caerulea.