Miracle-dz/UniFAR-FTUs huggingface dataset:
Professionalism, Ethics, and Realism of Television Medical Dramas as Perceived by Saudi Medical Students

Objectives Since their debut, medical dramas have been popular, particularly among medical students. This study examined how much time medical students spend watching medical TV shows and their impressions of the shows’ professionalism, ethics, and realism. Methods A survey on medical students’ medical drama viewing behavior and perceptions with regard to bioethics was given to 341 first- to fifth-year undergraduate medical students at a university in Al Majmaah, Saudi Arabia. The poll gathered demographic information, TV medical drama watching habits, bioethical and professional concerns in medical dramas, and the sources of bioethical knowledge. Results The most popular medical dramas were The Good Doctor, Grey's Anatomy, and House M.D. Compared with English-language television medical dramas, Arabic ones were less popular. Ninety-one percent of students watched television alone. There was no statistically significant difference in respondents’ perceptions of ethics accuracy based on year of study, gender, or whether they discussed it with friends. In comparison to students who had not seen any medical dramas, those who had were more inclined to view them as a credible source of ethical advice (P-value .04). Conclusions There is a high interest in medical programs among medical students, and these programs can be used to teach them about the ethical aspects of practicing medicine.
The Accuracy of Sex Identification Using CBCT Morphometric Measurements of the Mandible, with Different Machine-Learning Algorithms—A Retrospective Study

In forensics, predicting the sex is a crucial step in identification. Many studies have aimed to find an accurate and fast technique to estimate the sex. This study was conducted to determine the accuracy of volumetric and linear measurements of three-dimensional (3D) images of the mandible obtained from cone beam computed tomography (CBCT) radiographs, using different machine-learning (ML) models for sex identification. The CBCTs of 104 males and 104 females were included in this study. The radiographs were converted to 3D images, and the volume, surface area, and ten linear measurements of the mandible were obtained. The data were evaluated using statistical analysis and five different ML algorithms. All results were considered statistically significant at p < 0.05, and the precision, recall, f1-score, training accuracy, and testing accuracy were used to evaluate the performance of the ML models. All the studied parameters showed statistically significant differences between sexes p < 0.05. The right coronoid-to-gonion linear distance had the highest discriminative power of all the parameters. Meanwhile, Gaussian Naive Bayes (GNB) showed the best performance among all the ML models. The results of this study revealed promising outcomes; the sex can be easily determined, with high accuracy (90%).
Restricted permutations, antichains, atomic classes and stack sorting

Involvement is a partial order on all finite permutations, of infinite dimension and having subsets isomorphic to every countable partial order with finite descending chains. It has attacted the attention of some celebrated mathematicians including Paul Erdos and, due to its close links with sorting devices, Donald Knuth. We compare and contrast two presentations of closed classes that depend on the partial order of involvement: Basis or Avoidance Set, and Union of Atomic Classes. We examine how the basis is affected by a comprehensive list of closed class constructions and decompositions. The partial order of involvement contains infinite antichains. We develop the concept of a fundamental antichain. We compare the concept of 'fundamental' with other definitions of minimality for antichains, and compare fundamental permutation antichains with fundamental antichains in graph theory. The justification for investigating fundamental antichains is the nice patterns they produce. We forward the case for classifying the fundamental permutation antichains. Sorting devices have close links with closed classes. We consider two sorting devices, constructed from stacks in series, in detail.
Intravesical BCG in bladder cancer induces innate immune responses against SARS-CoV-2

BCG is the most efficient adjuvant therapy for high-risk, non-muscle-invasive bladder cancer (NMIBC). Both innate and adaptive immune responses have been implicated in BCG-mediated effects. BCG vaccination can boost innate immune responses via trained immunity (TI), resulting in an increased resistance to respiratory viral infections. Here we evaluated for the first time whether intravesical application of BCG triggers increased immunity against SARS-CoV-2 in patients with high-risk NMIBC. Serum and peripheral blood mononuclear cells (PBMCs) from heparinized whole blood samples of 11 unvaccinated SARS-CoV-2-naïve high-risk NMIBC patients were collected at baseline and during BCG treatment in a pre-COVID-19 era. To examine B-cell or T cell-dependent adaptive immunity against SARS-CoV-2, sera were tested for the presence of SARS-CoV-2 neutralizing antibodies. Using a SARS-CoV-2 peptide pool, virus-specific T cells were quantified via IFNγ ELISpot assays. To analyze innate immune responses, mRNA and protein expression levels of pro- and anti-inflammatory cytokines were measured after a 24-hour stimulation of PBMCs with either BCG or SARS-CoV-2 wildtype. ATAC- sequencing was performed to identify a potential epigenetic reprogramming in immune cells. We neither identified SARS-CoV-2 neutralizing antibodies nor SARS-CoV-2- reactive T cells, indicating that intravesical BCG did not induce adaptive immunity against SARS-CoV-2. However, a significant increase in mRNA as well as protein expression of IL-1β, IL-6 and TNFα, which are key cytokines of trained immunity, could be observed after at least four intravesical BCG instillations. Genomic regions in the proximity of TI genes (TLR2, IGF1R, AKT1, MTOR, MAPK14, HSP90AA1) were more accessible during BCG compared to baseline. Although intravesical BCG did not induce adaptive immune responses, repetitive intravesical instillations of BCG induced circulating innate immune cells that produce TI cytokines also in response to SARS-CoV-2.
Influence of magnetic field, chemical pressure and hydrostatic pressure on the structural and magnetocaloric properties of the Mn–Ni–Ge system

The magnetic, structural and thermomagnetic properties of the MM’X material system of MnNiGe are evaluated with respect to their utilization in magnetocaloric refrigeration. The effects of separate and simultaneous substitution of Fe for Mn and Si on the Ge site are analysed in detail to highlight the benefits of the isostructural alloying method. A large range of compounds with precisely tunable structural and magnetic properties and the tuning of the phase transition by chemical pressure are compared to the effect of hydrostatic pressure on the martensitic transition. We obtained very large isothermal entropy changes ΔSiso of up to −37.8 J kg−1 K−1 based on magnetic measurements for (Mn,Fe)NiGe in moderate fields of 2 T. The enhanced magnetocaloric properties for transitions around room temperature are demonstrated for samples with reduced Ge, a resource critical element. An adiabatic temperature change of 1.3 K in a magnetic field change of 1.93 T is observed upon direct measurement for a sample with Fe and Si substitution. However, the high volume change of 2.8% results in an embrittlement of large particles into several smaller fragments and leads to a sensitivity of the magnetocaloric properties towards sample shape and size. On the other hand, this large volume change enables to induce the phase transition with a large shift of the transition temperature by application of hydrostatic pressure (72 K GPa−1). Thus, the effect of 1.88 GPa is equivalent to a substitution of 10% Fe for Mn and can act as an additional stimulus to induce the phase transition and support the low magnetic field dependence of the phase transition temperature for multicaloric applications.
Simplified fringe order correction for absolute phase maps recovered with multiple-spatial-frequency fringe projections

Various kinds of fringe order errors may occur in the absolute phase maps recovered with multi-spatial-frequency fringe projections. In existing methods, multiple successive pixels corrupted by fringe order errors are detected and corrected pixel-by-pixel with repeating searches, which is inefficient for applications. To improve the efficiency of multiple successive fringe order corrections, in this paper we propose a method to simplify the error detection and correction by the stepwise increasing property of fringe order. In the proposed method, the numbers of pixels in each step are estimated to find the possible true fringe order values, repeating the search in detecting multiple successive errors can be avoided for efficient error correction. The effectiveness of our proposed method is validated by experimental results.
Applying Marine Predators Algorithm for Optimizing the Layout of Wind Turbines

-The extracted power from wind is clean, plentiful, and completely renewable. All over the world, researchers keep looking for the best layouts of wind parks to maximize captured energy. To design wind farms suitably, forecast their performance, and understand the strain loads of wind turbines, there is a persistent need to catch a perfect wake model. Wind turbine wakes are one of the most vital factors in the meteorology of wind power due to reducing the power production and the necessity to raise the downstream capacity of wind turbines. This study is divided into two main aspects: firstly, enhancing the optimal layout for the wind turbines at a farm using Marine Predators Algorithm (MPA). The Jensen wake model is applied to get the extracted power for each turbine, which is one of the mutual analytic models used to reach the optimized layout. By comparing the performance of the proposed algorithm with the previous studies achieved by several techniques, the obtained results revealed that the MPA achieves promising results. Secondly, the proposed algorithm is applied for four sites in Egypt as the fraction of occurrence for the selected locations has been adequately calculated using wind speed over five years.
Sectoral Differentiation of the Timeliness of the Publication of Periodic Reports in the Time of the Covid-19 Pandemic-Case of the Warsaw Stock Exchange

The aim of article is to verify the timeliness of periodic reports by issuers from Warsaw Stock Exchange (WSE) during COVID-19 pandemic years and period immediately preceding it. Main hypothesis of the study is following: COVID-19 pandemic had a negative impact on the timeliness of the publication of periodic reports by issuers belonging to selected WSE macrosectors, which resulted in the extension of the average time of their submission. The obtained research results did not allow for a positive verification of that hypothesis. Data on the median of publication days of periodic reports preceding the deadline indicate that in as many as 7 of the 8 indicated macrosectors it is higher in COVID-19 pandemic, which indicates that in 2020-2022 the issuers from the WSE published reports earlier than in 2018-2019. Only in the HEALTHCARE macrosector the results were consistent with the adopted research hypothesis. The considerations in the article deepen the knowledge of a feature of financial statements, which is timeliness. The authors point to the lack of research in this area in the period of COVID-19 pandemic. This study is intended to encourage related researchers with accounting to take up this important issue in their research.
When Two-Layer Federated Learning and Mean-Field Game Meet 5G and Beyond Security: Cooperative Defense Systems for 5G and Beyond Network Slicing

Cyber security for 5G and Beyond (5GB) network slicing is drawing much attention due to the increase of complex and dangerous cyber-attacks that could target the critical components of network slicing, such as radio access and core network. This paper proposes a new cyber defense approach based on two-layer Federated Learning (FL) to protect 5GB network slicing from the most dangerous network attacks and a mean-field game to safeguard the FL-enabled defense system from poisoning attacks. Our proposed distributed defense systems cooperate, intending to detect internal and external attacks targeting the critical components of 5GB network slicing and detecting infected parts in the 5GB defense system. Our experimental results show that our cooperative defense systems exhibit high accuracy detection rates against network attacks, namely (distributed) denial of service and botnets while being robust against poisoning attacks and requiring a few overheads generated by defense systems. To the best of our knowledge, we are the first to propose lightweight and accurate cooperative defense systems based on two-layer FL and non-cooperative games to enhance security against attackers in 5GB network slicing.
Manifold learning for fMRI time-varying functional connectivity

Whole-brain functional connectivity (FC) measured with functional MRI (fMRI) evolves over time in meaningful ways at temporal scales going from years (e.g., development) to seconds [e.g., within-scan time-varying FC (tvFC)]. Yet, our ability to explore tvFC is severely constrained by its large dimensionality (several thousands). To overcome this difficulty, researchers often seek to generate low dimensional representations (e.g., 2D and 3D scatter plots) hoping those will retain important aspects of the data (e.g., relationships to behavior and disease progression). Limited prior empirical work suggests that manifold learning techniques (MLTs)—namely those seeking to infer a low dimensional non-linear surface (i.e., the manifold) where most of the data lies—are good candidates for accomplishing this task. Here we explore this possibility in detail. First, we discuss why one should expect tvFC data to lie on a low dimensional manifold. Second, we estimate what is the intrinsic dimension (ID; i.e., minimum number of latent dimensions) of tvFC data manifolds. Third, we describe the inner workings of three state-of-the-art MLTs: Laplacian Eigenmaps (LEs), T-distributed Stochastic Neighbor Embedding (T-SNE), and Uniform Manifold Approximation and Projection (UMAP). For each method, we empirically evaluate its ability to generate neuro-biologically meaningful representations of tvFC data, as well as their robustness against hyper-parameter selection. Our results show that tvFC data has an ID that ranges between 4 and 26, and that ID varies significantly between rest and task states. We also show how all three methods can effectively capture subject identity and task being performed: UMAP and T-SNE can capture these two levels of detail concurrently, but LE could only capture one at a time. We observed substantial variability in embedding quality across MLTs, and within-MLT as a function of hyper-parameter selection. To help alleviate this issue, we provide heuristics that can inform future studies. Finally, we also demonstrate the importance of feature normalization when combining data across subjects and the role that temporal autocorrelation plays in the application of MLTs to tvFC data. Overall, we conclude that while MLTs can be useful to generate summary views of labeled tvFC data, their application to unlabeled data such as resting-state remains challenging.
A TOGAF based interoperable health information system needs assessment for practitioner–patient interaction

eHealth systems have been created in most developing countries to facilitate the functioning of healthcare operations and as such various healthcare applications are existing as fragmented silo systems. Such autonomous standalone systems do not communicate through a network thereby making it a challenge to share healthcare data. Therefore, to re‐engineer health information systems (HISs) to develop interoperable adaptive enterprise architecture (EA) systems for healthcare, the study aimed to discover and capture healthcare interoperability functional imperatives through understanding the expectations of healthcare practitioners and patients on post‐development of an interoperable HIS. Therefore, the study's aim was to determine the HISs interoperability perceived functional expectations by both patients and practitioners in fulfilling their healthcare receipt and provision needs in an integrated healthcare environment, respectively. The Open Group Architecture Framework (TOGAF) was used as the main study framework to guide the classification of the expectations from the HIS by patients and practitioners deriving the needs from the four domains which are, business architecture (BA), application architecture (AA), data architecture (DA), and technical architecture. The study used interviews and questionnaire surveys to collect qualitative and quantitative data respectively. The study used purposive sampling to select interview participants. A total of 19 interviews were conducted with healthcare practitioners. Questionnaires were collected from 71 healthcare practitioners and 143 patients and analyzed quantitatively respectively to understand the most significant needs anticipated in an interoperable HIS. The research targeted age groups of at least 20 years and above. The study discovered that patients and practitioners expect the interoperable healthcare environment to support the acquisition of disease knowledge through healthcare surveillance synergies; create healthcare awareness through coordinated digital interactions; augmentation of healthcare intelligence for patient‐care through the healthcare knowledgebase; allow treatment collaboration by various healthcare practitioners in the healthcare ecosystem and mostly achieving a guaranteed healthcare system security and assurance environment.
Monad transformers and modular algebraic effects: what binds them together

For over two decades, monad transformers have been the main modular approach for expressing purely functional side-effects in Haskell. Yet, in recent years algebraic effects have emerged as an alternative whose popularity is growing. While the two approaches have been well-studied, there is still confusion about their relative merits and expressiveness, especially when it comes to their comparative modularity. This paper clarifies the connection between the two approaches—some of which is folklore—and spells out consequences that we believe should be better known. We characterise a class of algebraic effects that is modular, and show how these correspond to a specific class of monad transformers. In particular, we show that our modular algebraic effects gives rise to monad transformers. Moreover, every monad transformer for algebraic operations gives rise to a modular effect handler.
Statistical-dynamical downscaling of the urban heat island in Hamburg, Germany

Regional climate models provide climate projections on a horizontal resolution in the order of 10 km. This is too coarse to sufficiently simulate urban climate related phenomena such as the urban heat island (UHI). Therefore, regional climate projections need to be downscaled. A statistical-dynamical method for the UHI was developed and applied to provide urban climate results at a high resolution with little computational costs. For the downscaling, weather situations relevant for the UHI are determined. This is done by combining objective weather pattern classification based on a k-means cluster analysis of ERA-40 reanalysis data and a regression-based statistical model of the observed UHI of Hamburg. The resulting days for each weather pattern are simulated with the mesoscale meteorological model METRAS at 1 km horizontal resolution. To obtain the average UHI for a climate period, the mesoscale model results are statistically recombined weighted by the frequency of the corresponding weather patterns. This is done for present-day climate (1971–2000) using reanalysis data to yield the current climate UHI. For the future climate periods 2036–2065 and 2070–2099 the results of regional climate projections are employed. Results are presented for Hamburg (Germany). The present day UHI pattern is well reproduced compared to temperature data based on floristic mapping data. The magnitude of the early night-time UHI is underestimated when compared to observed minimum temperature differences. The future UHI pattern does only slightly change towards the end of the 21st century based on A1B scenario results of the RCMs REMO and CLM. However, for CLM the number of days with high UHI intensities significantly increases mainly due to a decrease in near-surface relative humidity.
Dietary supplementation with different nano and organic selenium doses improves ovarian activity, fertility rate, and progesterone level in Ossimi ewes

Abstract This study aimed to investigate the effects of nano and organic selenium on ovarian activity, fertility rate, and progesterone hormone levels in Ossimi ewes. Thirty ewes were treated with intra-vaginal sponges impregnated with medroxyprogesterone acetate (40 mg) for 14 d and simultaneously assigned randomly to three groups. The 1st group ewe served as control (CG). Ewes in the 2nd and 3rd groups were treated orally with organic selenium 3 mg/ewe and Nano-selenium 0.2 mg/ewes during intra-vaginal sponges (IVS) treatment SeG and N-SeG, respectively. The ovarian activity was monitored using an ultrasonic device, and serum samples were harvested for progesterone measurement. The results revealed that ewes in SeG and N-SeG groups had a significantly (p < .05) higher number of small and large follicles after 48 h of IVS withdrawal than in the CG, respectively, on the left ovary. Also, the N-SeG group had a larger diameter of the Corpus luteum on both ovaries’ sides than CG, respectively, at the same time. However, the diameter of large follicles on both ovaries was significantly (p < .05) larger in the N-SeG after 72 h of IVS withdrawal than in SeG and CG. The ewes in N-SeG and SeG came in oestrus earlier (p < .05) than those in CG, and the oestrus durations were longer (p < .05) than that recorded in ewes of CG. The oestrus response and fertility rate were higher (p < .01) in N-SeG and SeG than in CG. Moreover, the progesterone concentrations were significantly higher (p < .05) in the N-SeG and SeG after 48 and 72 h of IVS withdrawal than in the CG. Also, the recorded serum progesterone concentrations in the included ewes non-significantly differed between groups during the first four months of pregnancy. It could be concluded that using nano and organic selenium can improve the quality of ovarian follicles and CL function and enhance the fertility rate in Ossimi ewes. HIGHLIGHTS Improvement of ovarian activity in ewes. Improvement of ewes fertility rate. Effect of Nano and organic selenium on Ossimi ewes