Miracle-dz/UniFAR-FTUs huggingface dataset:
Diamond Coating Reduces Nuclear Fuel Rod Corrosion at Accidental Temperatures: The Role of Surface Electrochemistry and Semiconductivity

If we want to decrease the probability of accidents in nuclear reactors, we must control the surface corrosion of the fuel rods. In this work we used a diamond coating containing <60% diamond and >40% sp2 “soft” carbon phase to protect Zr alloy fuel rods (ZIRLO®) against corrosion in steam at temperatures from 850 °C to 1000 °C. A diamond coating was grown in a pulse microwave plasma chemical vapor deposition apparatus and made a strong barrier against hydrogen uptake into ZIRLO® (ZIRLO) under all tested conditions. The coating also reduced ZIRLO corrosion in hot steam at 850 °C (for 60 min) and at 900 °C (for 30 min). However, the protective ability of the diamond coating decreased after 20 min in 1000 °C hot steam. The main goal of this work was to explain how diamond and sp2 “soft” carbon affect the ZIRLO fuel rod surface electrochemistry and semi conductivity and how these parameters influence the hot steam ZIRLO corrosion process. To achieve this goal, theoretical and experimental methods (scanning electron microscopy, Raman spectroscopy, electrochemical impedance spectroscopy, carrier gas hot extraction, oxidation kinetics, ab initio calculations) were applied. Deep understanding of ZIRLO surface processes and states enable us to reduce accidental temperature corrosion in nuclear reactors.
Readmission after index hospital discharge among patients with COVID‐19: Protocol for a systematic review and meta‐analysis

Hospital readmissions among COVID‐19 patients have increased the load on the healthcare systems and added more pressure to hospital capacity. This affects the ability to accommodate newly diagnosed COVID‐19 patients and other non‐COVID‐19 patients who require hospitalization. Therefore, this systematic review aims to understand the rates of and risk factors for hospital readmissions and all‐cause mortality among COVID‐19 patients who were hospitalized after being discharged following index hospitalization.
Unmet needs among Danish women with breast cancer in the transition from diagnosis into the early treatment phase

This study investigates the supportive needs of women diagnosed with breast cancer in the early treatment phase. The aim of this study was to identify unmet supportive care needs in the early treatment phase of women treated for breast cancer. This study employed a quantitative longitudinal design with baseline questionnaire (Time 1) and a six-month follow-up questionnaire (Time 2) that explored a variety of characteristics using both standardized scales and more explorative questions about employment situation, needs, rehabilitation, psychological, physical and social experiences. Eighty-five percent of the invited potential women agreed to participate, and 100 participants completed the baseline questionnaire. The analysis identified results concerning employment situation, depressive symptoms, unmet needs, distress, sexual life, social constraints and cancer-related quality of life. The study concludes that health professionals must embrace the challenges faced by the women and provide strategies to support women achieving meaningful lives.
StainView: A Fast and Reliable Method for Mapping Stains in Facades Using Image Classification in HSV and CIELab Colour Space

The new Construction 4.0 paradigm takes advantage of existing technologies. In this scope, the development and application of image-based methods for evaluating and monitoring the state of conservation of buildings has shown significant growth, including support for maintenance plans. Recently, powerful algorithms have been applied to automatically evaluate the state of conservation of buildings using deep learning frameworks, which are utilised as a black-box approach. The large amount of data required for training, the difficulty in generalising, and the lack of parameters to assess the quality of the results often make it difficult for non-experts to evaluate them. For several applications and scenarios, simple and more intuitive image-based approaches can be applied to support building inspections. This paper presents the StainView, which is a fast and reliable method. The method is based on the classification of the mosaic image, computed from a systematic acquisition, and allows one to (i) map stains in facades; (ii) locate critical areas; (iii) identify materials; (iv) characterise colours; and (v) produce detailed and comprehensive maps of results. The method was validated in three identical buildings in Bairro de Alvalade, in Lisbon, Portugal, that present different levels of degradation. The comparison with visual inspection demonstrates that StainView enables the automatic location and mapping of critical areas with high efficiency, proving to be a useful tool for building inspection: differences were of approximately 5% for the facade with the worst and average state of conservation, however, the values deteriorate for the facade under good conditions, reaching the double of percentage. In terms of processing speed, StainView allows a facade mapping that is 8–12 times faster, and this difference tends to grow with the number of evaluated façades.
Deep Learning-Based Method for Computing Initial Margin

Following the guidelines of the Basel III agreement (2013), large financial institutions are forced to incorporate additional collateral, known as Initial Margin, in their transactions in OTC markets. Currently, the computation of such collateral is performed following the Standard Initial Margin Model (SIMM) methodology. Focusing on a portfolio consisting of an interest rate swap, we propose the use of Artificial Neural Networks (ANN) to approximate the Initial Margin value of the portfolio over its lifetime. The goal is to find an optimal configuration of structural hyperparameters, as well as to analyze the robustness of the network to variations in the model parameters and swap features.
The Weight Problem: Overview of the Most Common Concepts for Body Mass and Fat Distribution and Critical Consideration of Their Usefulness for Risk Assessment and Practice

The prevalence of obesity already reached epidemic proportions many years ago and more people may die from this pandemic than from COVID-19. However, the figures depend on which measure of fat mass is used. The determination of the associated health risk also depends on the applied measure. Therefore, we will examine the most common measures for their significance, their contribution to risk assessment and their applicability. The following categories are reported: indices of increased accumulation of body fat; weight indices and mortality; weight indices and risk of disease; normal weight obesity and normal weight abdominal obesity; metabolically healthy obesity; the obesity paradox. It appears that BMI is still the most common measure for determining weight categories, followed by measures of abdominal fat distribution. Newer measures, unlike BMI, take fat distribution into account but often lack validated cut-off values or have limited applicability. Given the high prevalence of obesity and the associated risk of disease and mortality, it is important for a targeted approach to identify risk groups and determine individual risk. Therefore, in addition to BMI, a measure of fat distribution should always be used to ensure that less obvious but risky manifestations such as normal weight obesity are identified.
The Potential Role of Electronegative High-Density Lipoprotein H5 Subfraction in RA-Related Atherosclerosis

Although the heterogeneity of high-density lipoprotein-cholesterol (HDL-c) composition is associated with atherosclerotic cardiovascular risk, the link between electronegative subfractions of HDL-c and atherosclerosis in rheumatoid arthritis (RA) remains unknown. We examined the association of the percentage of the most electronegative subfraction of HDL-c (H5%) and RA-related atherosclerosis. Using anion-exchange purification/fast-protein liquid chromatography, we demonstrated significantly higher H5% in patients (median, 7.2%) than HC (2.8%, p < 0.005). Multivariable regression analysis revealed H5% as a significant predictor for subclinical atherosclerosis. We subsequently explored atherogenic role of H5 using cell-based assay. The results showed significantly higher levels of IL-1β and IL-8 mRNA in H5-treated (mean ± SD, 4.45 ± 1.22 folds, 6.02 ± 1.43-folds, respectively) than H1-treated monocytes (0.89 ± 0.18-folds, 1.03 ± 0.26-folds, respectively, both p < 0.001). In macrophages, H5 upregulated the mRNA and protein expression of IL-1β and IL-8 in a dose-dependent manner, and their expression levels were significantly higher than H1-treated macrophages (all p < 0.001). H5 induced more foam cell formation compared with H1-treated macrophages (p < 0.005). In addition, H5 has significantly lower cholesterol efflux capacity than H1 (p < 0.005). The results of nanoLC-MS/MS approach reveal that the best discriminator between high-H5% and normal-H5% is Apo(a), the main constituent of Lp(a). Moreover, Lp(a) level is a significant predictor for high-H5%. These observations suggest that H5 is involved in RA-related atherosclerosis.
CANCER TYPES;

Introduction: This study attempts to highlight the relation of various types ofcancers to abnormalities in liver and renal function tests. Objectives: Correlation of derangedrenal and liver function tests to different types of cancers in industrial workers who are exposed toindustrial chemicals and carcinogens. Patients and Methods: This non-interventional studyincluded 100 hundred confirmed cases of cancer were selected and studied from July 2011 toDecember 2011. The study was conducted at Fatima Memorial Hospital/College of Medicine &Dentistry, Lahore . Results: Out of these 100 patients, 21 had CA lung, 19 had lymphoma, 14 hadsarcoma, 13 had CA breast, 6 had CA prostate, 4 had CA liver, 3 had CA colon, 3 had CA ovary, 3had CA testis, 2 had leukemia, 2 had multiple myeloma, 2 had CA gall bladder, 2 had CA stomach,1 had CA esophagus, 1had CA tongue, 1had CA parotid, 1 had CA rectum, 1 had CA cervix and 1had CA larynx. Among lymphoma, non-Hodgkin lymphoma was more than Hodgkin lymphoma(out of 19 patients, 13 had non-Hodgkin and 6 had Hodgkin lymphoma). Out of 14 patients ofsarcomas, 5 had Ewing sarcoma, 4 had chondrosarcoma, 3 had fibrosarcoma and 2 hadliposarcoma. Similarly the serum bilirubin levels in liver cancer, ovarian cancer, multiplemyeloma, stomach and gall bladder cancer were 1.50±0.31, 1.17±0.10, 1.46±0.15 and1.40±0.50 mg% were 32.38±18.81, 37.73±4.35, 34.43±4.35, 45.00±46.17, 31.33±44.70,65.25±31.14, 33.67±80.90, 99.00±73.08, 50.00±98.49, 33.50±75.0, 36.50±85.0 and49.0±11.0 μ/l respectively which were statistically significantly higher (p<0.05) then controlindividuals. respectively which were significantly higher (p<0.05) than the control individualsSimilarly the ALT levels in lung cancer, lymphoma, sarcoma, breast cancer , prostate cancer, livercancer, leukemia, ovarian cancer, cancer of colon, multiple myeloma, cancer of testis andstomach. The mean serum creatinine levels in lung cancer, liver cancer and cancer of colon were0.89±0.05, 0.90±0.16 mg% respectively which were significantly higher than the controlindividuals (p<0.05). Conclusions: The renal and liver function tests should be frequentlymonitored in cancer patients from industrial back grounds.
Multi-label learning using label-specific features for simultaneous fault diagnosis of aircraft engine

Lacking of the management of simultaneous fault is one of the limitations of condition monitoring for a gas turbine, which is critical for the safety and decision-making of aircraft operation. To this end, this paper employed a multi-label (ML) learning strategy to address the simultaneous fault issues. Moreover, a feature selection algorithm is proposed, which is based on the viewpoint that different class labels might be distinguished by certain specific characteristics of their own. The proposed algorithm achieves the goal of label-specific feature selection by iteratively optimizing the weight reconstruction matrix, and the learned label-specific features for the corresponding label can be used for multi-label classification. As thus, sensor data for different components of aircraft engines can be determined by the proposed algorithm to deal with the simultaneous fault diagnosis. Finally, comprehensive experiments on the benchmark data sets of multi-label learning validate the advantages and feasibility of the presented approaches, and the effectiveness of their application to simultaneous fault diagnosis of aircraft engines is also proved by extensive experiments.
Using Outlier Analysis to Detect Partisan Gerrymanders: A Survey of Current Approaches and Future Directions

The prospect of convincing courts to intervene in partisan gerrymandering has inspired a great deal of research and, recently, public attention. But how can neutral parties such as courts and independent redistricting commissions discern when a district map is unfairly gerrymandered? We review the case law and the leading measures of substantive fairness, explaining why, alone, they are unlikely to be sufficient for neutral decision makers to ascertain gerrymanders. We then explain the concept of outlier analysis, a particularly promising intervention into the field, but one that is computationally challenging, as it requires generating a sample of reasonable district maps. We review the major approaches to assembling these samples, and we present recommendations for improvement and future research that could help lead to the de-velopment of a consensus approach to assessing whether a map is gerrymandered.
M

A stroke is the basic limb movement that both humans and animals naturally and repetitiously perform. Having been introduced into gestural interaction, mid-air stroke gestures saw a wide application range and quite intuitive use. In this paper, we present an approach for building command-to-gesture mapping that exploits the semantic association between interactive commands and the directions of mid-air unistroke gestures. Directional unistroke gestures make use of the symmetry of the semantics of commands, which makes a more systematic gesture set for users’ cognition and reduces the number of gestures users need to learn. However, the learnability of the directional unistroke gestures is varying with different commands. Through a user elicitation study, a gesture set containing eight directional mid-air unistroke gestures was selected by subjective ratings of the direction in respect to its association degree with the corresponding command. We evaluated this gesture set in a following study to investigate the learnability issue, and the directional mid-air unistroke gestures and user-preferred freehand gestures were compared. Our findings can offer preliminary evidence that “return”, “save”, “turn-off” and “mute” are the interaction commands more applicable to using directional mid-air unistrokes, which may have implication for the design of mid-air gestures in human–computer interaction.
Taylor rule and monetary policy transmission mechanism in a new keynesian economy

This paper estimates two SVAR models to assess Mexican Monetary policy rate for the period 2000-2015, which are recursively identifid according to Gali and Monacelli (2005) model. This paper shows that monetary policy rate responds to GDP, infltion and exchange rate as Taylor’s Rule predicts. When controlling for General Consumer’s Price Index infltion, monetary policy barely affcts aggregate demand even if exchange rate appreciates, nevertheless GDP diminish after contractive monetary policy takes place. Infltion rate lightly increases after interest rate rises, which does not coincide with New Keynesian predictions. A second model is estimated controlling for underlying infltion. Its results exhibit more interest rate sensitive consumption and net exports, while real exchange rate and GDP change as New Keynesian model predicts. Infltion decreases after monetary policy rise but its flctuations are close to zero. According to Gali (2008) such small changes indicate nominal rigidities existence.
Exploring Fuzzy Local Spatial Information Algorithms for Remote Sensing Image Classification

Fuzzy c-means (FCM) and possibilistic c-means (PCM) are two commonly used fuzzy clustering algorithms for extracting land use land cover (LULC) information from satellite images. However, these algorithms use only spectral or grey-level information of pixels for clustering and ignore their spatial correlation. Different variants of the FCM algorithm have emerged recently that utilize local spatial information in addition to spectral information for clustering. Such algorithms are seen to generate clustering outputs that are more enhanced than the classical spectral-based FCM algorithm. Nonetheless, the scope of integrating spatial contextual information with the conventional PCM algorithm, which has several advantages over the FCM algorithm for supervised classification, has not been explored much. This study proposed integrating local spatial information with the PCM algorithm using simpler but proven approaches from available FCM-based local spatial information algorithms. The three new PCM-based local spatial information algorithms: Possibilistic c-means with spatial constraints (PCM-S), possibilistic local information c-means (PLICM), and adaptive possibilistic local information c-means (ADPLICM) algorithms, were developed corresponding to the available fuzzy c-means with spatial constraints (FCM-S), fuzzy local information c-means (FLICM), and adaptive fuzzy local information c-means (ADFLICM) algorithms. Experiments were conducted to analyze and compare the FCM and PCM classifier variants for supervised LULC classifications in soft (fuzzy) mode. The quantitative assessment of the soft classification results from fuzzy error matrix (FERM) and root mean square error (RMSE) suggested that the new PCM-based local spatial information classifiers produced higher accuracies than the PCM, FCM, or its local spatial variants, in the presence of untrained classes and noise. The promising results from PCM-based local spatial information classifiers suggest that the PCM algorithm, which is known to be naturally robust to noise, when integrated with local spatial information, has the potential to result in more efficient classifiers capable of better handling ambiguities caused by spectral confusions in landscapes.
Cause-specific mortality rate of ovarian cancer in the presence of competing risks of death: a nationwide population-based cohort study

Objective This nationwide cohort study aimed to evaluate the cause-specific mortality (probability of death by ovarian cancer, probability of death by other causes) under the competing risks of death in women with ovarian cancer. Methods The Korea Central Cancer Registry was searched to identify women with primary ovarian cancer diagnosed between 2006 and 2016. Epithelial ovarian cancer cases were identified using the International Classification of Diseases for Oncology 3rd edition. We estimated the cause-specific mortality according to age (<65 years, ≥65 years), stage (local, regional, and distant), and histology (serous, mucinous, endometrioid, clear cell, and others) under the competing risks framework; moreover, cumulative incidences were estimated. Results We included 21,446 cases. Cause-specific mortality continuously increased throughout 10 year follow-up. Compared with women aged <65 years, ovarian cancer-specific mortality (5-year, 28.9% vs. 61.9%; 10-year, 39.0% vs. 68.6%, p<0.001) and other cause mortality (5-year, 1.7% vs. 4.8%; 10-year, 2.8% vs. 8.2%, p<0.001) increased in women aged ≥65 years. This trend was consistent across all the stages and histological types. There was a substantial increase in competing risks from 1.1% in women aged <65 years to 8.0% in women aged ≥65 years in patients with early-stage (p<0.001) non-serous ovarian cancer (p<0.001). Conclusion Older age at diagnosis is associated with increasing ovarian cancer-specific mortality and competing risks. Given the substantial effect of competing risks on elderly patients, there is a need for assessment tools to balance the beneficial and harmful effects to provide optimal treatment.
Approximation Solution of the Nonlinear Circular Sitnikov Restricted Four-Body Problem

In this paper, the approximated periodic solutions of the circular Sitnikov restricted four–body problem (RFBP) were constructed using the Lindstedt–Poincaré method, by removing the secular terms, and compared with numerical solution. It can be observed that, in the numerical as well as approximated solutions patterns, the initial conditions are important. In the sense of a numerical solution, the motion is periodic in a certain interval, but beyond this interval, the motion is not periodic. But, the Lindstedt–Poincaré method constantly gives regular and periodic motion all time. Finally, we observed that the solution obtained by the Lindstedt–Poincaré method gives the true motion of the circular Sitnikov RFBP and the fourth approximate solution has more accuracy than the first, second, and third approximate solutions.
Selective Separation and Analysis of Catecholamines in Urine Based on Magnetic Solid Phase Extraction by Mercaptophenylboronic Acid Functionalized Fe3O4-NH2@Au Magnetic Nanoparticles Coupled with HPLC

A novel magnetic solid phase extraction based on mercaptophenylboronic acid (MPBA)-functionalized Fe3O4-NH2@Au nanomaterial (Fe3O4-NH2@Au-MPBA) was developed for selective separation and enrichment of catecholamines (including dopamine, norepinephrine and adrenaline). Fe3O4-NH2@Au-MPBA nanoparticles were achieved by self-assembly-anchoring MPBA molecules on the surface of Fe3O4-NH2@Au nanocomposites, which were synthesized via a facial ultrasonic auxiliary in situ reduction process. The interaction between cis-diol from catecholamines and boronic acid was reversible and could be flexibly controlled by adjusting pH value. The catecholamines could be quickly adsorbed by Fe3O4-NH2@Au-MPBA in weak alkaline solution (pH 8.0–9.0) and subsequently released in acid solution (pH 1.0–2.0). The process of adsorption and dissociation was very fast. Furthermore, the three catecholamines could be detected in urine from children by high performance liquid chromatography (HPLC) with electrochemical detector. Under optimal conditions, norepinephrine (NE), epinephrine (EP) and dopamine (DA) were separated very well from internal standard and exhibited a good linearity in the range of 2.5–500.0 ng mL−1, with correlation coefficients of r2 > 0.9907. Limits of detection (LOD) (signal to noise = 3) were 0.39, 0.27 and 0.60 ng mL−1 for NE, EP and DA, respectively. Recoveries for the spiked catecholamines were in the range of 85.4–105.2% with the relative standard deviation (RSD) <11.5%.
Cybersecurity of Microgrid: State-of-the-Art Review and Possible Directions of Future Research

The infrastructure of and processes involved in a microgrid electrical system require advanced technology to facilitate connection among its various components in order to provide the intelligence and automation that can benefit users. As a consequence, the microgrid has vulnerabilities that can expose it to a wide range of attacks. If they are not adequately addressed, these vulnerabilities may have a destructive impact on a country’s critical infrastructure and economy. While the impact of exploiting vulnerabilities in them is understood, research on the cybersecurity of microgrids is inadequate. This paper provides a comprehensive review of microgrid cybersecurity. In particular, it (1) reviews the state-of-the-art microgrid electrical systems, communication protocols, standards, and vulnerabilities while highlighting prevalent solutions to cybersecurity-related issues in them; (2) provides recommendations to enhance the security of these systems by segregating layers of the microgrid, and (3) identifies the gap in research in the area, and suggests directions for future work to enhance the cybersecurity of microgrids.