Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of School-Based Educational Intervention on Promoting Healthy Dietary Habits in Danish Schoolchildren: The FOODcamp Case Study

The present study aimed to evaluate the effect of the school-based educational intervention “FOODcamp” on dietary habits among 6th–7th graders (aged 11–13 years), focusing on the food groups: fruits and vegetables, fish, meat, discretionary food, and sugar-sweetened beverages. In this cluster-based quasi-experimental controlled intervention study, 16 intervention classes (322 children) and 16 control classes (267 children) from nine schools were recruited during the school year 2019–2020. The children were asked to record their food intake for four consecutive days (Wednesday to Saturday) before (baseline) and after (follow-up) attending FOODcamp, using a validated self-administered web-based dietary record. Eligible dietary intake registrations from 124 and 118 children from the control and interventions classes, respectively, were included in the final statistical analysis. Hierarchical mixed model analysis was used to evaluate the effect of the intervention. No statistically significant effects of participating in FOODcamp were found on the average food intake of the food groups eaten regularly (vegetables, fruit, vegetables/fruit/juice combined, or meat) (p > 0.05). Among the food groups not eaten regularly (fish, discretionary foods, and sugar-sweetened beverages), a non-significant tendency to lower odds of consuming sugar-sweetened beverages from baseline to follow-up (OR = 0.512; 95% CI: 0.261–1.003; p = 0.0510) was seen among FOODcamp participants compared to control participants. In conclusion, this study found no effect of the educational intervention FOODcamp on the dietary intake of vegetables, fruit, vegetable/fruit/juice combined, meat, fish, or sugar-sweetened beverages. The intake frequency of sugar-sweetened beverages tended to decrease among FOODcamp participants.
Development of a Nonequilibrium Finite-Rate Ablation Model for Radiating Earth Reentry Flows

Thermal protection system design for atmospheric reentry vehicles remains a challenging and complex problem. Recent advances in computational modeling of air–carbon interactions consider competing finite-rate reactions on a limited number of available surface sites. One of the most advanced kinetic models is due to Zhluktov and Abe. However, the Zhluktov and Abe model only describes the oxidation and sublimation of carbon and has no nitridation mechanism. The following study develops several modifications to the Zhluktov and Abe air–carbon model that account for all three reaction mechanisms with the goal of improving cyanogen shock-layer radiation predictions to recent experimental results. First, the study examines two possible paths for carbon nitridation and then assesses the augmented surface reaction model in a representative blunt-body reentry flow. Second, a sensitivity analysis is performed to determine which surface reactions have the most impact on altering cyanogen radiance predictions. It was.
A Surrogate-Assisted Evolutionary Framework With Regions of Interests-Based Data Selection for Expensive Constrained Optimization

Optimization problems whose evaluations of the objective and constraints involve costly numerical simulations or physical experiments are referred to as expensive constrained optimization (ECO) problems. Such problems can be solved by evolutionary algorithms (EAs) in conjunction with computationally cheap surrogates that separately approximate the expensive objective and constraint functions. During the process of the ECO, the interested regions of surrogate models for the objective and constraints usually have a small overlap only. Specifically, the surrogate model for the objective function should focus on the prediction accuracy in the promising region, while the models for constraint functions should concentrate on the accuracy at the boundary of the feasible region. However, most existing methods neglect such differences and train those different models using the same training data, barely resulting in satisfactory performance. Therefore, we propose a general framework for solving expensive optimization problems with inequality constraints. In the proposed framework, the objective and constraints are separately trained with two different sets of training data to enhance the prediction accuracy and reliability in the interested regions. A novel infill sampling criterion is tailored to decide whether potentially better or more uncertain solutions should be sampled. Moreover, a new strategy, termed search intensity adjustment, is designed for adjusting the number of search generations on new surrogate models. We attempt to embed three competitive constrained EAs into our framework to verify its generality. The experimental results obtained on numerous benchmark functions from CEC2006, CEC2010, and CEC2017 have demonstrated the superiority of our approach over three state-of-the-art surrogate-assisted EAs.
The syntax of partitives

This dissertation is a study of the syntax of partitives. The main goal is to characterise this type of nominals and provide an analysis that accounts for its particular properties but at the same time reflects the similarities with other indefinite non-partitive nominals (i.e. quantitatives). The exhaustive comparison between partitives and quantitatives turns out very useful for determining the properties of the former, and it becomes clear from it that these two types of nominals have more in common than it seems at first sight or has been considered in the literature. In chapter 1 the properties of partitives are described and discussed in the light of previous analyses and data (mainly from Catalan but also other languages, especially Romance and Germanic languages), and a line of explanation is suggested. In chapter 2 two constructions are thoroughly studied that have been considered as partitives in the literature given that they look very similar and it is solidly argued that they are in fact quantitatives. These are nominals in which the quantifier is followed by a noun as in "dos llibres d'aquells" ('two books of those') and indefinite nominals that express partition through the preposition "entre" ('among') such as "una novel·la d'entre els llibres que em vas deixar" ('a novel among the books you lent me'). The conclusions in this chapter are very relevant for the analysis defended here: partitives have a single-noun structure and only preposition "de" (but not "entre") form partitives. In chapter 3 a unitary analysis for partitives and quantitatives is presented and defended according to which quantifiers are lexical elements that select a noun (projected into a DP in partitives or NP in quantitatives) and that are generated in a low position in the nominal structure. The preposition "de" has the same status and role in partitives and in quantitatives (una mica de pa 'a bit of bread', molts de dies 'many days'): it is a functional category that licenses the noun. The quantifier moves to the left in parallel with other cases of inversion in nominals (i.e. qualitative predicate inversion: l'idiota d'en Joan 'the idiot of Joan'). The advantage of this analysis is that the same basic structure accounts too for predicational uses of quantifiers in the clausal domain --as predicates in copular sentences (My students are many) and as secondary predicates (Els colons arribaven a milers 'Colons arrived by the thousand')-- or even within nominals such as "we two" or "the three books". The differences among all these constructions derive mainly from the lexical features quantifiers have and from the type of nominal projection they select (NP or DP).
The Development of Agile Enterprise Architecture for Digital Transformation in Higher Education Institutions

The rapid evolution of digital technologies has led to significant transformations in various industries, including higher education. This study explores the role of Agile Enterprise Architecture (AEA) in supporting digital transformation initiatives within Higher Education Institutions (HEIs). AEA provides a flexible, adaptive, and iterative framework to manage the complex and dynamic nature of digital transformation. We conducted a qualitative study using a multiple case-study approach, investigating four HEIs that implemented AEA in their digital transformation initiatives. Data were collected through semi-structured interviews, document analysis, and participant observation. We employed thematic analysis to identify the key factors contributing to the successful implementation of AEA in these institutions. Our findings revealed that AEA plays a crucial role in facilitating digital transformation by providing a holistic, systematic, and adaptive framework. The AEA approach enables HEIs to effectively manage the complexities of digital transformation, enhance their agility, and respond to changing stakeholder needs. Key success factors include strong leadership, effective communication, a skilled workforce, and a culture of collaboration and continuous improvement. The study contributes to the understanding of AEA's role in promoting digital transformation in higher education and offers practical implications for HEIs looking to leverage digital technologies for improved performance and stakeholder satisfaction. Further research is needed to explore the generalizability of these findings to other contexts and industries.
Individual Shrinkage-Based Fuzzy Variable Selection

Model selection has become a central task in science. The classical model selection such as variable selection in multiple linear regression delivers interpretable models, but its instability is also well known. On the other hand, some shrinkage estimators enjoy the property of stability. To combine these two strengths, we generalize the crisp variable selection by introducing the concept of fuzzy variable selection. After constructing a fuzzy model, we show that the optimal membership function can be estimated by shrinkage. By defining the effective model dimensionality, almost all the classical model selection methods can be extended to our fuzzy scheme. In order to optimize the fuzzy memberships, we also propose a hybrid optimization process, which has both the global and local searching capability. Finally, our numerical study shows its advantages over classical method selection methods.
The removal of heavy metals from refinery effluents by sulfide precipitation

This study investigated the selective precipitation of copper (Cu2+), nickel (Ni2+) and zinc (Zn2+) ions from industrial wastewater by using sodium sulfide (Na2S). The effects of pH, time and free sulfide concentration on the selective precipitation were systematically investigated using synthetically prepared solutions and gold refinery wastewater. First, the optimum precipitation pH values of metals, the amount of Na2S and time were determined by using synthetic solutions containing Cu, Ni and Zn single metal ions. The highest precipitation efficiencies were obtained at a pH of 4, 5.3 and 5.3 for Cu, Ni and Zn, respectively. Based on these results, the effects of Na2S stoichiometry and time of precipitation were investigated with synthetically prepared solutions containing mixed metal ions (100 mg/L Cu2+, 90 mg/L Ni2+ and 100 mg/L Zn2+). Metal sulfide precipitation efficiencies increased with increasing amounts of Na2S and reached a maximum at a ratio of 1:1.75 (Metal ion: Na2S molar ratio). Lastly, the results obtained from the experiments performed with synthetic solutions were used for the metal sulfide precipitation from industrial wastewater. The precipitation from refining wastewater containing 10 g/L Cu2+, 0.3 g/L Ni2+ and 3 g/L Zn2+ ions was achieved at pH values from 2.3 to 5.3. At a pH of 2.3, Cu2+ precipitated as CuS (Cu2S) at 100% efficiency. While the Ni2+ concentration in the solution decreased by only 44% at a pH of 2.3, the Zn2+ concentration decreased by 52%. At a pH of 5.3, Cu2+ and Zn2+ precipitated together at 100%.
The impact of artificial intelligence applications on the performance of accountants and audit firms in Saudi Arabia

The purpose of this study is to investigate the impact of using artificial intelligence applications on the performance of accountants and audit firms. The final sample for this study comprises 38 audit firms. This study uses a survey-based methodology in the context of Saudi Arabia. The results of the multiple regression revealed that the audit firms using artificial intelligence applications perceive them as useful instruments that increase the performance of accountants and audit firms. They can reduce the cost, effort, and time of the audit process, achieve a competitive advantage for the audit firms, help auditors better determine materiality, achieve a competitive advantage, improve the performance of the audit team, carry out the continuous audit process better than the traditional audit, enable auditors to select audit samples with high efficiency, improve the quality of control procedures on electronic transactions and files used by the client, contribute to the management of operations and tasks with more sophisticated and intelligent mechanisms, increase the efficiency and effectiveness of the audit process and the efficiency and effectiveness of planning and supervising the audit process, reduce uncertainty and audit risk. The results reported by this study can be valuable for the accounting and auditing professions, audit firms, and standards and auditing regulators to deeply understand the extent to which artificial intelligence applications influence the performance of accountants and audit firms.
Blanco DECam Bulge Survey (BDBS). VI. Extinction Maps Toward Southern Galactic Bulge Globular Clusters

We present wide-field, high resolution maps of the color excess for 14 globular clusters toward the Southern Galactic bulge. The maps were derived using Gaia EDR3 astrometry and stellar photometry from the Blanco DECam Bulge Survey, which is a deep, wide-field ugrizY photometric survey of the southern Galactic bulge. Comparisons with WISE 12 μm images of thermal continuum emission demonstrate that the maps presented here trace interstellar extinction by dust down to 5″ scales. We use the reddening-corrected photometry of proper motion-selected cluster stars to build color–magnitude diagrams for the target globular clusters, which show residual broadening in excess of that expected from the photometric errors alone. This residual broadening is likely to be driven by star-to-star elemental abundance variations.
The 2013 November 12 Solar Energetic Electron Event Associated with Solar Jets

We investigate the hard X-ray (HXR) flare-associated “prompt” solar energetic electron (SEE) 2013 November 12 event with joint EUV jet observations from Solar Dynamics Observatory/Atmospheric Imaging Assembly and STEREO-A/EUVI. The SEE energy spectrum observed by Wind/3D Plasma and Energetic Particle shows a triple-power-law shape with a low-energy break of 10.0 ± 1.7 keV and a high-energy break of 56.6 ± 8.9 keV, which has never been reported before for jet-related SEE events. Associated HXR emissions observed by RHESSI and FGST/Gamma-ray Burst Monitor show three distinctive peaks with different spectral indices β HPE of HXR-producing electrons (HPEs) derived by means of thick-target bremsstrahlung model. The high-energy spectral index β 3 = 4.63 ± 0.65 of SEE is consistent with the HPE spectral index β HPE derived in HXR peak 1 but different from β HPE of HXR peak 2 and peak 3. The main stream of EUV jets reaches a speed of 370 ± 25 km s−1 after an acceleration of up to 2.9 ± 0.4 km s−2 in a timescale of ∼2 minutes, and the acceleration time coincides with the decay phase of HXR peak 1. EUV observations from two different viewing directions help to reconstruct the jet magnetic configurations. After the investigation on HXR emissions and jet configurations, the interchange-reconnection model triggered by the emerging flux could be a satisfactory explanation for this jet event.
On exact quasilikelihood inference in generalized linear mixed models

It is well-known that the penalized quasi-likelihood (PQL) approach may not yield consistent estimators for the parameters of the generalized linear mixed model (GLMM). Jiang (1998) introduced a method of moments (MM) to estimate the parameters of the GLMM. The moment estimators may however be highly inefficient. To overcome this inefficiency problem, recently Jiang and Zhang (2001) suggest an improvement over the method of moments. It is however demonstrated in this paper that the estimators obtained based on the improved method of moments (IMM) may also be highly inefficient as compared to the estimators obtained based on a proposed quasi-likelihood (QL) approach. The QL estimators are consistent and highly efficient, the exact maximum likelihood estimators being fully efficient (i.e., optimal) which are however known to be difficult to compute.
Metabolic signature and proteasome activity controls synovial migration of CDC42hi CD14+ cells in rheumatoid arthritis

Objective: Activation of Rho-GTPases in macrophages causes inflammation and severe arthritis in mice. In this study, we explore if Rho-GTPases define the joint destination of pathogenic leukocytes in rheumatoid arthritis (RA) and how JAK inhibition mitigates these effects. Methods: CD14+ cells of 136 RA patients were characterized by RNA-sequencing, and cytokine measurement to identify biological processes and transcriptional regulators specific for CDC42hiCD14+ cells, which were summarized in a metabolic signature. Effect of hypoxia, and IFN-{gamma} signaling on the metabolic signature of CD14+ cells was assessed experimentally. To investigate its connection with joint inflammation, the signature was translated into the single cell characteristics of CDC42hi synovial tissue macrophages. Sensitivity of the metabolic signature to the RA disease activity and treatment effect was assessed experimentally and clinically. Results: CDC42hiCD14+ cells carried the metabolic signature of genes functional in the oxidative phosphorylation and proteasome-dependent cell remodeling, which correlated with the cytokine-rich migratory phenotype and antigen presenting capacity of these cells. Integration of CDC42hi CD14+ and synovial macrophages marked with the metabolic signature revealed the important role of the interferon-rich environment and immunoproteasome expression in homeostasis of these pathogenic macrophages. The CDC42hiCD14+ cells were targeted by JAK-inhibitors and responded with downregulation of immunoproteasome and MHC-II molecules, which disintegrated the immunological synapse, reduced cytokine production and alleviated arthritis. Conclusion: This study shows that the CDC42-related metabolic signature identifies the antigen-presenting CD14+ cells that migrate to joints to coordinate autoimmunity. Accumulation of CDC42hiCD14+ cells disclose patients perceptive to JAKi treatment.
Determinants of post-acute COVID-19 syndrome among hospitalized severe COVID-19 patients: a 2-year follow-up study

Abstract Background: Coronavirus disease-19 (COVID-19), emerged as a public health threat in December 2019. The number of COVID-19 cases worldwide is now more than 765 million with more than 6.9 million dead. During follow-up visits following discharge, a large percentage of patients were discovered to still be suffering from health issues that lower their quality of life and ability to return to work. This study assessed the prevalence and associated risk factors of post-acute COVID-19 syndrome (PACS) among severe COVID-19 patients who were discharged from Millennium COVID-19 care center, Addis Ababa, Ethiopia. Methods: A cross-sectional study using data collected from patient charts and a follow-up telephone interview after two years of discharge. Systematic random sampling was used to select a total of 400 patients. A structured questionnaire developed from the case report form for PACS of WHO was used. Frequency and cross-tabulation were used for descriptive statistics. Predictor variables with a p-value <0.25 in bivariate analyses were included in the logistic regression. Result: Out of the 400 patients, 20 patients were dead, 14 patients refused to give consent, and 26 patients could not be reached because their phone was not working. Finally, 340 were included in the study. The majority (68.5%) were males and the mean age was 53.9 ({+/-}13.3 SD) years. Most of the patients (60%) has one or more comorbidity. The most common symptom at presentation was cough (93.5%), followed by shortness of breath (82.1%) and fatigue (69.7%). The mean duration of hospital admission was 12.3 ({+/-}6.5 SD) days. More than a third (38.1%) of the patients reported the persistence of at least one symptom after hospital discharge. The most common symptoms were fatigue (27.5%) and Cough (15.3%). older age (AOR 1.04, 95% CI 1.02 - 1.07), female sex (AOR 1.82, 95% CI 1.00 - 3.29), presence of comorbidity (AOR 2.38, 95% CI 1.35 - 4.19), alcohol use (AOR 3.05, 95% CI 1.49 - 6.26), fatigue at presentation (AOR 2.18, 95% CI 1.21 - 3.95), and longer hospital stay (AOR 1.06, 95% CI 1.02 - 1.10) were foundto increase the odds of developing post-acute COVID-19 syndrome. Higher hemoglobin level was found to decrease the risk of subsequent post-acute COVID-19 syndrome (AOR 0.84, 95% CI 0.71 - 0.99). Conclusion: establishing a dedicated PACS followup clinic, especially for those with a higher risk can help to provide comprehensive care for the patients and improve their quality of life. Keywords: Post -acute COVID -19 syndrome, Long COVID, Chronic COVID -19
Evaluation of 3-D printed swabs for respiratory sampling and testing for SARS-CoV-2 during the early pandemic period

Appropriate collection of respiratory samples is essential for accurate diagnostic testing of respiratory pathogens such as, SARS-CoV-2. Early in the pandemic, there was a shortage of nasopharyngeal (NP) swabs and difficulty in sampling suspected cases. Therefore, we developed a 3D printed nasal swab for anterior nares, paired with in-house viral transport medium (VTM). The utility of this 3D swab kit was investigated in comparison with the standard NP commercial swab and VTM, in 200 individuals between August and September 2021. Subjects were those presenting for diagnostic testing for SARS-CoV-2 using the RT- PCR (cobas Roche assay) assay. NP samples were taken from each subject using the standard NP and 3D swabs followed by RT-PCR on paired specimens. CT values for amplification of gene targets were evaluated to determine assay parameters based on viral load cut offs of [≤] CT 35 or, [≤] CT 37. For high to medium viral loads, 3D swab based PCR testing had a sensitivity of 93%, specificity of 99%, positive predictive value (PPV) of 98.5% and negative predictive value (NPV) of 96.2% respectively. For low viral loads, 3D swab testing had a sensitivity of 88%, specificity of 99%, with a PPV of 98.5% and NPV of 93.2%. %. 3D swab sampling of anterior nares was comparable with NP sampling using standard swabs for SARS-CoV-2 specimens with a medium to high viral load. Therefore, 3D swab based sampling is a reliable and convenient local solution for collecting respiratory samples for SARS-CoV-2 diagnostic testing.