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Open universities have provided quality higher education through open and distance education to serve learners who could not attend traditional in-person universities. While open universities vary in terms of providing their services to learners at a distance, many have shifted to e-learning using cost-effective platforms. A common platform that is essential for online teaching and learning is the learning management system (LMS). While there are common processes observed among open universities in the way they use their LMS, there are also subtle differences caused by each institution's unique and special context. Their contextual challenges often drive them to develop technologies or techniques that may easily be adopted by others. Thus, benchmarking activities are often recommended for mutual improvement. In this regard, a comparative study of open universities in Indonesia, the Philippines, and Pakistan was conducted for decision-makers to understand how they could improve in providing quality education to their learners through Moodle, which is their primary LMS. The Delone and Mclean Model was used to systematically compare system, information, and service quality in e-learning delivered through their LMS. The authors hope that the results and recommendations from this study may help other educational institutions overcome similar challenges in their learning platforms.

AbstractIn this study, asymptotic formulas for complex order Tangent, Tangent-Bernoulli, and Tangent-Genocchi polynomials are obtained through the method of contour integration, strategically avoiding branch cuts in the process. By employing this technique, the paper elucidates the behavior of these polynomials in the complex plane. Additionally, the investigation expands upon the Taylor series expansion of these functions, revealing alternative asymptotic expansions. This dual approach not only enhances our understanding of the asymptotic properties of these polynomials but also offers alternative mathematical perspectives, enriching the existing body of knowledge in this field.

The study of special functions has become an enthralling area in mathematics because of its properties and wide range of applications that are relevant into other fields of knowledge. Developing topics in special functions involves the investigation of Apostol-type polynomials encompassing the combinations, extensions, and generalizations of some classical polynomials such as Bernoulli, Euler, Genocchi, and tangent polynomials. One particular type of these polynomials is the Apostol-Frobenius-Euler polynomials of order aa denoted by Hnα(z;u;λ){H}_{n}^{\alpha }\left(z;\hspace{0.33em}u;\hspace{0.33em}\lambda ). Using the saddle point method, Corcino et al. obtained approximations for the higher-order tangent polynomials. They also established a new method to derive its approximations with enlarged region of validity. In this article, it is found that these methods are applicable to the higher-order Apostol-Frobenius-Euler polynomials. Consequently, approximations of higher-order Apostol-Frobenius-Euler polynomials in terms of the hyperbolic functions are obtained for large values of the parameter nn, and its uniform approximations with enlarged region of validity are also derived. Moreover, approximations of the generalized Apostol-type Frobenius-Euler polynomials of order α\alpha with parameters a,b,a,b, and cc are obtained by applying the same methods. Graphs are provided to show the accuracy of the exact values of these polynomials and their corresponding approximations for some specific values of the parameters.

Advanced structural materials are expected to display significantly improved mechanical properties and this may be achieved, at least in part, by refining the grain size to the submicrometer or the nanocrystalline range. This report provides a detailed summary of the role of grain size in the mechanical properties of metals. The effect of grain size on the high temperature behavior and the development of superplasticity is illustrated using deformation mechanism maps and the development of exceptional strength through grain refinement hardening at low temperatures is also discussed. It is shown that the deformation mechanism of grain boundary sliding, as developed theoretically, can be used to effectively predict both the high and low temperature behavior of metals having different grain sizes. This analysis explains the increase in strain rate sensitivity in ultrafine-grained metals with low and moderate melting points and the ability to increase both the strength and ductility of these materials to thereby overcome the strength-ductility paradox. The recent development of hybrid materials is also reviewed and it is demonstrated that, although these hybrids have received only limited attention to date, they provide a potential for making significant advances in the production of new structural materials.

AbstractBackground The Covid-19 pandemic has affected patients with end-stage kidney disease (ESKD). Whether dialysis parameters have a prognostic value in ESKD patients with Covid-19 remains unclear.Materials and Methods We retrospectively evaluated clinical characteristics, blood pressure (BP) and dialysis parameters in ESKD patients undergoing maintenance outpatient hemodialysis, with (Covid-ESKD) and without (No-Covid-ESKD) Covid-19, at four Brazilian hemodialysis facilities. The Covid-ESKD (n = 107; 54% females; 60.8 ± 17.7 years) and No-Covid-ESKD (n = 107; 62% females; 58.4 ± 14.6 years) groups were matched by calendar time. The average BP and dialysis parameters were calculated during the pre-infection, acute infection, and post-infection periods. The main outcomes were Covid-19 hospitalization and all-cause mortality.Results Covid-ESKD patients had greater intradialytic and postdialysis systolic BP and lower predialysis weight, postdialysis weight, ultrafiltration rate, and interdialytic weight gain during acute-illness compared to 1-week-before-illness, while these changes were not observed in No-Covid-ESKD patients. After 286 days of follow-up (range, 276–591), there were 18 Covid-19-related hospitalizations and 28 deaths among Covid-ESKD patients. Multivariable logistic regression analysis showed that increases in predialysis systolic BP from 1-week-before-illness to acute-illness (OR, 95%CI = 1.06, 1.02–1.10; p = .004) and Covid-19 vaccination (OR, 95%CI = 0.16, 0.04–0.69; p = .014) were associated with hospitalization in Covid-ESKD patients. Multivariable Cox-regression analysis showed that Covid-19-related hospitalization (HR, 95%CI = 5.17, 2.07–12.96; p

This study investigates pronominal reference assignments across sentences that contain English verbs of transfer in monolingual English speakers and second-language (L2) learners having German as a first language and English as an L2. In a forced-choice task, participants were presented with sentences in perfective or imperfective aspect, like “Elizabeth took/was taking a meal to Mary” (adapted from Ferretti et al., 2009). They were then shown sentences that contained gender-matching pronouns, as in “She breathed in the smell of fresh basil”, and they were finally asked to choose who performed the relevant actions: “Who breathed in the smell of fresh basil? Elizabeth or Mary?”. We found that both groups preferred more often goal-oriented interpretations in the perfective condition, while in the imperfective condition only English monolingual speakers preferred more often source-oriented interpretations. The pattern observed in the perfective condition is consistent with previous studies and indicates that perfective aspect creates a strong bias towards end-states. For the imperfective condition, we argue that the different pattern observed in L2 learners may be due to some features of German, where an overall bias for end-states was previously observed. This indicates an effect of first-language strategies on L2 processing, consistent with previous research on different languages.

The study of atomic spectroscopy has a profound relationship with quantum mechanics and its comprehension. Since Bohr’s success with a theory for the atom, it has been established that spectrum lines originate from transitions between different states. Moreover, the analysis of complex spectra reveals that there are groups of spectral lines that compose what is called a multiplet. To rationalize the multiplet structure, we need models to characterize and label the quantum states. The characterization of the atomic states is a common subject in classes of physical chemistry and inorganic chemistry, and there are basically two approaches to infer and label the energy states: (i) the LS coupling scheme and (ii) the jj coupling scheme. Usually, only the LS coupling is presented, and this ends up omitting its underlying assumptions. Here, we present a survey of both approaches, highlighting their premises and their adequacy towards different groups of the periodic table, ions, and excited states configurations. We show that by using benchmark data together with the Landé interval rule, the appropriate use of the jj coupling to understand the atomic spectra of heavier atoms is easily conveyed.

Abstract Background The proportion of Canadian youth seeking mental health support from an emergency department (ED) has risen in recent years. As EDs typically address urgent mental health crises, revisiting an ED may represent unmet mental health needs. Accurate ED revisit prediction could aid early intervention and ensure efficient healthcare resource allocation. We examine the potential increased accuracy and performance of graph neural network (GNN) machine learning models compared to recurrent neural network (RNN), and baseline conventional machine learning and regression models for predicting ED revisit in electronic health record (EHR) data. Methods This study used EHR data for children and youth aged 4–17 seeking services at McMaster Children’s Hospital’s Child and Youth Mental Health Program outpatient service to develop and evaluate GNN and RNN models to predict whether a child/youth with an ED visit had an ED revisit within 30 days. GNN and RNN models were developed and compared against conventional baseline models. Model performance for GNN, RNN, XGBoost, decision tree and logistic regression models was evaluated using F1 scores. Results The GNN model outperformed the RNN model by an F1-score increase of 0.0511 and the best performing conventional machine learning model by an F1-score increase of 0.0470. Precision, recall, receiver operating characteristic (ROC) curves, and positive and negative predictive values showed that the GNN model performed the best, and the RNN model performed similarly to the XGBoost model. Performance increases were most noticeable for recall and negative predictive value than for precision and positive predictive value. Conclusions This study demonstrates the improved accuracy and potential utility of GNN models in predicting ED revisits among children and youth, although model performance may not be sufficient for clinical implementation. Given the improvements in recall and negative predictive value, GNN models should be further explored to develop algorithms that can inform clinical decision-making in ways that facilitate targeted interventions, optimize resource allocation, and improve outcomes for children and youth.

ABSTRACTBloodstream infection (BSI) caused by carbapenem-resistant Klebsiella pneumoniae (KP) poses significant challenges, particularly when the infecting isolate carries multiple antimicrobial resistance (AMR) genes/determinants. This study, employing short- and long-read whole-genome sequencing, characterizes six New Delhi metallo-β-lactamase (NDM) 1 and KP carbapenemase (KPC) 3 co-producing KP isolates, the largest cohort investigated in Europe to date. Five [sequence type (ST) 512] and one (ST11) isolates were recovered from patients who developed BSI from February to August 2022 or February 2023 at two different hospitals in Rome, Italy. Phylogenetic analysis revealed two distinct clusters among ST512 isolates and a separate cluster for the ST11 isolate. Beyond blaNDM-1 and blaKPC-3, various AMR genes, indicative of a multidrug resistance phenotype, including colistin resistance, were found. Each cluster-representative ST512 isolate harbored a blaNDM-1 plasmid (IncC) and a blaKPC-3 plasmid [IncFIB(pQil)/IncFII(K)], while the ST11 isolate harbored a blaNDM-1 plasmid [IncFII(pKPX1)] and a blaKPC-3 plasmid [IncFIB(K)/IncFII(K)]. The blaNDM-1 plasmids carried genes conferring resistance to clinically relevant antimicrobial agents, and the aminoglycoside resistance gene aac(6′)-Ib was found on different plasmids. Colistin resistance-associated mgrB/pmrB gene mutations were present in all isolates, and the yersiniabactin-encoding ybt gene was unique to the ST11 isolate. In conclusion, our findings provide insights into the genomic context of blaNDM-1/blaKPC-3 carbapenemase-producing KP isolates.IMPORTANCEThis study underscores the critical role of genomic surveillance as a proactive measure to restrict the spread of carbapenemase-producing KP isolates, especially when key antimicrobial resistance genes, such as blaNDM-1/blaKPC-3, are plasmid borne. In-depth characterization of these isolates may help identify plasmid similarities contributing to their intra-hospital/inter-hospital adaptation and transmission. Despite the lack of data on patient movements, it is possible that carbapenem-resistant isolates were selected to co-produce KP carbapenemase and New Delhi metallo-β-lactamase via plasmid acquisition. Studies employing long-read whole-genome sequencing should be encouraged to address the emergence of KP clones with converging phenotypes of virulence and resistance to last-resort antimicrobial agents.

The effect of high pressure torsion processing on mechanical properties and corrosion behavior of pure magnesium and Mg-Zn, Mg-Zn-Ca, Mg-Li-Y and Mg-Y-RE alloys is investigated. Micro-tomography and SEM characterization are used to estimate corrosion rate and evaluate non-uniform corrosion features. The results show that severe plastic deformation processing improves the strength of all magnesium alloys, but deformation localization can take place in the Mg-Zn-Ca and Mg-Y-RE alloys. The occurrence of deformation localization is associated with low strain rate sensitivity in these alloys and with severe corrosion localization. Pure magnesium and Mg-Zn and Mg-Li-Y alloys display good corrosion resistance with a low corrosion rate and maintain integrity after 28 days of immersion in Hank's solution.

BackgroundChronic kidney disease (CKD) is a global public health problem, exhibiting sharp increases in incidence, prevalence, and attributable morbidity and mortality. There is a critical need to better understand the demographics, clinical characteristics, and key risk factors for CKD; and to develop platforms for testing novel interventions to improve modifiable risk factors, particularly for the CKD patients with a rapid decline in kidney function.MethodsWe describe a novel collaboration between two large healthcare systems (Providence St. Joseph Health and University of California, Los Angeles Health) supported by leadership from both institutions, which was created to develop harmonized cohorts of patients with CKD or those at increased risk for CKD (hypertension/HTN, diabetes/DM, pre-diabetes) from electronic health record data.ResultsThe combined repository of candidate records included more than 3.3 million patients with at least a single qualifying measure for CKD and/or at-risk for CKD. The CURE-CKD registry includes over 2.6 million patients with and/or at-risk for CKD identified by stricter guide-line based criteria using a combination of administrative encounter codes, physical examinations, laboratory values and medication use. Notably, data based on race/ethnicity and geography in part, will enable robust analyses to study traditionally disadvantaged or marginalized patients not typically included in clinical trials.DiscussionCURE-CKD project is a unique multidisciplinary collaboration between nephrologists, endocrinologists, primary care physicians with health services research skills, health economists, and those with expertise in statistics, bio-informatics and machine learning. The CURE-CKD registry uses curated observations from real-world settings across two large healthcare systems and has great potential to provide important contributions for healthcare and for improving clinical outcomes in patients with and at-risk for CKD.