Global Rank Research Articles

Sacubitril/Valsartan in Pediatric Heart Failure (PANORAMA-HF): A Randomized, Multicenter, Double-Blind Trial.

Sacubitril/valsartan, an angiotensin receptor-neprilysin inhibitor (ARNI), is an established treatment for heart failure (HF) with reduced left ventricular ejection fraction. It has not been rigorously compared with angiotensin-converting enzyme inhibitors in children. PANORAMA-HF (Prospective Trial to Assess the Angiotensin Receptor Blocker Neprilysin Inhibitor LCZ696 Versus Angiotensin-Converting Enzyme Inhibitor for the Medical Treatment of Pediatric HF) is a randomized, double-blind trial that evaluated the pharmacokinetics and pharmacodynamics (PK/PD), safety, and efficacy of sacubitril/valsartan versus enalapril in children 1 month to <18 years of age with HF attributable to systemic left ventricular systolic dysfunction (LVSD). Children with HF attributable to LVSD were randomized to sacubitril/valsartan versus enalapril to assess the efficacy and safety of sacubitril/valsartan at 52 weeks of follow-up. The primary end point of the study was to determine whether sacubitril/valsartan was superior to enalapril for the treatment of pediatric patients with HF attributable to systemic LVSD, assessed using a primary global rank end point consisting of ranking patients from worst to best on the basis of clinical events such as death, listing for urgent heart transplant, mechanical life support requirement, worsening HF, New York Heart Association (NYHA)/Ross class, Patient Global Impression of Severity (PGIS), and Pediatric Quality of Life Inventory physical functioning domain. The change from baseline to 52 weeks in NT-proBNP (N-terminal pro-B-type natriuretic peptide) was an exploratory end point. A total of 375 children (mean age, 8.1±5.6 years; 52% female) were randomized to sacubitril/valsartan (n=187) or enalapril (n=188). At week 52, no significant difference was observed between the 2 treatment arms in the global rank end point (Mann-Whitney probability, 0.52 [95% CI, 0.47-0.58]; Mann-Whitney odds, 0.91 [95% CI, 0.72-1.14]; P=0.42). At week 52, clinically meaningful reductions were observed in both treatment arms in NYHA/Ross, PGIS, Patient Global Impression of Change, and NT-proBNP, without significant differences between groups. Adverse events were similar between treatment arms (incidence: sacubitril/valsartan, 88.8%; enalapril, 87.8%), and the safety profile of sacubitril/valsartan was acceptable in children. In this study, sacubitril/valsartan did not show superiority over enalapril in the treatment of children with HF attributable to systemic LVSD using the prespecified global rank end point. However, both treatment arms showed clinically meaningful improvements over 52 weeks. URL: https://www.clinicaltrials.gov; Unique identifier: NCT02678312.

Defining optimal left ventricular assist device short-term outcomes may provide insight into programmatic quality assessment

BackgroundPatients have substantial variability in perioperative outcomes after left ventricular assist device (LVAD) implant. A perioperative multidimensional tool integrating mortality, adverse events (AEs), and patient-reported outcomes to assist in quality improvement initiatives is needed. MethodsPatients undergoing HeartMate 3 LVAD implant (1/1/2017 and 1/31/2024) in the Society of Thoracic Surgeons’ Intermacs registry were studied. A Cox proportional hazard multivariable analysis incorporating AEs as time-varying covariates for mortality out to 180 days was used to generate the Intermacs Short-Term Composite Quality (INSITE) score, reflecting the adjusted hazard ratio (HR) for mortality contributed by each AE, applying global ranking methodology. In those alive and on support at 6 months, multivariable logistic regression (odds ratio, OR) was used to examine the impact of AEs on health-related quality of life (QOL) at 180 days, captured through the INSITE-QOL score. Failure to achieve ≥1 point increase in visual analog scale (VAS) from baseline was the event in the QOL analysis. ResultsOf 13,148 patients, 4,389 (33.4%) suffered at least one AE or death through 180 days. Stroke (survival: HR 13.1; QOL: HR 1.7), dialysis (survival: HR 31.4; QOL: HR 4.2), prolonged respiratory failure (survival: HR 5.7; QOL: HR 2.3), reoperation (Survival: HR 3.4; QOL: HR 1.6) and right heart failure (survival: 5.0; QOL: HR 1.4), contributed to both mortality and failure to improve QOL at 180 days (all p<0.05). The median INSITE and INSITE-QOL scores were 0.0 [0.0,1.6] and 0.0 [0.0,0.0], respectively. At 9.4% (n=17) of centers, a high INSITE score (≥13) was present in 15% of patients while the top 25% of centers had perfect INSITE-QOL scores in at least 75% of patients. ConclusionsAEs after LVAD confer differential impact on mortality and QOL, enabling development of global rank outcome scores. Given the high mortality hazard conferred by 180-day AEs, center-specific quality interventions aimed at reducing early complications provide the greatest opportunity to improve long-term survival and QOL.

A group decision-making method for technology selection of petrochemical enterprises

Selecting appropriate energy conservation and emission reduction technologies is essential for the sustainable developments of petrochemical enterprises. It usually involves experienced experts from multi-domain and thus can be regarded as a large-scale group decision-making problem. Existing methods assumed that different experts use the same linguistic term set following the same semantics to express their evaluation values, and derived the ranking of alternatives by an indirectly obtained group preference matrix. Such an approach may induce information loss. To avoid these defects, this study introduces a model based on rank centrality and expert differences to choose appropriate technologies for petrochemical enterprises. First, an attitude parameter is introduced into a linguistic scale function to unify different judgment benchmarks of experts regarding the same linguistic term. Next, a direct weight elicitation method based on rank centrality is developed to deduce the individual rank vector of alternatives. A grey clustering method based on the similarities of individual rank vectors is extended to cluster experts into small groups, and the silhouette coefficient is used to determine clusters weights. Afterwards, the rank vectors of alternatives corresponding to subgroups and the global rank vector are calculated by the weighted averaging operator. Alternatives are ranked by the rank vector of the global group. Finally, four technologies were evaluated by the model, and the preferred technology was identified. The findings indicate that the proposed technology selection model alleviates information loss, elevates group consensus level, and provides a perspective for petrochemical enterprises in search of energy conservation and emission reduction technologies.

Sample size planning for rank-based multiple contrast tests.

Rank methods are well-established tools for comparing two or multiple (independent) groups. Statistical planning methods for the computing the required sample size(s) to detect a specific alternative with predefined power are lacking. In the present paper, we develop numerical algorithms for sample size planning of pseudo-rank-based multiple contrast tests. We discuss the treatment effects and different ways to approximate variance parameters within the estimation scheme. We further compare pairwise with global rank methods in detail. Extensive simulation studies show that the sample size estimators are accurate. A real data example illustrates the application of themethods.

On Item-Sampling Evaluation for Recommender System

Personalized recommender systems play a crucial role in modern society, especially in e-commerce, news, and ads areas. Correctly evaluating and comparing candidate recommendation models is as essential as constructing ones. The common offline evaluation strategy is holding out some user-interacted items from training data and evaluating the performance of recommendation models based on how many items they can retrieve. Specifically, for any hold-out item or so-called target item for a user, the recommendation models try to predict the probability that the user would interact with the item and rank it among overall items, which is called global evaluation . Intuitively, a good recommendation model would assign high probabilities to such hold-out/target items. Based on the specific ranks, some metrics like Recall@K and NDCG@K can be calculated to further quantify the quality of the recommender model. Instead of ranking the target items among all items, Koren first proposed to rank them among a small sampled set of items , then quantified the performance of the models, which is called sampling evaluation . Ever since then, there has been a large amount of work adopting sampling evaluation due to its efficiency and frugality. In recent work, Rendle and Krichene argued that the sampling evaluation is “inconsistent” with respect to a global evaluation in terms of offline top- K metrics. In this work, we first investigate the “inconsistent” phenomenon by taking a glance at the connections between sampling evaluation and global evaluation. We reveal the approximately linear relationship between sampling with respect to its global counterpart in terms of the top- K Recall metric. Second, we propose a new statistical perspective of the sampling evaluation—to estimate the global rank distribution of the entire population. After the estimated rank distribution is obtained, the approximation of the global metric can be further derived. Third, we extend the work of Krichene and Rendle, directly optimizing the error with ground truth, providing not only a comprehensive empirical study but also a rigorous theoretical understanding of the proposed metric estimators. To address the “blind spot” issue, where accurately estimating metrics for small top- K values in sampling evaluation is challenging, we propose a novel adaptive sampling method that generalizes the expectation-maximization algorithm to this setting. Last but not least, we also study the user sampling evaluation effect. This series of works outlines a clear roadmap for sampling evaluation and establishes a foundational theoretical framework. Extensive empirical studies validate the reliability of the sampling methods presented.

Telemedicine Research Trends in 2001-2022 and Research Cooperation Between China and Other Countries Before and After the COVID-19 Pandemic: Bibliometric Analysis.

Advancements in technology have overcome geographical barriers, making telemedicine, which offers remote emergency services, healthcare, and medication guidance, increasingly popular. COVID-19 restrictions amplified its global importance by bridging distances. This study aimed to analyze Chinese and global literature data, present new global telemedicine research trends, and clarify the development potential, collaborations, and deficiencies in China's telemedicine research. We conducted bibliometrics and network analyses on relevant documents from the Web of Science database from 2001 to 2022. Data collection was completed on October 30, 2023. Considering COVID-19's impact, 2020 was used as a baseline, dividing the data into 2 periods: 2001-2019 and 2020-2022. The development potential was determined based on publication trends. An international coauthorship network analysis identified collaboration statuses and potential. Co-occurrence analysis was conducted for China and the world. We identified 25,333 telemedicine-related research papers published between 2001 and 2022, with a substantial increase during the COVID-19 period (2020-2022), particularly in China (1.93-fold increase), moving its global publication rank from tenth to sixth. The United States, the United Kingdom, and Australia contributed 62.96% of the literature, far ahead of China's 3.90%. Globally, telemedicine research increased significantly post-2020. Between 2001 and 2019, the United States and Australia were central in coauthor networks; post-2020, the United States remained the largest node. Network hubs included the United States, the United Kingdom, Australia, and Canada. Keyword co-occurrence analysis revealed 5 global clusters from 2001 to 2019 (system technology, health care applications, mobile health, mental health, and electronic health) and 2020 to 2022 (COVID-19, children's mental health, artificial intelligence, digital health, and rehabilitation of middle-aged and older adults). In China, the research trends aligned with global patterns, with rapid growth post-2020. From 2001 to 2019, China cooperated closely with Indonesia, India, Japan, Taiwan, and South Korea. From 2020 to 2022, cooperation expanded to Japan, Singapore, Malaysia, and South Korea, as well as Saudi Arabia, Egypt, South Africa, Ghana, Lebanon, and other African and Middle Eastern countries. Chinese keyword co-occurrence analysis showed focus areas in system technology, health care applications, mobile health, big data analysis, and electronic health (2001-2019) and COVID-19, artificial intelligence, digital health, and mental health (2020-2022). Although psychology research increased, studies on children's mental health and middle-aged and older adults' rehabilitation were limited. We identified the latest trends in telemedicine research, demonstrating its significant potential in China and providing directions for future development and collaborations in telemedicine research.

Secure software design evaluation and decision making model for ubiquitous computing: A two-stage ANN-Fuzzy AHP approach

Ubiquitous computing systems are deployed everywhere to process and service complex applications in real time. It has been noticed that most ubiquitous computing practitioners are trying to develop a highly secure design while maintaining usability. Unfortunately, the highly secure design of software becomes worthless because the usability of software is very low. When improving the security of software design, underlying security design threats (SDTs) and secure design practices (SDPs) play an important role in improving the design security of ubiquitous computing projects. Therefore, the objective of this study is to develop an SSD evaluation and decision-making model using artificial neural network (ANN) and fuzzy analytical hierarchy process (FAHP), for analyzing the SSD threats and its practices to improve ubiquitous computing. Initially, we identified 11 major SDTs and 50 SDPs to mitigate these threats by conducting an empirical study with 50 ubiquitous computing security experts. Moreover, 13 ubiquitous computing security expert panel reviews were conducted to analyze the interrelationships among the SDTs and SDPs using ANN and FAHP approaches. The ANN outcome presents that the foremost significant independent threat in predicting SSD intention is “SDT2: failure to adhere to accepted security design principles” (100%) importance. Furthermore, the FAHP was used to prioritize the list of SDPs. The results and analysis of this study provide a ranked-based decision-making model, which assists the ubiquitous computing practitioners in considering the most weighted SSD practice (“SDP: Implement Sandboxing and respect for User Privacy” having global weight: 0.05088 and global rank: 1) on priority. The study findings will assist real-world industry experts and academic researchers to improve and develop new techniques for the improvements of ubiquitous computing projects.

Metamorphosed Plagiogranite Veins In Salma Eclogites, Belomorian Eclogite Province

The Salma subduction eclogites of the Meso-Neoarchean Belomorian Eclogite Province have compositions of N-MORB oceanic crust protoliths and contain felsic veins of plagiogranite compositions. Felsic veins consist of metamorphic minerals sush as garnet, quartz and kyanite, the earliest high-pressure inclusion of phengite and zoisite in garnet. Veins have geochemistry characteristics that are typical for island arc granites or adakites. They are compositionally comparable with eclogite-bearing tonalite-trondhjemite-granodiorite (TTG) gneisses, modern adakite, and Archaean andesite association of the Vedlozero–Segozero greenstone belt. The origin of these felsic veins can be represented as a product of melting of fluid-modified oceanic mafic slab at PT-conditions of eclogite metamorphism.Geothermometry and pseudosection calculations of metamorphic conditions in felsic veins, for eclogitic mineral assemblage of pyropic garnet, kyanite, high-silica phengite and quartz gave high pressures conditions (ca. 16–22 kbar) at relatively low temperatures (ca. 600–650 °C); for secondary decompression plagioclase ± biotite coronas between quartz, kyanite and garnet gave 14–11 kbar at 650–700 °C. This estimate corresponds “warm” subduction, which is most consistent with the parameters of the modern subduction of the base of the Cascadia plate . Mineral assemblages in eclogites, that included the felsic veins, give 15–11 kbar at 750–700 °C. Thermodynamic modelling indicates that the eclogites hosting the felsic veins did not reach partial melting conditions under the estimated PT conditions: exposure temperatures to the mafic rocks must have been significantly higher. A probable explanation for high-pressure parageneses in felsic veins may be the formation of plagiogranites as a result of the melting of the mafic slab under higher temperature and pressure conditions than were measured in this work. Thus, the high-pressure paragenesis of garnet, phengite, zoisite, and kyanite reflects the path of rock exhumation through eclogitic conditions.Zircon from the felsic veins gave several Archean and Paleoproterozoic ages ∼ 2.87, 2.78, ∼2.70 and, ∼1.9 Ga. The oldest age gave the fluid-modified oceanic zircon (∼2.9 Ga) from Fe-Ti eclogites directly near the veins. The oldest igneous zircons in the veins are represented by a population with the age of ∼ 2.87 Ga and correspond to the igneous age of some TTG gneisses, which do not contradict a hypothesis of the plagiogranite formation during the melting of the mafic slab in the subduction zone. A Neoarchean zircon population of ∼ 2.78 Ga is often present in eclogitized mafic rocks and surrounding TTG gneisses which can be associated both with the formation of TTG gneiss protoliths in the subduction zone (?) and with the recrystallization of older zircon as a result of fluid or thermal impact. The ɛHf of the older population (∼2.87 Ga) is + 3 to + 4, indicating a juvenile origin; the ∼ 2.78 Ga population has similar Hf-isotope compositions, suggesting that their ages reflect resetting (non-zero Pb loss) of the older zircons. The Sm-Nd model TDM ages of Grt-Ky veins have Meso-Neoarchean values (3.0–2.75 Ga) with positive εNd = 1.8–3.8 that may indicate a juvenile nature of sources of the plagiogranite melt. The eclogitized rocks of the Belomorian province generally underwent the granulite facies overprint at ∼ 2.74–2.64 Ga ago that is coincides with the age of granulite facies metamorphism of regional and global rank. The Paleoproterozoic evolution of the Salma eclogites and the East European Craton as a whole: at 2.5–2.4 Ga and 2.0–1.9 Ga with formation of the Lapland–Mid-Russia–South Baltia intracontinental and Svecofennian accretionary orogens was terminated in Paleoproterozoic time at ∼ 1.87–1.7 Ga. These age values correspond to closure of Pb-Pb and Sm-Nd isotopic systems of rock-forming metamorphic minerals of the felsic veins.

Repetitive LevosimenDan infusions fOR patients with advanced chronic heart failure in the vulnerable post-discharge period: the multinational randomized LeoDOR trial

Abstract Background Studies of the repetitive use of intravenous levosimendan suggested a beneficial effect in patients with advanced heart failure (HF). It was the aim of the LeoDOR trial to test the efficacy and safety of intermittent levosimendan therapy in the vulnerable phase following a hospitalization for acute HF. Methods In this multicenter, double-blind, three-armed trial with an enrolment period from 3-2018 to 6-2021, patients with advanced HF were randomized 2:1 at the end of an index hospitalization for acute HF to intermittent levosimendan therapy or placebo for a period of 12 weeks. Levosimendan was administered according to center preference either as 6-hour infusion at a rate of 0.2 mcg/kg/min every 2 weeks, or as 24-hour infusion at a rate of 0.1 mcg/kg/min every 3 weeks. The primary efficacy assessment after 14 weeks was based on a global rank endpoint consisting of three hierarchical groups: Tier 1 = time to death or urgent heart transplantation or implantation of a ventricular assist device; Tier 2 = time to non-fatal HF requiring i.v. vasoactive therapy; Tier 3 = time-averaged proportional change in NTproBNP from baseline to week 14. Results Due to a forced interim interruption of the study as a result of the COVID-19 pandemic, the planned number of patients could not be recruited. The final modified intention-to-treat (mITT) analysis included 145 patients (93 in the combined levosimendan arm, 52 in the placebo arm). Patient characteristics were well balanced between treatment and placebo arms (mean age 69.3±9.7 vs 67.9±10.1, proportion of women 22.6% vs 21.2%, HF-hospitalizations in previous 12 months 1.9±1.4 vs. 1.7±1.1, NTproBNP at baseline 4740 ng/L (IQR 2235 – 9016) vs 5380 ng/L (IQR 2287 – 8204). There were no marked differences regarding HF medication and implanted devices. Compared with placebo, intermittent levosimendan had no significant effect on the primary endpoint (mean rank score 71.8 for the levosimendan group vs 75.1 for the placebo group; p = 0.65). However, there was a strong signal towards a higher incidence of the individual clinical components of the primary endpoint (tier 1 plus tier 2) in the levosimendan group vs the placebo group both after 14 weeks (HR 2.94, 95%CI 1.13–7.70; p = 0.028) and 26 weeks (HR 1.65 95%CI 0.87 – 3.12; p = 0.123). The number of investigator-reported adverse events during and immediately after drug application was 12.4% in the levosimendan group vs 11.8% in the placebo group (p = 0.905). Conclusion Our findings do not support the use of intermittent levosimendan to improve post-hospitalization clinical stability in patients who were recently hospitalized with HF and reduced LVEF. Further studies are warranted to find the correct use of levosimendan in advanced heart failure patients.

Hyperspectral Anomaly Detection With Tensor Average Rank and Piecewise Smoothness Constraints.

Anomaly detection in hyperspectral images (HSIs) has attracted considerable interest in the remote-sensing domain, which aims to identify pixels with different spectral and spatial features from their surroundings. Most of the existing anomaly detection methods convert the 3-D data cube to a 2-D matrix composed of independent spectral vectors, which destroys the intrinsic spatial correlation between the pixels and their surrounding pixels, thus leading to considerable degradation in detection performance. In this article, we develop a tensor-based anomaly detection algorithm that can effectively preserve the spatial-spectral information of the original data. We first separate the 3-D HSI data into a background tensor and an anomaly tensor. Then the tensor nuclear norm based on the tensor singular value decomposition (SVD) is exploited to characterize the global low rank existing in both the spectral and spatial directions of the background tensor. In addition, the total variation (TV) regularization is incorporated due to the piecewise smoothness. For the anomaly component, the l2.1 norm is exploited to promote the group sparsity of anomalous pixels. In order to improve the ability of the algorithm to distinguish the anomaly from the background, we design a robust background dictionary. We first split the HSI data into local clusters by leveraging their spectral similarity and spatial distance. Then we develop a simple but effective way based on the SVD to select representative pixels as atoms. The constructed background dictionary can effectively represent the background materials and eliminate anomalies. Experimental results obtained using several real hyperspectral datasets demonstrate the superiority of the proposed method compared with some state-of-the-art anomaly detection algorithms.

Impact of body mass index in patients with tricuspid regurgitation after transcatheter edge-to-edge repair

BackgroundObesity and underweight represent classical risk factors for outcome in patients treated for cardiovascular disease. This study describes the impact of different body mass index (BMI) categories on 1-year clinical outcome in patients with tricuspid regurgitation (TR) undergoing transcatheter-edge-to-edge repair (TEER).MethodsWe analyzed 211 consecutive patients (age 78.3 ± 7.2 years, 55.5% female, median EuroSCORE II 9.6 ± 6.7) with tricuspid regurgitation undergoing TEER from June 2015 until May 2021. Patients were prospectively enrolled in our single center registry and were retrospectively analyzed. Patients were stratified according to body mass index (BMI) into 4 groups: BMI < 20 kg/m2 (underweight), BMI 20.0 to < 25.0 kg/m2 (normal weight), BMI 25.0 to > 30.0 kg/m2 (overweight) and BMI ≥ 30 kg/m2 (obese).ResultsKaplan–Meier survival curves demonstrated inferior survival for underweight and obese patients, but comparable outcomes for normal and overweight patients (global log rank test, p < 0.01). Cardiovascular death was significantly higher in underweight patients compared to the other groups (24.1% vs. 7.0% vs. 6.3% vs. 6.4%; p < 0.01). Over all, there were comparable rates of bleeding, stroke and myocardial infarction. Multivariable Cox regression analysis (adjusted for age, gender, coronary artery disease, chronic obstructive pulmonary disease, tricuspid annular plane systolic excursion, left-ventricular ejection fraction) confirmed underweight (HR 3.88; 95% CI 1.64–7.66; p < 0.01) and obesity (HR 3.24; 95% CI 1.37–9.16; p < 0.01) as independent risk factors for 1-year all-cause mortality.ConclusionsCompared to normal weight and overweight patients, obesity and underweight patients undergoing TEER display significant higher 1-year all-cause mortality.Graphical abstract

Analyze of STEAM education research for three decades

The main objective of research is to ascertain the existing situation of STEAM education research over three decades based on the Scopus database. The entire documents are 256 findings globally data shorted by year, region, and highest cited to 100 documents. The analysis technique used VOSViewer, Microsoft Excel and word cloud generator. The result of document type articel is ranks first in Global and conference paper rank first in South East Asia. The sources that have published the top cited papers are “Journal of Small Business Management” in global and the “Education Sciences” in South East Asia. Meanwhile, the author with the most citations is Jeon M from the U.S.A. Specifically, the country with the most publications is US with 31 articles and 2553 citations. Whereas the majority of Southeast Asian countries have 9 articles and 10 citations. Supported the visualization analysis, VOSViewer's global region is divided into 4 clusters and 62 keywords to assist with the visualization analysis. A pair of clusters containing 14 keywords each for the South Asia region. The terms program, project, environment, model, and implication are frequently used in STEAM throughout the world. The keyword STEAM education appears in analyses conducted in South-East Asia. The outcome of this research can serve as a resource for scholars interested in STEAM and education. Further research into STEAM education trends can be conducted by focusing on a single region or on more specific issues.

Generalized Matrix Local Low Rank Representation by Random Projection and Submatrix Propagation.

Matrix low rank approximation is an effective method to reduce or eliminate the statistical redundancy of its components. Compared with the traditional global low rank methods such as singular value decomposition (SVD), local low rank approximation methods are more advantageous to uncover interpretable data structures when clear duality exists between the rows and columns of the matrix. Local low rank approximation is equivalent to low rank submatrix detection. Unfortunately, existing local low rank approximation methods can detect only submatrices of specific mean structure, which may miss a substantial amount of true and interesting patterns. In this work, we develop a novel matrix computational framework called RPSP (Random Probing based submatrix Propagation) that provides an effective solution for the general matrix local low rank representation problem. RPSP detects local low rank patterns that grow from small submatrices of low rank property, which are determined by a random projection approach. RPSP is supported by theories of random projection. Experiments on synthetic data demonstrate that RPSP outperforms all state-of-the-art methods, with the capacity to robustly and correctly identify the low rank matrices when the pattern has a similar mean as the background, background noise is heteroscedastic and multiple patterns present in the data. On real-world datasets, RPSP also demonstrates its effectiveness in identifying interpretable local low rank matrices.

A Comparison of the Checklist Scoring Systems, Global Rating Systems, and Borderline Regression Method for an Objective Structured Clinical Examination for a Small Cohort in a Saudi Medical School.

This study aims to compare the effectiveness of using the checklist and global rating scores to evaluate the clinical competency of medical students in Objective Structured Clinical Examinations (OSCEs). Additionally, the study assesses the appropriateness of using the borderline regression method to set standards for small-scale OSCE exams and determines if the estimated passing marks differ significantly from the university's prefixed passing score of 70%. The study also examines whether the university should utilize the borderline regression method to determine passing scores for each OSCE exam instead of a set passing score. The study analyzed medical students' grades in 11 OSCE exams in the 2022-2023 academic year at Alfaisal University, Riyadh, Saudi Arabia. Students received family medicine clerkship rotations, and after each rotation, they took an OSCE exam consisting of three stations that family medicine consultants graded. The exam included a checklist of 30 tasks and a five-level global rank scale. The study collected all the checklist marks and global rank grades and analyzed them using IBM® Statistical Package for Social Sciences (SPSS® Statistics) software. The statistical tests used were descriptive statistics, the T-test, chi-square tests, Fisher's exact test, and Pearson correlation. The study showed that students were more likely to pass when using the global rating system than the checklist scoring system. Additionally, students had a significantly lower passing rate when using the higher cut-off passing score estimated using the borderline regression method compared to the pre-set passing score of 70% established by the university (with a p-value of 0.00). Each scoring system has advantages and disadvantages, but they complement each other. Combining scoring systems can produce a more comprehensive and precise evaluation of a candidate's performance. The study also emphasizes the importance of carefully selecting and validating cut-off points in OSCE exams to ensure fairness and consistency in assessment.

Weakly Supervised U-Net with Limited Upsampling for Sound Event Detection

Sound event detection (SED) is the task of finding the identities of sound events, as well as their onset and offset timings from audio recordings. When complete timing information is not available in the training data, but only the event identities are known, SED should be solved by weakly supervised learning. The conventional U-Net with global weighted rank pooling (GWRP) has shown a decent performance, but extensive computation is demanded. We propose a novel U-Net with limited upsampling (LUU-Net) and global threshold average pooling (GTAP) to reduce the model size, as well as the computational overhead. The expansion along the frequency axis in the U-Net decoder was minimized, so that the output map sizes were reduced by 40% at the convolutional layers and 12.5% at the fully connected layers without SED performance degradation. The experimental results on a mixed dataset of DCASE 2018 Tasks 1 and 2 showed that our limited upsampling U-Net (LUU-Net) with GTAP was about 23% faster in training and achieved 0.644 in audio tagging and 0.531 in weakly supervised SED tasks in terms of F1 scores, while U-Net with GWRP showed 0.629 and 0.492, respectively. The major contribution of the proposed LUU-Net is the reduction in the computation time with the SED performance being maintained or improved. The other proposed method, GTAP, further improved the training time reduction and provides versatility for various audio mixing conditions by adjusting a single hyperparameter.

Pricing policies for international airports: Per-flight versus per-passenger charges

This study investigates the pricing regime choices between per-flight and/or per-passenger charges for international airports. Each country may choose (i) mix charges for per-flight and per-passenger charges, (ii) per-flight charges only, (iii) per-passenger charges only, and (iv) marginal-operating-cost pricing for each type of charge (Duo-MOC pricing). Each country’s airline competes on flight frequency and passenger volume in the next stage. The global welfare maximization shows that the global welfare levels rank as follows: mix charges (which leads to the first-best outcome), per-passenger charges only, and per-flight charges only (equivalent to Duo-MOC pricing). Regarding local welfare maximization, if both countries choose the same pricing regime in the first stage, then each country’s maximum local welfare achieved in the second airport stage is ranked as follows: Duo-MOC pricing, per-flight charges only, mix charges, and per-passenger charges only. If each country is allowed to choose different regimes, each chooses mix charges to achieve a unique equilibrium. A typical prisoner’s dilemma occurs. The local (and global) welfare rankings under local welfare maximization, combined with the unique equilibrium in the pricing regime choice game, provides clear and sharp policy implications for international airports.

Unicorn Start-Up in India: Byju’s - An Economic Analysis

With digital penetration, EdTech start-ups are growing tremendously. One of the leading top-position EdTech start-ups is Byju’s which emerged during the period of the Covid era, there were the lockdown days the need for Byju’s services was immense. In the world, India (115 unicorns) is the largest unicorn start-up ecosystem which crossed the hundredth unicorn mark in 2022 after the USA (661 unicorns) and China (312 unicorns). Byju’s unicorn Start-up is the leading one among all the EdTech start-ups in India. The start-up easily achieve the unicorn status in the pandemic era as the online education technology needed by all the students from schools, colleges, and universities. In a post-pandemic world, the enterprise optimised the productivity of the employees to maximise profitability. Byju’s is an EdTech start-up that has unicorn status, extended not only in India but also in various countries. Since it was founded in 2011, many strategies followed to come up as the leading company in India. Recent data says, Byju’s had a global rank of 14, and it is valued at US $ 22 billion, with funding about $800 million. Byju’s attains a profitable EdTech unicorn start-up. As of April 2023, this giant EdTech start-up has over 150 million student registrations. An attempt was taken to analyse the role of Byju’s EdTech start-up in economic growth, and how it tackles the challenges of the present era.

Covid-19 research in Bangladesh: A scientometric analysis during 2020-23

Objective. We examined the bibliometric characteristics of 3616 Bangladesh’s publications on Covid-19 during 2020-2023. For this purpose, we used quantitative and qualitative bibliometric indicators.&#x0D; Design/Methodology/Approach. Bangladesh publications on Covid-19 during 2020-2023 were retrieved using the Scopus database. From each record, we retrieved data on the title, author, organizations, journal, publication type and source, country, collaboration, funding, topic and keywords. They were exported to MS-Excell for bibliometric analysis, and VOSviewer software was used for analyzing keyword co-occurrence networks.&#x0D; Results/Discussion. The Bangladesh-related Covid-19 papers in the Scopus database constitute 3616 papers, which hold 50th global rank with 0.75% share in global output and registered 14.63 average citations per article (CPP). The 732 (21.52%) out of 3616 Bangladesh papers attracted external funding support, and 2493 (72.28%) have participated in international collaboration. The U.K., USA, India and Saudi Arabia contributed the most to Bangladesh’s international collaborative papers. The University of Dhaka, Jahangirnagar University, North South University and Daffodil International University were the most productive organizations. In contrast, CHINTA Research, Khulna University of Engineering &amp; Technology, Jahangirnagar University and Jashore University of Science &amp; Technology registered the highest impact. The most preferred communication channels were PLOS One, Heliyon, Frontiers in Public Health and Annals of Medicine &amp; Surgery. The Asian Journal of Psychiatry, IEEE Access, Science of the Total Environment and BMJ Global Health registered the highest citations. Clinical studies were the most frequent category, followed by epidemiology, risk factors and diagnosis. In contrast, adults and the middle-aged formed the largest population age groups, followed by aged, children and adolescents.&#x0D; Conclusion. The bibliometric analysis of Covid-19 research has evolved global interest, including in Bangladesh. For the first time, the present study throws light on the current status and publication trends of research and development efforts in Bangladesh.

PC26. Predicting a Successful Plastic Surgery Residency Applicant - The Linear Rank Model

PURPOSE: Linear rank modeling (LRM) is an algorithm that uses relevant inputs to model expert judgment. Our institution previously described how LRM improves consistency and fairness in resident selection. We have utilized LRM over the last five years to assist with ranking integrated plastic surgery residency (PRS) applicants. The primary aim of this study was to determine if LRM predicts applicant match success. Our secondary aim was to assess if LRM demonstrates bias against groups of underrepresented gender or self-identified race categories. METHODS: The LRM scores for applicants who applied to a single institution between 2018-2022 were calculated. Data was collected on applicant demographics, Step 1 & 2 scores, number of publications, extracurricular activity scores, letter of recommendation scores, global intuition rank, and match success. RESULTS: The mean LRM score of 231 applicants was 59. With every one-point increase in LRM score, there was a significant 8.3% increase in the likelihood of applicant match success (p <.001). An algorithm was developed to estimate the probability of match success based on the LRM score. No significant difference in average LRM scores was found between gender (p =.285) or self-identified race groups (p =.137). CONCLUSION: LRM is a valuable tool to help create rank lists by providing an objective assessment of residency applicants. This model can rank applicants based on a holistic set of criteria and estimate an applicant’s probability of successfully matching into an integrated PRS residency.

Objective Residency Applicant Assessment Using a Linear Rank Model

Residency applicant assessment is imperfect, with little objectivity built into the process, which, unfortunately, impacts recruitment diversity. Linear rank modeling (LRM) is an algorithm that standardizes applicant assessment to model expert judgment. Over the last 5 years, we have used LRM to assist with screening and ranking integrated plastic surgery (PRS) residency applicants. This study's primary objective was to determine if LRM scores are predictive of match success and, secondarily, to compare LRM scores between gender and self-identified race categories. Data was collected on applicant demographics, traditional application metrics, global intuition rank, and match success. LRM scores were calculated for screened and interviewed applicants, and scores were compared by demographic groups. Univariate logistic regression was used to evaluate the association of LRM scores and traditional application metrics with match success. University of Wisconsin, Division of Plastic and Reconstructive Surgery. Academic institution. Six hundred seventeen candidates who applied to a single institution over 4 application cycles (2019-2022). Using area under the curve modeling, LRM score was the most predictive indicator for match success. With every one-point increase in LRM score, there was an 11% and 8.3% increase in the likelihood of screened and interviewed applicant match success (p < 0.001). An algorithm was developed to estimate the probability of match success based on LRM score. No significant differences in LRM scores were appreciated for interviewed applicant gender or self-identified race groups. LRM score is the most predictive indicator of match success for PRS applicants and can be used to estimate an applicant's probability of successfully matching into an integrated PRS residency. Furthermore, it provides a holistic evaluation of the applicant that can streamline the application process and improve recruitment diversity. In the future, this model could be applied to assist in the match process for other specialties.