bluuebunny/crossref_metadata_2025_split huggingface dataset:
Marathi to Indian Sign Language Machine Translation
Authors: Bhagwat Suvarna Rajesh. , Bhavsar R. P. , Pawar B. V. 
Published: May 2024

Machine translation has been a prominent field of research, contributing significantly to human life enhancement. Sign language machine translation, a subfield, focuses on translating spoken language content into sign language and vice versa, thereby facilitating communication between the normal hearing and hard-of-hearing communities, promoting inclusivity.
This study presents the development of a ‘sign language machine translation system’ converting simple Marathi sentences into Indian Sign Language (ISL) glosses and animation. Given the low-resource nature of both languages, a phrase-level rule-based approach was employed for the translation. Initial encoding of translation rules relied on basic linguistic knowledge of Marathi and ISL, with subsequent incorporation of rules to address 'simultaneous morphological' features in ISL. These rules were applied during the ‘generation phase’ of translation to dynamically adjust phonological sign parameters, resulting in improved target sentence fluency.
The paper provides a detailed description of the system architecture, translation rules, and comprehensive experimentation. Rigorous evaluation efforts were undertaken, encompassing various linguistic features, and the findings are discussed herein.
The web-based version of the system serves as an interpreter for brief communications and can support the teaching and learning of sign language and its grammar in schools for hard-of-hearing students.
Electrochemical Sensor Modified with Graphene Oxide Decorated with Copper Oxide Nanoparticles for Velpatasvir’s Monitoring in Spiked Human Plasma
Authors: Hesham Nada , Mahmoud Amr M. , Hegazy Maha A. , Wagdy Hebatallah A. 
Published: May 2024

Velpatasvir, an antiviral agent co-formulated with sofosbuvir used to treat hepatitis C, has recently demonstrated beneficial therapeutic effects against COVID-19. Therefore, therapeutic drug monitoring of velpatasvir is essential to achieve the desired clinical outcomes. An electrochemical sensor modified with synthesized copper oxide nanoparticles on the surface of graphene oxide (CuO/GO-NPs) was fabricated for the analysis of velpatasvir for the first time. Characterization was carried out using Fourier-transform infrared spectroscopy, X-ray diffraction, and scanning electron microscopy with energy-dispersive X-ray spectroscopy. The voltammetric determinations were conducted using differential pulse and cyclic voltammetry, where the modified electrode exhibited better sensitivity than the unmodified one. The method was validated according to the International Council for Harmonisation (ICH) guidelines, exhibiting linearity within a range of 1.0 × 10−7 – 1.0 × 10−5 M, covering velpatasvir’s maximum plasma concentration (Cmax), with a quantification limit of 2.89 × 10−7 M and a detection limit of 9.03 × 10−8 M. The developed sensor was successfully applied to spiked human plasma at velpatasvir’s Cmax level. The method’s greenness was assessed using the Analytical Eco-scale and the Green analytical procedure index tools. This method holds promise as a green simple approach to implemented in future velpatasvir’s therapeutic drug monitoring studies.
Using Machine Learning to Predict Convection-Allowing Ensemble Forecast Skill: Evaluation with the NSSL Warn-on-Forecast System
Authors: Potvin Corey K. , Flora Montgomery L. , Skinner Patrick S. , Reinhart Anthony E. , Matilla Brian C. 
Published: May 2024

Abstract
Forecasters routinely calibrate their confidence in model forecasts. Ensembles inherently estimate forecast confidence but are often underdispersive, and ensemble spread does not strongly correlate with ensemble-mean error. The misalignment between ensemble spread and skill motivates new methods for “forecasting forecast skill” so that forecasters can better utilize ensemble guidance. We have trained logistic regression and random forest models to predict the skill of composite reflectivity forecasts from the NSSL Warn-on-Forecast System (WoFS), a 3-km ensemble that generates rapidly updating forecast guidance for 0–6-h lead times. The forecast skill predictions are valid at 1-, 2-, or 3-h lead times within localized regions determined by the observed storm locations at analysis time. We use WoFS analysis and forecast output and NSSL Multi-Radar/Multi-Sensor composite reflectivity for 106 cases from the 2017 to 2021 NOAA Hazardous Weather Testbed Spring Forecasting Experiments. We frame the prediction task as a multiclassification problem, where the forecast skill labels are determined by averaging the extended fraction skill scores (eFSSs) for several reflectivity thresholds and verification neighborhoods and then converting to one of three classes based on where the average eFSS ranks within the entire dataset: POOR (bottom 20%), FAIR (middle 60%), or GOOD (top 20%). Initial machine learning (ML) models are trained on 323 predictors; reducing to 10 or 15 predictors in the final models only modestly reduces skill. The final models substantially outperform carefully developed persistence- and spread-based models and are reasonably explainable. The results suggest that ML can be a valuable tool for guiding user confidence in convection-allowing (and larger-scale) ensemble forecasts.

Significance Statement
Some numerical weather prediction (NWP) forecasts are more likely to verify than others. Forecasters often recognize situations where NWP output should be trusted more or less than usual, but objective methods for “forecasting forecast skill” are notably lacking for thunderstorm-scale models. Better estimates of forecast skill can benefit society through more accurate forecasts of high-impact weather. Machine learning (ML) provides a powerful framework for relating forecast skill to the characteristics of model forecasts and available observations over many previous cases. ML models can leverage these relationships to predict forecast skill for new cases in real time. We demonstrate the effectiveness of this approach to forecasting forecast skill using a cutting-edge thunderstorm prediction system and logistic regression and random forest models. Based on this success, we recommend the adoption of similar ML-based methods for other prediction models.
A Comprehensive Analysis of Uncertainties in Warm-Rain Parameterizations in Climate Models Based on In Situ Measurements
Authors: Zhang Zhibo , Mechem David B. , Chiu J. Christine , Covert Justin A. 
Published: May 2024

Abstract
Because of the coarse grid size of Earth system models (ESMs), representing warm-rain processes in ESMs is a challenging task involving multiple sources of uncertainty. Previous studies evaluated warm-rain parameterizations mainly according to their performance in emulating collision–coalescence rates for local droplet populations over a short period of a few seconds. The representativeness of these local process rates comes into question when applied in ESMs for grid sizes on the order of 100 km and time steps on the order of 20–30 min. We evaluate several widely used warm-rain parameterizations in ESM application scenarios. In the comparison of local and instantaneous autoconversion rates, the two parameterization schemes based on numerical fitting to stochastic collection equation (SCE) results perform best. However, because of Jessen’s inequality, their performance deteriorates when grid-mean, instead of locally resolved, cloud properties are used in their simulations. In contrast, the effect of Jessen’s inequality partly cancels the overestimation problem of two semianalytical schemes, leading to an improvement in the ESM-like comparison. In the assessment of uncertainty due to the large time step of ESMs, it is found that the rainwater tendency simulated by the SCE is roughly linear for time steps smaller than 10 min, but the nonlinearity effect becomes significant for larger time steps, leading to errors up to a factor of 4 for a time step of 20 min. After considering all uncertainties, the grid-mean and time-averaged rainwater tendency based on the parameterization schemes is mostly within a factor of 4 of the local benchmark results simulated by SCE.
Atmospheric Teleconnections Responsible for the Dominant Patterns of Interannual Variability in Summer Drought over Northern Asia
Authors: Ju Dingyu , Sun Jianqi , Hong Haixu , Zhang Mengqi 
Published: May 2024

Abstract
Summer drought over northern Asia (NA) seriously threatens the local fragile ecological environment and social economy development. In this study, using the standardized precipitation evapotranspiration index (SPEI), we first identify the dominant modes of interannual variability in summer drought condition over NA and then explore the atmospheric patterns responsible for the formation of the modes. The results show that the first empirical orthogonal function (EOF1) mode of summer SPEI over NA exhibits a meridional dipole pattern, which is influenced primarily by the polar–Eurasian teleconnection (POL) and circumglobal teleconnection (CGT) patterns. Under the influence of negative POL and positive CGT patterns, there is an anomalous anticyclone (cyclone) over northwestern Siberia (Lake Baikal to Northeast China). Such atmospheric circulations lead to meridional dipole patterns in air temperature, moisture condition, vertical motion, and cloud cover over NA, favoring decreased (increased) precipitation and increased (decreased) potential evapotranspiration over northern (southern) NA, finally contributing to the formation of EOF1. The second EOF (EOF2) shows an approximate zonal dipole pattern, which is influenced by the British–Baikal Corridor (BBC) and Scandinavia (SCA) teleconnection patterns. The positive BBC and SCA patterns can lead to an anomalous anticyclone over the Ural Mountains and a cyclone over Lake Baikal. Such atmospheric circulations result in a zonal dipole pattern in precipitation and potential evapotranspiration over NA through changing the local moisture condition, air temperature, and radiation, consequently favoring the formation of EOF2. Fitting analysis indicates that the aforementioned atmospheric factors can explain 76% (55%) of the interannual variability of EOF1 (EOF2).
Exploring the Usefulness of Machine Learning Severe Weather Guidance in the Warn-on-Forecast System: Results from the 2022 NOAA Hazardous Weather Testbed Spring Forecasting Experiment
Authors: Flora Montgomery L. , Gallo Burkely , Potvin Corey K. , Clark Adam J. , Wilson Katie 
Published: May 2024

Abstract
Artificial intelligence (AI) is gaining popularity for severe weather forecasting. Recently, the authors developed an AI system using machine learning (ML) to produce probabilistic guidance for severe weather hazards, including tornadoes, large hail, and severe winds, using the National Severe Storms Laboratory’s (NSSL) Warn-on-Forecast System (WoFS) as input. Known as WoFS-ML-Severe, it performed well in retrospective cases, but its operational usefulness had yet to be determined. To examine the potential usefulness of the ML guidance, we conducted a control and treatment (experimental) group experiment during the 2022 NOAA Hazardous Weather Testbed Spring Forecasting Experiment (HWT-SFE). The control group had full access to WoFS, while the experimental group had access to WoFS and ML products. Explainability graphics were also integrated into the WoFS web viewer. Both groups issued 1-h convective outlooks for each hazard. After issuing their forecasts, we surveyed participants on their confidence, the number of products viewed, and the usefulness of the ML guidance. We found the ML-based outlooks outperformed non-ML-based outlooks for multiple verification metrics for all three hazards and were rated subjectively higher by the participants. However, the difference in confidence between the two groups was not significant, and the experimental group self-reported viewing more products than the control group. Participants had mixed sentiments toward explainability products as it improved their understanding of the input/output relationships, but viewing them added to their workload. Although the experiment demonstrated the usefulness of ML guidance for severe weather forecasting, there are avenues to improve upon the ML guidance, and more training and exposure are needed to exploit its benefits fully.

Significance Statement
We developed an artificial intelligence (AI) system to predict tornadoes, large hail, and damaging straight-line winds. The AI system was leveraged in real time during the 2022 NOAA Hazardous Weather Testbed Spring Forecasting Experiment. This study reveals that forecasters using AI guidance produced more reliable and spatially accurate outlooks than those without. While AI and complementary explainability products did not reduce forecaster workload, both demonstrated great potential for improving severe weather forecasting. This research also highlights the importance of user feedback in refining AI tools for severe weather forecasting.
On the essential work of fracture of polylactic acid/thermoplastic polyurethane/halloysite
Authors: Hosseinjani Sadegh , Ashenai Ghasemi Faramarz , Fasihi Mohammad , Rajaee Pouya 
Published: May 2024

The goal of this work is to investigate the mechanical and fracture characteristics of polylactic acid (PLA)/thermoplastic polyurethane (TPU)/halloysite (HNT). HNT with three levels (0, 3, and 6 wt.%) and TPU with three levels (10, 20, and 30 wt.%) were used as variables. The investigation of tensile properties revealed contrasting effects of TPU and HNT levels. Increasing TPU content to 30 wt.% led to a decrease in strength and modulus by 36% and 42% in turn, however resulted in a remarkable 338% improvement in elongation at break. Conversely, the addition of 3 wt.% HNT enhanced both tensile strength and modulus, although it caused a reduction in elongation at break. The results concerning fracture properties showed by incorporating TPU up to 30 wt.% EWF ( w e) and non-EWF ( βw p) rose by 459% and 178%, respectively. Additionally, the inclusion of 3 wt.% HNT further elevated the EWF compared to the compound containing 10 wt.% TPU without HNT. Through optimization, the compound with 10 wt.% TPU and 3 wt.% HNT was shown to have the optimum strength, stiffness, and toughness balance.
Mechanical recycling of poly(lactic acid)/agave fiber biocomposites
Authors: Ramos-Hernández Tomas , Robledo-Ortíz Jorge Ramón , Martín del Campo Alan Salvador , Rodrigue Denis , Cano Alfredo , Pérez-Fonseca Aida Alejandra 
Published: May 2024

This paper focuses on evaluating the mechanical recycling potential of poly(lactic acid) (PLA) biocomposites reinforced with agave fibers (AF). The biocomposites were prepared by extrusion using 5, 15, and 30 wt.% of agave fibers and reprocessed up to eight times. The results show that the fiber dimensions substantially decrease during reprocessing, especially after the first extrusion cycle, followed by a more gradual decrease in each subsequent cycle. The melt flow index (MFI) and the mechanical properties (except impact strength) tend to decrease as the fiber concentration increases. On the other hand, the glass transition temperature ( T g) and the crystallinity ( X c) of the biocomposites increased with increasing fiber concentration. It is important to highlight that closed-loop reprocessing does not significantly affect the overall behavior of the biocomposites under the conditions investigated. Therefore, PLA reinforced with AF is suitable for primary recycling since the final properties are mainly influenced by the fiber concentration and less by the number of reprocessing cycles.
Characteristics of patients and facility of peritoneal dialysis in Korea: Results from the Peritoneal Dialysis Outcomes and Practice Patterns Study (PDOPPS) Korea
Authors: Kim Ji Hye , Kim Seon-Mi , Kang Minjung , Kang Eunjeong , Park Sun-Hee , Kim Yong-Lim , Pecoits-Filho Roberto , Bieber Brian , Pisoni Ronald L , Oh Kook-Hwan ,   PDOPPS Korea and Arbor Research Investigators
Published: May 2024

Background: Varying peritoneal dialysis (PD)-related clinical outcomes have been reported in different countries. As a participant of the Peritoneal Dialysis Outcomes and Practice Patterns Study (PDOPPS), this study investigated the characteristics of Korean PD patients, PD facilities and the incidence rates of clinical outcomes including mortality and PD-related outcomes. Methods: From July 2019 to December 2021, a total of 766 Korean PD patients were included for analysis. Poisson regression analysis was used to explore the incidence rates of various clinical events including mortality, modality transfer, exit site or catheter tunnel infection and peritonitis. Results: Among the 766 patients (median age 55.5 years, males 59.5%), 276 were incident and 490 were prevalent PD patients. The incidence rates of events were as follows: all-cause mortality (0.048), modality transfer (0.051), exit site or catheter tunnel infection (0.054) and peritonitis (0.136) events per person year. The most common causative organism for exit site or tunnel infection was staphylococcus species (47%) and that for peritonitis was streptococcus (28%) followed by staphylococcus (27%) species. Conclusions: Up to now, PDOPPS Korea has recruited 766 Korean PD patients and started documentation of major PD-related outcomes which occurred during the follow-up period. The overall incidence rates of clinical outcomes in Korean PD patients were relatively favourable. There was no statistically significant difference in the incidence rates of clinical outcomes according to both facility and patient factors.
Strategic technological processes in hospitals: Conflicts and personal experiences of healthcare teams
Authors: Naamati-Schneider Lior , Arazi-Fadlon Mirit , Daphna-Tekoah Shir 
Published: May 2024

Background Global health systems operate amid dynamic factors, including demographic shifts, economic variations, political changes, technological progress, and societal trends that lead to VUCA reality (Volatility, Uncertainty, Complexity, and Ambiguity). To address these challenges, healthcare organizations are increasingly turning to Strategic Technological Processes and digital transformation. Research objective Against this background, the current study examined the personal experiences, conflicts, difficulties, and moral dilemmas attendant upon accommodating this digital transformation of healthcare professionals. Participants The study involved 27 healthcare professionals working in Israeli hospitals, whose experiences and perspectives were central to understanding the impact of digital transformation in healthcare settings. Research design The study methodology rested on in-depth interviews, which were analyzed through the prism of the Listening Guide analytical technique. Ethical considerations The research obtained pre-approval from the Ethics Committee at the researcher’s institution. Findings The study revealed that the healthcare professionals are indeed facing ethical conflicts and personal challenges related to digitalization (such as providing the best quality of care, being the best caregiver, and acting for the betterment of the hospital). It also identified dilemmas resulting from conflicts between the needs and demands of the health system and the healthcare professionals’ values and resources. Discussion The study findings highlight the impact of ethical and moral challenges accompanying strategic organizational and digital transformation changes implemented by healthcare organizations worldwide. These challenges arise as healthcare institutions adapt to the demands of the 21st century, potentially leading to burnout and moral distress among healthcare professionals, further exacerbating the already stressful reality they face. Conclusion In conclusion, the study emphasizes the critical necessity for comprehensive support strategies to alleviate stress and burnout among healthcare professionals. As healthcare organizations and personnel navigate significant organizational changes to address contemporary challenges, prioritizing the well-being of healthcare workers through effective support mechanisms becomes imperative.
Navigating Emotional Discomfort in Developing Equity-Driven School Leaders: A Conceptual-Pedagogical Framework
Authors: Kim Taeyeon , Wright James 
Published: May 2024

Background Given that K-12 schools necessitate leaders who can advance equity and justice, preparation programs in higher education institutions have prioritized the development of equity-oriented school leaders. However, there has been relatively limited exploration of pedagogical approaches that equip educational leaders to navigate adverse emotional responses and utilize their discomforting emotions as a source of transformation toward equity-oriented principles. When negative emotions are suppressed and/or unexplored within leadership development programs, adult learners will likely miss crucial opportunities for personal growth and transformative change. Purpose This theoretical article aims to enhance and expand existing scholarship on the pedagogies of emotional discomfort by developing a conceptual-pedagogical framework for preparing equity-driven school leaders. Conceptual Model We explore the role of emotions in/as learning, drawing insights from the learning science literature, and analyze empirical studies in leadership education to unravel how and why discomforting emotions are triggered and operationalized when learning about racism and inequities. We present a framework, the pedagogy of discomfort toward critical hope, drawing on scholarly work bridging emotional discomfort and critical pedagogy, and exemplified by various examples of pedagogies of discomfort. Building on this foundation, we introduce an emotional scaffolding design centered around three concepts: generating discomfort to make oppression and privilege visible, guided emotion participation for engaging critical reflection and dialogue, and appraising emotions with metacognitive and meta-affect tools. Discussion and Implications This article extends the scholarship on teaching and learning for developing equity leaders by bridging insights from learning science and critical pedagogy.