bluuebunny/crossref_metadata_2025_split huggingface dataset:
AAPM WGPE report 394: Simulated error training for the physics plan and chart review
Authors: Johnson Perry B. , Schubert Leah , Kim Grace Gwe‐Ya , Faught Jacqueline , Buckey Courtney , Conroy Leigh , Luk Samuel M. H. , Schofield Deborah , Parker Stephanie 
Published: April 2024

AbstractBackgroundSimulated error training is a method to practice error detection in situations where the occurrence of error is low. Such is the case for the physics plan and chart review where a physicist may check several plans before encountering a significant problem. By simulating potentially hazardous errors, physicists can become familiar with how they manifest and learn from mistakes made during a simulated plan review.PurposeThe purpose of this project was to develop a series of training datasets that allows medical physicists and trainees to practice plan and chart reviews in a way that is familiar and accessible, and to provide exposure to the various failure modes (FMs) encountered in clinical scenarios.MethodsA series of training datasets have been developed that include a variety of embedded errors based on the risk‐assessment performed by American Association of Physicists in Medicine (AAPM) Task Group 275 for the physics plan and chart review. The training datasets comprise documentation, screen shots, and digital content derived from common treatment planning and radiation oncology information systems and are available via the Cloud‐based platform ProKnow.ResultsOverall, 20 datasets have been created incorporating various software systems (Mosaiq, ARIA, Eclipse, RayStation, Pinnacle) and delivery techniques. A total of 110 errors representing 50 different FMs were embedded with the 20 datasets. The project was piloted at the 2021 AAPM Annual Meeting in a workshop where participants had the opportunity to review cases and answer survey questions related to errors they detected and their perception of the project's efficacy. In general, attendees detected higher‐priority FMs at a higher rate, though no correlation was found between detection rate and the detectability of the FMs. Familiarity with a given system appeared to play a role in detecting errors, specifically when related to missing information at different locations within a given software system. Overall, 96% of respondents either agreed or strongly agreed that the ProKnow portal and training datasets were effective as a training tool, and 75% of respondents agreed or strongly agreed that they planned to use the tool at their local institution.ConclusionsThe datasets and digital platform provide a standardized and accessible tool for training, performance assessment, and continuing education regarding the physics plan and chart review. Work is ongoing to expand the project to include more modalities, radiation oncology treatment planning and information systems, and FMs based on emerging techniques such as auto‐contouring and auto‐planning.
Competing‐risks model for predicting the prognosis of patients with regressive melanoma based on the SEER database
Authors: Huang Chaodi , Huang Liying , Huang Jianguo , Zheng Xinkai , Jiang Congjun , Tom Kong Ching , Wu U. Tim , Huang WenHsien Ethan , Gao Yunfei , Situ Fangmin , Yu Hai , Deng Liehua , Lyu Jun 
Published: April 2024

AbstractBackgroundThe relationship between the regression and prognosis of melanoma has been debated for years. When competing‐risk events are present, using traditional survival analysis methods may induce bias in the identified prognostic factors that affect patients with regressive melanoma.MethodsData on patients diagnosed with regressive melanoma were extracted from the Surveillance, Epidemiology, and End Results (SEER) database during 2000–2019. Cumulative incidence function and Gray's test were used for the univariate analysis, and the Cox proportional‐hazards model and the Fine–Gray model were used for the multivariate analysis.ResultsA total of 1442 eligible patients were diagnosed with regressive melanoma, including 529 patients who died: 109 from regressive melanoma and 420 from other causes. The multivariate analysis using the Fine–Gray model revealed that SEER stage, surgery status, and marital status were important factors that affected the prognosis of regressive melanoma. Due to the existence of competing‐risk events, the Cox model may have induced biases in estimating the effect values, and the competing‐risks model was more advantageous in the analysis of multiple‐endpoint clinical survival data.ConclusionThe findings of this study may help clinicians to better understand regressive melanoma and provide reference data for clinical decisions.
Thermodynamically consistent numerical modeling of immiscible two‐phase flow in poro‐viscoelastic media
Authors: Kou Jisheng , Salama Amgad , Chen Huangxin , Sun Shuyu 
Published: April 2024

AbstractNumerical modeling of immiscible two‐phase flow in deformable porous media has become increasingly significant due to its applications in oil reservoir engineering, geotechnical engineering and many others. The coupling between two‐phase flow and geomechanics gives rise to a major challenge to the development of physically consistent mathematical models and effective numerical methods. In this article, based on the concept of free energies and guided by the second law of thermodynamics, we derive a thermodynamically consistent mathematical model for immiscible two‐phase flow in poro‐viscoelastic media. The model uses the fluid and solid free energies to characterize the fluid capillarity and solid skeleton elasticity, so that it rigorously follows an energy dissipation law. The thermodynamically consistent formulation of the pore fluid pressure is naturally derived for the solid mechanical equilibrium equation. Additionally, the model ensures the mass conservation law for both fluids and solids. For numerical approximation of the model, we propose an energy stable and mass conservative numerical method. The method herein inherits the energy dissipation law through appropriate energy approaches and subtle treatments for the coupling between two phase saturations, the effective pore pressure and porosity. Using the locally conservative cell‐centered finite difference methods on staggered grids with the upwind strategies for saturations and porosity, we construct the fully discrete scheme, which has the ability to conserve the masses of both fluids and solids as well as preserve the energy dissipation law at the fully discrete level. In particular, the proposed method is an unbiased algorithm, that is, treating the wetting phase, the non‐wetting phase and the solid phase in the same way. Numerical results are also given to validate and verify the features of the proposed model and numerical method.
On sparse regression, Lp‐regularization, and automated model discovery
Authors: McCulloch Jeremy A. , St. Pierre Skyler R. , Linka Kevin , Kuhl Ellen 
Published: April 2024

AbstractSparse regression and feature extraction are the cornerstones of knowledge discovery from massive data. Their goal is to discover interpretable and predictive models that provide simple relationships among scientific variables. While the statistical tools for model discovery are well established in the context of linear regression, their generalization to nonlinear regression in material modeling is highly problem‐specific and insufficiently understood. Here we explore the potential of neural networks for automatic model discovery and induce sparsity by a hybrid approach that combines two strategies: regularization and physical constraints. We integrate the concept of Lp regularization for subset selection with constitutive neural networks that leverage our domain knowledge in kinematics and thermodynamics. We train our networks with both, synthetic and real data, and perform several thousand discovery runs to infer common guidelines and trends: L2 regularization or ridge regression is unsuitable for model discovery; L1 regularization or lasso promotes sparsity, but induces strong bias that may aggressively change the results; only L0 regularization allows us to transparently fine‐tune the trade‐off between interpretability and predictability, simplicity and accuracy, and bias and variance. With these insights, we demonstrate that Lp regularized constitutive neural networks can simultaneously discover both, interpretable models and physically meaningful parameters. We anticipate that our findings will generalize to alternative discovery techniques such as sparse and symbolic regression, and to other domains such as biology, chemistry, or medicine. Our ability to automatically discover material models from data could have tremendous applications in generative material design and open new opportunities to manipulate matter, alter properties of existing materials, and discover new materials with user‐defined properties.
Factor structure and psychometric properties of the Jordanian version of the depression anxiety stress scale (DASS‐21)
Authors: Al‐Dassean Khaled A. , Murad Odeh S. 
Published: April 2024

AbstractAimEarly diagnosis of health conditions such as depression, anxiety, and stress, which have been recognized as global health issues, is essential for providing psychological support to people who experience negative emotions. Therefore, it is important to identify a reliable method for diagnosing depression, anxiety, and stress. To this end, this study investigated the factor structure and evaluated the psychometric properties of the Depression Anxiety Stress Scale‐21 (DASS‐21) in Jordan.MethodsA university sample of 336 Jordanians completed the Arabic version of the DASS‐21 and several psychopathology measures. Cross‐sectional study and confirmatory factor analysis (CFA) were applied to achieve the study's aims.ResultsCFA favored the bifactor model compared to the other four models. In contrast, the Beck Anxiety Inventory (BAI) and the Beck Depression Inventory‐II (BDI‐II) strongly correlated with the DASS‐21, indicating that the DASS‐21 has adequate convergent and divergent validity. Subsequently, the scale's discriminant validity was tested using the heterotrait–monotrait (HTMT) correlation ratio, which was found to be relatively insufficient. While DASS‐21 showed strong reliability, Cronbach's α and McDonald's omega values ranged between 0.83 and 0.93.ConclusionThe bifactor model best fits the Jordanian sample data. The DASS‐21 has optimum psychometric properties; therefore, it can be used to assess the general distress experienced by the Jordanian community during research investigations and in non‐clinical settings.
Enhancing CO2/N2 and CO2/CH4 separation in mixed matrix membrane: A comprehensive study on Pebax®1657 with SSMMP/IL for improved efficiency
Authors: Ferrari Henrique Z. , Bernard Franciele , dos Santos Leonardo , Dias Guilherme , Le Roux Christophe , Micoud Pierre , Martin François , Einloft Sandra 
Published: April 2024

AbstractMixed matrix membranes (MMMs) have been proposed as a solution to surmount Robeson's trade‐off curves and have demonstrated efficacy in gas separation processes, particularly for CO2 capture. In this study, MMMs based on Pebax®1657 were obtained utilizing synthetic silico‐metallic mineral particles (SSMMP) functionalized with ionic liquids (ILs). The objective was to attain enhanced CO2 separation performance, thereby showcasing the potential to mitigate the environmental repercussions of industrial processes that entail greenhouse gas emissions. For membrane production, an ethanol/water mixture was used as solvent, with the SSMMP/IL content varying from 0.5 to 20% by weight of the polymer. The primary aim of this study was to assess the effect of filler addition on permeability and selectivity for CO2, CH4, and N2. Comprehensive analyses, including SEM, FTIR, TGA, DSC, and DMA were conducted to evaluate the properties of the produced membranes. Gas permeability and ideal selectivity were measured at 25°C and different pressures, ranging from 1 to 7 bar. Characterization results demonstrate that the glass transition temperature (Tg) of MMMs increased compared to pure Pebax®1657, indicating that the addition of SSMMP/IL reduces the flexibility of the PEO chains, forming a rigid interface at the polymer/filler, which may enhance selectivity. This effect, corroborated by gas permeation, was observed for both CO2/N2 and CO2/CH4. For CO2/N2, the highest selectivity was achieved at lower filler concentrations, gradually decreasing as the filler load increased. MMM‐0.5 wt% achieved the highest selectivity of 91.96. The membrane CO2 permeability rose with an elevated filler content, rising from 84.21 for pure Pebax®1657 to 192.17 Barrer for MMM‐20 wt% at 4 bar. The permeability results were influenced by the gas diffusion coefficients of the MMMs, which increased with increasing SSMMP/IL content. The effect of feed pressure on MMM‐5 wt% was also assessed, revealing that CO2 permeability increased with increasing pressure, from 126.72 Barrer at 1 bar to 165.56 Barrer at 7 bar. This work showcased the viability of MMMs incorporating SSMMP/IL for industrial use, as they displayed separation capabilities that exceeded the 2008 Robeson upper bound.Highlights
Mixed matrix membranes based on Pebax®1657 and SSMMP‐20%‐[bmim][Tf2N] were prepared.
CO2 permeability of the MMMs was increased by 128% and CO2/N2 selectivity by 83%.
Higher CO2 pressures increase MMMs permeability.
The obtained MMMs have separation performances above the Robeson upper Bound.
A new ordering method of basic events in dynamic fault tree for sequential binary decision diagram
Authors: Huang Tudi , Zeng Ying , Li Yan‐Feng , Liu Jun , Huang Hong‐Zhong 
Published: April 2024

AbstractDynamic fault tree (DFT) is a reliable method for modeling complex systems with dynamic characteristics. However, evaluating the reliability of such systems can be complex. The sequential binary decision diagram (SBDD) method simplifies this process by reducing computational complexity. This study investigates factors influencing SBDD size and proposes a new method, the complete priority ordering method, for ordering basic events in SBDDs. Results demonstrate the method's effectiveness in comparison to existing approaches.
Additives for Aqueous Zinc‐Ion Batteries: Recent Progress, Mechanism Analysis, and Future Perspectives
Authors: Cao Jianghui , Zhao Fang , Guan Weixin , Yang Xiaoxuan , Zhao Qidong , Gao Liguo , Ren Xuefeng , Wu Gang , Liu Anmin 
Published: April 2024

AbstractAqueous zinc‐ion batteries (ZIBs) stand out as a promising next‐generation electrochemical energy storage technology, offering notable advantages such as high specific capacity, enhanced safety, and cost‐effectiveness. However, the application of aqueous electrolytes introduces challenges: Zn dendrite formation and parasitic reactions at the anode, as well as dissolution, electrostatic interaction, and by‐product formation at the cathode. In addressing these electrode‐centric problems, additive engineering has emerged as an effective strategy. This review delves into the latest advancements in electrolyte additives for ZIBs, emphasizing their role in resolving the existing issues. Key focus areas include improving morphology and reducing side reactions during battery cycling using synergistic effects of modulating anode interface regulation, zinc facet control, and restructuring of hydrogen bonds and solvation sheaths. Special attention is given to the efficacy of amino acids and zwitterions due to their multifunction to improve the cycling performance of batteries concerning cycle stability and lifespan. Additionally, the recent additive advancements are studied for low‐temperature and extreme weather applications meticulously. This review concludes with a holistic look at the future of additive engineering, underscoring its critical role in advancing ZIB performance amidst the complexities and challenges of electrolyte additives.
Personal value of Alzheimer's disease biomarker testing and result disclosure from the patient and care partner perspective
Authors: Patel Khushbu J. , Yang David , Feldman Howard H. , Hsiung Ging‐Yuek R. , Nygaard Haakon B. , Best John R. , Dwosh Emily , Robillard Julie M. , DeMarco Mari L. 
Published: April 2024

AbstractINTRODUCTIONWe described patients’ and care partners’ experiences with Alzheimer's disease (AD) cerebrospinal fluid (CSF) biomarker testing and result disclosure in routine care.METHODSIMPACT‐AD BC is an observational study of clinic patients who underwent AD CSF biomarker testing as part of their routine medical care (n = 142). In the personal utility arm of the study, semi‐structured phone interviews were conducted with a subset of patients (n = 34), and separately with their care partners (n = 31). Post‐disclosure interviews were conducted ∼1 month and ∼6 months after biomarker result disclosure and investigated the patients’ decision‐making process around testing, impact of receiving results, wellness and lifestyle changes, and future planning.RESULTSA majority of patients (90%) rated their decision to undergo testing as “easy.” Post‐disclosure, the majority (82%) reported overall positive feelings from having greater certainty and the ability to plan ahead, and results spurred them to adopt/continue healthy behaviors such as exercise (84%) and cognitive activities (54%). Care partners expressed relief from having more diagnostic certainty, increased appreciation of future caregiving responsibilities, and a desire to connect with support resources.DISCUSSIONPerspectives of persons with lived experience in dementia provide new insight into the value of biomarker testing and should be included as part of evidence‐guided considerations for pre‐test counseling and result disclosure. Moreover, study findings identify an interval when patients and care partners are highly receptive to positive lifestyle and medical interventions.
Opportunities and challenges for precipitation forcing data in post‐wildfire hydrologic modeling applications
Authors: Partridge Trevor F. , Johnson Zachary C. , Sleeter Rachel R. , Qi Sharon L. , Walvoord Michelle A. , Murphy Sheila F. , Peterman‐Phipps Cara , Ebel Brian A. 
Published: April 2024

AbstractThe frequency and extent of wildfires have increased in recent decades with immediate and cascading effects on water availability in many regions of the world. Precipitation is used as primary input to hydrologic models and is a critical driver of post‐wildfire hydrologic hazards including debris flows, flash floods, water‐quality effects, and reservoir sedimentation. These models are valuable tools for understanding the hydrologic response to wildfire but require accurate precipitation data at suitable spatial and temporal resolutions. Wildfires often occur in data‐sparse, headwater catchments in complex terrain, and post‐wildfire hydrologic effects are particularly sensitive to high‐intensity, short‐duration precipitation events, which are highly variable and difficult to measure or estimate. Therefore, the assessment and prediction of wildfire‐induced changes to watershed hydrology, including the associated effects on ecosystems and communities, are complicated by uncertainty in precipitation data. When direct measurements of precipitation are not available, datasets of indirect measurements or estimates are often used. Choosing the most appropriate precipitation dataset can be difficult as different datasets have unique trade‐offs in terms of spatial and temporal accuracy, resolution, and completeness. Here, we outline the challenges and opportunities associated with different precipitation datasets as they apply to post‐wildfire hydrologic models and modeling objectives. We highlight the need for expanded precipitation gage deployment in wildfire‐prone areas and discuss potential opportunities for future research and the integration of precipitation data from disparate sources into a common hydrologic modeling framework.This article is categorized under:
Science of Water > Hydrological Processes
Science of Water > Methods
Science of Water > Water and Environmental Change
Cement augmentation for proximal humerus fractures: a meta-analysis of randomized trials and observational studies
Authors: Lecoultre Yannic , Beeres Frank J. P. , Link Björn C. , Pretz Fabian , Tillmann Franz , Babst Reto , van de Wall Bryan J. M. 
Published: April 2024

Abstract
Introduction
It is unclear if elderly patients treated with plate osteosynthesis for proximal humerus fractures benefit from cement augmentation. This meta-analysis aims to compare cement augmentation to no augmentation regarding healing, complications, and functional results.

Methods
PubMed, Embase, and Cochrane Central Register of Controlled Trials were searched for randomized clinical trials and observational studies. Effect estimates were pooled across studies using random effects models. The primary outcome is overall complication rate. Stratified analyses were performed for types of complication (implant-related or systemic). Secondary outcomes include re-interventions, hospital stay, operation time, functional scores, and general quality of life.

Results
Five observational studies and one randomized controlled trial with a total of 541 patients were included. The overall complication rate was significantly lower in the augmented group (15.6% versus 25.4%, OR 0.54 (95%CI 0.33–0.87)). This was caused by a reduction of implant-related complications (10.4% vs. 19.9%, OR 0.49 (95%CI 0.28, 0.88)). No difference in humeral head necrosis was found. Data on re-intervention, hospital stay, and operation time was limited but did not show significant differences. No impact on functional scores and general quality of life was detected.

Conclusion
This meta-analysis shows that cement augmentation may reduce overall complications, mainly by preventing implant-related complications. No difference was detected regarding need for re-intervention, functional scores, general quality of life, and hospital stay. This is the first meta-analysis on this topic. It remains to be seen whether conclusions will hold when more and better-quality data becomes available.
The improvement of ground truth annotation in public datasets for human detection
Authors: Nou Sotheany , Lee Joong-Sun , Ohyama Nagaaki , Obi Takashi 
Published: April 2024

AbstractThe quality of annotations in the datasets is crucial for supervised machine learning as it significantly affects the performance of models. While many public datasets are widely used, they often suffer from annotations errors, including missing annotations, incorrect bounding box sizes, and positions. It results in low accuracy of machine learning models. However, most researchers have traditionally focused on improving model performance by enhancing algorithms, while overlooking concerns regarding data quality. This so-called model-centric AI approach has been predominant. In contrast, a data-centric AI approach, advocated by Andrew Ng at the DATA and AI Summit 2022, emphasizes enhancing data quality while keeping the model fixed, which proves to be more efficient in improving performance. Building upon this data-centric approach, we propose a method to enhance the quality of public datasets such as MS-COCO and Open Image Dataset. Our approach involves automatically retrieving missing annotations and correcting the size and position of existing bounding boxes in these datasets. Specifically, our study deals with human object detection, which is one of the prominent applications of artificial intelligence. Experimental results demonstrate improved performance with models such as Faster-RCNN, EfficientDet, and RetinaNet. We can achieve up to 32% compared to original datasets in the term of mAP after applying both proposed methods to dataset which is transformed the grouped of instances to individual instance. In summary, our methods significantly enhance the model’s performance by improving the quality of annotations at a lower cost with less time than manual improvement employed in other studies.