bluuebunny/crossref_metadata_2025_split huggingface dataset:
Efficient Estimation of Climate State and Its Uncertainty Using Kalman Filtering with Application to Policy Thresholds and Volcanism
Authors: Nicklas J. Matthew , Fox-Kemper Baylor , Lawrence Charles 
Published: October 2024

Abstract
We present the energy-balance model–Kalman filter (EBM–KF), a hybrid model projecting and assimilating the global mean surface temperature (GMST) and ocean heat content anomaly (OHCA). It combines an annual energy-balance model (difference equations) with 17 parameters drawn from the literature and a statistical extended Kalman filter assimilating GMST and OHCA, either observed time series or simulated by Earth system models. Our motivation is to create an efficient and natural estimator of the climate state and its uncertainty, which we believe to be Gaussian at a global scale. We illustrate four applications: 1) EBM–KF generates a similar estimate to the 30-yr time-averaged climate state 15 years sooner, or a model-simulated hindcasts’ annual ensemble average, depending on whether volcanic forcing is filtered or not; 2) EBM–KF conveniently assesses annually likelihoods of crossing a policy threshold. For example, based on temperature records up to the end of 2023, p = 0.0017 indicates that the climate state was 1.5°C over preindustrial, but there is a 16% likelihood that the GMST in 2023 itself could have been over that threshold; 3) a variant of the EBM–KF also approximates the spread of an entire climate model large ensemble using only one or a few ensemble members; and 4) all variants of the EBM–KF are sufficiently fast to allow thorough sampling from non-Gaussian probabilistic futures, e.g., the impact of rare but significant volcanic eruptions. This sampling with the EBM–KF better determines how future volcanism may affect when policy thresholds will be crossed and what an ensemble with thousands of members exploring future intermittent volcanism reveals.

Significance Statement
The global average of Earth’s historical climate over the past 150 years can be explained by a thermal/radiation physical equation involving a small number of constants, atmospheric carbon dioxide (CO2) concentration, anthropogenic clouds, and volcanic emissions. Global mean surface temperature measurements vary around this climate state within a consistent normal distribution. This physical equation and statistical depiction allowed us to construct a simple model that can rapidly estimate the uncertainty in Earth’s current climate, aid in policy discussions, and provide an alternative for some applications to expensive ensemble modeling.
Effect of the synergistic interaction of zinc adipate, hydrotalcite and ethylene propylene rubber on the performances of polypropylene
Authors: Zhao Yaqin , Kou Jixiong , Shen Liang , Zhao Haoning , Chen Zhenbin 
Published: October 2024

Polypropylene (PP) is widely used in a range of areas due to its excellent physical and chemical properties, but its low-temperature brittleness and low-impact resistance largely limits its application. Hence, this study investigated the changes in mechanical and thermal properties of isotactic polypropylene (iPP) modified by the addition of the β-nucleating agent zinc adipate (ZnAA), the inorganic rigid particle hydrotalcite (HT), and the rubber elastomer bipropylene rubber (EPR). The structure and properties of modified PP were investigated by Fourier transform infrared (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM), differential scanning calorimetry (DSC), electronic universal material testing machine, and simple-beam impact testing machine. At last, the non-isothermal crystallization kinetics of iPP was investigated by the Caze method. It was found that with the addition of EPR reached 30 wt%, the notched impact strength of iPP was 34.74 kJ/m2, which was 542.43 % higher than that of pure iPP, and more than twice as high as that of ZnAA and HT modification, and the relative crystallinity ( K β) value of the β-crystalline form reached 79.66 % when 10 wt% EPR was added to the iPP. The addition of ZnAA, HT and EPR could significantly increase the crystallisation peak temperature ( T c), shorten the half-crystallisation time ( t 1/2) and increase the crystallisation activation energy ( ΔE) of iPP.
The Influence of Kinesiophobia and Pain Catastrophizing on Disease-Specific Quality of Life in Patients With Recurrent Patellofemoral Instability
Authors: Hiemstra Laurie A. , Lafave Mark R. , Bentrim Allegra , Kerslake Sarah 
Published: October 2024

Background: The Banff Patellofemoral Instability Instrument 2.0 (BPII 2.0) is a disease-specific, quality of life patient-reported outcome measure (PROM) that is valid and reliable in patients with recurrent lateral patellofemoral instability (LPI). Quality of life encompasses the physical, emotional, and psychological aspects of patient functioning and recovery. Purposes: To concurrently validate the BPII 2.0 to the Tampa Scale for Kinesiophobia (TSK-11), the Pain Catastrophizing Scale (PCS), and the Anterior Cruciate Ligament–Return to Sport after Injury Scale (ACL-RSI) in patients presenting with recurrent LPI and to assess baseline values for the PROMs in patients with LPI. Study Design: Cohort study (Diagnosis); Level of evidence, 2. Methods: A total of 107 consecutive patients with recurrent LPI were assessed between January and October 2021. Patients completed the BPII 2.0, TSK-11, PCS, and ACL-RSI. A Pearson r correlation coefficient was employed to examine relationships between the PROMs. Baseline values, as well as floor and ceiling effects and Cronbach alpha, were assessed for all PROMs. Results: All 107 patients completed the 4 PROMs. Patients included 28 men (26.2%) and 79 women (73.8%), with a mean (SD) age of 25.7 (9.8) years. The mean (SD) age at first dislocation was 14.8 (6.3) years. The TSK-11, PCS, and ACL-RSI were all statistically significantly correlated with the BPII 2.0 ( P < .01; 2-tailed), with moderate correlations ( r = -0.361-0.628) The R2 values indicated an overlap of the constructs measured by the PROMs. Conclusion: A statistically significant correlation was evident between the BPII 2.0 and the other PROMs. The BPII 2.0 does not explicitly measure kinesiophobia or pain catastrophizing; however, the significant statistical relationship of the TSK-11 and PCS to the BPII 2.0 suggests that this information is being captured and reflected.
Intellectual Monopoly and Income Inequality in the United States, 1948–2021: A Long-Run Analysis
Authors: Rotta Tomás N. 
Published: October 2024

Proponents of intellectual property claim that it fosters innovation and benefits companies and workers by increasing long-run growth. A growing body of literature challenges these claims by arguing that the cumulative nature of intellectual monopoly amplifies asymmetries between winners and losers. Intellectual monopolies pose disadvantages for countries, firms, consumers, and workers who struggle to maintain a leading position. Using data at the aggregate level from 1948 to 2021 in the United States, this article estimates the long-run effects of proprietary knowledge accumulation on income shares and tests the hypothesis that intellectual monopoly amplifies income inequality. The empirical evidence shows that companies in the United States transferred to wages a significant share of their profits from intellectual property. But these transfers have widened income inequality by benefiting the top 10 and top 25 percent, to the detriment of lower income brackets. Intellectual property alone can explain 23 percent of the increase in the income share of the top 10 percent in the 1948–2021 period. JEL Classification: O34, E25, C22
Optimization through combined compromise solution and weight determination through SHANNON-ENTROPY for multi criteria decision making in tribological testing of ultrahigh molecular weight polyethylene nano-composite in SBF environment and compression testing with it’s invitro analysis
Authors: Srivastava Parijat , Singh Vinay Pratap 
Published: October 2024

Aluminium oxide and graphene reinforced with ultrahigh molecular weight polyethylene (UHMWPE) composites offer significant potential for prosthetic and implant applications due to their unique combination of properties. In this article, a series of aluminium oxide and graphene reinforced with ultrahigh molecular weight polyethylene (UHMWPE) composites were successfully fabricated through compression moulding. The average hardness of all the composites was observed to be higher than the UHMWPE (6.1 Hv). Composite U-15A-5 G was found to be the hardest with hardness of 10.5 Hv owing to strong bond between Al2O3 and UHMWPE. The compression strength of the U-15A-5 G material was about 81.936 MPa, which is 40.9% better than the pure UHMWPE. The compression modulus was about 330 MPa which is 22.2% higher than neat UHMWPE. Composite samples were tribo tested under simulated body fluid (SBF) lubricating environment. Using combined entropy-COCOSO method it was found that 50 mm/s speed, 200N load and 5% graphene are the optimum tribological parameters. with Cof of 0.04 and wear rate of 4.8745*10−4 mm3/Nm. Microstructure analysis indicated that long-range structured lamellar structures and microfibers were created between the crystals with the assistance of graphene layers. Apatite layer was also found on the sample. The apatite layer included phosphate and carbonate ions, which is good for in vitro bioactivity on polymeric films. This composite is a good candidate for prosthetics and implant components.
Integrating ytopt and libEnsemble to autotune OpenMC
Authors: Wu Xingfu , Tramm John R , Larson Jeffrey , Navarro John-Luke , Balaprakash Prasanna , Videau Brice , Kruse Michael , Hovland Paul , Taylor Valerie , Hall Mary 
Published: October 2024

Ytopt is a Python machine-learning-based autotuning software package developed within the ECP PROTEAS-TUNE project. The ytopt software adopts an asynchronous search framework that consists of sampling a small number of input parameter configurations and progressively fitting a surrogate model over the input-output space until exhausting the user-defined maximum number of evaluations or the wall-clock time. libEnsemble is a Python toolkit for coordinating workflows of asynchronous and dynamic ensembles of calculations across massively parallel resources developed within the ECP PETSc/TAO project. libEnsemble helps users take advantage of massively parallel resources to solve design, decision, and inference problems and expands the class of problems that can benefit from increased parallelism. In this paper we present our methodology and framework to integrate ytopt and libEnsemble to take advantage of massively parallel resources to accelerate the autotuning process. Specifically, we focus on using the proposed framework to autotune the ECP ExaSMR application OpenMC, an open source Monte Carlo particle transport code. OpenMC has seven tunable parameters some of which have large ranges such as the number of particles in-flight, which is in the range of 100,000 to 8 million, with its default setting of 1 million. Setting the proper combination of these parameter values to achieve the best performance is extremely time-consuming. Therefore, we apply the proposed framework to autotune the MPI/OpenMP offload version of OpenMC based on a user-defined metric such as the figure of merit (FoM) (particles/s) or energy efficiency energy-delay product (EDP) on Crusher at Oak Ridge Leadership Computing Facility. The experimental results show that we achieve the improvement up to 29.49% in FoM and up to 30.44% in EDP.
Multicategory Survival Outcomes Classification via Overlapping Group Screening Process Based on Multinomial Logistic Regression Model With Application to TCGA Transcriptomic Data
Authors: Wang Jie-Huei , Hou Po-Lin , Chen Yi-Hau 
Published: October 2024

Objectives: Under the classification of multicategory survival outcomes of cancer patients, it is crucial to identify biomarkers that affect specific outcome categories. The classification of multicategory survival outcomes from transcriptomic data has been thoroughly investigated in computational biology. Nevertheless, several challenges must be addressed, including the ultra-high-dimensional feature space, feature contamination, and data imbalance, all of which contribute to the instability of the diagnostic model. Furthermore, although most methods achieve accurate predicted performance for binary classification with high-dimensional transcriptomic data, their extension to multi-class classification is not straightforward. Methods: We employ the One-versus-One strategy to transform multi-class classification into multiple binary classification, and utilize the overlapping group screening procedure with binary logistic regression to include pathway information for identifying important genes and gene-gene interactions for multicategory survival outcomes. Results: A series of simulation studies are conducted to compare the classification accuracy of our proposed approach with some existing machine learning methods. In practical data applications, we utilize the random oversampling procedure to tackle class imbalance issues. We then apply the proposed method to analyze transcriptomic data from various cancers in The Cancer Genome Atlas, such as kidney renal papillary cell carcinoma, lung adenocarcinoma, and head and neck squamous cell carcinoma. Our aim is to establish an accurate microarray-based multicategory cancer diagnosis model. The numerical results illustrate that the new proposal effectively enhances cancer diagnosis compared to approaches that neglect pathway information. Conclusions: We showcase the effectiveness of the proposed method in terms of class prediction accuracy through evaluations on simulated synthetic datasets as well as real dataset applications. We also identified the cancer-related gene-gene interaction biomarkers and reported the corresponding network structure. According to the identified major genes and gene-gene interactions, we can predict for each patient the probabilities that he/she belongs to each of the survival outcome classes.
Trends of fully alcohol-attributable mortality rates before and during COVID-19 in the Baltic and other European countries
Authors: Rehm Jürgen , Tran Alexander , Hassan Ahmed Syed , Jiang Huan , Lange Shannon , Reile Rainer , ŠtelemĖkas Mindaugas 
Published: October 2024

Aims: We tested the polarization hypothesis, which postulates that during times of crises, such as the COVID-19 pandemic, alcohol consumption increases among the heaviest drinkers but decreases among most other drinkers, resulting in an overall decrease in consumption among the population. We posited the increase in heavy drinking would lead to increases in 100% alcohol-attributable (AA) mortality. Furthermore, based on the high level of alcohol consumption in the Baltic countries compared to other European countries, we predicted that the increases in AA mortality would be more pronounced in these countries. Methods: Data for 100% AA deaths were obtained from the World Health Organization for the period 2010 to 2022, and standardized to the regional age distribution for 2010. Parametric and non-parametric tests were used to test the study hypotheses. Results: The mean age-standardized 100% AA mortality rate increased during the COVID-19 pandemic in the 19 European countries with data by 3.12 deaths per 100,000 population, constituting an 18% increase (tested against no change: t = 2.52; df = 18; p = 0.021). This increase was higher in the Baltic countries (mean difference = 13.41 deaths per 100,000 population; standard deviation (SD) = 7.44; 46% increase) than for other European countries (mean difference = 1.19; SD = 1.55; 8% increase). The increases in 100% AA mortality were associated with decreases in the level of alcohol consumption in the majority of countries. Conclusions: As predicted, 100% AA mortality increased in 19 European countries during the COVID-19 pandemic, with the Baltic countries seeing a higher increase. Renewed alcohol control policy efforts should be considered.
The Incidence of Shoulder and Elbow Injuries in High School and Collegiate Softball Athletes: A Systematic Review
Authors: Enata Nichelle M. , Inclan Paul M. , Brophy Robert H. , Knapik Derrick , Smith Matthew V. 
Published: October 2024

Context: Despite increased youth and adolescent participation in fast-pitch softball and the reporting of upper extremity injuries, there remains a relative paucity of research examining shoulder and elbow injuries in high school and collegiate softball athletes. Objective: To evaluate the reported incidence, setting, and positional factors associated with shoulder and elbow injuries in high school and collegiate fast-pitch softball players. Data Sources: PubMed, Ovid, Medline, EMBASE, Scopus, Cochrane Central, and Clinicaltrials.gov. Study Selection: English-language articles reporting the incidence of shoulder and/or elbow injuries occurring in high school or collegiate fast-pitch softball players were included. Biomechanical studies, review articles, abstract only texts, previous systematic reviews, and meta-analyses were excluded. Study Design: Systematic review. Level of Evidence: Level 4. Data Extraction: Two reviewers independently evaluated studies. Data related to the reported incidence of shoulder and elbow injuries, injury setting, position, and rate of return to play after injury were recorded. Results: A total of 22 studies were identified. In high school athletes, shoulder injury rates ranged from 0.88 to 1.14 per 10,000 athletic exposures (AE), with elbow injury rates ranging from 0.41 to 0.71 per 10,000 AE. In collegiate athletes, reported injury rates ranged from 3.76 to 5.93 per 10,000 AE for shoulder and 1.5 to 3.39 per 10,000 AE for elbow injuries. Shoulder and elbow injuries were reported more commonly during competition in high school athletes, and with greater frequency in the practice setting in collegiate athletes. No association between injury incidence and position was appreciated at either the high school or collegiate level. Most (81%-96%) athletes were able to return to sport within 3 weeks of injury. Conclusion: The incidence of shoulder and elbow injuries was greater in collegiate softball athletes than in high school athletes.
Noninsulin antidiabetic prescription patterns in Colombia: a cross-sectional study
Authors: Machado-Alba Jorge Enrique , Gaviria-Mendoza Andrés , Machado-Duque Manuel Enrique , Valladales-Restrepo Luis Fernando , Alvarado-Segovia Andrés 
Published: October 2024

Background: The prevalence of type 2 diabetes mellitus (T2DM) continues to increase; the clinical practice guidelines continue to modify the recommendations for its treatment. Objective: The aim was to determine the prescription patterns of noninsulin antidiabetics in a group of patients from Colombia. Design: Cross-sectional study. Methods: The use of noninsulin antidiabetic drugs based on a population database of patients under treatment in 2022. Comorbidities were identified, including total numbers, proportions, and defined daily doses of each antidiabetic agent per 1000 inhabitants/day (DHD). Results: A total of 155,381 patients with T2DM were identified, with a mean age of 67.1 ± 12.0 years. The most widely used antidiabetics according to DHD were metformin (9.46 DHD), empagliflozin (5.3), sitagliptin (2.8), linagliptin (2.4), and dapagliflozin (2.3), mainly in combination therapy (55.5%), most often two (31.2% of patients) or three antidiabetics (22.4% of patients). The most frequent cardiovascular comorbidities were hypertension (67.6%), chronic kidney disease (6.3%), and coronary ischemic heart disease (2.5%), treated with angiotensin 2 receptor antagonists, followed by diuretics, calcium antagonists, and β-blockers. Conclusion: This group of patients with T2DM has been treated mainly with metformin alone or in combination with other antidiabetic drugs, but despite the changes in treatment in recent years, a significant number of patients with concomitant cardiovascular conditions are not receiving appropriate antidiabetic agents. Sodium-glucose type 2 cotransporter or glucagon-like peptide-1 receptor agonists may offer additional benefits with reduced cardiovascular risk.
Enhancing medication management of older adults in Qatar: healthcare professionals’ perspectives on challenges, barriers and enabling solutions
Authors: Alyazeedi Ameena , Stewart Carrie , Soiza Roy L. , Stewart Derek , Awaisu Ahmed , Ryan Cristin , Alhail Moza , Aldarwish Abdulaziz , Myint Phyo Kyaw 
Published: October 2024

Background: Polypharmacy and potentially inappropriate medications are significant challenges in older adults’ medication management. The Consolidated Framework for Implementation Research (CFIR) is a comprehensive approach used to explore barriers and enablers to the healthcare system in guiding the effective implementation of evidence-based practices. Objectives: This study examines the barriers and enablers to promote safe medication management among older adults in Qatar from healthcare professionals’ perspectives. This includes identifying critical factors within the healthcare system influencing medication management and suggesting practical solutions to improve it. Design: The study employs a qualitative design. Focus Groups (FGs) were conducted with healthcare professionals from the geriatric, mental health and medicine departments of Hamad Medical Corporation (HMC), the leading governmental sector in Qatar serving the older adult population. Methods: Utilising the CFIR, this study analysed feedback from healthcare professionals through FGs at HMC. A combined inductive and deductive thematic analysis was applied to transcripts from five FGs, focusing on identifying barriers and enablers to safe medication management among older adults. Two researchers transcribed the audio-recorded FG discussions verbatim, and two researchers analysed the data using a mixed inductive and deductive thematic analysis approach utilising CFIR constructs. Results: We engaged 53 healthcare professionals (31 physicians, 10 nurses and 12 clinical pharmacists) in FGs. The analysis identified current barriers and enabler themes under different CFIR constructs, including inner settings, outer settings, individual characteristics and intervention characteristics. We identified 44 themes, with 25 classifieds as barriers and 19 as enablers. The findings revealed that barriers and enablers within the inner settings were primarily related to structural characteristics, resources, policies, communication and culture. On the other hand, barriers and enablers from the outer settings included patients and caregivers, care coordination, policies and laws, and resources. Conclusion: This study identified several barriers and enablers to promote medication management for older adults using the CFIR constructs from the perspective of healthcare professionals. The multifaceted findings emphasise involving stakeholders like clinical leaders, policymakers and decision-makers to address medication safety factors. A robust action plan, continuously monitored under Qatar’s national strategy, is vital. Further research is needed to implement recommended interventions.