bluuebunny/crossref_metadata_2025_split huggingface dataset:
Importance of internal variability for climate model assessment
Authors: Jain Shipra , Scaife Adam A. , Shepherd Theodore G. , Deser Clara , Dunstone Nick , Schmidt Gavin A. , Trenberth Kevin E. , Turkington Thea 
Published: June 2023

AbstractBenchmarking climate model simulations against observations of the climate is core to the process of building realistic climate models and developing accurate future projections. However, in many cases, models do not match historical observations, particularly on regional scales. If there is a mismatch between modeled and observed climate features, should we necessarily conclude that our models are deficient? Using several illustrative examples, we emphasize that internal variability can easily lead to marked differences between the basic features of the model and observed climate, even when decades of model and observed data are available. This can appear as an apparent failure of models to capture regional trends or changes in global teleconnections, or simulation of extreme events. Despite a large body of literature on the impact of internal variability on climate, this acknowledgment has not yet penetrated many model evaluation activities, particularly for regional climate. We emphasize that using a single or small ensemble of simulations to conclude that a climate model is in error can lead to premature conclusions on model fidelity. A large ensemble of multidecadal simulations is therefore needed to properly sample internal climate variability in order to robustly identify model deficiencies and convincingly demonstrate progress between generations of climate models.
New formation and fate of Isoprene SOA markers revealed by field data-constrained modeling
Authors: Zhang Jie , Liu Junyi , Ding Xiang , He Xiao , Zhang Tianle , Zheng Mei , Choi Minsu , Isaacman-VanWertz Gabriel , Yee Lindsay , Zhang Haofei , Misztal Pawel , Goldstein Allen H. , Guenther Alex B. , Budisulistiorini Sri Hapsari , Surratt Jason D. , Stone Elizabeth A. , Shrivastava Manish , Wu Dui , Yu Jian Zhen , Ying Qi 
Published: June 2023

AbstractParticulate 2-methyltetrols (2-MT) and 2-methylglyceric acid (2-MG) are typically used to indicate the abundance of isoprene-derived secondary organic aerosols (SOA). However, their formation and fate are not fully understood. In this study, we showed that particulate 2-MT and 2-MG collected at multiple monitoring sites under a wide range of atmospheric and emission conditions, with concentrations spanning six orders of magnitudes, are well reproduced with an expanded isoprene-SOA scheme implemented into the Community Multiscale Air Quality (CMAQ) model. The scheme considers their three-phase (gas-aqueous-organic phase) partitioning, formation from acid-driven multiphase reactions, and degradation by OH radicals in the gas and aqueous phases. The model results reveal that a non-aqueous formation pathway or direct biogenic emission is needed to supplement the commonly assumed acid-driven multiphase reaction process to explain the observed 2-MT concentrations. This missing pathway contributes to 20–40% of 2-MT in areas with aerosol pH<2 and more than 70% under less acidic conditions (pH~2–5), such as those encountered in the western US and China. The typical summertime gas-phase photochemical lifetimes of 2-MT and 2-MG are estimated to be 4–6 and 20–30 h, respectively, and their aqueous lifetimes are approximately 20–40 h. Our simulations show that predicted 2-MT is mainly influenced by its aqueous phase loss to OH, but 2-MG is more sensitive to gas phase OH loss due to the preferential partitioning of the two tracers in the aqueous and gas phases, respectively.
Non-invasive chronic kidney disease risk stratification tool derived from retina-based deep learning and clinical factors
Authors: Joo Young Su , Rim Tyler Hyungtaek , Koh Hee Byung , Yi Joseph , Kim Hyeonmin , Lee Geunyoung , Kim Young Ah , Kang Shin-Wook , Kim Sung Soo , Park Jung Tak 
Published: June 2023

AbstractDespite the importance of preventing chronic kidney disease (CKD), predicting high-risk patients who require active intervention is challenging, especially in people with preserved kidney function. In this study, a predictive risk score for CKD (Reti-CKD score) was derived from a deep learning algorithm using retinal photographs. The performance of the Reti-CKD score was verified using two longitudinal cohorts of the UK Biobank and Korean Diabetic Cohort. Validation was done in people with preserved kidney function, excluding individuals with eGFR <90 mL/min/1.73 m2 or proteinuria at baseline. In the UK Biobank, 720/30,477 (2.4%) participants had CKD events during the 10.8-year follow-up period. In the Korean Diabetic Cohort, 206/5014 (4.1%) had CKD events during the 6.1-year follow-up period. When the validation cohorts were divided into quartiles of Reti-CKD score, the hazard ratios for CKD development were 3.68 (95% Confidence Interval [CI], 2.88–4.41) in the UK Biobank and 9.36 (5.26–16.67) in the Korean Diabetic Cohort in the highest quartile compared to the lowest. The Reti-CKD score, compared to eGFR based methods, showed a superior concordance index for predicting CKD incidence, with a delta of 0.020 (95% CI, 0.011–0.029) in the UK Biobank and 0.024 (95% CI, 0.002–0.046) in the Korean Diabetic Cohort. In people with preserved kidney function, the Reti-CKD score effectively stratifies future CKD risk with greater performance than conventional eGFR-based methods.
Out‐of‐distribution detection based on multi‐classifiers
Authors: Jiang Weijie , Yu Yuanlong 
Published: June 2023

AbstractExisting out‐of‐distribution detection models rely on the prediction of a single classifier and are sensitive to classifier bias, making it difficult to discriminate similar feature out‐of‐distribution data. This article proposed a multi‐classifier‐based model and two strategies to enhance the performance of the model. The model first trains several different base classifiers and obtains the predictions of the test data on each base classifier, then uses cross‐entropy to calculate the dispersion between these predictions, and finally uses the dispersion as a metric to identify the out‐of‐distribution data. A large scatter implies inconsistency in the predictions of the base classifier, and the greater the probability of belonging to the out‐of‐distribution data. The first strategy is applied in the training process of the model to increase the difference between base classifiers by using various scales of Label smoothing regularisation. The second strategy is applied to the inference process of the model by changing the mean and variance of the activations in the neural network to perturb the inference results of the test data. These two strategies can effectively amplify the discrepancy in the dispersion of the in‐distribution and out‐of‐distribution data. The experimental results show that the method in this article can effectively improve the performance of the model in the detection of different types of out‐of‐distribution data, improve the robustness of deep neural networks (DNN) in the face of unknown classes, and promote the application of DNN in systems and engineering with high security requirements.
Adaptive dynamic programming discrete‐time LQR control on electromagnetic levitation system
Authors: Abdollahzadeh Mohammad 
Published: June 2023

AbstractIn this paper, an adaptive dynamic programming discrete‐time linear quadratic Riccati (ADP‐DTLQR) control is designed for an electromagnetic levitation system (EMS) to track the reference trajectories and preserve stability in severe situations efficiently. The EMS system has a strongly nonlinear and unstable dynamic in nature. The ADP‐DTLQR approach is a novel method based on the Q‐learning algorithm for unknown discrete‐time systems in a causal manner. The ADP‐DTLQR can determine the online gains of the controller in different conditions as a model‐free structure, which can minimize the combination of state errors and control efforts of the systems. Moreover, the DTLQR controller and linear matrix inequality controller on the EMS system are designed and compared with the present strategy in different scenarios in the simulation section to verify the robustness and strongness of the proposed method. The evaluation of simulation results demonstrates that the proposed control scheme is suitable not only for preserving the levitated object stability but also for compensating the disturbances and uncertainties in the EMS structure.
Control of a transformerless static synchronous series compensator for unbalanced voltage compensation
Authors: Pan Xuejiao , Zhang Li 
Published: June 2023

AbstractThis paper explores a transformerless static synchronous series compensator (TL‐SSSC) for mitigating voltage distortion in a power distribution line with renewable generations. A novel deadbeat current control scheme with time delay compensation is proposed which models the a‐b‐c three phase line with TL‐SSSC plus its LC filters and predicts the compensating voltages to be injected by the TL‐SSSC. Without resolving measured voltages and currents into positive and negative sequences, the scheme is computationally efficient and effective in mitigating voltage distortion due to faults occurring at different locations along the distribution line. It also eliminates the problem of inaccurate compensation voltage caused by inverter filter choking voltage drop. The paper also analyses the power controllable ranges for a TL‐SSSC under different unbalanced voltage ratios. The proposed TL‐SSSC and control scheme is validated using Typhoon Hardware‐In –Loop (HIL) device. Experimental results on mitigation unbalanced voltages of a 11kV transmission line confirm the effectiveness and high performance.
Charging load forecasting of electric vehicles based on sparrow search algorithm‐improved random forest regression model
Authors: Wang Dongdong , Ge Yuan , Cao Jin , Lin Qiyou , Chen Renfeng 
Published: June 2023

AbstractIn order to solve the problem that the current charging load forecasting accuracy is not high, it is difficult to simulate the actual charging load distribution of Electric Vehicles (EVs), and it is impossible to reasonably predict the future load, a charging load forecasting model based on Sparrow Search Algorithm (SSA) improved Random Forest Regression (RFR) is proposed. The SSA is used to enhance the ability of global optimization and local exploration. Combined with the advantages of the RFR model, such as low generalization error, fast convergence speed, and few adjustment parameters, the SSA was used to optimize the parameters of the decision tree number and the number of split nodes in the RFR, and the optimal value of the parameters is obtained, so as to obtain the optimal performance of the RFR. Firstly, based on the concept of travel chain and conditional probability distribution, the user's travel habits are described. Monte Carlo simulation method was used to simulate the driving, parking, and charging behaviours of a large number of EVs in different regions, so as to obtain the charging load of EVs in different regions. Then, a charging load forecasting model based on SSA improved RFR is established. Monte Carlo simulation results are used as sample data to predict the charging load of EVs in different regions. Finally, taking a certain area as an example, the experimental results show that the charging load prediction model based on Sparrow Search Algorithm improved Random Forest Regression (SSA‐RFR) can accurately predict the charging load of EVs in different regions, and the charging load of different regional types is obviously different. Compared with the RFR model and other literature models, the SSA‐RFR model has better prediction accuracy, which verifies the feasibility and superiority of SSA‐RFR model in EVs charging load prediction.
Systematic meta-analysis of research on AI tools to deal with misinformation on social media during natural and anthropogenic hazards and disasters
Authors: Vicari Rosa , Komendatova Nadejda 
Published: June 2023

AbstractThe spread of misinformation on social media has led to the development of artificial intelligence (AI) tools to deal with this phenomenon. These tools are particularly needed when misinformation relates to natural or anthropogenic disasters such as the COVID-19 pandemic. The major research question of our work was as follows: what kind of gatekeepers (i.e. news moderators) do we wish social media algorithms and users to be when misinformation on hazards and disasters is being dealt with? To address this question, we carried out a meta-analysis of studies published in Scopus and Web of Science. We extracted 668 papers that contained keyterms related to the topic of “AI tools to deal with misinformation on social media during hazards and disasters.” The methodology included several steps. First, we selected 13 review papers to identify relevant variables and refine the scope of our meta-analysis. Then we screened the rest of the papers and identified 266 publications as being significant for our research goals. For each eligible paper, we analyzed its objective, sponsor’s location, year of publication, research area, type of hazard, and related topics. As methods of analysis, we applied: descriptive statistics, network representation of keyword co-occurrences, and flow representation of research rationale. Our results show that few studies come from the social sciences (5.8%) and humanities (3.5%), and that most of those papers are dedicated to the COVID-19 risk (92%). Most of the studies deal with the question of detecting misinformation (68%). Few countries are major funders of the development of the topic. These results allow some inferences. Social sciences and humanities seem underrepresented for a topic that is strongly connected to human reasoning. A reflection on the optimum balance between algorithm recommendations and user choices seems to be missing. Research results on the pandemic could be exploited to enhance research advances on other risks.
Against epistemic accounts of luck
Authors: Hill Jesse 
Published: June 2023

Abstract
Epistemic accounts of luck define luck’s chanciness condition relative to a subject’s epistemic position. This could be put in terms of a subject’s evidence or knowledge about whether the event will occur. I argue that both versions of the epistemic account fail. In §2, I give two types of counterexamples to the evidence-based approach. In §3, I argue—contrary to the knowledge-based view—that an event can be a matter of good or bad luck for a subject even if she knows that it will occur. In §4, I argue that epistemic accounts cannot explain some instances of constitutive luck. Because of these problems, luck’s chanciness condition cannot be adequately defined in epistemic terms.
A-01 Trait Mindfulness Gains Associated with Executive Functioning Improvements Following Physical Exercise in Older Adults
Authors: Agate F T , Sánchez J P , Webber A , Contreras A , Garcia-Barrera M A 
Published: June 2023

Abstract

Purpose
Although there are certain injury risks associated with sports participation (e.g., concussion), there are also neuropsychological benefits relating to physical activity. Evidence suggests that trait mindfulness (TM) may contribute to the neuropsychological benefits of physical activity and exercise, including those pertaining to executive functioning (EF) and mood. The present study aimed to investigate the degree to which changes in TM accounted for changes in neuropsychological functioning following a remote physical exercise training program for older adults. It was hypothesized that increases in TM would predict improvements in self-reported EF and symptoms of depression and anxiety.

Methods
As part of a larger RCT, 43 older adults (70% females, 65-81 years-old) from across Canada completed an online assessment measuring TM, EF, and mood before and after engaging in an 8-week remote physical exercise training program.

Results
Linear regression analyses showed that pre-post changes in TM explained a significant proportion of variance in self-reported changes in EF, r2=.18, F(1,24)=5.11, p&lt;.05, but did not for symptoms of depression, r2=.06, F(1,24)= 1.53, p= 23 or anxiety, r2=.04, F(1,24)=1.08, p=.31. More specifically, bivariate correlations demonstrated that gains in TM were significantly associated with increases in reported impulse control, r=.48, p=.01, and empathy, r=.42, p&lt;.05.

Conclusions
These results suggest that for older adults engaging in exercise, gains in trait mindfulness may be associated with benefits to certain aspects of executive functioning. These findings also invite sports neuropsychologists to consider level of mindfulness when evaluating individuals (e.g., athletes) engaged in exercise.
A-03 Preliminary Evidence for Motor Vehicle Accidents and Associated Concussion Response Severity in Older Adults
Authors: Albrecht T , Holland C , French J , Perry C , Kontos A 
Published: June 2023

Abstract

Purpose
To determine if mechanism of injury is associated with differences in initial response to concussion in older adults.

Methods
Fifty older adults (54% female) aged 50-84 (59.14±6.54) were recruited from a sports medicine concussion clinic at initial appointment. Inclusion criteria included age (50+) and injury date within the last year. Exclusion criteria included prior diagnosis of a neurological disorder and/or brain malformation, psychiatric diagnosis other than anxiety/depression, prior history of moderate or severe brain injury, and/or ongoing litigation. Mechanism of injury in the sample included 22 falls/accidents (44%), 20 MVAs (40%), 2 sport/recreation (4%), 1 assault (2%), and 5 other (10%). Participants were assigned to two independent groups, MVA and Non-MVA. Outcome measures included Vestibular/Ocular-Motor Screening (VOMS), Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT), and Concussion Clinical Profile Screening (CP Screen). Response severity was operationalized by VOMS Total and CP Screen Total. Two-tailed independent t-tests (alpha&lt;.05) examined differences between groups at initial visit.

Results
There were no significant differences between groups in terms of demographics or other common risk factors. There were no differences between groups for response severity. The MVA group reported more severe vestibular complaints t(41) =2.07, p=.044, more severe cognitive complaints, t(44)=3.68, p&lt;.001, and demonstrated worse scores on domains of visual memory, t(43)=-2.16, p=.037, reaction time, t(43)=3.07, p=.004, and visual motor speed, t(43)=-2.85, p=.007.

Conclusions
MVA-related concussions may result in more focal disruption of vestibular and cognitive performance in older adults. Future research is warranted to confirm the findings of this study, to explore potential explanations for these findings, and to further explore how concussion presents in older adults.
A-10 Virginians Report Unequal Access to Appropriate Licensed Healthcare Providers for Concussion
Authors: Blackstone A M , Werner S , McGill C , Hacherl S , Kelshaw P M , Lincoln A E , DeSalvo G , Cook N E , Caswell S V 
Published: June 2023

Abstract

Purpose
Virginia state law requires athletes with suspected concussions to be evaluated and receive written clearance from an appropriately licensed healthcare provider (HCP) before returning to play. This HCP is defined as a physician, physician assistant, athletic trainer (AT), neuropsychologist, physical therapist, or nurse practitioner. However, little is known regarding whether access to HCPs in Virginia differs by locality. We explored stakeholders’ access to HCPs.

Methods
An electronic survey assessed school and community stakeholders’ perceptions of accessibility to HCPs. Results were stratified by locale (National Center for Education Statistics locality classifications). Participants were a convenience sample recruited through collaboration with state professional organizations.

Results
400 respondents (city [n=61, 15%], suburban [n=142, 36%], town [n=67, 17%], and rural [n=130, 33%]) completed the survey. Overall, three out of four (77%) reported having access to community HCPs, though rates were substantially higher among those from cities (92% city, 80% suburban, 80% town). However, only 66% of respondents from rural areas reported access to an HCP. Additionally, when asked about access to an AT, 45% of stakeholders responded that they had no access to an AT. Most respondents with no AT access were from suburban (34%) or rural (39%) locales, and 43% reported traveling &gt;30 minutes to access an HCP.

Conclusions
Our findings indicate access disparities such that rural Virginians report less access to licensed HCPs for concussion clearance. In communities without an AT, it may require considerable travel to reach an HCP. Further investigation is needed into strategies that promote equal access to HCPs.
A-12 The Impact of Injury Mechanism and Skill on Concussion in School and All Star Cheerleaders
Authors: Boucher S J , Datoc A E , Mathew A S , Choi D , Jones C A , Ellis C , Abt J P , Burkhart S O 
Published: June 2023

Abstract

Purpose
Previous research determined that sports-related concussions (SRC) occurred at higher rates in cheerleading club practices compared to school practice. The purpose of the current study was to determine if mechanism of injury and skill during which injury occurred impacted symptom reporting and overall recovery days in school and All Star cheerleaders.

Methods
This study was a retrospective comparison of 101 female cheerleaders (aged 8-18 years) evaluated in a specialty concussion clinic and diagnosed with SRC from 2020-2023. Cheerleaders were stratified based on type of cheer, mechanism of injury, and skill during which they were injured. Post-Concussion Symptom Scale (PCSS) total score and recovery time in days were compared for each group. Chi square and factorial analyses were used.

Results
Most concussions occurred during stunting incidents (76.2%) with higher rates for school cheerleaders (43.6%) compared to All Star (32.7%). Tumbling incidents accounted for 11.9%, with higher rates for All Star (7.9%) compared to school cheerleaders (4.0%). Head to ground contact (49.5%) and head to body contact (42.6%) were the most reported mechanism of injury, with school cheerleaders having slightly higher rates (28.7%; 21.8%) compared to All Star cheerleaders (20.8%; 20.8%) respectively. School cheerleaders who suffered a SRC through head-to-head contact had significantly higher PCSS scores (P= 0.39) compared to other mechanisms across groups.

Conclusions
A majority of the SRCs in cheer athletes resulted from head to ground and head to body contacts. The results suggest that cheerleaders suffer higher rates of SRC when participating in stunting activities compared to other cheerleading skills. Future research examining the trajectory of recovery following SRC suffered in cheerleading, will identify the need for skill-specific protocols.