bluuebunny/crossref_metadata_2025_split huggingface dataset:
An adaptive and late multifusion framework in contextual representation based on evidential deep learning and Dempster–Shafer theory
Authors: El-Din Doaa Mohey , Hassanein Aboul Ella , Hassanien Ehab E. 
Published: July 2024

AbstractThere is a growing interest in multidisciplinary research in multimodal synthesis technology to stimulate diversity of modal interpretation in different application contexts. The real requirement for modality diversity across multiple contextual representation fields is due to the conflicting nature of data in multitarget sensors, which introduces other obstacles including ambiguity, uncertainty, imbalance, and redundancy in multiobject classification. This paper proposes a new adaptive and late multimodal fusion framework using evidence-enhanced deep learning guided by Dempster–Shafer theory and concatenation strategy to interpret multiple modalities and contextual representations that achieves a bigger number of features for interpreting unstructured multimodality types based on late fusion. Furthermore, it is designed based on a multifusion learning solution to solve the modality and context-based fusion that leads to improving decisions. It creates a fully automated selective deep neural network and constructs an adaptive fusion model for all modalities based on the input type. The proposed framework is implemented based on five layers which are a software-defined fusion layer, a preprocessing layer, a dynamic classification layer, an adaptive fusion layer, and an evaluation layer. The framework is formalizing the modality/context-based problem into an adaptive multifusion framework based on a late fusion level. The particle swarm optimization was used in multiple smart context systems to improve the final classification layer with the best optimal parameters that tracing 30 changes in hyperparameters of deep learning training models. This paper applies multiple experimental with multimodalities inputs in multicontext to show the behaviors the proposed multifusion framework. Experimental results on four challenging datasets including military, agricultural, COIVD-19, and food health data provide impressive results compared to other state-of-the-art multiple fusion models. The main strengths of proposed adaptive fusion framework can classify multiobjects with reduced features automatically and solves the fused data ambiguity and inconsistent data. In addition, it can increase the certainty and reduce the redundancy data with improving the unbalancing data. The experimental results of multimodalities experiment in multicontext using the proposed multimodal fusion framework achieve 98.45% of accuracy.
Importance of students’ social resources for their academic achievement and well-being in elementary school
Authors: Schwerter Jakob , Stang-Rabrig Justine , Kleinkorres Ruben , Bleher Johannes , Doebler Philipp , McElvany Nele 
Published: July 2024

AbstractBased on the relationships motivation theory, it can be assumed that social interactions in elementary school are essential for students’ development and especially for their school success. Thus, this study examined how vital social resources, more precisely social interactions with peers and teachers, are for two central aspects of school success, namely academic achievement and well-being. To this end, the representative German PIRLS 2016 data of 3959 fourth-grade students (MAge = 10.34 years; N = 1,940 girls, 71% white) were analyzed. Social interactions were operationalized using factors indicating whether students experienced bullying from peers, and how much teacher support they perceived. We found that fewer bullying experiences and more perceived teacher support were positively related to academic achievement and enjoyment of school as a prominent aspect of school-related well-being. Applying machine-learning methods to avoid overfitting while including important control variables, only the effects of bullying experiences and perceived teacher support on well-being remained robust. The results underlined that positive relationship experiences were particularly important for students’ well-being but not necessarily incremental to students’ academic achievement.
Influence of a 2-week transcutaneous auricular vagus nerve stimulation on memory: findings from a randomized placebo controlled trial in non-clinical adults
Authors: Cibulcova Veronika , Koenig Julian , Jackowska Marta , Jandackova Vera Kr 
Published: July 2024

Abstract

Purpose
Memory plays an essential role in daily life and is one of the first functions to deteriorate in cognitive impairment and dementia. Transcutaneous vagus nerve stimulation (tVNS) is a promising therapeutic method; however, its ability to enhance memory is underexplored, especially considering long-term stimulation. We aimed to investigate the effect of a 2-week course of auricular tVNS (taVNS) on memory in a non-clinical population.

Methods
This single-blind randomized placebo-wait-list controlled trial recruited 76 participants (30 men; mean age 48.32 years) and randomized them into four groups: early active/sham taVNS and late active/sham taVNS. Participation in the study lasted 4 weeks; early groups underwent 2 weeks intervention immediately following the first study site visit (days 0–13) and late groups 2 weeks after the first study site visit (days 14–27). Active and sham taVNS included 2 weeks of daily 4-h neurostimulation at the tragus or earlobe, respectively. To assess memory, we used the Rey Auditory Verbal Learning Test.

Results
Two weeks of active taVNS, but not sham taVNS, improved immediate recall and short-term memory score both in early and late groups. Furthermore, the improvements persisted over subsequent follow-up in early active taVNS. Importantly, the effect of active taVNS was superior to sham for immediate recall in both early and late groups. There were no statistical differences in delayed recall.

Conclusion
Our findings suggest that taVNS has potential to improve memory, particularly immediate recall, and may be an effective method in preventing memory loss and mitigating cognitive aging.
Reliability of heart rate in reflecting cardiac sympathetic overdrive in type 2 diabetes mellitus
Authors: Dell’Oro Raffaella , Quarti-Trevano Fosca , Ciardullo Stefano , Perseghin Gianluca , Mancia Giuseppe , Grassi Guido 
Published: July 2024

Abstract
Purpose
Clinical trials have shown that in type 2 diabetes mellitus (T2D) resting office heart rate (HR) values > 70 beats/minute are associated with an increased cardiovascular risk, a worse prognosis and an unfavorable outcome. The present study was aimed at investigating whether the above mentioned treshold HR values reflect a sympathetic overdrive of marked degree.

Methods
In 58 T2D patients (age range: 39–57 years) without signs of autonomic neuropathy and in 52 age-matched healthy controls, we assessed muscle sympathetic nerve activity (MSNA, microneurography) and venous plasma norepinephrine (NE, HPLC), subdividing the study population in different subgroups according to their clinic and 24-h HR values.

Results
In T2D progressively greater clinic and 24-h HR values were accompanied by progressive increases in MSNA and NE. HR cutoff values indicated by clinical trials as associated with an increased cardiovascular risk (> 70 beats/minute) were accompanied by MSNA values significantly higher than those detected in patients with lower HR, this being the case also for NE. In T2D both MSNA and NE were significantly related to clinic (r = 0.93, P < 0.0001 and r = 0.87, P < 0.0001, respectively) and 24-h (r = 0.92, P < 0.0001 and r = 0.84, P < 0.0001, respectively) HR. The MSNA and NE behaviour observed in T2D was not detected in healthy controls.

Conclusions
In T2D clinic HR values allow to detect patients with a greater sympathetic overactivity. Considering the adverse clinical impact of the sympathetic overdrive on prognosis, our data emphasize the need of future studies investigating the potential usefulness of lifestyle and pharmacological interventions exerting sympathomodulatory effects.
The 2023 revised diagnostic criteria for IgG4-related ophthalmic disease
Authors: Takahira Masayuki , Goto Hiroshi , Azumi Atsushi 
Published: July 2024

AbstractImmunoglobulin G4 (IgG4)-related disease is a clinical entity characterized by elevated serum IgG4 concentrations and infiltration of IgG4-immunopositive plasmacytes in various organs, including ophthalmic lesions. Diagnostic criteria for IgG4-related ophthalmic disease (IgG4-ROD) were established in 2014 and describe the most affected ocular adnexal tissues such as lacrimal glands, trigeminal nerves and extraocular muscles, but do not mention optic neuropathy, the most severe indication of ophthalmic lesions. We reviewed published case reports of optic neuropathy in IgG4-related disease (n = 44), and in many cases, decreased visual acuities recovered well following treatment such as systemic corticosteroids, rituximab, and orbital surgery. However, some patients did not recover, especially when pretreatment visual acuities were as low as light perception or less. Herein, we propose a 2023 revised diagnostic criteria for IgG4-ROD, which include a reminder not to overlook optic neuropathy. The 2014 diagnostic criteria specify mucosa-associated lymphoid tissue (MALT) lymphoma as an important differential diagnosis for the relationship between IgG4-ROD and orbital lymphoma. The 2023 revision directs physicians’ attention toward lymphomas other than MALT lymphoma, considering that the 2014 criteria might have placed too much emphasis on MALT lymphoma.
A Comprehensive review of data-driven approaches for forecasting production from unconventional reservoirs: best practices and future directions
Authors: Rahmanifard Hamid , Gates Ian 
Published: July 2024

AbstractPrediction of well production from unconventional reservoirs is a complex problem given an incomplete understanding of physics despite large amounts of data. Recently, Data Analytics Techniques (DAT) have emerged as an effective approach for production forecasting for unconventional reservoirs. In some of these approaches, DAT are combined with physics-based models to capture the essential physical mechanisms of fluid flow in porous media, while leveraging the power of data-driven methods to account for uncertainties and heterogeneities. Here, we provide an overview of the applications and performance of DAT for production forecasting of unconventional reservoirs examining and comparing predictive models using different algorithms, validation benchmarks, input data, number of wells, and formation types. We also discuss the strengths and limitations of each model, as well as the challenges and opportunities for future research in this field. Our analysis shows that machine learning (ML) based models can achieve satisfactory performance in forecasting production from unconventional reservoirs. We measure the performance of the models using two dimensionless metrics: mean absolute percentage error (MAPE) and coefficient of determination (R2). The predicted and actual production data show a high degree of agreement, as most of the models have a low error rate and a strong correlation. Specifically, ~ 65% of the models have MAPE less than 20%, and more than 80% of the models have R2 higher than 0.6. Therefore, we expect that DAT can improve the reliability and robustness of production forecasting for unconventional resources. However, we also identify some areas for future improvement, such as developing new ML algorithms, combining DAT with physics-based models, and establishing multi-perspective approaches for comparing model performance.
Recommendations for 46,XY Disorders/Differences of Sex Development Across Two Decades: Insights from North American Pediatric Endocrinologists and Urologists
Authors: Khorashad Behzad Sorouri , Gardner Melissa , Lee Peter A. , Kogan Barry A. , Sandberg David E. 
Published: July 2024

AbstractClinical decision-making for individuals with 46,XY disorders/differences of sex development (DSD) remains unsettled and controversial. The North American DSD Clinician Survey examines the recommendations of a large group of clinical specialists over the last two decades. Active members of the (Lawson Wilkins) Pediatric Endocrine Society and the Societies for Pediatric Urology were invited to respond to a web-based survey at three different timepoints: 2003–2004 (T1), 2010–2011 (T2), and 2019–2020 (T3). Data from 429 participants in T1, 435 in T2, and 264 in T3 were included in this study. The participants were presented with three XY newborn clinical case scenarios—micropenis, partial androgen insensitivity syndrome, and iatrogenic penile ablation—and asked for clinical management recommendations. The main outcomes assessed included the recommended gender of rearing, surgical decision-maker (parent or patient), timing of genital surgery, and age at which to disclose medical details and surgical history to the patient. For all scenarios, the overwhelming majority recommended rearing as male, including a significant increase across timepoints in those recommending a male gender of rearing for the infant with penile ablation. The proportions recommending female gender of rearing declined significantly across timepoints. In general, most recommended parents (in consultation with the physician) serve as surgical decision-makers, but these proportions declined significantly across timepoints. Recommendations on the timing of surgery varied based on the patient’s gender and type of surgery. There has been a shift in recommendations away from the “optimal gender policy” regarding gender of rearing and surgical interventions for patients with XY DSD.
Patterns of climate-change coping among late adolescents: Differences in emotions concerning the future, moral responsibility, and climate-change engagement
Authors: Rikner Martinsson Amanda , Ojala Maria 
Published: July 2024

AbstractYoung people both are and will be greatly affected by climate change, an insight which can trigger a range of stressful emotions concerning the future. How young people cope with climate change as a stressor can be of importance for both moral responsibility and climate-change engagement. People often use a combination of coping strategies; however, the focus thus far has merely been on isolated coping strategies. Using a person-centered approach, the aim of this study was to examine: (1) patterns of climate-change coping among late adolescents and (2) if late adolescents characterized by unique patterns of coping differ regarding emotions concerning the future (worry, pessimism, optimism), moral responsibility, and climate-change engagement (outcome expectancy and climate-friendly food choices). A questionnaire study was conducted with 474 Swedish senior high-school students (16–22 years old, mean age: 17.91). A cluster analysis revealed three unique patterns of coping: The solution-oriented group (43%, high on problem- and meaning-focused coping), the avoidant group (33%, high on de-emphasizing and meaning-focused coping), and the uninvolved group (24%, low on all coping strategies). The solution-oriented group differed from the other two groups in reporting more climate-change worry, moral responsibility, outcome expectancy, and climate-friendly food choices. The uninvolved group reported more climate-change worry, moral responsibility, and climate-friendly food choices than the avoidant group, and the least optimism. The avoidant group was the least pessimistic. Our results reveal the importance of exploring patterns of climate-change coping to understand young people’s engagement concerning this global threat.
Phylogeny predicts sensitivity in aquatic animals for only a minority of chemicals
Authors: Coleman Alice L. , Edmands Suzanne 
Published: July 2024

AbstractThere are substantial gaps in our empirical knowledge of the effects of chemical exposure on aquatic life that are unlikely to be filled by traditional laboratory toxicity testing alone. One possible alternative of generating new toxicity data is cross-species extrapolation (CSE), a statistical approach in which existing data are used to predict the effect of a chemical on untested species. Some CSE models use relatedness as a predictor of chemical sensitivity, but relatively little is known about how strongly shared evolutionary history influences sensitivity across all chemicals. To address this question, we conducted a survey of phylogenetic signal in the toxicity data from aquatic animal species for a large set of chemicals using a phylogeny inferred from taxonomy. Strong phylogenetic signal was present in just nine of thirty-six toxicity datasets, and there were no clear shared properties among those datasets with strong signal. Strong signal was rare even among chemicals specifically developed to target insects, meaning that these chemicals may be equally lethal to non-target taxa, including chordates. When signal was strong, distinct patterns of sensitivity were evident in the data, which may be informative when assembling toxicity datasets for regulatory use. Although strong signal does not appear to manifest in aquatic toxicity data for most chemicals, we encourage additional phylogenetic evaluations of toxicity data in order to guide the selection of CSE tools and as a means to explore the patterns of chemical sensitivity across the broad diversity of life.
How Can Risk-Averse and Risk-Taking Approaches be Considered in a Group Multi-Criteria Decision-Making Problem?
Authors: Kheybari Siamak , Mehrpour Mohammad Reza , Bauer Paul , Ishizaka Alessio 
Published: July 2024

AbstractWe propose an alternative decision-making methodology based on adopting a mixed risk-averse and risk-taking behavior, improving the objectivity of decision-making. We demonstrate the methodology by prioritizing Iranian tourism centers’ activity under pandemic conditions, providing insights to policymakers on those to keep active or reduce the activity of – hence, those worth developing ahead of future disease outbreaks. This research follows a three-step methodology. First, criteria for evaluation are identified and categorized into tourist attractions, infrastructure, and healthcare dimensions. Second, criterion weights are calculated based on expert opinions, collected using a best-worst method-based questionnaire. Third, tourism centers are evaluated by employing risk-averse and risk-taking best-worst methods. We identify popular attractions, general services, and drugstore accessibility as the primary indicators of tourist attractions, infrastructure, and healthcare, respectively. By clustering tourism centers using K-means algorithm, we find that, in order, the cities of Semnan, Kerman and Zahedan are the tourism centers most suited to staying active during disease outbreaks. For multi-criteria decision-making problems that rely on experts’ evaluations, the proposed methodology can improve the reliability of decision-making. The methodology and framework presented can be used to support various types of decision-making, including evaluation, ranking, selection or sorting.
A systematic literature review of predicting patient discharges using statistical methods and machine learning
Authors: Pahlevani Mahsa , Taghavi Majid , Vanberkel Peter 
Published: July 2024

AbstractDischarge planning is integral to patient flow as delays can lead to hospital-wide congestion. Because a structured discharge plan can reduce hospital length of stay while enhancing patient satisfaction, this topic has caught the interest of many healthcare professionals and researchers. Predicting discharge outcomes, such as destination and time, is crucial in discharge planning by helping healthcare providers anticipate patient needs and resource requirements. This article examines the literature on the prediction of various discharge outcomes. Our review discovered papers that explore the use of prediction models to forecast the time, volume, and destination of discharged patients. Of the 101 reviewed papers, 49.5% looked at the prediction with machine learning tools, and 50.5% focused on prediction with statistical methods. The fact that knowing discharge outcomes in advance affects operational, tactical, medical, and administrative aspects is a frequent theme in the papers studied. Furthermore, conducting system-wide optimization, predicting the time and destination of patients after discharge, and addressing the primary causes of discharge delay in the process are among the recommendations for further research in this field.
Debating the Vulnerability Zeitgeist: Introduction to an Interdisciplinary Trialogue
Authors: Nungesser Frithjof , Schirgi Antonia 
Published: July 2024

AbstractThe article serves as an introduction to the special section “Vulnerability: An Interdisciplinary Trialogue,” which brings together three researchers who have made important contributions to the field of vulnerability studies from different perspectives and in different disciplines: Elodie Boublil, Kate Brown, and Erinn Gilson. At the outset, the article discusses some characteristic features of the current discourse on vulnerability, in particular the question of why the concept of vulnerability is not only very popular but is also often used in such a diverse and vague manner. In addition to an increased multidisciplinary interest, transdisciplinary conflicts over fundamental epistemological, ontological, and methodological positions are identified as an essential factor; furthermore, the plurality and vagueness of the notion are attributed to an increasing “duplication” of vulnerability as an academic concept on the one hand and as an element of cultural semantics and political discourse on the other. The special section discusses this peculiar mixture of popularity, plurality, and vagueness of the vulnerability concept in an interdisciplinary, reflexive, and critical manner. The introduction outlines key aspects of the three contributions to the trialogue and highlights some convergences and common themes. At the end, the three authors are also briefly introduced.
Bullying Victimization is Associated with Heightened Rates of Anxiety and Depression Among Autistic and ADHD Youth: National Survey of Children’s Health 2016–2020
Authors: Accardo Amy L. , Neely Leslie C. , Pontes Nancy M. H. , Pontes Manuel C. F. 
Published: July 2024

AbstractAutistic youth and youth with ADHD have heightened rates of bullying victimization, anxiety, and depression. The purpose of this research is to use nationally representative US data to 1) estimate the prevalence of anxiety and depression among bullied neurodivergent youth and 2) investigate whether the association between bullying victimization and anxiety or depression is significantly greater among autistic youth and youth with ADHD. For this research, we used five years of data (2016–2020) from the nationally representative National Survey of Children’s Health (NSCH), youth ages 12–17 years (n = 71,973). Data were analyzed with R and the R survey package to estimate average marginal percentages, risk differences, and additive interactions as recommended by STROBE guidelines. The study identified heightened anxiety and depression among bullied autistic or ADHD youth. Results also showed that the increase in the rate of anxiety or depression associated with bullying victimization was significantly greater among autistic youth and youth with ADHD relative to non-autistic non-ADHD youth; interactions were significant among both male and female youth. Autistic youth, youth with ADHD, and youth with co-occurring autism and ADHD are particularly vulnerable to bullying victimization and associated depression and anxiety. Future research is needed to understand why the association between bullying victimization and depression/anxiety is significantly greater among autistic and non-autistic ADHD youth. Recommendations include exploring school-wide anti-stigma initiatives to stop the reciprocal bullying–anxiety/depression cycle, routine bullying and mental health screening of autistic and ADHD youth, and clinical management of bullied autistic and ADHD youth with anxiety or depression.