bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of different surgical approaches on health-related quality of life in pediatric and adolescent patients with papillary thyroid carcinoma
Authors: Su Yanling , Wang Feng , Chen Shunjin , Yao Xiyu 
Published: March 2024

Abstract
Objective
We aimed to compare the health-related quality of life (HRQOL) in pediatric and adolescent patients with papillary thyroid carcinoma who underwent bilateral or unilateral thyroidectomy.

Methods
This prospective observational study recruited children and adolescents diagnosed with papillary thyroid carcinoma at low risk of recurrence. Participants were categorized into bilateral or unilateral thyroidectomy groups. They were asked to complete one questionnaire regarding basic information and three others regarding quality of life. HRQOL data from patients who underwent different procedures at various time points were compared.

Results
Thirty patients underwent unilateral thyroidectomy and 54 bilateral thyroidectomy (median age = 14.27 years). HRQOL of the unilateral thyroidectomy group was higher than the bilateral group.

Conclusion
For children and adolescents with papillary thyroid carcinoma at low risk of recurrence, bilateral thyroidectomy is associated with a lower HRQOL than unilateral thyroidectomy. Surgeons should consider tumor impact and HRQOL when selecting the most appropriate surgical method.
“In the end, the story of climate change was one of hope and redemption”: ChatGPT’s narrative on global warming
Authors: Sommer Bernd , von Querfurth Sarah 
Published: March 2024

AbstractAI chatbots such as ChatGPT help people produce texts. According to media reporting, these texts are also used for educational purposes. Thus, AI influences people’s knowledge and perception of current issues. This paper examines the narrative of ChatGPT's stories on climate change. Our explorative analysis reveals that ChatGPT’s stories on climate change show a relatively uniform structure and similar content. Generally, the narrative is in line with scientific knowledge on climate change; the stories convey no significant misinformation. However, specific topics in current debates on global warming are conspicuously missing. According to the ChatGPT narrative, humans as a species are responsible for climate change and specific economic activities or actors associated with carbon emissions play no role. Analogously, the social structuration of vulnerability to climate impacts and issues of climate justice are hardly addressed. ChatGPT’s narrative consists of de-politicized stories that are highly optimistic about technological progress.
Why Care About Sustainable AI? Some Thoughts From The Debate on Meaning in Life
Authors: Rüther Markus 
Published: March 2024

AbstractThe focus of AI ethics has recently shifted towards the question of whether and how the use of AI technologies can promote sustainability. This new research question involves discerning the sustainability of AI itself and evaluating AI as a tool to achieve sustainable objectives. This article aims to examine the justifications that one might employ to advocate for promoting sustainable AI. Specifically, it concentrates on a dimension of often disregarded reasons — reasons of “meaning” or “meaningfulness” — as discussed more recently in the “meaning in life” literature of analytic ethics. To proceed, the article first elucidates the working definitions of “sustainable AI” and “meaning in life”, while also setting the criteria for evaluating the plausibility of these reasons. Subsequently, it presents and scrutinises three arguments for the claim that one has reasons to care about sustainable AI from a perspective of meaning: the Meaning-conferring-action Argument, the Afterlife Argument, and the Harm Argument. In conclusion, this article asserts that only the Harm Argument presents a viable line of reasoning. However, it also outlines the presuppositions of this argument and the additional steps necessary to make it compelling.
Investigations on the influence of angular and linear misalignment on the fatigue strength of HFMI-treated structural steel butt joints
Authors: Ripsch Benjamin , Gabriel Göran , Gericke Andreas , Henkel Knuth-Michael 
Published: March 2024

AbstractThe effect of High-Frequency Mechanical Impact (HFMI) treatment on the fatigue resistance of structural steel S355 butt welds with macro geometric imperfections outside of the usual fatigue design limits was investigated. Thin-walled specimens (t = 8 mm) were manufactured with different degrees of angular and linear misalignment and fatigue tested in the as welded and HFMI treated condition. The fatigue test results show that HFMI treatment can compensate the fatigue strength-reducing effects of macro geometric imperfections (ΔσC,HFMI = 127 N/mm2, m = 5), but also that linear and angular misalignment does not reduce the fatigue strength of butt welds as severely as rules and guidelines suggest (ΔσC,AW = 107 N/mm2, m = 3). Underestimating the fatigue strength determined by testing by more than 40%, numerical analysis and strain gauge measurements show that the application of the effective notch stress approach and the structural hot-spot stress approach delivers conservative assessment results, which might not always be representative for the actual fatigue strength benefit that can be gained by HFMI treatment.
The effect of xylitol chewing gums and candies on caries occurrence in children: a systematic review with special reference to caries level at study baseline
Authors: Pienihäkkinen K. , Hietala-Lenkkeri A. , Arpalahti I. , Söderling E. 
Published: March 2024

Abstract
Purpose
A systematic review of published data was carried out to assess the caries-preventive effects of xylitol chewing gums and candies in children.

Methods
Electronic and hand searches were performed to find clinical studies on the effects of xylitol chewing gums and candies on dental caries in children (≤ 18 years). Prospective randomised or controlled clinical trials published before 2023 were included in the review.

Results
The initial search identified 365 titles to be evaluated. After applying inclusion and exclusion criteria, 15 articles with either fair or low quality were reviewed. Nine articles studied chewing gums, five candies, and one both of them. In the ten evaluated xylitol chewing gum studies xylitol consumption significantly reduced caries occurrence when compared with no treatment or a placebo polyol gum. The effect was clinically significant in studies with high or moderate caries level at study baseline. The results also suggested that the caries-reducing effect of xylitol gums may differ from sorbitol/polyol gums. In five of the six heterogenous xylitol candy studies, no caries-reducing effect was found independent of caries level. In addition to caries level, also the daily xylitol dose was a confounding factor.

Conclusion
The present findings suggest that the caries-reducing effect of adding xylitol chewing gum to the daily diet has been well demonstrated in children and adolescents with high or moderate caries level at study baseline. Xylitol gum use could benefit subjects with active incipient caries lesions on smooth tooth surfaces.
Using Machine Learning for Motion Analysis to Early Detect Autism Spectrum Disorder: A Systematic Review
Authors: Simeoli Roberta , Rega Angelo , Cerasuolo Mariangela , Nappo Raffaele , Marocco Davide 
Published: March 2024

AbstractDiagnosis of autism spectrum disorder (ASD) is typically performed using traditional tools based on behavioral observations. However, these diagnosis methods are time-consuming and can be misleading. Integrating machine learning algorithms with technological screening tools within the typical behavioral observations can possibly enhance the traditional assessment and diagnostic process. In the last two decades, to improve the accuracy and reliability of autism detection, many clinicians and researchers began to develop new screening methods by means of advanced technology like machine learning (ML). These methods include artificial neural networks (ANN), support vector machines (SVM), a priori algorithms, and decision trees (DT). Mostly, these methods have been applied to pre-existing datasets, derived from the standard diagnostic and assessment tools, to implement and test predictive models. On the other hand, the detection of new objective behavioral measures such as biomarkers could lead to a significant strengthening of existing screening tools. In the present study, we carried out a critical review of the literature about the latest findings in this field. The aim was to shed light about the effectiveness of using ML systems for motion analysis to enhance both clinical assessment and diagnostic processes. Specifically, we discussed the contribution of ML systems in promoting early diagnosis of ASD. The literature review showed that motion patterns ML analysis predicts ASD classification as accurately as that of classical gold standard tools. However, the application of these methods is still challenging, as discussed in this review.
A data decomposition and attention mechanism-based hybrid approach for electricity load forecasting
Authors: Oqaibi Hadi , Bedi Jatin 
Published: March 2024

AbstractAn accurate and reliable prediction of future energy patterns is of utmost significance for the smooth operation of several related activities such as capacity or generation unit planning, transmission network optimization, better resources availability, and many more. With the availability of historical load datasets through smart grid systems, artificial intelligence and machine learning-based techniques have been extensively developed for achieving the desired objectives. However, effectively capturing strong randomness and non-linear fluctuations in the load time-series remains a critical issue that demands concrete solutions. Considering this, the current research proposes a hybrid approach amalgamating data smoothing and decomposition strategy with deep neural models for improving forecasting results. Moreover, an attention mechanism is integrated to capture relevant portions of the time series, thus achieving the desired ability to capture long-term dependencies among load demand observations. This integration enhances the prediction and generalization capabilities of the proposed model. To validate the performance benefits achieved by the proposed approach, a comparative evaluation is conducted with state-of-the-art neural-based load series prediction models. The performance assessment is carried out on a novel real-world dataset of five southern states of India, and the superiority of the proposed in capturing load time-series variations is well observed and demonstrated in terms of several performance indicators.
A novel implicit decision variable classification approach for high-dimensional robust multi-objective optimization in order scheduling
Authors: Xiao Youkai , Du Wei , Tang Yang 
Published: March 2024

AbstractThis paper efficiently addresses the high-dimensional robust order scheduling problem. A novel algorithm named dynamic cooperative coevolution based on an implicit decision variable classification approach (DCC/IDVCA) is developed to search for robust order schedules. To significantly reduce the computational resources required for solving the high-dimensional robust order scheduling problem, we propose decomposing the original decision variables through implicit classification methods. First, a novel estimation method is introduced to evaluate the weighted contribution of variables to robustness. This method utilizes historical information, including the variation of the overall mean effective fitness and the frequency of variables being classified into highly robustness-related subcomponents in previous cycles, for evaluating their weighted contribution to robustness. Then, based on the corresponding weighted robustness contributions, the original variables are classified into highly and weakly robustness-related variables. Finally, these two types of variables are decomposed into highly and weakly robustness-related subgroups within a dynamic cooperative coevolution framework and optimized separately. In the experimental section, the proposed algorithm is applied to two practical order scheduling problems in discrete manufacturing industry. The experimental results demonstrate that the proposed algorithm achieves competitive outcomes compared to state-of-the-art high-dimensional robust multi-objective optimization algorithms.
Time-aware MADDPG with LSTM for multi-agent obstacle avoidance: a comparative study
Authors: Zhao Enyu , Zhou Ning , Liu Chanjuan , Su Houfu , Liu Yang , Cong Jinmiao 
Published: March 2024

AbstractIntelligent agents and multi-agent systems are increasingly used in complex scenarios, such as controlling groups of drones and non-player characters in video games. In these applications, multi-agent navigation and obstacle avoidance are foundational functions. However, problems become more challenging with the increased complexity of the environment and the dynamic decision-making interactions among agents. The Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm is a classical multi-agent reinforcement learning algorithm successfully used to improve agents’ performance. However, it ignores the temporal message hidden in agents’ interaction with the environment and needs to be more efficient in scenarios with many agents due to its training technique. To address the limitations of MADDPG, we propose to explore modified algorithms of MADDPG for multi-agent navigation and obstacle avoidance. By combining MADDPG with Long Short-Term Memory (LSTM), we obtain the MADDPG-LSTMactor algorithm, which leverages continuous observations over time as input for the policy network, enabling the LSTM layer to capture hidden temporal patterns. Moreover, by simplifying the input of the critic network, we obtain the MADDPG-L algorithm for efficiency improvement in scenarios with many agents. Experimental results demonstrate that these algorithms outperform existing networks in the OpenAI multi-agent particle environment. We also conducted a comparative study of the LSTM-based approach with Transformer and self-attention models in the task of multi-agent navigation and obstacle avoidance. The results reveal that Transformer and self-attention do not consistently outperform LSTM. The LSTM-based model exhibits a favorable tradeoff across varying sequence lengths. Overall, this work addresses the limitations of MADDPG in multi-agent navigation and obstacle avoidance tasks, providing insights for developing intelligent agents and multi-agent systems.
Which characteristics do religious school administrators value in teachers? Experimental evidence from the global Christian school sector
Authors: Johnson Alison , Lee Matthew H. , Cheng Albert 
Published: March 2024

AbstractResearch shows that teacher quality is the most important school-related input correlated with student success. In religious private schools, teachers do not merely influence academic outcomes; they may also play a role in spiritual formation. Religious school administrators report that their faith informs their hiring decisions. However, little research has examined the ways that religious school administrators choose individuals to place in teaching positions. In this study, we use conjoint analysis to experimentally examine the hiring preferences of 170 Christian school administrators. We find that having modest (compared to below-average) academic achievement and qualifications or extensive (compared to limited) experience each increases a teacher candidate’s likelihood of being hired by 26 percentage points. Having strong (compared to modest) academics, being a graduate of a Christian (compared to secular) postsecondary institution, or being a graduate of the administrator’s K-12 school (compared to a different school) produces smaller effects.
Effect of Additives on Heat Hardened Inorganic Solid Foundry Binder
Authors: Anwar Nurul , Major-Gabryś Katarzyna , Jalava Kalle , Orkas Juhani 
Published: March 2024

Abstract
Renewed interest in inorganic binders for sand molding has also intensified research on different forms of it. In this study, solid inorganic sodium silicate binder was tested with different additives to see how these affected the silica mold quality. The five additives used were: glucose, sucrose, boric acid, aluminum oxide and iron(III)oxide powders. The mold quality was assessed through tests like bending strength, tensile strength, hot distortion, wear resistance, gas evolution and collapsibility tests. In addition, SEM imaging was done on some select mold fracture samples. In the end, a casting trial was carried out followed by a surface roughness and defects analysis. A reduction in mold strength was noticed with glucose and boric acid, while collapsibility was improved by glucose, sucrose and boric acid additives. Casting trials have shown the best surface finish to be obtained with sucrose additive. All the casts in general showed some penetration; however, repeat casts have proven that altering some casting parameters could result in casts with excellent surface finish using solid silicates.
Implications of Flat‐Slab Subduction on Hydration, Slab Seismicity, and Arc Volcanism in the Pampean Region of Chile and Argentina
Authors: Liu Xiaowen , Wagner Lara S. , Currie Claire A. , Caddick Mark J. 
Published: March 2024

AbstractThe Pampean flat slab in central Chile and Argentina is characterized by the inland migration and subsequent cessation of arc volcanism since the mid‐Miocene. Slab flattening also affects the distribution and number of intermediate‐depth earthquakes and the evolution of the overlying continental thermal structure. In this study, we combine thermal‐mechanical models with petrological models to examine temporal changes in pressure, temperature, and composition during flat‐slab subduction and estimate water carrying capacity, predicted melt distributions and predicted changes in melt composition. Model results indicate that the present‐day flattened Nazca plate carries water to ∼700 km inland from the trench and could cause flux melting if the material above the slab remains fertile. Observed slab seismicity matches areas where hydrated materials have ∼>3 wt% H2O in the oceanic crust and mantle lithosphere. Seismicity increases as slab water carrying capacity decreases (slab dehydration). As P‐T conditions and compositions of the rock trapped above the slab change during slab flattening, flux melting switches from a peridotite‐dominated early phase to a combined mid‐ocean ridge basalt/eclogite and peridotite melting at ∼8 Ma. The results provide broad consistency with known earthquake distributions, seismic velocities, and observed temporal and spatial changes in volcanic patterns above the Pampean flat slab and point toward the role of melt depletion in the decrease and ultimate cessation of arc volcanism in this region.
Geophysical Responses to an Environmentally‐Boosted Volcanic Unrest
Authors: De Siena L. , Amoruso A. , Petrosino S. , Crescentini L. 
Published: March 2024

AbstractThe spatiotemporal relationship between geophysical, environmental, and geochemical responses during volcanic unrest is essentially unknown, making their joint use and interpretation for eruption forecasting challenging. Here, Empirical Orthogonal Functions analysis applied to GPS data allows the separation of the dominant deep‐sourced inflation from environmentally controlled signals associated with extension at Campi Flegrei caldera. This separation bridges the gap between deformation, seismic and geochemical responses, clarifying the processes underlying the ongoing volcanic unrest. Persistent meteoric forcing during the 2017–2018 hydrological year changed the decadal trend of seismic energy and secondary deformation components, pairing their spatial patterns. The result was a block in the carbon dioxide released in 2018 at Solfatara, the primary stress‐release valve at the caldera. The subsequent overpressure weakened the fractured eastern caldera, opening pathways for deep, hot materials to reach the surface. Our results give insight into how environmental forcing can favor volcanic unrest in pressurized calderas.
Hybrid Simulation of Magnetosheath Jet‐Driven Bow Waves
Authors: Ren Junyi , Lu Quanming , Gao Xinliang , Gedalin Michael , Qiu Huixuan , Han Desheng , Wang Rongsheng 
Published: March 2024

AbstractHigh‐speed jets (HSJs) are commonly observed in the Earth's magnetosheath. The HSJs can drive shock‐like bow waves when compressing the ambient plasma, which are important for the HSJ's evolution and the energization of charged particles. Here we present the first two‐dimensional hybrid simulation of the formation and evolution of jet‐driven bow waves. The simulated bow waves exhibit localized enhanced magnetic field and ion density, with their peaks separated by the order of ion inertial length. The bow waves are formed when a super‐magnetosonic HSJ encounters a magnetic structure with the magnetic field nearly perpendicular to the HSJ's velocity. The magnetic field structure acts as an obstacle to deflect and decelerate the jet, causing the pile up of ions on the jet side and the compression of the magnetic structure on the downstream side. Our study explains the observed properties of bow waves, and helps to better understand the evolution of HSJs.
Large TEC Variations Between Mars and Earth: Simulation and Observation Comparisons
Authors: He Qingbao , Liu Kaijun , Wang Zhichao , Liu Qinghui , Guo Li 
Published: March 2024

AbstractLarge total electron content (TEC) variations along the line from Mars to Earth have been demonstrated by analyzing the ground received Tianwen‐1 differential one‐way range (DOR) signals when corona mass ejections (CMEs) and co‐rotating interaction regions (CIRs) pass through the signal path. Here, the TEC variations along the line from Mars to Earth are calculated from the Wang‐Sheeley‐Arge (WSA)‐Enlil space weather forecast model. The results are compared with the Tianwen‐1 line of sight (LOS) observations as well as the STEREO‐A in situ solar wind density measurements. It is found that the TEC variations calculated from the WSA‐Enlil model are usually much smaller in amplitude, especially in the cases of CIRs passing through the signal path. In addition, the timings of CMEs and CIRs passing through the DOR signal path in the WSA‐Enlil model can be half a day earlier or later than the observed ones. In comparison with in situ solar wind measurements, the ground received Tianwen‐1 DOR signals carry solar wind density information over a large spatial region along the signal path. Such remote sensing measurement can be valuable for constraining and improving global solar wind forecast models.