bluuebunny/crossref_metadata_2025_split huggingface dataset:
Science students' social bonds and knowledge construction
Authors: Bellocchi Alberto 
Published: December 2021

AbstractEmerging research is beginning to explore the role of social bonds in science learning. In this study, I develop a novel conceptual framework extending recent science education research that has adopted Scheff's social bond theory in understanding science learning. I use microsociological methods to understand social bonds and knowledge construction as contemporaneous phenomena through analysis of interactional dynamics. Drawing on ethnomethodology and an interpretive paradigm, I analyze multiple data sources including video recordings of group interactions, social bond diaries, reflective discussions, and social bond quizzes to understand how a student group cocreates science knowledge and how the same social practices co‐construct social bond status within the group. Complexity in the nature of trio's interactions highlights the need for researchers to produce holistic understandings of knowledge construction and social bonds. Implications for future research on social bonds and their role in science learning experiences are offered.
Low‐Speed Control Experiment of Motorcycles Using SPACAR Model
Authors: Hara Susumu , Tsuchiya Mitsuo , Kimura Tetsuya , Akai Naoki 
Published: December 2021

The main stream of development of autonomous driving technology (ADT) is for four‐wheel motor vehicles. ADT for motorcycles has received scant attention. The motorcycle stability tends to diminish when being driven at extremely low speed. This study addresses how a motorcycle should be stabilized under low‐speed driving. To obtain a linearized motorcycle model without skidding, we introduce a model based on SPACAR, a finite element method computation program. Moreover, velocity‐dependent gain‐scheduling linear quadratic regulator (LQR) is applied. The experimental results demonstrate stabilized driving responses at 1.5 km/h, which is slower than a person's typical walking speed. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Integration of metabolomics and transcriptomics data to further characterize Gliricidia sepium (Jacq.) Kunth under high salinity stress
Authors: Carvalho da Silva Thalliton Luiz , Belo Silva Vivianny Nayse , Braga Ítalo de Oliveira , Rodrigues Neto Jorge Candido , Leão André Pereira , Ribeiro José Antônio de Aquino , Valadares Leonardo Fonseca , Abdelnur Patrícia Verardi , de Sousa Carlos Antônio Ferreira , Souza Manoel Teixeira 
Published: December 2021

AbstractSoil salinity is one abiotic stress that threatens agriculture in more than 100 countries. Gliricidia [Gliricidia sepium (Jacq.) Kunth] is a multipurpose tree known for its ability to adapt to a wide range of soils; however, its tolerance limits and responses to salt stress are not yet well understood. In this study, after characterizing the morphophysiological responses of young gliricidia plants to salinity stress, leaf metabolic and transcription profiles were generated and submitted to single and integrated analyses. RNA from leaf samples were subjected to RNA sequencing using an Illumina HiSeq platform and the paired‐end strategy. Polar and lipidic fractions from leaf samples were extracted and analyzed on an ultra‐high‐performance liquid chromatography (UHPLC) coupled with electrospray ionization quadrupole time‐of‐flight high‐resolution mass spectrometry (MS) system. Acquired data were analyzed using the OmicsBox, XCMS Online, MetaboAnalyst, and Omics Fusion platforms. The substrate salinization protocol used allowed the identification of two distinct responses to salt stress: tolerance and adaptation. Single analysis on transcriptome and metabolome data sets led to a group of 5,672 transcripts and 107 metabolites differentially expressed in gliricidia leaves under salt stress. The phenylpropanoid biosynthesis was the most affected pathway, with 15 metabolites and three genes differentially expressed. Results showed that the differentially expressed metabolites and genes from this pathway affect mainly short‐term salt stress (STS). The single analysis of the transcriptome identified 12 genes coding for proteins that might play a role in gliricidia response at both STS and long‐term salt stress (LTS). Further studies are needed to reveal the mechanisms behind the adaptation response.
Phase transitions in tensorial group field theories: Landau-Ginzburg analysis of models with both local and non-local degrees of freedom
Authors: Marchetti Luca , Oriti Daniele , Pithis Andreas G. A. , Thürigen Johannes 
Published: December 2021

Abstract
In the tensorial group field theory approach to quantum gravity, the theory is based on discrete building blocks and continuum spacetime is expected to emerge from their collective dynamics, possibly at criticality, via a phase transition. On a compact group of fixed volume this can be expected to be only possible in a large-volume or thermodynamic limit. Here we show how phase transitions are possible in TGFTs in two cases: a) considering the non-local group degrees of freedom on a non-compact Lie group instead of a compact one (or taking a large-volume limit of a compact group); b) in models including ℝ-valued local degrees of freedom (that can be interpreted as discrete scalar fields, often used in this context to provide a matter reference frame). After adapting the Landau-Ginzburg approach to this setting of mixed local/non-local degrees of freedom, we determine the critical dimension beyond which there is a Gaussian fixed point and a continuous phase transition which can be described by mean-field theory. This is an important step towards the realization of a phase transition to continuum spacetime in realistic TGFT models for quantum gravity.
Estimates for Operators Related to the Sub-Laplacian with Drift in Heisenberg Groups
Authors: Li Hong-Quan , Sjögren Peter 
Published: December 2021

AbstractIn the Heisenberg group of dimension $$2n+1$$

2
n
+
1

, we consider the sub-Laplacian with a drift in the horizontal coordinates. There is a related measure for which this operator is symmetric. The corresponding Riesz transforms are known to be $$L^p$$

L
p

bounded with respect to this measure. We prove that the Riesz transforms of order 1 are also of weak type (1, 1), and that this is false for order 3 and above. Further, we consider the related maximal Littlewood–Paley–Stein operators and prove the weak type (1, 1) for those of order 1 and disprove it for higher orders.
Tracking consumption at Pompeii: the graffiti lists
Authors: Bowes Kim 
Published: December 2021

AbstractThis paper examines five graffiti expense lists from Pompeii for information on the habits of consumption in the Vesuvian cities. It is intended as a contribution to the growing literature on economic well-being in Pompeii, focusing on the diet and consumption strategies of the nonelite Roman majority. These lists provide rare quantitative evidence for a portion of a whole diet, as well as nonfood expenses. They also shed light on the place of cereals in the overall Vesuvian diet, the importance of consumer goods, and cycles of plenty and want.
Anti-ELAB Movement, National Security Law, and heterogeneous institutional trust in Hong Kong
Authors: Tung Hans H. , Lin Ming-Jen , Lin Yi-Fan 
Published: December 2021

AbstractHow does repression on opposition protests affect citizens' institutional trust under dictatorships? There has been a burgeoning literature investigating empirically both long- and short-term impacts of protests and their repression on citizens' political preferences in both democratic and nondemocratic contexts. Yet, the literature tells us relatively little about how the above question could be answered. This paper tries to answer this question by taking advantage of a recent natural experiment in Hong Kong when Beijing suddenly adopted the National Security Law (NSL) in June 2020 to repress dissidents' protest mobilization. Our findings are twofold. First of all, the NSL drove a wedge in the Hong Kong society by making the pro-establishment camp more satisfied with the post-NSL institutions on the one hand, while alienating the pro-democracy camp who lost tremendous trust in them on the other. Second, our study also reveals that one's trust in institutions is significantly associated with the regimes' ability to curb protesters' contentious mobilization. The Hong Kongers who had higher confidence in the NSL to rein in protests would also have a greater level of trust than those who didn't. The effect, however, is substantially smaller among pro-democracy Hong Kongers except for their trust in monitoring institutions. As Beijing is transforming Hong Kong's current institutions from within hopes of bringing about a new political equilibrium, our study helps provide a timely assessment of Hong Kong's institutional landscape and sheds light on how likely this strategy can work.
Implications of Increasing Household Air Conditioning Use Across the United States Under a Warming Climate
Authors: Obringer Renee , Nateghi Roshanak , Maia‐Silva Debora , Mukherjee Sayanti , CR Vineeth , McRoberts Douglas Brent , Kumar Rohini 
Published: December 2021

AbstractSoaring temperatures and increased occurrence of heatwaves have drastically increased air‐conditioning demand, a trend that will likely continue into the future. Yet, the impact of anthropogenic warming on household air conditioning is largely unaccounted for in the operation and planning of energy grids. Here, by leveraging the state‐of‐the‐art in machine learning and climate model projections, we find substantial increases in future residential air conditioning demand across the U.S.—up to 8% with a range of 5%–8.5% (13% with a range of 11%–15%) after anthropogenic warming of 1.5°C (2.0°C) in global mean temperature. To offset this climate‐induced demand, an increase in the efficiency of air conditioners by as much as 8% (±4.5%) compared to current levels is needed; without this daunting technological effort, we estimate that some states will face supply inadequacies of up to 75 million “household‐days” (i.e., nearly half a month per average current household) without air conditioning in a 2.0°C warmer world. In the absence of effective climate mitigation and technological adaptation strategies, the U.S. will face substantial increases in air conditioning demand and, in the event of supply inadequacies, there is increased risk of leaving millions without access to space cooling during extreme temperatures.
Analysis of Long‐Term GIC Measurements in Transformers in Austria
Authors: Albert D. , Schachinger P. , Bailey R. L. , Renner H. , Achleitner G. 
Published: December 2021

AbstractGeomagnetically induced currents (GICs), a result of solar wind interaction with the Earth's magnetic field and the resistive ground, are known to flow in power transmission grids, where they can lead to transformer damage and grid operation problems. In this study we present an analysis of five years of continuous GIC measurements in transformer neutral points in Austria. Seven self‐designed stand‐alone measurement systems are currently installed in the Austrian 220 and 380 kV transmission levels, measuring currents up to 25 A. We identify recurrent geomagnetic activity in the measurements, and also find man‐made sources of low frequency currents using frequency analysis. In order to support the transmission grid operators, two GIC simulation approaches are used to simulate GICs in the power grid. The first model uses measurements to derive the sensitivity of the location to northward and eastward geoelectric field components (which requires no detailed grid data), and the second model uses the detailed grid model to compute GICs from a geoelectric field. We evaluate two geomagnetic storms from September 2017 and May 2021 to discuss the effects of GICs on the power transmission grid and its assets.
Natural Prenylated Xanthones as Potential Inhibitors of PI3k/Akt/mTOR Pathway in Triple Negative Breast Cancer Cells
Authors: Nguyen Thi Thu Ha , Qu Zhao , Nguyen Van Tuyen , Nguyen Thanh Tra , Le Thi Tu Anh , Chen Sibao , Ninh Son The 
Published: December 2021

AbstractThree prenylated xanthones, garcinone E (1), bannaxanthone D (2) and bannanxanthone E (3) were isolated from the leaves of Garcinia mckeaniana Graib. Their structures were elucidated by spectral methods and compared with literature data. To evaluate their anti-proliferative effects in tumor cells, firstly, cisplatin was used as a positive control and the effects of compound 1 – 3 were determined by performing MTT assay in MDA-MB-231, CNE-2 and A549 cancer cells. The results showed compound 1 – 3 exhibited stronger inhibitory effect than cisplatin in MDA-MB-231. Further effects of compound 1 – 3 in TNBC MDA-MB-231 and MDA-MB-468 cells were examined by performing cell cycle and apoptosis assays. The results indicated that compound 1 – 3 had ability to arrest cell cycle at G2/M phase and induce apoptosis. Furthermore, compound 2 significantly down-regulated PI3K, Akt and mTOR levels in both total proteins and phosphorylated form, which is its potential anti-cancer mechanism. These findings indicated that those prenylated xanthones might serve as promising leading compounds for the development of anticancer drug for TNBC.
A Data-Driven Iterative Approach for Semi-automatically Assessing the Correctness of Medication Value Sets: A Proof of Concept Based on Opioids
Authors: Li Linyi , Grando Adela , Sarker Abeed 
Published: December 2021

Abstract
Background Value sets are lists of terms (e.g., opioid medication names) and their corresponding codes from standard clinical vocabularies (e.g., RxNorm) created with the intent of supporting health information exchange and research. Value sets are manually-created and often exhibit errors.
Objectives The aim of the study is to develop a semi-automatic, data-centric natural language processing (NLP) method to assess medication-related value set correctness and evaluate it on a set of opioid medication value sets.
Methods We developed an NLP algorithm that utilizes value sets containing mostly true positives and true negatives to learn lexical patterns associated with the true positives, and then employs these patterns to identify potential errors in unseen value sets. We evaluated the algorithm on a set of opioid medication value sets, using the recall, precision and F1-score metrics. We applied the trained model to assess the correctness of unseen opioid value sets based on recall. To replicate the application of the algorithm in real-world settings, a domain expert manually conducted error analysis to identify potential system and value set errors.
Results Thirty-eight value sets were retrieved from the Value Set Authority Center, and six (two opioid, four non-opioid) were used to develop and evaluate the system. Average precision, recall, and F1-score were 0.932, 0.904, and 0.909, respectively on uncorrected value sets; and 0.958, 0.953, and 0.953, respectively after manual correction of the same value sets. On 20 unseen opioid value sets, the algorithm obtained average recall of 0.89. Error analyses revealed that the main sources of system misclassifications were differences in how opioids were coded in the value sets—while the training value sets had generic names mostly, some of the unseen value sets had new trade names and ingredients.
Conclusion The proposed approach is data-centric, reusable, customizable, and not resource intensive. It may help domain experts to easily validate value sets.
Effects of Modified Osteoplastic Pterional Craniotomy on Temporal Muscle Volume and Frontal Muscle Nerve Function
Authors: Seçer Mehmet , Çam İsa , Gökbel Aykut , Ulutaş Murat , Çakır Özgür , Ergen Anıl , Çınar Kadir 
Published: December 2021

Abstract
Introduction Pterional craniotomy is a surgical approach frequently used in aneurysm and skull base surgery. Pterional craniotomy may lead to cosmetic and functional problems, such as eyebrow drop due to facial nerve frontal branch damage, temporal muscle atrophy, and temporomandibular joint pain. The aim was to compare the postoperative effects of our modified osteoplastic craniotomy with classical pterional craniotomy in terms of any change in volume of temporal muscle and in the degree of frontal muscle nerve damage.
Materials and Methods Aneurysm cases were operated with either modified osteoplastic pterional craniotomy or free bone flap pterional craniotomy according to the surgeon's preference. Outcomes were compared in terms of temporal muscle volume and frontal muscle nerve function 6 months postoperatively.
Results Preoperative temporal muscle volume in the modified osteoplastic pterional and free bone flap pterional craniotomy groups were not different (p > 0.05). However, significantly less atrophy was observed in the postoperative temporal muscle volume of the osteoplastic group compared with the classical craniotomy group (p < 0.001). In addition, when comparing frontal muscle nerve function there was less nerve damage in the modified osteoplastic pterional craniotomy group compared with the classical craniotomy group, although this did not reach significance (p > 0.05).
Conclusion Modified osteoplastic pterional craniotomy significantly reduced atrophy of temporal muscle and caused proportionally less frontal muscle nerve damage compared with pterional craniotomy, although this latter outcome was not significant. These findings suggest that osteoplastic craniotomy may be a more advantageous intervention in cosmetic and functional terms compared with classical pterional craniotomy.
Volumetric Resection and Complications in Nonfunctioning Pituitary Adenoma by Fully Endoscopic Transsphenoidal Approach along 15 Years of Single-Center Experience
Authors: Palpan Flores Alexis , Sáez Alegre Miguel , Vivancos Sanchez Catalina , Pérez Alvaro Zamarrón , Pérez-López Carlos 
Published: December 2021

AbstractObjective The aim of this study was to evaluate the rate of complications and the extent of resection (EOR) of nonfunctioning pituitary adenomas by endoscopic endonasal approach (EEA) in a 15-year learning curve.Methods A total of 100 patients operated by the same surgical team were divided chronologically into two, three, and four groups, comparing differences in EOR measured by a semiautomatic software (Smartbrush, Brainlab), rate of immediate postoperative complications, and the visual and hormonal status at 6 months.Results There were no significant differences over the years in rates of postoperative complications and in visual status at 6 months. A significant linear correlation between the EOR and the number of surgeries (rho = 0.259, p = 0.007) was found. The analysis was performed in three groups because of the remarkable differences among them; the EOR were: 87.2% (early group), 93.03% (intermediate group), and 95.1% (late group) (p = 0.019). Gross total resection was achieved in 30.3, 51.5, and 64%, respectively (p = 0.017); also, the rate of reoperation and the worsening of at least one new hormonal axis were worse in the early group. Consequently, the early group had a higher risk of incomplete resection compared with the late group (odds ratio: 4.2; 95% confidence interval: 1.5–11.7). The three groups were not different in demographic and volume tumor variables preoperatively.Conclusions The first 33 interventions were associated with a lower EOR, a high volume of residual tumor, a high reoperation rate, and a higher rate of hormonal dysfunction. We did not find differences in terms of postoperative complications and the visual status at 6-month follow-up.
Bearing fault diagnosis using a speed-adaptive network based on vibro-speed data fusion and majority voting
Authors: Yuan Zonghao , Ma Zengqiang , Li Xin , Gao Dayong , Fu Zhipeng 
Published: December 2021

Abstract
Fault diagnosis of rolling bearings is key to maintain and repair modern rotating machinery. Rolling bearings are usually working in non-stationary conditions with time-varying loads and speeds. Existing diagnosis methods based on vibration signals only do not have the ability to adapt to rotational speed. And when the load changes, their accuracy rate will be obviously reduced. A method is put forward which fuses multi-modal sensor signals to fit speed information. Firstly, the features are extracted from raw vibration signals and instantaneous rotating speed signals, and fused by 1D-convolution neural network-based networks. Secondly, to improve the robustness of the model when the load changes, a majority voting mechanism is proposed in the diagnosis stage. Lastly, multiple variable speed samples of four bearings under three loads are obtained to evaluate the performance of the proposed method by analyzing the loss function, accuracy rate and F
1 score under different variable speed samples. It is empirically found that the proposed method achieves higher diagnostic accuracy and speed-adaptive ability than the algorithms based on vibration signal only. Moreover, a couple of ablation studies are also conducted to investigate the inner mechanism of the proposed speed-adaptive network.
Vision-based measurement for decoupling identification of geometric errors of rotating axes for five-axis platform
Authors: Yin Song , Zhou Haibo , Ju Xia , Li Zhiqiang 
Published: December 2021

Abstract
In this paper, a method for identifying and decoupling geometric errors of rotation axes using vision measurement is proposed. Based on screw theory and exponential product formula, identification equations of position-dependent geometric errors (PDGEs) and position-independent geometric errors (PIGEs) of the rotation axes are established. The mapping relationships between the error twist and geometric errors are established. The error model provides the coupling mechanism of PDGEs and PIGEs. Furthermore, a progressive decoupling method is proposed to separate PDGEs and PIGEs without additional assumptions. The pose parameters, required for solving the identification equations, are obtained by visual measurement. Then, the error terms of PIGEs and PDGEs are determined. Lastly, the error calibration of the rotation axes is investigated, thus providing an average rotary table orientation error reduction of 28.1% compared to the situation before calibration.
Applications of machine learning in computational nanotechnology
Authors: Liu Wenxiang , Wu Yongqiang , Hong Yang , Zhang Zhongtao , Yue Yanan , Zhang Jingchao 
Published: December 2021

Abstract
Machine learning (ML) has gained extensive attention in recent years due to its powerful data analysis capabilities. It has been successfully applied to many fields and helped the researchers to achieve several major theoretical and applied breakthroughs. Some of the notable applications in the field of computational nanotechnology are ML potentials, property prediction, and material discovery. This review summarizes the state-of-the-art research progress in these three fields. ML potentials bridge the efficiency versus accuracy gap between density functional calculations and classical molecular dynamics. For property predictions, ML provides a robust method that eliminates the need for repetitive calculations for different simulation setups. Material design and drug discovery assisted by ML greatly reduce the capital and time investment by orders of magnitude. In this perspective, several common ML potentials and ML models are first introduced. Using these state-of-the-art models, developments in property predictions and material discovery are overviewed. Finally, this paper was concluded with an outlook on future directions of data-driven research activities in computational nanotechnology.