bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deformation Assessment of Deep Excavations Retaining Structure in Collapsible Loess Areas
Authors: Xu Jianzhuang , Sun Benli , Wu Ming , Yang Haifeng 
Published: January 2023

Abstract
In this study, the deformation characteristics of deep excavations retaining structure in loess areas are investigated statistically by analyzing filed measurements in three typical loess areas of Shaanxi, Shanxi, and Gansu. Generally, the study results showed that the maximum lateral displacement of the retaining structure ranges between 0.277‰H to 3.712‰H, with an average of 0.1029%H (H is the final excavation depth of excavations). The maximum lateral displacement is approximately located at 2/3 of the excavation depth. In addition, a quantitative analysis is carried out on the influencing factors for the lateral deformation of the retaining structure, such as the insertion ratio of retaining piles (walls), the length-wide ratio of the excavation plane, synthetical stiffness of the support system, heave-resistant safety factor, and the set depth of support in the first tunnel.
Research for the Influence of Ferris Wheel Deep Foundation Pit Excavation on Adjacent Existing Tunnel
Authors: Wu Xiaofeng , Zhang Di 
Published: January 2023

Abstract
The excavation of deep foundation pit could have a great impact on the adjacent existing structures. The proposed Ferris wheel is located in Jianghehui Block. The construction site is narrow and adjacent existing structures. In this paper, the finite element model was used to compare the influence of the underground separation wall protective measures on the deformation and internal force of adjacent existing tunnel. It was found that the foundation pit bottom presented a tendency of large deformation in the middle and small deformation on both sides. Based on the principle of soil-structure interaction, the optimization study of the underground separation wall was carried out, and it was found that the excessive thickness has little contribution to the reduction of lateral displacement.
Development of lower hybrid wave power feedback control and experiments on EAST
Authors: Wang Mao , Ma Wendong , Yang Yong , Li Miaohui , Wu Zege , Hu Huaichuan , Feng Jianqiang , Liu Liang , Zhao Lianmin , Zhang Jian , Zhu Liang , Cheng Min , Zhou Taian , Jia Hua , Xu Li , Ding Bojiang , Shan Jiafang , Liu Fukun 
Published: January 2023

Abstract
A new method of power feedback control for the lower hybrid wave (LHW) systems on the experimental advanced superconducting tokamak (EAST) is presented. Different methods are applied to control the power of two LHW systems with different frequency at 2.45 GHz and at 4.6 GHz, respectively. The old method is applied to control the 4.6 GHz LHW power through the exciter of klystrons, and the new one is used to control the 2.45 GHz LHW power through the high voltage power supply (HVPS) system. The new method has been applied in EAST and successfully assisted the development of relevant experiments such as loop voltage control, q profile control.
Transverse emittance measurement in 2D and 4D performed on a Low Energy Beam Transport line: benchmarking and data analysis
Authors: Osswald F. , Durand T. , Heine M. , Michaud J. , Poirier F. , Thomas J.C. , Traykov E. 
Published: January 2023

Abstract
2D and 4D transverse phase-space of a low-energy ion-beam is measured with two of the most common emittance scanners. The article covers the description of the installation, the setup, the settings, the experiment and the benchmark of the two emittance meters. We compare the results from three series of measurements and present the advantages and drawbacks of the two systems. Coupling between phase-space planes, correlations and mitigation of deleterious effects are discussed. The influence of background noise and aberrations of trace-space figures on emittance measurements and RMS calculations is highlighted, especially for low density beams and halos. A new data analysis method using noise reduction, filtering, and reconstruction of the emittance figure is described. Finally, some basic concepts of phase-space theory and application to beam transport are recalled.
Contribution of biomass burning to black carbon deposition on Andean glaciers: consequences for radiative forcing
Authors: Bonilla E X , Mickley L J , Beaudon E G , Thompson L G , Rodriguez W E , Encarnación R Cruz , Whicker C A , Flanner M G , Schmitt C G , Ginot P 
Published: January 2023

Abstract
Andean glaciers have melted rapidly since the 1960s. While some melting is likely due to anthropogenic climate change driven by increasing greenhouse gases, deposition of light-absorbing particles such as black carbon (BC) may also play a role. We hypothesize that BC from fires in the Amazon Basin and elsewhere may be deposited on Andean glaciers, reducing the surface albedo and inducing further melting. Here we investigate the role of BC deposition on albedo changes in the Andes for 2014–2019 by combining atmospheric chemistry modeling with observations of BC in snow or ice at four mountain sites in Peru (Quelccaya, Huascarán, Yanapaccha, and Shallap) and at one site in Bolivia (Illimani). We find that annual mean ice BC concentrations simulated by the chemical transport model GEOS-Chem for 2014–2019 are roughly consistent with those observed at the site with the longest record, Huascarán, with overestimates of 15%–40%. Smoke from fires account for 20%–70% of total wet and dry deposition fluxes, depending on the site. The rest of BC deposited comes from fossil fuel combustion. Using a snow albedo model, we find that the annual mean radiative forcing from the deposition of smoke BC alone on snow ranges from +0.1 to +3.2 W m−2 under clear-sky conditions, with corresponding average albedo reductions of 0.04%–1.1%. These ranges are dependent on site and snow grain size. This result implies a potentially significant climate impact of biomass burning in the Amazon on radiative forcing in the Andes.
High-accuracy identification of interferograms between two vortex beams via deep learning without adequate experimental data
Authors: Rui-Jia Lu , Zhi-Kun Su 
Published: January 2023

Abstract
The on-axis interference intensity patterns between a vortex beam and its conjugated beam can be used to measure the fractional topological charge of vortex beams. However, it is still challenging to efficiently recognize these intensity diagrams. On one hand, the difference of the patterns for adjacent modes with interval 0.1 is too subtle to be identified precisely. On the other hand, the interferograms are susceptible to undesirable experimental conditions such as the misalignment of the beams, the unequal arms of the interferometer and the deviation of splitting ratio of the beam splitters in the interferometer. Here, we propose a deep learning method to recognize these intensity diagrams with up to

97
%

accuracy. In particular, our method has reference values for deep learning model training when there is not adequate experimental data.
The crucial role of defect structure in understanding the electrical properties of spark plasma sintered antimony doped barium stannate
Authors: Vukašinović Jelena , Rapljenović Željko , Počuča-Nešić Milica , Ivek Tomislav , Branković Zorica , Branković Goran 
Published: January 2023

Abstract
The influence of structural defects in spark plasma sintered BaSn1-xSbxO3 (BSSO, x = 0.00 and 0.08) ceramic samples on their electrical properties was investigated in the temperature range of 300–4 K. X-ray photoelectron spectroscopy (XPS) revealed the presence of point defects, primarily oxygen vacancies (VO) and mixed oxidation states of tin (Sn2+/Sn4+) in both samples. As a result, the undoped BSSO sample exibited a non-standard semiconductor behavior, retaining its temperature-dependent resistivity. The electrical resistivity of the doped samples was two orders of magnitude lower than that of the undoped sample. The presence of structural defects such as VO, mixed oxidation states of the constituent elements, and significant amounts of O− species make the electrical resistivity of the doped sample constant in the temperature range of 300–70 K, indicating heavily-doped semiconductor behavior.
Investigation of paint removal by atmospheric pressure plasma jet
Authors: WANG Zhijun , CHEN Zhongwen , KANG Hao , YE Zongbiao 
Published: January 2023

Abstract
Acrylic polyurethane paint on the surface of 2A12 Al alloy was cleaned utilizing an atmospheric pressure plasma jet in this work. The dynamic evolution of the paint removal process during plasma treatment with time was explored through analysis of morphology and chemical states. The results showed that although the thickness of paint could be reduced effectively with an increase in cleaning time, the removal rate of paint gradually decreased with time. During the initial cleaning process range, its original smooth morphology of paint turned rugged quickly and was almost unchanged with further plasma treatment. Element and chemical state analysis showed that the content of C in the paint layer decreased obviously after plasma treatment. In contrast, the O content increased remarkably. The cleaning mechanism could be mainly attributed to the reaction between active O-containing species in air plasma and organic components in the paint. After removal of superficial organic matter, residue inorganic metal oxide substances aggregated on the base. The exposed metal oxides on the one hand elevated the superficial O content, but on the other hand hindered further plasma penetration, resulting in a gradual decrease in cleaning rate with cleaning time. Therefore, physical wiping was proposed to be incorporated with the plasma method and effective removal of paint was realized.
Complex systems in the spotlight: next steps after the 2021 Nobel Prize in Physics
Authors: Bianconi Ginestra , Arenas Alex , Biamonte Jacob , Carr Lincoln D , Kahng Byungnam , Kertesz Janos , Kurths Jürgen , Lü Linyuan , Masoller Cristina , Motter Adilson E , Perc Matjaž , Radicchi Filippo , Ramaswamy Ramakrishna , Rodrigues Francisco A , Sales-Pardo Marta , San Miguel Maxi , Thurner Stefan , Yasseri Taha 
Published: January 2023

Abstract
The 2021 Nobel Prize in Physics recognized the fundamental role of complex systems in the natural sciences. In order to celebrate this milestone, this editorial presents the point of view of the editorial board of JPhys Complexity on the achievements, challenges, and future prospects of the field. To distinguish the voice and the opinion of each editor, this editorial consists of a series of editor perspectives and reflections on few selected themes. A comprehensive and multi-faceted view of the field of complexity science emerges. We hope and trust that this open discussion will be of inspiration for future research on complex systems.
Pre-synaptic DC bias controls the plasticity and dynamics of three-terminal neuromorphic electrolyte-gated organic transistors
Authors: Rondelli Federico , Salvo Anna De , Sebastianella Gioacchino Calandra , Murgia Mauro , Fadiga Luciano , Biscarini Fabio , Lauro Michele Di 
Published: January 2023

Abstract
The role of pre-synaptic DC bias is investigated in three-terminal organic neuromorphic architectures based on electrolyte-gated organic transistors—EGOTs. By means of pre-synaptic offset it is possible to finely control the number of discrete conductance states in short-term plasticity experiments, to obtain, at will, both depressive and facilitating response in the same neuromorphic device and to set the ratio between two subsequent pulses in paired-pulse experiments. The charge dynamics leading to these important features are discussed in relationship with macroscopic device figures of merit such as conductivity and transconductance, establishing a novel key enabling parameter in devising the operation of neuromorphic organic electronics.
1365 PROACTIVE IT-ASSISTED CGA IN CARE HOMES IMPROVES ADHERENCE TO PREFERRED PLACE OF CARE AND DEATH, HOSPITALISATION AND MORTALITY RATES
Authors: Attwood D , Vafidis J , Boorer J , Ellis W , Earley M , Denovan J , Hart G , Williams M , Burdett N , Lemon M , Hope S V 
Published: January 2023

Abstract

Introduction
Primary care-based frailty identification and proactive comprehensive geriatric assessment (CGA) remains challenging. Our Devon-based Primary Care Network has developed and introduced an innovative, community-based IT-assisted CGA (i-CGA) process, which includes advance care planning (ACP). We wished to see if this process could improve effectiveness of ACP in residential care home (CH) residents.

Methods/Intervention
Interim analysis focused on adherence to ACP-documentation in severely frail residents, comparing groups:

Results
i-CGA group: 196 residents (16 mild/69 moderate/111 severe frailty); control group: 100(13 mild/31 moderate/56 severe). No significant baseline differences. 100% residents in the i-CGA group had documented resuscitation decisions, vs 72% (72/100) controls: in 97% of both groups (191/196,70/72) to ‘allow a natural death'. 85% (94/111) severely frail i-CGA residents preferred not to be hospitalised. 55% (52/94) died, 90% (47/52) in their CH. Compared to the preceding year, unplanned hospitalisation rates fell:0.86 to 0.68/person years alive. In severely frail control residents, unplanned hospitalisations increased: 0.87 to 2.05/person years alive. 29% (16/56) had no hospitalisation preferences documented. 16/16 died, 25% (4/16) in hospital. 40/56 had documented decisions, not all recent:38% (15/40) wished for admission. Significant group mortality difference was seen: 55% (62/111) severely frail i-CGA residents died compared to 77% (43/56) controls, p=0.0013.

Conclusions
Proactive primary care-led i-CGA in severely frail CH residents promotes up-to-date discussions regarding preferred place of care and death. Most prefer not to be hospitalised, despite traditionally high rates of unplanned admissions. Our i-CGA/ACP process improves adherence to preferences, reduces unplanned hospitalisations and mortality rates. Progressive i-CGA completion and annual/opportunistic reviews should confer progressive benefits.
A social theory-enhanced graph representation learning framework for multitask prediction of drug–drug interactions
Authors: Feng Yue-Hua , Zhang Shao-Wu , Feng Yi-Yang , Zhang Qing-Qing , Shi Ming-Hui , Shi Jian-Yu 
Published: January 2023

Abstract
Current machine learning-based methods have achieved inspiring predictions in the scenarios of mono-type and multi-type drug–drug interactions (DDIs), but they all ignore enhancive and depressive pharmacological changes triggered by DDIs. In addition, these pharmacological changes are asymmetric since the roles of two drugs in an interaction are different. More importantly, these pharmacological changes imply significant topological patterns among DDIs. To address the above issues, we first leverage Balance theory and Status theory in social networks to reveal the topological patterns among directed pharmacological DDIs, which are modeled as a signed and directed network. Then, we design a novel graph representation learning model named SGRL-DDI (social theory-enhanced graph representation learning for DDI) to realize the multitask prediction of DDIs. SGRL-DDI model can capture the task-joint information by integrating relation graph convolutional networks with Balance and Status patterns. Moreover, we utilize task-specific deep neural networks to perform two tasks, including the prediction of enhancive/depressive DDIs and the prediction of directed DDIs. Based on DDI entries collected from DrugBank, the superiority of our model is demonstrated by the comparison with other state-of-the-art methods. Furthermore, the ablation study verifies that Balance and Status patterns help characterize directed pharmacological DDIs, and that the joint of two tasks provides better DDI representations than individual tasks. Last, we demonstrate the practical effectiveness of our model by a version-dependent test, where 88.47 and 81.38% DDI out of newly added entries provided by the latest release of DrugBank are validated in two predicting tasks respectively.
GPDOCK: highly accurate docking strategy for metalloproteins based on geometric probability
Authors: Wang Kai 
Published: January 2023

Abstract
Accurately predicting the interaction modes for metalloproteins remains extremely challenging in structure-based drug design and mechanism analysis of enzymatic catalysis due to the complexity of metal coordination in metalloproteins. Here, we report a docking method for metalloproteins based on geometric probability (GPDOCK) with unprecedented accuracy. The docking tests of 10 common metal ions with 9360 metalloprotein–ligand complexes demonstrate that GPDOCK has an accuracy of 94.3% in predicting binding pose. What is more, it can accurately realize the docking of metalloproteins with ligand when one or two water molecules are engaged in the metal ion coordination. Since GPDOCK only depends on the three-dimensional structure of metalloprotein and ligand, structure-based machine learning model is employed for the scoring of binding poses, which significantly improves computational efficiency. The proposed docking strategy can be an effective and efficient tool for drug design and further study of binding mechanism of metalloproteins. The manual of GPDOCK and the code for the logistical regression model used to re-rank the docking results are available at https://github.com/wangkai-zhku/GPDOCK.git.
Reducing false positive rate of docking-based virtual screening by active learning
Authors: Wang Lei , Shi Shao-Hua , Li Hui , Zeng Xiang-Xiang , Liu Su-You , Liu Zhao-Qian , Deng Ya-Feng , Lu Ai-Ping , Hou Ting-Jun , Cao Dong-Sheng 
Published: January 2023

Abstract
Machine learning-based scoring functions (MLSFs) have become a very favorable alternative to classical scoring functions because of their potential superior screening performance. However, the information of negative data used to construct MLSFs was rarely reported in the literature, and meanwhile the putative inactive molecules recorded in existing databases usually have obvious bias from active molecules. Here we proposed an easy-to-use method named AMLSF that combines active learning using negative molecular selection strategies with MLSF, which can iteratively improve the quality of inactive sets and thus reduce the false positive rate of virtual screening. We chose energy auxiliary terms learning as the MLSF and validated our method on eight targets in the diverse subset of DUD-E. For each target, we screened the IterBioScreen database by AMLSF and compared the screening results with those of the four control models. The results illustrate that the number of active molecules in the top 1000 molecules identified by AMLSF was significantly higher than those identified by the control models. In addition, the free energy calculation results for the top 10 molecules screened out by the AMLSF, null model and control models based on DUD-E also proved that more active molecules can be identified, and the false positive rate can be reduced by AMLSF.
Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding
Authors: Yuan Qitong , Chen Keyi , Yu Yimin , Le Nguyen Quoc Khanh , Chua Matthew Chin Heng 
Published: January 2023

AbstractAnticancer peptides (ACPs) are the types of peptides that have been demonstrated to have anticancer activities. Using ACPs to prevent cancer could be a viable alternative to conventional cancer treatments because they are safer and display higher selectivity. Due to ACP identification being highly lab-limited, expensive and lengthy, a computational method is proposed to predict ACPs from sequence information in this study. The process includes the input of the peptide sequences, feature extraction in terms of ordinal encoding with positional information and handcrafted features, and finally feature selection. The whole model comprises of two modules, including deep learning and machine learning algorithms. The deep learning module contained two channels: bidirectional long short-term memory (BiLSTM) and convolutional neural network (CNN). Light Gradient Boosting Machine (LightGBM) was used in the machine learning module. Finally, this study voted the three models’ classification results for the three paths resulting in the model ensemble layer. This study provides insights into ACP prediction utilizing a novel method and presented a promising performance. It used a benchmark dataset for further exploration and improvement compared with previous studies. Our final model has an accuracy of 0.7895, sensitivity of 0.8153 and specificity of 0.7676, and it was increased by at least 2% compared with the state-of-the-art studies in all metrics. Hence, this paper presents a novel method that can potentially predict ACPs more effectively and efficiently. The work and source codes are made available to the community of researchers and developers at https://github.com/khanhlee/acp-ope/.
Median regularity and honest inference
Authors: Kuchibhotla Arun Kumar , Balakrishnan Sivaraman , Wasserman Larry 
Published: January 2023

Summary
We introduce a new notion of regularity of an estimator called median regularity. We prove that uniformly valid honest inference for a functional is possible if and only if there exists a median regular estimator of that functional. To the best of our knowledge, such a notion of regularity that is necessary for uniformly valid inference is unavailable in the literature.
MAGP1 maintains tumorigenicity and angiogenesis of laryngeal cancer by activating Wnt/β-catenin/MMP7 pathway
Authors: Lv Fei , Li Xiaoqi , Wang Ying , Hao Liying 
Published: January 2023

Abstract
Microfibril-associated glycoprotein-1 (MAGP1), a crucial extracellular matrix protein, contributes to the initiation and progression of different cancers. However, the role of MAGP1 in laryngeal cancer is not clear. The purpose of this study was to investigate the clinical significance and biological function of MAGP1 in laryngeal cancer. MAGP1 was upregulated in public databases and laryngeal cancer tissues, and high MAGP1 expression led to a poor prognosis and was identified as an independent prognostic marker. Knocking-down MAGP1 inhibited laryngeal cancer cell growth and metastasis. According to gene set enrichment analysis, high MAGP1 expression revealed enrichment in Wnt/β-catenin signaling and knocking-down MAGP1 in laryngeal cancer cells also caused degradation, de-activation, re-location and loss of stability of β-catenin. Additionally, we observed MAGP1 in laryngeal cancer cells inhibits angiogenesis in an MMP7-dependent way. In conclusion, our study suggests a clinical role of MAGP1 in laryngeal cancer, signifying its potential as a therapeutic target in the future.