bluuebunny/crossref_metadata_2025_split huggingface dataset:
Coastal vulnerability assessment of the West African coast to flooding and erosion
Authors: Dada Olusegun A. , Almar Rafael , Morand Pierre 
Published: January 2024

AbstractGlobal coastal areas are at risk due to geomorphological issues, climate change-induced sea-level rise, and increasing human population, settlements, and socioeconomic activities. Here, the study examines the vulnerability of the West African (WA) coast using six satellite-derived geophysical variables and two key socioeconomic parameters as indicators of coastal vulnerability index (CVI). These geophysical and socioeconomic variables are integrated to develop a CVI for the WA coast. Then, the regional hotspots of vulnerability with the main indicators that could influence how the WA coast behaves and can be managed are identified. The results indicate that 64, 17 and 19% of WA coastal areas had high to very high CVI, moderate CVI, and low to very low CVI, respectively. The study reveals that while geophysical variables contribute to coastal vulnerability in WA, socioeconomic factors, particularly high population growth and unsustainable human development at the coast, play a considerably larger role. Some sections of the WA coast are more vulnerable and exposed than others, particularly those in the region's northwestern and Gulf of Guinea regions. Climate change and human presence may amplify the vulnerability in these vulnerable areas in the future. Hence, future coastal economic development plans should be based on a deep understanding of local natural conditions, resource status, and geophysical parameters to prevent negative coastal ecosystem transformation. It is also essential to establish a coastal management plan that would facilitate the development of desired actions and stimulate sustainable management of West African coastal areas.
Prediction method of longitudinal surface settlement caused by double shield tunnelling based on deep learning
Authors: Shang Wentao , Li Yan , Wei Huanwei , Qiu Youbao , Chen Chaowei , Gao Xiangrong 
Published: January 2024

AbstractThe deep learning method faces the challenges of small sample data and high dimensional shield operational parameters in predicting the longitudinal surface settlement caused by shield excavation. In this study, various optimization algorithms were compared, and the slime mould algorithm (SMA) was optimally chosen to optimize the hyperparameters of random forest (RF), and SMA-RF was used for dimensionality reduction and feature contribution analysis. A double-input deep neural network (D-DNN) framework was proposed for the prediction of surface settlement, which considers the influence of twin tunnels and effectively increases the high-fidelity data in the database. The results show that SMA performs best among various optimization algorithms; employing features that have a cumulative contribution value exceeding 90% as input can result in high prediction accuracy; there is significant uncertainty in the feature contribution analysis for small sample data; the reduced shield running parameters show a strong nonlinear relationship with surface settlement; compared with S-DNN, D-DNN takes into account the excavation of twin tunnels and expands the database capacity by more than 1.5 times, with an average increase of 27.85% in the R2 and an average decrease of 53.2% in the MAE.
Humans create more novelty than ChatGPT when asked to retell a story
Authors: Breithaupt Fritz , Otenen Ege , Wright Devin R. , Kruschke John K. , Li Ying , Tan Yiyan 
Published: January 2024

AbstractWe compare how humans retell stories to how ChatGPT retells stories in chains of three retellings by different people or different accounts on ChatGPT. ChatGPT provides competent summaries of the original narrative texts in one step of retelling. In subsequent retellings few additional changes occur. Human retellers, by contrast, reduce the original text incrementally and by creating 55–60% of novel words and concepts (synsets) at each iteration. The retellings by both ChatGPT and humans show very stable emotion ratings, which is a puzzle for human retellers given the high degree of novel inventions across retellings. ChatGPT maintains more nouns, adjectives, and prepositions and also uses language later acquired in life, while humans use more verbs, adverbs, and negations and use language acquired at a younger age. The results reveal that spontaneous retelling by humans involves ongoing creativity, anchored by emotions, beyond the default probabilistic wording of large language models such as ChatGPT.
Apatinib weakens proliferation, migration, invasion, and angiogenesis of thyroid cancer cells through downregulating pyruvate kinase M2
Authors: Yang Xia , Li Wenhong , Han Xiaoying , Wang Jiao , Dai Jianjian , Ye Xin , Meng Min 
Published: January 2024

AbstractThyroid cancer (TC) is the most frequent malignancy of the endocrine system. Apatinib, as an anti-angiogenic agent, has been applied in the therapy of several cancers. However, the function and mechanism of Apatinib in TC have not been clearly elucidated. After processing with Apatinib alone or combined PKM2 overexpression plasmids, cell proliferation, migration, and invasion were analyzed by EdU staining, CCK-8, wound healing, and Transwell. Meanwhile. HUVECs were incubated with the conditioned medium prepared from cell culture medium, and tube formation and VEGFR2 expression in HUVECs were examined using tube formation and immunofluorescence (IF) assays. Besides, we established a nude mouse xenograft model by lentivirus-mediated PKM2 shRNAs, and tested the growth of tumors; the pathological structure was analyzed with H&E staining. And the expressions of N-cadherin, Vimentin, E-cadherin, PKM2, VEGFA, VEGFR2, and Ki67 were determined by immunohistochemistry or Western blot. Apatinib could prominently suppress proliferation, migration, invasion, and HUVEC tube formation in SW579 and TPC-1 cells. Besides, we discovered that Apatinib had a significant inhibitory role on the expression of pyruvate kinase M2 (PKM2) in TC cells. And PKM2 overexpression also could notably reverse Apatinib-mediated inhibition of TC progression. Moreover, PKM2 shRNAs were applied to TC xenografts, resulting in significant reduction in tumor volume and suppression of angiogenesis-related protein expression. In summary, Apatinib has a regulatory role in TC progression, and Apatinib can block cancer cell angiogenesis by downregulating PKM2. This will provide a theoretical basis for therapy of TC.
The semantics of gaze in person perception: a novel qualitative-quantitative approach
Authors: Landmann Eva , Breil Christina , Huestegge Lynn , Böckler Anne 
Published: January 2024

AbstractInterpreting gaze behavior is essential in evaluating interaction partners, yet the ‘semantics of gaze’ in dynamic interactions are still poorly understood. We aimed to comprehensively investigate effects of gaze behavior patterns in different conversation contexts, using a two-step, qualitative-quantitative procedure. Participants watched video clips of single persons listening to autobiographic narrations by another (invisible) person. The listener’s gaze behavior was manipulated in terms of gaze direction, frequency and direction of gaze shifts, and blink frequency; emotional context was manipulated through the valence of the narration (neutral/negative). In Experiment 1 (qualitative-exploratory), participants freely described which states and traits they attributed to the listener in each condition, allowing us to identify relevant aspects of person perception and to construct distinct rating scales that were implemented in Experiment 2 (quantitative-confirmatory). Results revealed systematic and differential meanings ascribed to the listener’s gaze behavior. For example, rapid blinking and fast gaze shifts were rated more negatively (e.g., restless and unnatural) than slower gaze behavior; downward gaze was evaluated more favorably (e.g., empathetic) than other gaze aversion types, especially in the emotionally negative context. Overall, our study contributes to a more systematic understanding of flexible gaze semantics in social interaction.
Estimation of pore structure and permeability in tight carbonate reservoir based on machine learning (ML) algorithm using SEM images of Jaisalmer sub-basin, India
Authors: Yalamanchi Pydiraju , Datta Gupta Saurabh 
Published: January 2024

AbstractAnalyzing the pore structure in carbonate reservoirs plays a crucial role in predicting fluid flow characteristics within these formations. The goal of the study was to use machine learning techniques for pore structure analysis and estimation of permeability in carbonate reservoirs. We implemented these algorithms by examining 2D scanning electron microscope (SEM) images of carbonate samples from the Jaisalmer sub-basin captured at various magnifications. In the initial stage of the analysis, various binarization algorithms were applied to determine carbonate sample porosity. Among these algorithms, the MaxEntropy algorithm gave a porosity value closely aligned with those obtained through petrography analysis. We employed the watershed algorithm to find the pore network parameters of carbonate samples at various magnifications. We observed that changes in magnification affected pore network parameters, resulting in a reduction in pore size distribution, throat radius, and grain size. Subsequently, we employed the numerical lattice Boltzmann method (LBM) to estimate the permeability of carbonate samples and compared to values derived from well logs. We employed machine learning (ML) algorithms, specifically Artificial Neural Network (ANN) and Support Vector Machine (SVM), to predict the permeability of carbonate samples. The input features for these models were the pore network parameters, while the LBM permeability values served as the output. We examine the prediction performance of these methods against the measured LBM permeability by conducting the error analysis and the coefficient of determination ($${R}^{2}$$R2) calculation. Our findings revealed that the ANN models outperformed the SVM models. Specifically, the ANN model displayed an impressive R2value of 0.892, along with root mean square error (RMSE), mean squared error (MSE) and, mean absolute error (MAE) values of 1.927, 3.716 and 1.580, respectively. In contrast, the SVM model yielded an R2value of 0.849, with RMSE, MSE and, MAE values of 2.324, 5.401 and, 2.166 respectively, when assessed on testing data of measured permeability. This study found that ANN is more dependable, robust, and precise than SVM in forecasting carbonate sample permeability.
QBO modulation of MJO teleconnections in the North Pacific: impact of preceding MJO phases
Authors: Kang Min-Jee , Kim Hera , Son Seok-Woo 
Published: January 2024

AbstractThis study examines the influence of the Quasi-Biennial Oscillation (QBO) on the Madden-Julian Oscillation (MJO) teleconnections in the North Pacific using ERA5 data. It is found that the Rossby wave trains induced by MJO phase 6–7 exhibit greater strength and robustness during the westerly QBO winter (WQBO) than during the easterly QBO winter (EQBO), although the MJO itself is weaker during the former. This counter-intuitive dependency of MJO teleconnections on the QBO is attributed to the preexisting MJO teleconnections prior to the MJO phase 6–7. The MJO phase 6–7 is more frequently preceded by stronger MJO phase 3–4 during the EQBO than during the WQBO. The preceding MJO phase 3–4 teleconnections, which have opposed signs to the MJO phase 6–7 teleconnections, result in a considerable attenuation of the MJO phase 6–7 teleconnections by destructive interference. This result is supported by linear model experiments. The subseasonal-to-seasonal prediction models also indicate improved prediction skills of MJO phase 6–7 teleconnections during the WQBO compared to the EQBO. These results suggest that enhanced MJO activities during the EQBO do not necessarily result in stronger and more robust MJO teleconnections in the North Pacific.
Metastable nickel–oxygen species modulate rate oscillations during dry reforming of methane
Authors: Sandoval-Diaz Luis , Cruz Daniel , Vuijk Maurits , Ducci Gianmarco , Hävecker Michael , Jiang Wulyu , Plodinec Milivoj , Hammud Adnan , Ivanov Danail , Götsch Thomas , Reuter Karsten , Schlögl Robert , Scheurer Christoph , Knop-Gericke Axel , Lunkenbein Thomas 
Published: January 2024

AbstractWhen a heterogeneous catalyst is active, it forms metastable structures that constantly transform into each other. These structures contribute differently to the catalytic function. Here we show the role of different metastable oxygen species on a Ni catalyst during dry reforming of methane by combining environmental scanning electron microscopy, near ambient pressure X-ray photoelectron spectroscopy, on-line product detection and computer vision. We highlight the critical role of dissociative CO2 adsorption in regulating the oxygen content of the catalyst and in CH4 activation. We also discover rate oscillations during dry reforming of methane resulting from the sequential transformation of metastable oxygen species that exhibit different catalytic properties: atomic surface oxygen, subsurface oxygen and bulk NiOx. The imaging approach allowed the localization of fluctuating surface regions that correlated directly with catalytic activity. The study highlights the importance of metastability and operando analytics in catalysis science and provides impetus towards the design of catalytic systems.
Oral d-ribose causes depressive-like behavior by altering glycerophospholipid metabolism via the gut-brain axis
Authors: Xu Ke , Ren Yi , Zhao Shuang , Feng Jinzhou , Wu Qingyuan , Gong Xue , Chen Jianjun , Xie Peng 
Published: January 2024

AbstractOur previous work has shown that d-ribose (RIB)-induced depressive-like behaviors in mice. However, the relationship between variations in RIB levels and depression as well as potential RIB participation in depressive disorder is yet unknown. Here, a reanalysis of metabonomics data from depressed patients and depression model rats is performed to clarify whether the increased RIB level is positively correlated with the severity of depression. Moreover, we characterize intestinal epithelial barrier damage, gut microbial composition and function, and microbiota-gut-brain metabolic signatures in RIB-fed mice using colonic histomorphology, 16 S rRNA gene sequencing, and untargeted metabolomics analysis. The results show that RIB caused intestinal epithelial barrier impairment and microbiota-gut-brain axis dysbiosis. These microbial and metabolic modules are consistently enriched in peripheral (fecal, colon wall, and serum) and central (hippocampus) glycerophospholipid metabolism. In addition, three differential genera (Lachnospiraceae_UCG-006, Turicibacter, and Akkermansia) and two types of glycerophospholipids (phosphatidylcholine and phosphatidylethanolamine) have greater contributions to the overall correlations between differential genera and glycerophospholipids. These findings suggest that the disturbances of gut microbiota by RIB may contribute to the onset of depressive-like behaviors via regulating glycerophospholipid metabolism, and providing new insight for understanding the function of microbiota-gut-brain axis in depression.
Cerebrospinal fluid proteomics in patients with Alzheimer’s disease reveals five molecular subtypes with distinct genetic risk profiles
Authors: Tijms Betty M. , Vromen Ellen M. , Mjaavatten Olav , Holstege Henne , Reus Lianne M. , van der Lee Sven , Wesenhagen Kirsten E. J. , Lorenzini Luigi , Vermunt Lisa , Venkatraghavan Vikram , Tesi Niccoló , Tomassen Jori , den Braber Anouk , Goossens Julie , Vanmechelen Eugeen , Barkhof Frederik , Pijnenburg Yolande A. L. , van der Flier Wiesje M. , Teunissen Charlotte E. , Berven Frode S. , Visser Pieter Jelle 
Published: January 2024

AbstractAlzheimer’s disease (AD) is heterogenous at the molecular level. Understanding this heterogeneity is critical for AD drug development. Here we define AD molecular subtypes using mass spectrometry proteomics in cerebrospinal fluid, based on 1,058 proteins, with different levels in individuals with AD (n = 419) compared to controls (n = 187). These AD subtypes had alterations in protein levels that were associated with distinct molecular processes: subtype 1 was characterized by proteins related to neuronal hyperplasticity; subtype 2 by innate immune activation; subtype 3 by RNA dysregulation; subtype 4 by choroid plexus dysfunction; and subtype 5 by blood–brain barrier impairment. Each subtype was related to specific AD genetic risk variants, for example, subtype 1 was enriched with TREM2 R47H. Subtypes also differed in clinical outcomes, survival times and anatomical patterns of brain atrophy. These results indicate molecular heterogeneity in AD and highlight the need for personalized medicine.
Airborne transmission efficiency of SARS-CoV-2 in Syrian hamsters is not influenced by environmental conditions
Authors: Yinda Claude Kwe , Port Julia R. , Bushmaker Trenton , Schulz Jonathan E. , Gallogly Shane , Fischer Robert J. , Munster Vincent J. 
Published: January 2024

AbstractSeveral human respiratory viruses display a clear seasonal pattern with a higher incidence in the winter season in temperate regions. We previously determined that SARS-CoV-2 is more stable at low-temperature and low-humidity conditions compared to warmer temperature and higher-humidity. To determine if this translates into differential airborne transmission rates in vivo, we performed airborne transmission experiments in the Syrian hamster model under three different environmental conditions (10 °C, 45% relative humidity (RH), 22 °C, 45% RH, and 27 °C, 65% RH). We compared the ancestral SARS-CoV-2 Lineage A with the more transmissible Delta Variant of Concern (VOC). Airborne transmission was evaluated using SARS-CoV-2 infected donor animals at 24 h post inoculation. Sentinels were placed at a 90 cm distance in a transmission set-up and exposed for 1-h to infected donor animals. While environmental conditions moderately impacted lung RNA titers, the shedding kinetics of the donors were not affected by the environmental conditions and did not differ significantly between variants on day 1. Overall, the highest transmission efficiency was observed at 22 °C, 40%RH for Delta (62.5%, based on seroconversion), and ranged between 37.5 and 50% for all other conditions. However, these differences were not significant. To elucidate this further, we performed aerosol stability comparisons and found that infectious virus remained stable during a 1-h time window across all conditions. Our data suggest that even when environmental conditions affect the stability of SARS-CoV-2, this may not directly be translatable to measurable impacts on transmission in an experimental setting when exposure time is restricted.
Noncovalent interaction network of chalcogen, halogen and hydrogen bonds for supramolecular β-sheet organization
Authors: Cao Jinlian , Weng Peimin , Qi Yuanwei , Lin Kexin , Yan Xiaosheng 
Published: January 2024

The alanine-based bilateral building block adopts a β-sheet organization in both the solid state and CH3CN solution, achieved via a noncovalent interaction network composed of intramolecular chalcogen bonds and intermolecular halogen/hydrogen bonds.
An adaptive neural design for planar rigid formation of three coleaders in unknown flowfields
Authors: Chen Weibin , Xu Peng , Chen Yang‐Yang 
Published: January 2024

AbstractThis article deals with the robust planar rigid formation control problem of three second‐order coleaders with unknown flowfields acting on the velocity and acceleration respectively. To yield the uniform boundedness property of the resulting system, an adaptive projection is introduced to design the novel adaptive neural control law. To avoid the derivatives of Gaussian functions of neural networks, dynamic surface is used. Simulation results are provided to illustrate the effectiveness of the proposed control law.
Application and analysis of superconducting magnetic eddy current heater used in wind thermal power generation system
Authors: Zhang Wenfeng , Chen Chen , Liu Zeyuan , Wang Youhua , Liu Chengcheng 
Published: January 2024

AbstractIn order to cope with the grid fluctuation caused by large‐scale wind power connected to the grid, the wind thermal power generation system has been proposed and extensively studied. The wind thermal power generation system uses a wind turbine to drive a heat generation device to heat the heat storage medium, which is further exchanged to drive a turbine to generate electricity. A superconducting magnetic eddy current heater (SMH) is proposed for the characteristics of wind thermal power generation system, which uses non‐resistive, large current‐carrying superconducting coils for excitation, and has high output efficiency and power density. The working principle of magnetic eddy current heating is analysed, and the structure of SMH with no ferromagnetic material and two heating screens inside and outside is proposed according to the characteristics of SMH. An analytical model of the SMH is established, and the influence of the structure and materials of the SMH on the magnetic field distribution is analysed. Based on this, a 22‐pole SMH was designed and analysed for output power at 10–20 rpm. A three‐dimensional magnetism‐stress combined analysis model of SMH is established, and the strain and stress characteristics of SMH are simulated under the condition of maximum output power, which verifies the feasibility of the mechanical properties of existing superconducting materials for application in SMH.
Galaxy morphology classification based on Convolutional vision Transformer (CvT)
Authors: Cao Jie , Xu Tingting , Deng Yuhe , Deng Linhua , Yang Mingcun , Liu Zhijing , Zhou Weihong 
Published: January 2024

Context.The classification of galaxy morphology is among the most active fields in astronomical research today. With the development of artificial intelligence technology, deep learning is a useful tool in the classification of the morphology of galaxies and significant progress has been made in this domain. However, there is still some room for improvement in terms of classification accuracy, automation, and related issues.Aims.Convolutional vision Transformer (CvT) is an improved version of the Vision Transformer (ViT) model. It improves the performance of the ViT model by introducing a convolutional neural network (CNN). This study explores the performance of the CvT model in the area of galaxy morphology classification.Methods.In this work, the CvT model was applied, for the first time, in a five-class classification task of galaxy morphology. We added different types and degrees of noise to the original galaxy images to verify that the CvT model achieves good classification performance, even in galaxy images with low signal-to-noise ratios (S/Ns). Then, we also validated the classification performance of the CvT model for galaxy images at different redshifts based on the low-redshift dataset GZ2 and the high-redshift dataset Galaxy Zoo CANDELS. In addition, we visualized and analyzed the classification results of the CvT model based on the t-distributed stochastic neighborhood -embedding (t-SNE) algorithm.Results.We find that (1) compared with other five-class classification models of galaxy morphology based on CNN models, the average accuracy, precision, recall, and F1_score evaluation metrics of the CvT classification model are all higher than 98%, which is an improvement of at least 1% compared with those based on CNNs; (2) the classification visualization results show that different categories of galaxies are separated from each other in multi-dimensional space.Conclusions.The application of the CvT model to the classification study of galaxy morphology is a novel undertaking that carries important implications for future studies.