bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mechanical characteristics of hoisting construction of segmental cantilever assembled box girder bridge
Authors: Cheng Xuansheng , Sun Yue , Hong Junliang 
Published: April 2024

Abstract
In order to study the safety of the segmental box girder in the hoisting process, this paper designed the construction method of the gantry crane hoisting segmental box girder in combination with the actual project. By establishing a mathematical model, the mechanical properties of the segmental box girder under two different hoisting schemes were analyzed. Under different load conditions, the lifting analyses, translation analyses and descending analyses of the box girder were carried out respectively, and the deflection and stress corresponding to the box girder were obtained. Considering the displacement and stress results of the box girder under the two hoisting schemes, it was concluded that the hoisting construction of the segmental box girder should give priority to the second hoisting scheme. That is, the hoisting holes were opened at the flange position of the roof. Provide feasible technical support for future construction.
Neural networks for separation of cosmic gamma rays and hadronic cosmic rays in air shower observation with a large area surface detector array
Authors: Okukawa Sousuke , Hara Kazuyuki , Hibino Kinya , Katayose Yusaku , Kawata Kazumasa , Ohnishi Munehiro , Sako Takashi , Sako Takashi K , Shibata Makio , Shiomi Atsushi , Takita Masato 
Published: April 2024

Abstract
The Tibet ASγ experiment has been observing cosmic gamma rays and cosmic rays in the energy range from teraelectron volts to several tens of petaelectron volts with a surface detector array since 1990. The derivation of cosmic gamma-ray flux is made by finding the excess distribution of the arrival direction of air showers above background cosmic rays. In 2014, the underground water Cherenkov muon detector (MD) was added to separate cosmic gamma rays from the background on the basis of the muon-less feature of the air showers of gamma-ray origin; hybrid observations using these two detectors were started at this time. In the present study, we developed methods to separate gamma-ray-induced air showers and hadronic cosmic-ray-induced ones using the measured particle number density distribution to improve the sensitivity of cosmic gamma-ray measurements using the Tibet air shower array data alone before the installation of the MD. We tested two approaches based on neural networks. The first method used feature values representing the lateral spread of the secondary particles, and the second method used the shower image data. To compare the separation performance of each method, we analyzed Monte Carlo air shower events in the vertically incident direction with mono-initial-energy gamma rays and protons. When discriminated by a single feature, the feature with the highest separation performance has an area under the curve (AUC) value of 0.701 for a gamma-ray energy of 10 TeV and 0.808 for 100 TeV. A separation method with a multilayer perceptron (MLP) based on multiple features has AUC values of 0.761 for a gamma-ray energy of 10 TeV and 0.854 for 100 TeV, which represents an improvement of approximately 5% in the AUC value compared with the single-feature case. We also found that the feature values that effectively contribute to the separation vary depending on the energy. A separation method with a convolutional neural network (CNN) using the shower image data has AUC values of 0.781 for a gamma-ray energy of 10 TeV and 0.901 for 100 TeV, which are approximately 5% higher than those of the MLP method. We applied the CNN separation method to Monte Carlo gamma-ray and cosmic-ray events from the Crab Nebula in the energy range 10–100 TeV. The AUC values range from 0.753 to 0.879, and the significance of the observed gamma-ray excess is improved by 1.3 to 1.8 times compared with the case without the separation procedure.
Contributions of atmospheric forcing and ocean preconditioning in the 2016 Antarctic sea ice extent drop
Authors: Mezzina Bianca , Goosse Hugues , Huot Pierre-Vincent , Marchi Sylvain , Van Lipzig Nicole 
Published: April 2024

Abstract
The 2016 Antarctic sea ice extent (SIE) drop was a rapid decrease that led to persistent low sea ice conditions. The event was triggered by atmospheric anomalies, but the potential preconditioning role of the ocean is unsettled. Here, we use sensitivity experiments with a fully-coupled regional climate model to elucidate the impact of the ocean conditions on the drop and on the persistence of the negative SIE anomalies during 2017. In particular, we re-initialize the model in January 2016 using different ocean and sea ice conditions, keeping lateral boundary forcings in the atmosphere and ocean unchanged. We find that the state of the Southern Ocean in early 2016 does not determine whether the drop occurs or not, but indeed has an impact on its amplitude and regional characteristics. Our results also indicate that the ocean initialization affects the sea ice recovery after the drop in the short term (one year), especially in the Weddell sector. The ocean’s influence appears not to be linked to the ocean surface and sea-ice initialization, but rather to the sub-surface conditions (between 50 m and 150 m) and heat exchange fluctuations at the regional scale, while the atmospheric forcing triggering the drop is driven by the large-scale circulation.
Exploiting a variational auto-encoder to represent the evolution of sudden stratospheric warmings
Authors: Chen Yi-Chang , Liang Yu-Chiao , Wu Chien-Ming , Huang Jin-De , Lee Simon H , Wang Yih , Zeng Yi-Jhen 
Published: April 2024

Abstract
Sudden stratospheric warmings (SSWs) are the most dramatic events in the wintertime stratosphere. Such extreme events are characterized by substantial disruption to the stratospheric polar vortex, which can be categorized into displacement and splitting types depending on the morphology of the disrupted vortex. Moreover, SSWs are usually followed by anomalous tropospheric circulation regimes that are important for subseasonal-to-seasonal prediction. Thus, monitoring the genesis and evolution of SSWs is crucial and deserves further advancement. Despite several analysis methods that have been used to study the evolution of SSWs, the ability of deep learning methods has not yet been explored, mainly due to the relative scarcity of observed events. To overcome the limited observational sample size, we use data from historical simulations of the Whole Atmosphere Community Climate Model version 6 to identify thousands of simulated SSWs, and use their spatial patterns to train the deep learning model. We utilize a convolutional neural network combined with a variational auto-encoder (VAE)—a generative deep learning model—to construct a phase diagram that characterizes the SSW evolution. This approach not only allows us to create a latent space that encapsulates the essential features of the vortex structure during SSWs, but also offers new insights into its spatiotemporal evolution mapping onto the phase diagram. The constructed phase diagram depicts a continuous transition of the vortex pattern during SSWs. Notably, it provides a new perspective for discussing the evolutionary paths of SSWs: the VAE gives a better-reconstructed vortex morphology and more clearly organized vortex regimes for both displacement-type and split-type events than those obtained from principal component analysis. Our results provide an innovative phase diagram to portray the evolution of SSWs, in which particularly the splitting SSWs are better characterized. Our findings support the future use of deep learning techniques to study the underlying dynamics of extreme stratospheric vortex phenomena, and to establish a benchmark to evaluate model performance in simulating SSWs.
Divergent future change in South Atlantic Ocean Dipole impacts on regional rainfall in CMIP6 models
Authors: Nworgu Uzoma C , Nnamchi Hyacinth C , Rosário Nilton E 
Published: April 2024

Abstract
The South Atlantic Ocean Dipole (SAOD) exerts strong influence on climate variability in parts of Africa and South America. Here we assess the ability of an ensemble of 35 state-of-the-art coupled global climate models to simulate the SAOD impacts on regional rainfall for the historical period (1950–2014), and their future projections (2015–2079). For both periods we consider the peak phase of the dipole in austral winter. Observational analysis reveals four regions with spatially coherent SAOD impacts on rainfall; Northern Amazon, Guinea Coast, Central Africa, and Southeast Brazil. The observed rainfall response to the SAOD over Northern Amazon (0.31 mm d−1), Guinea Coast (0.38 mm d−1), and Southeast Brazil (0.12 mm d−1) are significantly underestimated by the modeled ensemble-mean response of 0.10 ± 0.15 mm d−1, 0.05 ± 0.15 mm d−1, −0.01 ± 0.04 mm d−1, respectively. A too southerly rain belt in the ensemble, associated with warmer-than-observed Atlantic cold tongue, leads to better performance of models over Central Africa (46% simulate observations-consistent SAOD-rainfall correlations) and poor performance over the Guinea Coast (only 5.7% simulate observations-consistent SAOD-rainfall correlations). We also find divergent responses among the projections of ensemble members precluding a categorical statement on the future strength of the SAOD-rainfall relationship in a high-emissions scenario. Our results highlight key uncertainties that must be addressed to enhance the value of SAOD-rainfall projections for the affected African and South American countries.
Assessing canopy height measurements from ICESat-2 and GEDI orbiting LiDAR across six different biomes with G-LiHT LiDAR
Authors: Yu Qiuyan , Ryan Michael G , Ji Wenjie , Prihodko Lara , Anchang Julius Y , Kahiu Njoki , Nazir Abid , Dai Jingyu , Hanan Niall P 
Published: April 2024

Abstract
The height of woody plants is a defining characteristic of forest and shrubland ecosystems because height responds to climate, soil and disturbance history. Orbiting LiDAR instruments, Ice, Cloud and land Elevation Satellite-2 (ICESat-2) and Global Ecosystem Dynamics Investigation LiDAR (GEDI), can provide near-global datasets of plant height at plot-level resolution. We evaluate canopy height measurements from ICESat-2 and GEDI with high resolution airborne LiDAR in six study sites in different biomes from dryland shrub to tall forests, with mean canopy height across sites of 0.5–40 m. ICESat-2 and GEDI provide reliable estimates for the relative height with RMSE and mean absolute error (MAE) of 7.49 and 4.64 m (all measurements ICESat-2) and 6.52 and 4.08 m (all measurements GEDI) for 98th percentile relative heights. Both datasets slightly overestimate the height of short shrubs (1–2 m at 5 m reference height), underestimate that of tall trees (by 6–7 m at 40 m reference height) and are highly biased (>3 m) for reference height <5 m, perhaps because of the difficulty of distinguishing canopy from ground signals. Both ICESat-2 and GEDI height estimates were only weakly sensitive to canopy cover and terrain slope (R
2 < 0.06) and had lower error for night compared to day samples (ICESat-2 RMSE night: 5.57 m, day: 6.82 m; GEDI RMSE night: 5.94 m, day: 7.03 m). For GEDI, the day versus night differences varied with differences in mean sample heights for the day and night samples and had little effect on bias. Accuracy of ICESat-2 and GEDI canopy heights varies among biomes, and the highest MAE was observed in the tallest, densest forest (GEDI: 7.85 m; ICESat-2: 7.84 m (night) and 12.83 m (day)). Improvements in canopy height estimation would come from better discrimination of canopy photons from background noise for ICESat-2 and improvements in the algorithm for decomposing ground and canopy returns for GEDI. Both would benefit from methods to distinguish outlier samples.
A Multicenter, Randomized, Evaluator-Blinded Study to Examine the Safety and Effectiveness of Hyaluronic Acid Filler in the Correction of Infraorbital Hollows
Authors: Biesman Brian S , Green Jeremy B , George Rosalyn , Jacob Carolyn , Palm Melanie , Jones Derek H , Grunebaum Lisa , Beer Kenneth , Cho Young , Joseph John H , Almegård Birgitta , Weinberg Felipe , Bromée Torun 
Published: April 2024

Abstract

Background
Hyaluronic acid injections are increasingly administered for correction of infraorbital hollows (IOHs).

Objectives
The objective of this study was to examine the effectiveness (IOH correction) and safety of Restylane Eyelight hyaluronic acid (HAEYE) injections.

Methods
Patients with moderate/severe IOHs, assessed with the Galderma infraorbital hollows scale (GIHS), were randomized to HAEYE injections (by needle/cannula) (Day 1 + optional Month 1 touch-up) or no-treatment control. The primary endpoint was blinded evaluator–reported Month 3 response, defined as ≥1-point GIHS improvement from baseline (both sides, concurrently). Other endpoints examined investigator-reported aesthetic improvement on the Global Aesthetic Improvement Scale (GAIS), patient-reported satisfaction (FACE-Q satisfaction with outcome; satisfaction questionnaire), and adverse events.

Results
Overall, 333 patients were randomized. Month 3 GIHS responder rate was significantly higher for HA­EYE (87.4%) vs control (17.7%; P &lt; .001), and comparable between HA­EYE-needle and HA­EYE-cannula groups (P = .967). HAEYE GAIS responder rate was 87.5-97.7% (Months 3-12). Mean FACE-Q Rasch-transformed scores were 64.3-73.5 (HAEYE) vs 14.1-16.2 (control) through Month 12. Patients reported looking younger (≥71%) and less tired (≥79%) with reduced undereye shadows (≥76%) and recovered within 3-5 hours posttreatment. Efficacy was maintained through Month 12 (63.5% GIHS responders) and through Month 18, after Month 12 retreatment (80.3% GIHS responders; 99.4% GAIS responders; FACE-Q scores 72.5-72.8). Forty patients (12.7%) reported typically mild adverse events (4.9% HAEYE-needle; 20.9% HAEYE-cannula).

Conclusions
HAEYE treatment was effective in correcting moderate/severe IOHs at the primary endpoint (Month 3). Efficacy was sustained through Month 12 after first treatment for 63.5% and through Month 18 for 80.3% (after 1 retreatment) with needle or cannula administration. Safety outcomes were reassuring.

Level of Evidence: 1
Regional analysis to delineate intrasample heterogeneity with RegionalST
Authors: Lyu Yue , Wu Chong , Sun Wei , Li Ziyi 
Published: April 2024

Abstract

Motivation
Spatial transcriptomics has greatly contributed to our understanding of spatial and intra-sample heterogeneity, which could be crucial for deciphering the molecular basis of human diseases. Intra-tumor heterogeneity, e.g. may be associated with cancer treatment responses. However, the lack of computational tools for exploiting cross-regional information and the limited spatial resolution of current technologies present major obstacles to elucidating tissue heterogeneity.

Results
To address these challenges, we introduce RegionalST, an efficient computational method that enables users to quantify cell type mixture and interactions, identify sub-regions of interest, and perform cross-region cell type-specific differential analysis for the first time. Our simulations and real data applications demonstrate that RegionalST is an efficient tool for visualizing and analyzing diverse spatial transcriptomics data, thereby enabling accurate and flexible exploration of tissue heterogeneity. Overall, RegionalST provides a one-stop destination for researchers seeking to delve deeper into the intricacies of spatial transcriptomics data.

Availability and implementation
The implementation of our method is available as an open-source R/Bioconductor package with a user-friendly manual available at https://bioconductor.org/packages/release/bioc/html/RegionalST.html.
High-dimensional multisubject time series transition matrix inference with application to brain connectivity analysis
Authors: Lyu Xiang , Kang Jian , Li Lexin 
Published: April 2024

ASTRACT
Brain-effective connectivity analysis quantifies directed influence of one neural element or region over another, and it is of great scientific interest to understand how effective connectivity pattern is affected by variations of subject conditions. Vector autoregression (VAR) is a useful tool for this type of problems. However, there is a paucity of solutions when there is measurement error, when there are multiple subjects, and when the focus is the inference of the transition matrix. In this article, we study the problem of transition matrix inference under the high-dimensional VAR model with measurement error and multiple subjects. We propose a simultaneous testing procedure, with three key components: a modified expectation-maximization (EM) algorithm, a test statistic based on the tensor regression of a bias-corrected estimator of the lagged auto-covariance given the covariates, and a properly thresholded simultaneous test. We establish the uniform consistency for the estimators of our modified EM, and show that the subsequent test achieves both a consistent false discovery control, and its power approaches one asymptotically. We demonstrate the efficacy of our method through both simulations and a brain connectivity study of task-evoked functional magnetic resonance imaging.
Revisiting the Bond between Social Work and Ethnography
Authors: Sinai-Glazer H 
Published: April 2024

Abstract
Ethnographic research in social work is extremely valuable, yet not very commonly applied. In this theoretical article, I delineate the historical bond and ethical alliance between ethnography and social work, as well as highlight the empirical value that ethnography can offer the social work profession. By so doing I render visible the affinity between ethnographic research and the discipline of social work. I conclude by examining the relations between slow scholarship and ethnographic research as a means of resistance to the corporate neo-liberal university setting, encouraging social work scholars to do more ethnographic work. Such work can make a potentially significant contribution to social work practitioners, service users, policy makers, educators and scholars and encourage social science with meaning.
Two-point connected complex of zinc aminoporphyrin with histidine protected at α-amino and imidazolyl-π nitrogen atoms
Authors: Yuasa Mio , Nakamura Soichi , Tamiaki Hitoshi 
Published: April 2024

Abstract
Zinc porphyrins possessing an (N,N-dialkylamino)methyl group at the peripheral position were prepared from β-octaethylporphyrin. The synthetic zinc aminoporphyrins were complexed with an Nα,Nim(τ)-protected histidine through the coordination of the histidyl imidazolyl-τ nitrogen atom with the central zinc atom and the concomitant hydrogen bonding of the histidyl carboxy group with the peripheral amino group. The 1:1 complexation in the chloroform was dependent on the size of the alkyl groups on the peripheral aminomethyl nitrogen atom. The diethyl substitute complexed more strongly than the dimethyl and dipropyl counterparts. The complexation of zinc (diethylamino)methyl-porphyrin with an Nα,Nim(π)-protected histidine was 260-fold stronger than that with the above Nα,Nim(τ)-protected histidine to exhibit the association constant of 2.6 × 106 M−1. The significant enhancement was ascribable to the less sterically demanding and more coordinating Nim(τ)-atom compared with the Nim(π)-atom. The achiral zinc aminoporphyrin was complexed with the chiral Nα,Nim(π)-protected histidine to give a circular dichroism active species in a visible Soret region.
Functional brain networks in Developmental Topographical Disorientation
Authors: Faryadras Mahsa , Burles Ford , Iaria Giuseppe , Davidsen Jörn 
Published: April 2024

AbstractDespite a decade-long study on Developmental Topographical Disorientation, the underlying mechanism behind this neurological condition remains unknown. This lifelong selective inability in orientation, which causes these individuals to get lost even in familiar surroundings, is present in the absence of any other neurological disorder or acquired brain damage. Herein, we report an analysis of the functional brain network of individuals with Developmental Topographical Disorientation ($n = 19$) compared against that of healthy controls ($n = 21$), all of whom underwent resting-state functional magnetic resonance imaging, to identify if and how their underlying functional brain network is altered. While the established resting-state networks (RSNs) are confirmed in both groups, there is, on average, a greater connectivity and connectivity strength, in addition to increased global and local efficiency in the overall functional network of the Developmental Topographical Disorientation group. In particular, there is an enhanced connectivity between some RSNs facilitated through indirect functional paths. We identify a handful of nodes that encode part of these differences. Overall, our findings provide strong evidence that the brain networks of individuals suffering from Developmental Topographical Disorientation are modified by compensatory mechanisms, which might open the door for new diagnostic tools.
The causal relationship between physical activity, sedentary behavior and brain cortical structure: a Mendelian randomization study
Authors: Sun Yulin , Ma Di , Jiang Zhenping , Han Qifeng , Liu Yining , Chen Guoyang 
Published: April 2024

Abstract
Physical activity and sedentary behavior, both distinct lifestyle behaviors associated with brain health, have an unclear potential relationship with brain cortical structure. This study aimed to determine the causal link between physical activity, sedentary behavior, and brain cortical structure (cortical surface area and thickness) through Mendelian randomization analysis. The inverse-variance weighted method was primarily utilized, accompanied by sensitivity analyses, to confirm the results’ robustness and accuracy. Analysis revealed nominally significant findings, indicating a potential positive influence of physical activity on cortical thickness in the bankssts (β = 0.002 mm, P = 0.043) and the fusiform (β = 0.002 mm, P = 0.018), and a potential negative association of sedentary behavior with cortical surface area in the caudal middle frontal (β = −34.181 mm2, P = 0.038) and the pars opercularis (β = −33.069 mm2, P = 0.002), alongside a nominally positive correlation with the cortical surface area of the inferior parietal (β = 58.332 mm2, P = 0.035). Additionally, a nominally significant negative correlation was observed between sedentary behavior and cortical thickness in the paracentral (β = −0.014 mm, P = 0.042). These findings offer insights into how lifestyle behaviors may influence brain cortical structures, advancing our understanding of their interaction with brain health.