bluuebunny/crossref_metadata_2025_split huggingface dataset:
Failure of Pavlik Harness Treatment in Infants Under 6 Months Old with Dislocated Hips: Short- and Intermediate-Term Results of Subsequent Treatment Modalities
Authors: Imerci Ahmet , Thacker Mihir M. , Bowen James Richard 
Published: June 2024

Abstract
Objective
This study aimed to determine the short- and medium-term outcomes of hip dislocation in infants who failed Pavlik harness therapy and were subsequently treated with brace, closed reduction (CR) or open reduction (OR) before 6 months of age.

Methods
Fifty infants (66 hip dislocations) who failed Pavlik harness therapy between 2000 and 2018 and were treated with a rigid abduction brace or undergoing a CR or OR/cast were evaluated. All demographic data obtained from the medical system, developments and complications during the follow-up and treatment process were recorded and evaluated.

Results
Fifty infants (66 hips) with dislocated hips failed Pavlik harness therapy. Of these, 9 infants (12 hips) underwent rigid abduction splint therapy: 9 hips were successful, 2 hips had CR and 1 had OR. Thirty-eight infants (51 hips) had index CR, of which 3 (3 hips) failed and had OR. Radiographs of 49 hips (44 patients) were normal at the final evaluation. Pavlik harness therapy starting after 3 weeks (P = 0.028) and unilateral dislocations (P = 0.028) increased the risk of needing operating room. There was an association between OR and avascular necrosis (P = 0.025), but not between OR and other complications—dysplasia and re-dislocation/subluxation (P = 0.257 and P = 0.508, respectively).

Conclusion
Closed treatment of hip dislocation is possible in most babies who fail Pavlik treatment. Babies who are started on Pavlik therapy after 3 weeks of age may be at increased risk of needing an operating room.

Level of Evidence
IV.
A review of point cloud segmentation for understanding 3D indoor scenes
Authors: Sun Yuliang , Zhang Xudong , Miao Yongwei 
Published: June 2024

AbstractPoint cloud segmentation is an essential task in three-dimensional (3D) vision and intelligence. It is a critical step in understanding 3D scenes with a variety of applications. With the rapid development of 3D scanning devices, point cloud data have become increasingly available to researchers. Recent advances in deep learning are driving advances in point cloud segmentation research and applications. This paper presents a comprehensive review of recent progress in point cloud segmentation for understanding 3D indoor scenes. First, we present public point cloud datasets, which are the foundation for research in this area. Second, we briefly review previous segmentation methods based on geometry. Then, learning-based segmentation methods with multi-views and voxels are presented. Next, we provide an overview of learning-based point cloud segmentation, ranging from semantic segmentation to instance segmentation. Based on the annotation level, these methods are categorized into fully supervised and weakly supervised methods. Finally, we discuss open challenges and research directions in the future.
Ecological dynamics and conservation implications of Indo-Pacific Tarpon (Megalops cyprinoides) in Lake Siombak, North Sumatra, Indonesia
Authors: Muhtadi Ahmad , Maiyah Nur , Suryanti Ani , Abdelrahman Amal Saber , Akbar Helmy , Putra Singgih Afifa 
Published: June 2024

AbstractThis study presents a comprehensive examination of the Indo-Pacific Tarpon (IPT) population in Lake Siombak, North Sumatra Province, Indonesia. Through a year-long investigation, we analyzed various facets of IPT ecology, encompassing population structure, growth dynamics, reproductive behavior, feeding habits, and mortality parameters. Male dominance in collected specimens, coupled with consistently immature gonads in females, suggests a unique reproductive pattern influenced by the brackish nature of Lake Siombak. Utilizing the von Bertalanffy method, we determined a moderate growth rate in IPT, with a predominance of medium-sized, sexually immature individuals. The dietary analysis revealed an omnivorous nature, with a notable reliance on zooplankton, particularly Cladocera and Copepods. However, the most significant concern arises from the estimation of the exploitation rate (E), surpassing the annual optimum exploitation threshold, indicating a state of overexploitation. This underscores the urgent need for regulatory measures to ensure the sustainability of IPT resources in Lake Siombak. Our findings contribute to a nuanced understanding of IPT ecology in this specific ecosystem, emphasizing the importance of balanced conservation and management practices. The documented reproductive, growth, and feeding patterns provide crucial insights for informing targeted conservation strategies and safeguarding the enduring viability of the IPT population in Lake Siombak. This research serves as a foundational contribution to the broader field of fisheries science, advocating for evidence-based management to preserve the ecological balance of aquatic ecosystems.
Landslide Deformation Prediction and Automatic Warning by Coupling Machine Learning and Physical Models
Authors: Wang Xianmin , Diao Mupeng , Guo Haonan , Wang Lizhe , Guo Haixiang , Li Dongdong 
Published: June 2024

AbstractAutomatic deformation forecast and warning of a catastrophic landslide can effectively avoid significant casualties and economic losses. However, currently it has not come to a comprehensive forecast framework covering all the deformation stages of a landslide. Moreover, landslide deformation prediction possesses high error and false alarm rates. This work suggests a novel integrated framework of landslide deformation forecast and warning by coupling machine learning and physical models. The framework can relatively accurately predict all the deformation stages from creeping deformation to critical sliding and features 4 advantages. (a) The forecast indices are established by combining deformation and disaster‐triggering characteristics to improve the prediction accuracy. (b) It leverages the advantage of C5.0 decision tree algorithm in knowledge interpretability to automatically extract deformation forecast criteria. (c) It capitalizes on the precision superiority of a graph convolutional network in time‐series data learning to predict the four deformation stages from creeping deformation to rapidly accelerated deformation. (d) It utilizes the physical and mechanical bases of Morgenstern‐Price method to forecast the critical sliding stage. Zhujiatang Landslide is a large‐scale deep‐seated soil landslide with significant deformation. It is selected as a case study because it has endangered 1,131 persons and may cause a direct financial loss of 100 million RMB. The validating and predicting Accuracy values attain 97.39% and 95.72%, respectively, and the Kappa values reach 0.91 and 0.93, respectively. The landslide will run out when it suffers from a rainstorm with cumulative rainfall of 79.57 mm and an earthquake with a horizontal coefficient of 0.04.
Drivers of the Extreme Early Spring Glacier Melt of 2022 on the Central Tibetan Plateau
Authors: Zhu Fei , Zhu Meilin , Yang Wei , Wang Zhongyan , Guo Yanhong , Yao Tandong 
Published: June 2024

AbstractExtreme glacier melt has important implications for climate change studies and glacier‐related hazards. Few attempts have been made to study the characteristics and mechanisms of extreme melt on the Tibetan Plateau (TP) and surroundings, one of the largest concentrations of glaciers beyond the polar regions. To address this gap, we investigated the magnitude and possible drivers of anomalous early spring glacier melt in 2022 on the central TP using multi‐source observations (October 2020–July 2022) of Kuoqionggangri and Sangqu glaciers and an energy and mass balance model. Our results show that anomalous melt of both glaciers occurred in early spring (18 March–10 April 2022), when glaciers are traditionally expected to be frozen. There were 89 cumulative hours with air temperatures above 0°C, with a positive cumulative air temperature of 121.80°C hour at 5,700 m on Kuoqionggangri Glacier. The glacier‐wide cumulative melts on Kuoqionggangri and Sangqu glaciers from 18 March to 10 April 2022 were 22 ± 14.2 and 48 ± 37.5 mm w.e., respectively. Such anomalous glacier melt was driven by high incoming shortwave radiation and sensible heat flux, which were influenced by low cloud fraction and precipitation, and high air temperature during early spring. The weakening and northward‐shift of the westerlies, coupled with descending air motions over the central TP, reduced cloud fraction and hindered the transport of cold, moist air to the central TP. These changes occurred both in the central TP and the Himalaya, indicating the influence of macroscale atmospheric circulation patterns on this extreme glacier melt.
An Omnidirectional Filtering Method for Destriping Lunar Satellite Gravity Anomalies Data
Authors: Yang Jing , Guo Lianghui , Tang Hanhan , Yao Shuo 
Published: June 2024

AbstractHigh‐resolution gravity field models of the Moon established from Gravity Recovery and Interior Laboratory satellite gravity data have been playing an important role in understanding the interior structure and tectonic evolution of the Moon. Due to the correlations in the high degree and order term coefficients of the gravity field models, the difference between satellite flight orbits, and the random noise of instruments, the high‐resolution gravity anomalies data derived from the models of high degree and order usually present serious multi‐directional striping and random noise, which clearly affect the subsequent interpretation of the data. We provide an omnidirectional filtering method based on the polynomial‐fitting principle to remove multi‐directional striping noise in the lunar satellite gravity anomalies data. A set of azimuth parameters are chosen to suppress all the directions of striping noise. The algorithms of data partitioning and iterative optimization are utilized to make our method suitable and stable for large‐scale data sets. Tests on the synthetic data and the real data from the Moon's Rümker region and globally verified the feasibility of our method with a better destriping effect than the traditional Gaussian filtering or degree‐order‐truncation methods.
Deciphering the Biophysical Impact of Permafrost Greening on Summer Surface Offset
Authors: Wang Jian , Liu Desheng 
Published: June 2024

AbstractSatellite observations have shown widespread greening during the last few decades over the northern permafrost region, but the impact of vegetation greening on permafrost thermal dynamics remains poorly understood, hindering the understanding of permafrost‐vegetation‐climate feedbacks. Summer surface offset (SSO), defined as the difference between surface soil temperature and near‐surface air temperature in summer (June‐August), is often predicted as a function of surface thermal characteristics for permafrost modeling. Here we examined the impact of leaf area index (LAI), detected by satellite as a proxy to permafrost vegetation dynamics, on SSO variations from 2003 to 2021 across the northern permafrost region. We observed latitude‐ and biome‐dependent patterns of SSO changes, with a pronounced increase in Siberian shrublands and a decrease in Tibetan grasslands. Based on partial correlation and sensitivity analyses, we found a strong LAI signal (∼30% of climatic signal) on SSO with varying elevation‐ and canopy height‐dependent patterns. Positive correlations or sensitivities, that is, increases in LAI lead to higher SSO, were distributed in relatively cold and wet areas. Biophysical effects of permafrost greening on surface albedo, evapotranspiration, and soil moisture (SM) could link the connection between LAI and SSO. Increased LAI substantially reduced surface albedo and enhanced evapotranspiration, influenced energy redistribution, and further controlled interannual variability of SSO. We also found contrasting effects of LAI on surface SM, consequently leading to divergent impacts on SSO. The results offer a fresh perspective on how greening affects the thermal balance and dynamics of permafrost, which is enlightening for improved permafrost projections.
Is Only the Wind Field Controlling the Maximum Sea Ice Area in the Bering Sea?
Authors: Wang Weibo , Jing Chunsheng , Guo Xiaogang 
Published: June 2024

AbstractOver the past four decades, temporal variability in March sea ice area (SIA) within the Bering Sea occasionally contradicts prevailing northeasterly wind, raising doubt about the primary regulatory role of wind speed in governing maximum SIA. We argue that at least two spatial modes, extracted through the Empirical Orthogonal Function analysis, are necessary to explain the variations in the maximum SIA in the Bering Sea. Wind field emerges as the primary regulator of EOF1, governing both the direct influence of wind divergence (WD) and the indirect influence of meridional heat transport on sea ice. EOF2 is directly governed by ocean heat transport (OHT). Considering only the direct impact on sea ice, the OHT is directly responsible for the maximum SIA changes in 1995–2007, while in 2008–2014, the wind field is the largest driver. Since 2015, historically low SIA is attributed to significantly enhanced wind convergence and reduced zonal heat transport in the Gulf of Anadyr. Wind field directly controls the maximum SIA for less than half of the observation time, with OHT driving the rest. Declining trends observed in both WD and OHT suggests a future distribution pattern for the maximum SIA in the Bering Sea, characterized by an increase in the east and a decrease in the west. Long‐term retreat of sea ice on the western side of the Bering Sea is anticipated to exert significant impacts on local ecosystems, commercial activities, and even indigenous communities.
Reply to: “Comment on ‘Stratospheric Aerosol Composition Observed by the Atmospheric Chemistry Experiment Following the 2019 Raikoke Eruption’ by Boone et al.” by Ansmann et al.
Authors: Boone C. D. 
Published: June 2024

AbstractThe question of stratospheric aerosol type following the Raikoke eruption is revisited. Raman lidar measurements suggest the aerosols are predominately smoke, while Atmospheric Chemistry Experiment Fourier transform spectrometer (ACE‐FTS) results indicate the aerosols are predominately sulfate aerosols. The suggested mechanism of smoke particles self‐lofting into the stratosphere is inconsistent with observations in 2020, when more severe Siberian fires failed to invoke a response even vaguely similar to 2019. A side‐by side comparison of the Sarychev and Raikoke eruptions invalidates model calculations that suggest sulfate aerosols should be at levels too low to explain the observed aerosol loading. Structure in infrared absorption spectra provides conclusive evidence of composition, a unique fingerprint for identifying aerosol type. Such information cannot be misinterpreted so long as there is sufficient resolution and spectral coverage. ACE‐FTS infrared aerosol spectra often have an order of magnitude stronger absorption than that of background sulfate aerosols. These spectra can be accurately reproduced by laboratory measured sulfate aerosol spectroscopic information, providing unambiguous identification of the aerosols as sulfate. Visual inspection of thousands of infrared aerosol spectra from the period following the Raikoke eruption indicates the aerosols in the lower stratosphere are predominately sulfate, with no indication of smoke. The lidar study's identification of the aerosols as smoke was based primarily on observed lidar ratios that were more consistent with a material that absorbed significantly at the lidar wavelengths, inconsistent with expectations for sulfate aerosols. However, this could indicate the presence of a substance dissolved in the sulfate aerosols absorbing at those wavelengths rather than smoke particles.
Four Years of Meander‐Bend Evolution Captured by Drone‐Based Lidar Reveals Lack of Width Maintenance on the White River, Indiana, USA
Authors: Martin H. K. , Edmonds D. A. , Lewis Q. W. 
Published: June 2024

AbstractMeandering rivers experience fluctuations in width whenever riverbanks migrate in different directions or at different rates, which can be observed after individual floods. However, meandering rivers maintain approximately constant widths over decadal timescales. This implies some timescale below which width fluctuates as banks migrate independently, and above which width is maintained by a bank‐coupling process. This coupling is thought to occur either as point bar deposition events induce cutbank erosion (bar‐push), or as cutbank erosion events induce point bar deposition (bank‐pull). This coupling, however, has been challenging to observe in natural rivers due to limited event‐scale field data. We present results from a 4.5‐year campaign with 22 drone‐based lidar surveys of a single point bar and cutbank (∼0.35 km2 in area) on the White River near Worthington, Indiana, USA. The middle point bar experienced net erosion (5,400 m3), but net aggradation (17,100 m3) between 2019 and 2022 when including perennially submerged regions. This aggradation was less than the 35,700 m3 of cutbank erosion over the same period. Combined, we have observed widening (1.58 m/yr bend‐averaged; 3.08 m/yr near apex) over the study period as point bar deposition has not kept up with cutbank erosion. Finally, we suggest that the difference between bar‐push and bank‐pull as width‐maintenance mechanisms may not be resolvable by observing bend widening or narrowing alone without an advancement of current theory, such as determining a long‐term equilibrium width and measuring deviations relative thereto.
The Growth and Carbon Sink of Tundra Peat Patches in Arctic Alaska
Authors: Cleary Kathleen G. , Xia Zhengyu , Yu Zicheng 
Published: June 2024

AbstractRecent amplified climate warming in the Arctic has caused profound changes in terrestrial ecosystems, with the potential for strong feedback on climate change. Arctic tundra landscapes have developed patchy and thin organic soil (peat) layers at the surface that may continue to grow into mature peatlands and become a larger carbon sink under future warming. Here we use paleoecological analyses of multiple soil and peat cores collected from the North Slope of Alaska to document and understand the formation and development histories of tundra peat patches and permafrost peatlands. We find a consistent peat development sequence for peat patches, first from mineral soils to sedge peat during the Little Ice Age, and then to Sphagnum peat during the recent warming with high carbon accumulation rates. These findings suggest that climate cooling is likely critical for the initial peat buildup on tundra soils due to reduced decomposition, whereas climate warming triggers the regime shift into an increased abundance of Sphagnum mosses that are likely central to enhancing their carbon sink capacity. Additionally, peat patches become similar to permafrost peatlands in the vicinity in terms of ecosystem processes and carbon dynamics, and therefore may have developed the same ecohydrological feedback system to maintain their long‐term stability. This study implies that the potential future expansion of peat patches into peatlands may strongly alter the carbon balance of Arctic tundra, supporting the new United Nations Environment Programme's report that calls for incorporating widespread shallow peat into understanding the peatland–carbon–climate nexus.
FaIRGP: A Bayesian Energy Balance Model for Surface Temperatures Emulation
Authors: Bouabid Shahine , Sejdinovic Dino , Watson‐Parris Duncan 
Published: June 2024

AbstractEmulators, or reduced complexity climate models, are surrogate Earth system models (ESMs) that produce projections of key climate quantities with minimal computational resources. Using time‐series modeling or more advanced machine learning techniques, data‐driven emulators have emerged as a promising avenue of research, producing spatially resolved climate responses that are visually indistinguishable from state‐of‐the‐art ESMs. Yet, their lack of physical interpretability limits their wider adoption. In this work, we introduce FaIRGP, a data‐driven emulator that satisfies the physical temperature response equations of an energy balance model. The result is an emulator that (a) enjoys the flexibility of statistical machine learning models and can learn from data, and (b) has a robust physical grounding with interpretable parameters that can be used to make inference about the climate system. Further, our Bayesian approach allows a principled and mathematically tractable uncertainty quantification. Our model demonstrates skillful emulation of global mean surface temperature and spatial surface temperatures across realistic future scenarios. Its ability to learn from data allows it to outperform EBMs, while its robust physical foundation safeguards against the pitfalls of purely data‐driven models. We also illustrate how FaIRGP can be used to obtain estimates of top‐of‐atmosphere radiative forcing and discuss the benefits of its mathematical tractability for applications such as detection and attribution or precipitation emulation. We hope that this work will contribute to widening the adoption of data‐driven methods in climate emulation.
Development of a Distributed Physics‐Informed Deep Learning Hydrological Model for Data‐Scarce Regions
Authors: Zhong Liangjin , Lei Huimin , Yang Jingjing 
Published: June 2024

AbstractClimate change has exacerbated water stress and water‐related disasters, necessitating more precise streamflow simulations. However, in the majority of global regions, a deficiency of streamflow data constitutes a significant constraint on modeling endeavors. Traditional distributed hydrological models and regionalization approaches have shown suboptimal performance. While current deep learning (DL)‐related models trained on large data sets excel in spatial generalization, the direct applicability of these models in certain regions with unique hydrological processes can be challenging due to the limited representativeness within the training data set. Furthermore, transfer learning DL models pre‐trained on large data sets still necessitate local data for retraining, thereby constraining their applicability. To address these challenges, we present a physics‐informed DL model based on a distributed framework. It involves spatial discretization and the establishment of differentiable hydrological models for discrete sub‐basins, coupled with a differentiable Muskingum method for channel routing. By introducing upstream‐downstream relationships, model errors in sub‐basins propagate through the river network to the watershed outlet, enabling the optimization using limited downstream streamflow data, thereby achieving spatial simulation of ungauged internal sub‐basins. The model, when trained solely on the downstream‐most station, outperforms the distributed hydrological model in streamflow simulation at both the training station and upstream held‐out stations. Additionally, in comparison to transfer learning models, our model requires fewer gauge stations for training, but achieves higher precision in simulating streamflow on spatially held‐out stations, indicating better spatial generalization ability. Consequently, this model offers a novel approach to hydrological simulation in data‐scarce regions, especially those with poor hydrological representativeness.