bluuebunny/crossref_metadata_2025_split huggingface dataset:
Climate Driven Trends in Historical Extreme Low Streamflows on Four Continents
Authors: Hodgkins Glenn A. , Renard Benjamin , Whitfield Paul H. , Laaha Gregor , Stahl Kerstin , Hannaford Jamie , Burn Donald H. , Westra Seth , Fleig Anne K. , Araújo Lopes Walszon Terllizzie , Murphy Conor , Mediero Luis , Hanel Martin 
Published: June 2024

AbstractUnderstanding temporal trends in low streamflows is important for water management and ecosystems. This work focuses on trends in the occurrence rate of extreme low‐flow events (5‐ to 100‐year return periods) for pooled groups of stations. We use data from 1,184 minimally altered catchments in Europe, North and South America, and Australia to discern historical climate‐driven trends in extreme low flows (1976–2015 and 1946–2015). The understanding of low streamflows is complicated by different hydrological regimes in cold, transitional, and warm regions. We use a novel classification to define low‐flow regimes using air temperature and monthly low‐flow frequency. Trends in the annual occurrence rate of extreme low‐flow events (proportion of pooled stations each year) were assessed for each regime. Most regimes on multiple continents did not have significant (p < 0.05) trends in the occurrence rate of extreme low streamflows from 1976 to 2015; however, occurrence rates for the cold‐season low‐flow regime in North America were found to be significantly decreasing for low return‐period events. In contrast, there were statistically significant increases for this period in warm regions of NA which were associated with the variation in the Pacific Decadal Oscillation. Significant decreases in extreme low‐flow occurrence rates were dominant from 1946 to 2015 in Europe and NA for both cold‐ and warm‐season low‐flow regimes; there were also some non‐significant trends. The difference in the results between the shorter (40‐year) and longer (70‐year) records and between low‐flow regimes highlights the complexities of low‐flow response to changing climatic conditions.
Strong El Niño Events Lead to Robust Multi‐Year ENSO Predictability
Authors: Lenssen N. , DiNezio P. , Goddard L. , Deser C. , Kushnir Y. , Mason S. J. , Newman M. , Okumura Y. 
Published: June 2024

AbstractThe El Niño‐Southern Oscillation (ENSO) phenomenon—the dominant source of climate variability on seasonal to multi‐year timescales—is predictable a few seasons in advance. Forecast skill at longer multi‐year timescales has been found in a few models and forecast systems, but the robustness of this predictability across models has not been firmly established owing to the cost of running dynamical model predictions at longer lead times. In this study, we use a massive collection of multi‐model hindcasts performed using model analogs to show that multi‐year ENSO predictability is robust across models and arises predominantly due to skillful prediction of multi‐year La Nina events following strong El Niño events.
Statistical Comparison of Southern and Northern Auroral Electrojet Indices as a Function of Solar Wind and IMF Conditions
Authors: Boudouridis A. , Weygand J. M. , Zesta E. 
Published: June 2024

AbstractA Southern Auroral Electrojet (SAE) index has been recently constructed using several Antarctica magnetometer stations. It has been compared for case studies with the standard Auroral Electrojet (AE) index, and a near‐conjugate to the southern stations Northern Auroral Electrojet (NAE) index. We compare the three indices statistically as a function of the accompanying solar wind (SW) and Interplanetary Magnetic Field (IMF) conditions to further explore conjugacy issues. We use 274 days of common north/south data presence between December 2005 and August 2010. We calculate the cross‐correlation coefficients and differences between all three pairs. We estimate the effect of the SW/IMF conditions on the index correlations and differences using three groups of data: (a) the entire data set, (b) two separate sets based on the presence or not of Southern Hemisphere stations within the 21‐03 Magnetic Local Time (MLT) sector where substorms occur, and (c) separately for the four different seasons. We find that high north‐south correlation coefficients are more common during strong SW/IMF driving, while the index differences are also higher, suggesting that the SAE index follows better the northern indices' trend, but has even lower values during active times. The UT study shows that the number of high AE/SAE correlations is slightly lower at all clock angles and dynamic pressure levels for the periods within 1454–1941 UT (when no southern station is within 21–03 MLT). Finally, the results show that the number of high correlations is greater during the northern spring than the winter period.
Relating Pore‐Scale Observations to Continuum‐Scale Models: Impact of Ganglion Dynamics on Flow Transport Kinetics
Authors: Nhunduru Rumbidzai A. E. , Jahanbakhsh Amir , Shahrokhi Omid , Wlodarczyk Krystian L. , Garcia Susana , Maroto‐Valer M. Mercedes 
Published: June 2024

AbstractContinuum‐scale models used to model and predict two‐phase flow in the subsurface are often based on averaged flow parameters and do not consider pore‐scale fluid flow phenomena, for example, ganglion dynamics and thin‐film flow. As such, a major challenge in upscaling two‐phase flow for groundwater engineering applications is understanding the impact of disconnected flow and ganglion dynamics on continuum‐scale flow functions such as relative permeability‐saturation and capillary pressure‐saturation curves. In this study, we explored how changes in wettability and fluid velocity affect ganglion dynamics. We conducted pore‐scale numerical simulations with OpenFOAM to investigate the displacement of decane by water. Additionally, we examined how displaced phase saturation (a continuum‐scale flow function) responds to changes in dynamic fluid connectivity. We identified three different fluid flow regimes, that is, the connected pathway flow regime, ganglion dynamics (GD) flow regime, and droplet traffic flow regime, and studied the effects of changes in the wettability of the porous medium and the velocity of the invading fluid on the transitions between these different regimes. Our research showed that transitions between connected and disconnected pore‐scale flow regimes, which are induced by changes in fluid velocity and wettability, have a significant impact on both fluid displacement efficiency and average fluid flow transport kinetics.
An Electrical Parameter Characterizing Solute Heterogeneity: The Mixing Factor M
Authors: Fernandez Visentini Alejandro , Linde Niklas 
Published: June 2024

AbstractQuantitative estimates of hydrological state variables using electrical or electromagnetic geophysical methods are systematically biased by overlooked heterogeneity below the spatial scale resolved by the method. We generalize the high‐salinity asymptotic limit of electrical conduction in porous media at the continuous (e.g., Darcy) scale, by introducing a new petrophysical parameter, the mixing factor M, which accounts for the effect of fluid conductivity heterogeneity on the equivalent electrical conductivity tensor; it is expressed in terms of the volume‐average of the product of mean‐removed fluid conductivity and electric fields. We investigate the behavior of M for static and evolving fluid conductivity scenarios. Considering 2‐D ergodic log‐normal random fields of fluid conductivity, we demonstrate, in absence of surface conductivity, that observing the components of the M‐tensor allows univocally determining the variance and anisotropy of the field. Further, time‐series of the M‐tensor under diffusion‐limited mixing allows distinguishing between different characteristic temporal scales of diffusion, which are directly related to the initial integral scales of the salinity field. Under advective‐diffusive transport and for a pulse injection, the time‐series of M have a strong dependence on the Péclet number. Since M is defined in the absence of surface conductivity, we investigate how to correct measurements for surface conductivity effects. The parameter M provides conceptual understanding about the impact of saline heterogeneity on electrical measurements. Further work will investigate how it can be incorporated into hydrogeophysical inverse formulations and interpretative frameworks.
Geographic Characteristics and Meteorological Factors Dominate the Variation of Chlorophyll‐a in Lakes and Reservoirs With Higher TP Concentrations
Authors: Zhang Hanxiao , Huo Shouliang , Feng Lian , Ma Chunzi , Li Wenpan , Liu Yong , Wu Fengchang 
Published: June 2024

AbstractNutrients such as phosphorus and nitrogen lead to extensive growth of harmful algae in lakes and reservoirs, which results in eutrophication. The driving mechanism of primary productivity change in lakes and reservoirs at a wide spatial and temporal scale remains largely unknown. We establish a water quality database using a stacking machine learning model, including monthly chlorophyll‐a (Chl‐a), total nitrogen (TN) and phosphorus (TP) concentrations in 255 lakes and 332 reservoirs from 1980 to 2018. We find an increase in the number of lakes and reservoirs at risk of algal blooms, with approximately 2.66% exhibiting Chl‐a concentrations exceeding 30 μg L−1 by 2018. Variations in Chl‐a concentrations were not entirely synchronized at the individual and regional levels with TN and TP concentrations, or their stoichiometric ratios, as estimated by a hierarchical linear model spanning 1980 to 2018. Further, we discovered unexpected effects of geographical features (i.e., latitude, longitude, and slope) and meteorological factors (i.e., air temperature) on Chl‐a concentrations in the lakes and reservoirs with higher TP concentrations (>0.041 mg L−1 for lakes and >0.027 mg L−1 for reservoirs), through the use of multiple regression trees and structural equation model analysis. Our findings underscore the importance of (a) implementing flexible nutrient pollution control approaches based on nutrient ecoregions that consider geographical variations, and (b) developing mitigation and adaptation strategies to address the uncertain risks posed by climate change in the prevention and control of eutrophication in lakes and reservoirs.
Comparative Analysis of Supervised Classification Algorithms for Residential Water End Uses
Authors: Heydari Zahra , Stillwell Ashlynn S. 
Published: June 2024

AbstractWater sustainability in the built environment requires an accurate estimation of residential water end uses (e.g., showers, toilets, faucets, etc.). In this study, we evaluate the performance of four models (Random Forest, RF; Support Vector Machines, SVM; Logistic Regression, Log‐reg; and Neural Networks, NN) for residential water end‐use classification using actual (measured) and synthetic labeled data sets. We generated synthetic labeled data using Conditional Tabular Generative Adversarial Networks. We then utilized grid search to train each model on their respective optimized hyperparameters. The RF model exhibited the best model performance overall, while the Log‐reg model had the shortest execution times under different balanced and imbalanced (based on number of events per class) synthetic data scenarios, demonstrating a computationally efficient alternative for RF for specific end uses. The NN model exhibited high performance with the tradeoff of longer execution times compared to the other classification models. In the balanced data set scenario, all models achieved closely aligned F1‐scores, ranging from 0.83 to 0.90. However, when faced with imbalanced data reflective of actual conditions, both the SVM and Log‐reg models showed inferior performance compared to the RF and NN models. Overall, we concluded that decision tree‐based models emerge as the optimal choice for classification tasks in the context of water end‐use data. Our study advances residential smart water metering systems through creating synthetic labeled end‐use data and providing insight into the strengths and weaknesses of various supervised machine learning classifiers for end‐use identification.
Chromosome-level genome assembly of the cereal cyst nematode Heterodera flipjevi
Authors: Yao Ke , Cui Jiangkuan , Jian Jinzhuo , Peng Deliang , Huang Wenkun , Kong Lingan , Wang Qianghui , Peng Huan 
Published: June 2024

AbstractAs an economically important plant parasitic nematode (PPN), Heterodera filipjevi causes great damage on wheat, and now it was widely recorded in many countries. While multiple genomes of PPNs have been published, high-quality genome assembly and annotation on H. filipjevi have yet to be performed. This study presents a chromosome-scale genome assembly and annotation for H. filipjevi, utilizing a combination of Illumina short-read, PacBio long-read, and Hi-C sequencing technologies. The genome consists of 9 pseudo-chromosomes that contain 134.19 Mb of sequence, with a scaffold N50 length of 11.88 Mb. In total, 10,036 genes were annotated, representing 75.20% of the total predicted protein-coding genes. Our study provides the first chromosome-scale genome for H. filipjevi, which is also the inaugural high-quality genome of cereal cyst nematodes (CCNs). It provides a valuable genomic resource for further biological research and pest management of cereal cyst nematodes disease.
Efficient data acquisition and reconstruction for air-coupled ultrasonic robotic NDE
Authors: Hamilton Ciaron , Karpenko Oleksii , Udpa Lalita , Haq Mahmoodul , Deng Yiming 
Published: June 2024

AbstractNon-destructive evaluation of complex parts using surface scanning techniques, such as ultrasonic testing and eddy current testing, requires complex manipulation of such sensors to ensure quantitative results. A robotic arm may function as a complex manipulator for surface scanning, controlling the position and tilt between the probe and specimen’s surface. To ensure accuracy in probe manipulation, accurate geometric information of the specimen is required. This article explores a methodology that uses structured light for physical-to-virtual reconstruction, providing submillimeter scale and accurate surface geometries. Reconstruction aids in path planning through a novel ray-triangle intersection array algorithm, establishing movements for the NDE probe to orient itself on the specimen at a constant probe to specimen surface distance, or lift-off. The proposed technique is demonstrated and validated through experimental air-coupled ultrasonic inspection of automotive CFRP composite samples with simulated flaws such as interlaminar delamination. The proposed method employs guided waves and a pitch-catch configuration of air-coupled ultrasonic probes, enabling single-side access scans. A Fanuc 100ib robot arm was used to manipulate the ultrasonic probes along a sample reconstructed with a CR-Scan 01 structured light sensor. The probes were excited at 200khz from a SonoAir system, while also recovering defect vs background information synchronized with the probe’s orientation. Additionally, a framework for potential automation is proposed, with further details to be explored in future works.
Numerical research on disastrous mechanism of seepage instability of karst collapse column considering variable mass effect
Authors: Zhengzheng Cao , Shuaiyang Zhang , Zhenhua Li , Feng Du , Cunhan Huang , Wenqiang Wang 
Published: June 2024

AbstractIn order to reveal the disastrous mechanism of seepage instability of karst collapse column considering variable mass effect, a variable mass fluid–solid coupling mechanical model of water inrush is established, by considering the random distribution characteristics of a collapse column. Taking Qianjin coal mine as the research background, based on the Weibull distribution theory, the heterogeneous distribution characteristics of rock mass is described, and COMSOL Multiphysics numerical simulation software is employed to simulate the seepage characteristics and inrush water changes in collapse columns under different conditions of homogeneity, water pressure, and initial porosity. The research results show that the greater the homogeneity is, the more water conduction channels are formed, and the porosity increases accordingly, when considering the influence of different homogeneity on the seepage characteristics of broken rock mass, which eventually leads to water inrush accidents and a sharp increase in water inflow. Besides, when studying the seepage evolution law of different water pressures on a broken rock mass, an elevation of water pressure dramatically increases the porosity and seepage rate of the water. Over time, the broken rock particles gradually migrate and the fine particles are transported and eroded by the water flow, resulting in changes in the seepage characteristics and the formation of potential water diversion channels. Finally, when taking into account the effect of different initial porosity on the fractured rock mass seepage characteristics, the greater the original porosity is, the higher the seepage velocity is, and the particle migration increases the permeability. This leads to a more pronounced conductive water passage formation, which reveals the disastrous mechanism of seepage instability of karst collapse column considering variable mass effect.
A hybrid deep approach to recognizing student activity and monitoring health physique based on accelerometer data from smartphones
Authors: Xiao Lei , Luo Kangrong , Liu Juntong , Foroughi Andia 
Published: June 2024

AbstractSmartphone sensors have gained considerable traction in Human Activity Recognition (HAR), drawing attention for their diverse applications. Accelerometer data monitoring holds promise in understanding students’ physical activities, fostering healthier lifestyles. This technology tracks exercise routines, sedentary behavior, and overall fitness levels, potentially encouraging better habits, preempting health issues, and bolstering students’ well-being. Traditionally, HAR involved analyzing signals linked to physical activities using handcrafted features. However, recent years have witnessed the integration of deep learning into HAR tasks, leveraging digital physiological signals from smartwatches and learning features automatically from raw sensory data. The Long Short-Term Memory (LSTM) network stands out as a potent algorithm for analyzing physiological signals, promising improved accuracy and scalability in automated signal analysis. In this article, we propose a feature analysis framework for recognizing student activity and monitoring health based on smartphone accelerometer data through an edge computing platform. Our objective is to boost HAR performance by accounting for the dynamic nature of human behavior. Nonetheless, the current LSTM network’s presetting of hidden units and initial learning rate relies on prior knowledge, potentially leading to suboptimal states. To counter this, we employ Bidirectional LSTM (BiLSTM), enhancing sequence processing models. Furthermore, Bayesian optimization aids in fine-tuning the BiLSTM model architecture. Through fivefold cross-validation on training and testing datasets, our model showcases a classification accuracy of 97.5% on the tested dataset. Moreover, edge computing offers real-time processing, reduced latency, enhanced privacy, bandwidth efficiency, offline capabilities, energy efficiency, personalization, and scalability. Extensive experimental results validate that our proposed approach surpasses state-of-the-art methodologies in recognizing human activities and monitoring health based on smartphone accelerometer data.
Evidence of introduced honeybees (Apis mellifera) as pollen wasters in orchid pollination
Authors: Scaccabarozzi Daniela , Guzzetti Lorenzo , Pioltelli Emiliano , Brundrett Mark , Aromatisi Andrea , Polverino Giovanni , Vallejo-Marin Mario , Cozzolino Salvatore , Ren Zong-Xin 
Published: June 2024

AbstractBiological invasions threaten global biodiversity, altering landscapes, ecosystems, and mutualistic relationships like pollination. Orchids are one of the most threatened plant families, yet the impact of invasive bees on their reproduction remains poorly understood. We conduct a global literature survey on the incidence of invasive honeybees (Apis mellifera) on orchid pollination, followed by a study case on Australian orchids. Our literature survey shows that Apis mellifera is the primary alien bee visiting orchids worldwide. However, in most cases, introduced honeybees do not deposit orchid pollen. We also test the extent to which introduced honeybees affect orchid pollination using Diuris brumalis and D. magnifica. Diuris brumalis shows higher fruit set and pollination in habitats with both native and invasive bees compared to habitats with only introduced bees. Male and female reproductive success in D. magnifica increases with native bee abundance, while conversely pollinator efficiency decreases with honeybee abundance and rises with habitat size. Our results suggest that introduced honeybees are likely involved in pollen removal but do not effectively deposit orchid pollen, acting as pollen wasters. However, Apis mellifera may still contribute to pollination of Diuris where native bees no longer exist. Given the global occurrence of introduced honeybees, we warn that certain orchids may suffer from pollen depletion by these invaders, especially in altered habitats with compromised pollination communities.
Knowledge graph-derived feed efficiency analysis via pig gut microbiota
Authors: Zhang Junmei , Jiang Qin , Du Zhihong , Geng Yilin , Hu Yuren , Tong Qichang , Song Yunfeng , Zhang Hong-Yu , Yan Xianghua , Feng Zaiwen 
Published: June 2024

AbstractFeed efficiency (FE) is essential for pig production, has been reported to be partially explained by gut microbiota. Despite an extensive body of research literature to this topic, studies regarding the regulation of feed efficiency by gut microbiota remain fragmented and mostly confined to disorganized or semi-structured unrestricted texts. Meanwhile, structured databases for microbiota analysis are available, yet they often lack a comprehensive understanding of the associated biological processes. Therefore, we have devised an approach to construct a comprehensive knowledge graph by combining unstructured textual intelligence with structured database information and applied it to investigate the relationship between pig gut microbes and FE. Firstly, we created thepgmReadingknowledge base and the domain ontology of pig gut microbiota by annotating, extracting, and integrating semantic information from 157 scientific publications. Secondly, we created thepgmPubtatorby utilizing PubTator to expand the semantic information related to microbiota. Thirdly, we created thepgmDatabaseby mapping and combining the ADDAGMA, gutMGene, and KEGG databases based on the ontology. These three knowledge bases were integrated to form the Pig Gut Microbial Knowledge Graph (PGMKG). Additionally, we created five biological query cases to validate the performance of PGMKG. These cases not only allow us to identify microbes with the most significant impact on FE but also provide insights into the metabolites produced by these microbes and the associated metabolic pathways. This study introduces PGMKG, mapping key microbes in pig feed efficiency and guiding microbiota-targeted optimization.