bluuebunny/crossref_metadata_2025_split huggingface dataset:
Per-acetic acid effect on separation of banana fiber and their dyeing with natural dyes
Authors: Patel B. H. , Panchal D. P. , Chaudhari S. B. 
Published: July 2024

AbstractThe fiber derived from the pseudo-stem of the banana plant is the subject of this investigation. It describes the processes involved in creating pseudo-stem fiber from bananas, such as fiber extraction, retting, and degumming. Banana fiber has been separated using per-acetic acid. To evaluate the efficacy of the process, the fiber's appearance and form have been altered. A homogeneous, spotless surface was observed under Scanning Electron Microscopy. Weight loss is the outcome of the treatment and absorbency increases. 3.66% of the weight loss is attributed to the per-acetic acid treatment. The fiber’s average diameter was reduced 81.9% compared to the raw fiber. Fourier transform infrared spectroscopy analysis was utilized to assess the chemical changes brought about by the treatment, which verified the presence of cellulose in the fiber. The separated fibers were colored using natural dyes, which were then assessed based on how well they characterized the color. The results demonstrated the use of natural colors derived from extracts of madder and myrobalan. The standard test indicates that ferrous sulfate (FeSO4) exhibits strong resistance to light, washing, and rubbing fastness, making it a viable mordant for achieving brown to black color.
Graphical Abstract
Integration of facility manager in planning, design, and execution of construction projects
Authors: Jari Yasir A. , Abdulghafour Abdulrazak B. 
Published: July 2024

AbstractConstruction projects typically progress through four primary phases, namely planning, design, implementation, and operation. This study discusses the facility manager’s (FM) role during the project development phases prior to operation to improve the facility’s operational efficiency. The study applied deductive positivism approaches and quantitative analysis through the application of stratified sampling and cross-sectional survey to identify the potential roles before the operation phase. The study identified the primary roles in which FM might participate throughout the project’s lifecycle. The results confirmed that FM has a crucial role in the decision-making process in the design phase while having minimal roles in the planning and execution phases. Additionally, results showed that among stakeholders, clients are the most reluctant to support FM integration in projects while consultants are the most supportive group. The client’s reluctance can be linked with potential added administrative costs and complexities or concerns related to duplication of roles. The study proposed a model to effectively integrate FM throughout the project while preserving managerial flexibility to fit different client institutions' needs and their various administrative systems.
Conversing with AI chatbots: examining what OpenAI ChatGPT-4, Microsoft Bing Chatbot, and google bard know, think they know, do not know, and would like to know about engineering
Authors: Naser M. Z. , AlOgla Saleh , Anand N. , Zhou Huanting , Zhang Gang 
Published: July 2024

AbstractOver the past few weeks, chatbots have become increasingly popular, and these are likely to revolutionize our lives. While the most common three chatbots, namely, OpenAI ChatGPT, Microsoft Bing Chatbot, and Google Bard, remain in their early release days, these chatbots present exciting opportunities to various users, including engineers. This short paper hopes to shed light on the current state of these three AI chatbots’ understanding of engineering history, principles, concepts, and societal challenges and compare their responses to those noted by the National Academy of Engineering and the National Society of Professional Engineers. By exploring the capabilities and limitations of these chatbots, this piece hopes to foster discussions on the role of chatbots in engineering and their potential impact on our future. Then, this paper presents a case for the future integration of chatbots as co-creators of knowledge.
Characterizing Satellite Soil Moisture Drydown: A Bivariate Filtering Approach
Authors: Sinha Jhilam , Sharma Ashish , Marshall Lucy , Kim Seokhyeon 
Published: July 2024

AbstractDrying of soil impacts land energy and water balance, influences the sustainability of vegetation growth, and modulates hydrological extremes including floods. While satellite soil moisture data are widely used for a range of environmental applications, systematic differences from regional in‐situ data prevent their optimal use as key physical signatures (such as soil moisture recession, also termed drydown) are represented differently. This study investigates differences in drydowns from the Soil Moisture Active Passive (SMAP) level 4 product with reference to in‐situ observations. A bivariate filtering alternative is proposed to minimize the disparity noted by modeling the relationship between the rate of drying and initial soil wetness and representing the same as in‐situ. Considerable improvements are observed in the resulting SMAP soil moisture filtered estimates. Although the algorithm assumes spatial stationarity, improvements exist across different soil properties and climatic conditions, providing a parsimonious alternative to better capture the dynamics of soil moisture loss.
Parameter Regionalization With Donor Catchment Clustering Improves Urban Flood Modeling in Ungauged Urban Catchments
Authors: Hu Chen , Xia Jun , She Dunxian , Jing Zhaoxia , Hong Si , Song Zhihong , Wang Gangsheng 
Published: July 2024

AbstractThe lack of discharge observations and reliable drainage information is a pervasive problem in urban catchments, resulting in difficulties in parameterizing urban hydrological models. Current parameterization methods for ungauged urban catchments mostly rely on subjective experiences or simplified models, resulting in inadequate accuracy for urban flood prediction. Parameter regionalization has been widely used to tackle model parameterization issues, but has rarely been employed for urban hydrological models. How to conduct effective parameter regionalization for urban hydrological models remains to be investigated. Here we propose a parameter regionalization framework (PRF) that integrates donor catchment clustering and the optimal regression‐based methods in each cluster. The PRF is applied to an urban hydrological model, the Time Variant Gain Model in urban areas (TVGM_Urban), in 37 urban catchments in Shenzhen City, China. We first show satisfactory flood simulation performance of TVGM_Urban for all urban catchments. Subsequently, we employ the PRF for parameter regionalization of TVGM_Urban. PRF classifies 37 urban catchments into three groups, and the partial least‐squares regression is identified as optimal regression‐based method for Groups 1 and 2, while the random forest model is found to be best for Group 3. To evaluate the simulation performance of PRF, we compare it with eight single regionalization methods. The results indicate better simulation performance and lower uncertainty of PRF, and donor catchment clustering can effectively enhance the simulation performance of linear regression‐based methods. Lastly, we identify curve number, land cover area ratios, and slope as critical factors for most TVGM_Urban parameters based on PRF results.
Isogeochemical Characterization of Mountain System Recharge Processes in the Sierra Nevada, California
Authors: Armengol Sandra , Ajami Hoori , Acero Triana Juan S. , O. Sickman James , Ortega Lucia 
Published: July 2024

AbstractMountain System Recharge processes are significant natural recharge pathways in many arid and semi‐arid mountainous regions. However, Mountain System Recharge processes are often poorly understood and characterized in hydrologic models. Mountains are the primary water supply source to valley aquifers via lateral groundwater flow from the mountain block (Mountain Block Recharge) and focused recharge from mountain streams contributing to focused Mountain Front Recharge at the piedmont zone. Here, we present a multi‐tool isogeochemical approach to characterize mountain flow paths and Mountain System Recharge in the northern Tulare Basin, California. We used groundwater chemistry data to delineate hydrochemical facies and explain the chemical evolution of groundwater from the Sierra Nevada to the Central Valley aquifer. Stable isotopes and radiogenic groundwater tracers validated Mountain System Recharge processes by differentiating focused from diffuse recharge, and estimating apparent groundwater age, respectively. Novel application of End‐Member Mixing Analysis using conservative chemical components revealed three Mountain System Recharge end‐members: (a) evaporated Ca‐HCO3 water type associated with focused Mountain Front Recharge, (b) non‐evaporated Ca‐HCO3 and Na‐HCO3 water types with short residence times associated with shallow Mountain Block Recharge, and (c) Na‐HCO3 groundwater type with long residence time associated with deep Mountain Block Recharge. We quantified the contribution of each Mountain System Recharge process to the valley aquifer by calculating mixing ratios. Our results show that deep Mountain Block Recharge is a significant recharge component, representing 31%–53% of the valley groundwater. Greater hydraulic connectivity between the Sierra Nevada and Central Valley has significant implications for parameterizing groundwater flow models. Our framework is useful for understanding Mountain System Recharge processes in other snow‐dominated mountain watersheds.
Effective Characterization of Fractured Media With PEDL: A Deep Learning‐Based Data Assimilation Approach
Authors: Nan Tongchao , Zhang Jiangjiang , Xie Yifan , Cao Chenglong , Wu Jichun , Lu Chunhui 
Published: July 2024

AbstractGeological formations with fractures are frequently encountered in various research fields. Accurately characterizing these fractured media is of paramount importance when it comes to tasks that demand precise predictions of liquid flow and solute transport within them. Since directly measuring fractured media poses inherent challenges, data assimilation (DA) techniques are typically employed to derive inverse estimates of media properties using observable state variables. Nonetheless, the considerable difficulties arising from the strong heterogeneity and non‐Gaussian nature of fractured media have diminished the effectiveness of existing DA methods. In this study, we formulate a novel DA approach known as parameter estimator with deep learning (PEDL) that harnesses the capabilities of DL to capture nonlinear relationships and extract non‐Gaussian features. To evaluate PEDL's performance, we conduct three case studies, comprising two numerical cases and one real‐world case. In these cases, we systematically compare PEDL with three widely used DA methods: ensemble smoother with multiple DA (ESMDA), iterative local updating ES (ILUES), and ES with DL‐based update (ESDL). Notably, in the problems characterized by highly non‐Gaussian features, ESMDA and ILUES produce significantly divergent results. Conversely, employing the DL‐based update, ESDL demonstrates improved performance. However, its estimation uncertainty remains high, potentially attributable to ESDL's updating mechanism. Comprehensive analyses confirm PEDL's validity and adaptability across various ensemble sizes and DL model architectures. Moreover, even in scenarios where structural difference exists between the accurate reference model and the simplified forecast model, PEDL adeptly identifies the primary characteristics of fracture networks.
Observational and Numerical Modeling Constraints on the Global Ocean Biological Carbon Pump
Authors: Doney Scott C. , Mitchell Kayla A. , Henson Stephanie A. , Cavan Emma , DeVries Tim , Gruber Nicolas , Hauck Judith , Mouw Colleen B. , Müller Jens D. , Primeau Francois W. 
Published: July 2024

AbstractThis study characterized ocean biological carbon pump metrics in the second iteration of the REgional Carbon Cycle Assessment and Processes (RECCAP2) project. The analysis here focused on comparisons of global and biome‐scale regional patterns in particulate organic carbon (POC) production and sinking flux from the RECCAP2 ocean biogeochemical model ensemble against observational products derived from satellite remote sensing, sediment traps, and geochemical methods. There was generally good model‐data agreement in mean large‐scale spatial patterns, but with substantial spread across the model ensemble and observational products. The global‐integrated, model ensemble‐mean export production, taken as the sinking POC flux at 100 m (6.08 ± 1.17 Pg C yr−1), and export ratio defined as sinking flux divided by net primary production (0.154 ± 0.026) both fell at the lower end of observational estimates. Comparison with observational constraints also suggested that the model ensemble may have underestimated regional biological CO2 drawdown and air‐sea CO2 flux in high productivity regions. Reasonable model‐data agreement was found for global‐integrated, ensemble‐mean sinking POC flux into the deep ocean at 1,000 m (0.65 ± 0.24 Pg C yr−1) and the transfer efficiency defined as flux at 1,000 m divided by flux at 100 m (0.122 ± 0.041), with both variables exhibiting considerable regional variability. The RECCAP2 analysis presents standard ocean biological carbon pump metrics for assessing biogeochemical model skill, metrics that are crucial for further modeling efforts to resolve remaining uncertainties involving system‐level interactions between ocean physics and biogeochemistry.
Electron Beams at Europa
Authors: Allegrini F. , Saur J. , Szalay J. R. , Ebert R. W. , Kurth W. S. , Cervantes S. , Smith H. T. , Bagenal F. , Bolton S. J. , Clark G. , Connerney J. E. P. , Louarn P. , Mauk B. , McComas D. J. , Pontoni A. , Sarkango Y. , Valek P. , Wilson R. J. 
Published: July 2024

AbstractJupiter's moon Europa contains a subsurface ocean whose presence is inferred from magnetic field measurements, the interpretation of which depends on knowledge of Europa's local plasma environment. A recent Juno spacecraft flyby returned new observations of plasma electrons with unprecedented resolution. Specifically, powerful magnetic field‐aligned electron beams were discovered near Europa. These beams, with energies from ∼30 to ∼300 eV, locally enhance electron‐impact‐excited emissions and ionization in Europa's atmosphere by more than a factor three over the local space environment, and are associated with large jumps of the magnetic fields. The beams therefore play an essential role in shaping Europa's plasma and magnetic field environment and thus need to be accounted for electromagnetic sounding of Europa's ocean and plume detection by future missions such as JUICE and Europa Clipper.
Field‐Aligned Current Structures During the Terrestrial Magnetosphere's Transformation Into Alfvén Wings and Recovery
Authors: Beedle J. M. H. , Chen L.‐J. , Shuster J. R. , Gurram H. , Gershman D. J. , Chen Y. , Rice R. C. , Burkholder B. L. , Ardakani A. S. , Genestreti K. J. , Torbert R. B. 
Published: July 2024

AbstractOn 24 April 2023, a Coronal Mass Ejection event caused the solar wind to become sub‐Alfvénic, leading to the development of an Alfvén Wing configuration in the Earth's magnetosphere. Alfvén Wings have previously been observed as cavities of low flow around moons in Jupiter's and Saturn's magnetospheres, but the observing spacecraft did not have the ability to directly measure the Alfvén Wings' current structures. Through in situ measurements made by the Magnetospheric Multiscale spacecraft, the 24 April event provides us with the first direct measurements of current structures during an Alfvén Wing configuration. These structures are observed to be significantly more anti‐field‐aligned and electron‐driven than the typical diamagnetic magnetopause current, indicating the disruption caused to the magnetosphere current system by the Alfvén Wing formation. The magnetopause current is then observed to recover more of its typical, perpendicular structure during the magnetosphere's recovery from the Alfvén Wing formation.
Northern Hemisphere Stratosphere‐Troposphere Circulation Change in CMIP6 Models: 2. Mechanisms and Sources of the Spread
Authors: Karpechko Alexey Yu. , Wu Zheng , Simpson Isla R. , Kretschmer Marlene , Afargan‐Gerstman Hilla , Butler Amy H. , Domeisen Daniela I.V. , Garny Hella , Lawrence Zachary , Manzini Elisa , Sigmond Michael 
Published: July 2024

AbstractWe analyze the sources for spread in the response of the Northern Hemisphere wintertime stratospheric polar vortex (SPV) to global warming in Climate Model Intercomparison Project Phase 5 (CMIP5) and Phase 6 (CMIP6) model projections. About half of the intermodel spread in SPV projections by CMIP6 models, but less than a third in CMIP5 models, can be attributed to the intermodel spread in stationary planetary wave driving. In CMIP6, SPV weakening is mostly driven by increased upward wave flux from the troposphere, while SPV strengthening is associated with increased equatorward wave propagation away from the polar stratosphere. We test hypothesized factors contributing to changes in the upward and equatorward planetary wave fluxes and show that an across‐model regression using projected global warming rates, strengthening of the subtropical jet and basic state lower stratospheric wind biases as predictors can explain nearly the same fraction in the CMIP6 SPV spread as the planetary wave driving (r = 0.67). The dependence of the SPV spread on the model biases in the basic state winds offers a possible emergent constraint; however, a large uncertainty prevents a substantial reduction of the projected SPV spread. The lack of this dependence in CMIP5 further calls for better understanding of underlying causes. Our results improve understanding of projected SPV uncertainty; however, further narrowing of the uncertainty remains challenging.
Sea Surface Wind Speed Estimation From the Combination of Satellite Scatterometer and Radiometer Parameters
Authors: Xiang Kunsheng , Zhou Wu , Bao Qingliu 
Published: July 2024

AbstractSatellite research on global sea surface winds is crucial for understanding and monitoring the dynamics of earth's oceans, providing valuable insights into weather patterns, climate changes, and oceanographic processes. This study investigates the fusion of active (microwave scatterometer) and passive (microwave radiometer) satellite data using Machine learning (ML) for sea surface wind speed estimation. Employing Random Forest Regression (RF), Convolutional Neural Network Regression (CNN), and Multiple Linear Regression (MLR). Evaluation against reference data sets (Advanced Scatterometer, ERA5, Cross‐Calibrated Multi‐Platform (CCMP), buoy wind speeds) highlights the robustness of the proposed models. The research findings indicate that both RF and CNN exhibit superior accuracy in the active, passive, and joint active‐passive models compared to the simplistic MLR model. The joint models of the three regression methods outperform the individual active or passive models. The root mean square deviation (RMSD) accuracy of RF and CNN joint models, when compared with ASCAT, is in the order of 0.7 m/s, and when compared with buoys, the RMSD accuracy is around 1.1 m/s. The ML models, especially RF and CNN, demonstrate superior performance, providing accurate and reliable estimations crucial for meteorological and oceanographic applications. These findings underscore the potential operational use of ML techniques in enhancing remote sensing applications.
Oceanography of the Eastern Equatorial Pacific Ocean Across the Oligocene‐Miocene Transition
Authors: Liebrand Diederik , Wade Bridget S. , Beddow Helen M. , King David J. , Harrison Alexander D. , Johnstone Heather J. H. , Drury Anna Joy , Pälike Heiko , Sluijs Appy , Lourens Lucas J. 
Published: July 2024

AbstractThe functioning of the Pacific Ocean—the world's largest ocean—during a warmer‐than‐present paleoclimate state remains underexplored. We present planktonic and benthic foraminiferal stable oxygen (δ18O) and carbon (δ13C) isotope records from Integrated Ocean Drilling Program (IODP) Site U1334 that span the Oligocene‐Miocene Transition (OMT) interval, from 24.15 to 21.95 million years ago (Ma). We reconstruct (sub‐)surface and deep‐water conditions and provide better constraints on the physical and chemical oceanography of the eastern equatorial Pacific Ocean (EEP). Positive trends in planktonic and benthic foraminiferal δ18O values, mark a largely uniform imprint of increased land‐ice volume/global cooling on surface‐ and deep‐waters. We document a delayed planktonic foraminiferal δ18O increase across the OMT as well as an increase in the amplitude variability of planktonic foraminiferal δ18O values on eccentricity timescales during the early Miocene. We interpret this as an enhanced glacioeustatic sea‐level control on Atlantic‐Pacific salinity exchange through the Central American Seaway (CAS) or as the onset of more variable surface currents and oceanic fronts in the EEP. Positive trends in planktonic and benthic foraminiferal δ13C values characterize the whole‐ocean depletion in 12C linked to organic carbon burial during the Oligocene‐Miocene carbon maximum (CM‐OM). However, this depletion is more pronounced in the planktonic foraminiferal δ13C record, especially during ∼400 Kyr eccentricity minima, reflecting an increase in nutrient upwelling and the efficacy of the biological carbon pump (BCP) when global temperatures decreased across the OMT and during the early Miocene. Our study highlights the dynamic behavior of the EEP in a warmer‐than‐present unipolar icehouse state.
Advocating for the inclusion of kidney health outcomes in neonatal research: best practice recommendations by the Neonatal Kidney Collaborative
Authors: Reidy Kimberly J. , Guillet Ronnie , Selewski David T. , Defreitas Marissa , Stone Sadie , Starr Michelle C. , Harer Matthew W. , Todurkar Namrata , Vuong Kim T. , Gogcu Semsa , Askenazi David , Tipple Trent E. , Charlton Jennifer R. 
Published: July 2024

AbstractAcute kidney injury (AKI) occurs in nearly 30% of sick neonates. Chronic kidney disease (CKD) can be detected in certain populations of sick neonates as early as 2 years. AKI is often part of a multisystem syndrome that negatively impacts developing organs resulting in short- and long-term pulmonary, neurodevelopmental, and cardiovascular morbidities. It is critical to incorporate kidney-related data into neonatal clinical trials in a uniform manner to better understand how neonatal AKI or CKD could affect an outcome of interest. Here, we provide expert opinion recommendations and rationales to support the inclusion of short- and long-term neonatal kidney outcomes using a tiered approach based on study design: (1) observational studies (prospective or retrospective) limited to data available within a center’s standard practice, (2) observational studies involving prospective data collection where prespecified kidney outcomes are included in the design, (3) interventional studies with non-nephrotoxic agents, and (4) interventional studies with known nephrotoxic agents. We also provide recommendations for biospecimen collection to facilitate ancillary kidney specific research initiatives. This approach balances the costs of AKI and CKD ascertainment with knowledge gained. We advocate that kidney outcomes be included routinely in neonatal clinical study design. Consistent incorporation of kidney outcomes across studies will increase our knowledge of neonatal morbidity.