bluuebunny/crossref_metadata_2025_split huggingface dataset:
Advanced oxidation processes for pharmaceutical degradation and disinfection of wastewater: peracetic acid and graphene oxide quantum dots
Authors: Tshangana C. , Mubiayi M. P. , Kuvarega A. , Mamba B. , Muleja A. 
Published: April 2023

AbstractA combination of graphene oxide quantum dots and peracetic acid (GQDs/PAA) was used to degrade sulfasalazine in municipal wastewater. The impact of reaction parameters such as initial concentrations of oxidant (peracetic acid) and drug (sulfasalazine) and different water matrices was evaluated. The degradation efficiency when using GQDs/PAA (50 mg/L: 0.10 mM) was almost 100% in synthetic water and 80% in municipal wastewater. The primary reactive radicals that caused the degradation of sulfasalazine in wastewater were identified as hydroxy (·OH) as well as the peroxy radicals (CH3C(=O)OO·, CH3C(=O)O·). 83.7% of total organic carbon were eliminated when 0.15 mM PAA was used while nearly 100% degradation of SZZ was achieved. A degradation pathway was proposed using the degradation intermediates obtained on quadrupole time-of-flight liquid chromatography mass spectrometry. The genotoxic and mutagenic potential of the degradation products formed during the degradation of sulfasalazine was assessed using the Ames test. It was demonstrated that none of the intermediates were mutagenic. GQDs/PAA was further tested as a potential disinfectant, and S. aureus was completely inactivated as verified by using LIVE/DEAD Baclight staining. In raw municipal wastewater, GQDs/PAA eliminated more than 90% of bacteria, thus confirming the synergy of GQDs/PAA as both a disinfectant and a photocatalyst.
Überschätzen die Lehrmittelautor:innen den authentischen Lebensweltbezug von MINT-Aufgaben? Eine Studie zur Lernendenperspektive
Authors: Stuppan Sebastian , Bölsterli Bardy Katrin , Schmid Andrea Maria , Wilhelm Markus 
Published: April 2023

ZusammenfassungIn der Scientific Literacy und der Context-Based Science Education wird dem Lebensweltbezug eine hohe Bedeutung zugeschrieben. In vielen Forschungsprojekten zum Lebensweltbezug, ein Aspekt der Aufgabenqualität, werden schriftliche Aufgabenstellungen beurteilt und ausgewertet. Eine Schwierigkeit beim Analysieren von Aufgaben ist die Tatsache, dass die Bewertung von geschulten Beurteilenden anders ausfallen kann als durch die Zielgruppe im Unterricht. Die Dokumentenanalyse bildet das objektive Potenzial der Aufgaben aus Sicht der jeweiligen Beurteiler:innen ab. Die Einbettung der Aufgaben im Unterricht, das Niveau der Lösungen sowie die Aufgabeneinschätzung der Lernenden werden weggelassen. Studien, die sich mit der Analyse der schriftlichen Aufgaben und der Aufgabenbeurteilungen durch Lernende im Unterricht auseinandersetzen, existieren derzeit kaum. Um diese Forschungslücke zu schmälern, wird in diesem Vorhaben verglichen, wie fachdidaktische Lehrmittelautor:innen ihre entwickelten Aufgaben einschätzen und Lernende dieselben Aufgaben im Unterricht nach deren Bearbeitung wahrnehmen. Hierzu beurteilten Lernende (N = 805) ausgewählte MINT-Aufgaben (N = 16) auf deren Lebensweltbezug (min. 66, max. 182 Lernende pro Aufgabe) mithilfe des Aufgaben-Analyse-Instruments. Die Lernenden-Einschätzungen werden anschließend inferenzstatistisch der fachdidaktischen Perspektive der Lehrmittelautor:innen gegenübergestellt und diskutiert. Die Befunde dieser Studie zeigen, dass die Lernenden in den untersuchten MINT-Aufgaben die Lebenswelt divers wahrnehmen und es keine geschlechtsspezifischen Unterschiede gibt. Die Gegenüberstellung der Lernenden-Einschätzungen und der Setzung der Lehrmittelautor:innen macht deutlich, dass die Autor:innen in der Regel die Aufgaben authentischer beurteilen, als die Lernenden diese wahrnehmen. Aufgrund der doch deutlichen Diskrepanz zwischen den zwei Perspektiven wird diskutiert, welchen Mehrwert der Einbezug der Praxis in einem iterativen Verfahren beim Erstellen von Lernaufgaben haben könnte, um den Lebensweltbezug aus der Perspektive der Lernenden stringenter aufnehmen zu können.
Advanced Procedures for Series Production with 3D-Printed Core Packages
Authors: Erhard Patricia , Hartmann Christoph , Li Rui , Volk Wolfram , Günther Daniel 
Published: April 2023

AbstractThe application of additive-manufactured cores and molds is of great interest for complex cast components. Nevertheless, several challenges still exist in utilizing binder jetting in the multi-step additive manufacturing process for foundry applications to its fullest extent. This contribution shows methods that facilitate the use of 3D-printed sand molds and cores in casting series applications. The binder jetting process itself is assessed from an overall process chain perspective to highlight the benefits of its application in series production. The challenges associated with automating mold cleaning for highly complex casting contours are depicted. In particular, employing the method of cleanable mold partitioning is shown to enhance the automation level of the overall process. Mold design tailored to 3D printing is demonstrated to contribute to overall cost and time savings in enhanced core packages. Topology-optimized, lightweight part designs involving complex freeform surfaces may require mold partitioning associated with laborious burr removal processes. A new approach in answer to the shortage of skilled workers in the harsh and hazardous foundry environment is shown. Implementing motion tracking technology is demonstrated to enable economical automated burr removal for minor quantities or high variant diversity in the future foundry. All the methods shown are of great importance for introducing printed core packages into series production.
Adjustment of initial estimates of voter transition probabilities to guarantee consistency and completeness
Authors: Pavía Jose M. 
Published: April 2023

AbstractThe estimation of voter shifts (stayers and switchers) between elections is an active area of research that, for decades, has attracted the interest of many scholars. The voter transitions are typically summarised in a row-standardised proportion (probability) matrix. This matrix is usually unknown, despite it being of interest to many agents, including party teams, the media and political scientists. When surveys are used to approximate this matrix, it is not uncommon for the estimated matrix to be inconsistent and even incomplete. The iterative proportional fitting algorithm solves inconsistency but cannot fix incompleteness. Hierarchical Bayesian models that combine aggregate and survey estimates can solve both problems, but are extremely complex and data-demanding. This paper details all the scenarios concerning the available information that can be reasonably considered and, within the linear programming framework, develops specific models to reach consistency and completeness. The models are, moreover, quite flexible as they allow analysts to have missing values and to introduce through weights their relative confidences in the different a priori transition proportions. The usefulness of the proposed models is illustrated with real data. Interested readers can easily use these new models with their data as they have been programmed in the function lp_apriori of the R-package lphom.
The Proteome Landscape of Human Placentas for Monochorionic Twins with Selective Intrauterine Growth Restriction
Authors: Meng Xin-Lu , Yuan Peng-Bo , Wang Xue-Ju , Hang Jing , Shi Xiao-Ming , Zhao Yang-Yu , Wei Yuan 
Published: April 2023

Abstract
In perinatal medicine, intrauterine growth restriction (IUGR) is one of the greatest challenges. The etiology of IUGR is multifactorial, but most cases are thought to arise from placental insufficiency. However, identifying the placental cause of IUGR can be difficult due to numerous confounding factors. Selective IUGR (sIUGR) would be a good model to investigate how impaired placentation affects fetal development, as the growth discordance between monochorionic twins cannot be explained by confounding genetic or maternal factors. Herein, we constructed and analyzed the placental proteomic profiles of IUGR twins and normal cotwins. Specifically, we identified a total of 5481 proteins, of which 233 were differentially expressed (57 up-regulated and 176 down-regulated) in IUGR twins. Bioinformatics analysis indicates that these differentially expressed proteins (DEPs) are mainly associated with cardiovascular system development and function, organismal survival, and organismal development. Notably, 34 DEPs are significantly enriched in angiogenesis, and diminished placental angiogenesis in IUGR twins has been further elaborately confirmed. Moreover, we found decreased expression of metadherin (MTDH) in the placentas of IUGR twins and demonstrated that MTDH contributes to placental angiogenesis and fetal growth in vitro. Collectively, our findings reveal the comprehensive proteomic signatures of placentas for sIUGR twins, and the DEPs identified may provide in-depth insights into the pathogenesis of placental dysfunction and subsequent impaired fetal growth.
Assessing the Potential of Backscattering as a Proxy for Phytoplankton Carbon Biomass
Authors: Serra‐Pompei Camila , Hickman Anna , Britten Gregory L. , Dutkiewicz Stephanie 
Published: April 2023

AbstractDespite phytoplankton contributing roughly half of the photosynthesis on earth and fueling marine food‐webs, field measurements of phytoplankton biomass remain scarce. The particulate backscattering coefficient (bbp) has often been used as an optical proxy to estimate phytoplankton carbon biomass (Cphyto). However, total observed bbp is impacted by phytoplankton size, cell composition, and non‐algal particles. The lack of phytoplankton field data has prevented the quantification of uncertainties driven by these factors. Here, we first review and discuss existing bbp algorithms by applying them to bbp data from the BGC‐Argo array in surface waters (<10 m). We find a bbp threshold where estimated Cphyto differs by more than an order of magnitude. Next, we use a global ocean circulation model (the MITgcm Biogeochemical and Optical model) that simulates plankton dynamics and associated inherent optical properties to quantify and understand uncertainties from bbp‐based algorithms in surface waters. We do so by developing and calibrating an algorithm to the model. Simulated error‐estimations show that bbp‐based algorithms overestimate/underestimate Cphyto between 5% and 100% in surface waters, depending on the location and time. This is achieved in the ideal scenario where Cphyto and bbp are known precisely. This is not the case for algorithms derived from observations, where the largest source of uncertainty is the scarcity of phytoplankton biomass data and related methodological inconsistencies. If these other uncertainties are reduced, the model shows that bbp could be a relatively good proxy for phytoplankton carbon biomass, with errors close to 20% in most regions.
Decoupling of Barium and Silicon at the Congo River‐Dominated Southeast Atlantic Margin: Insights From Combined Barium and Silicon Isotopes
Authors: Zhang Zhouling , Yu Yang , Hathorne Ed C. , Vieira Lucia H. , Grasse Patricia , Siebert Christopher , Rahlf Peer , Frank Martin 
Published: April 2023

AbstractThe correlation between concentrations of dissolved barium (dBa) and silicon (dSi) in the modern ocean supports the use of Ba as a paleoceanographic proxy. However, the mechanisms behind their linkage and the exact processes controlling oceanic Ba cycling remain enigmatic. To discern the extent to which this association arises from biogeochemical processes versus physical mixing, we examine the behavior of Ba and Si at the Congo River‐dominated Southeast Atlantic margin where active biological processes and large boundary inputs override the large‐scale ocean circulation. Here we present the first combined measurements of dissolved stable Ba (δ138Ba) and Si (δ30Si) isotopes as well as Ba and Si fluxes estimated based on 228Ra from the Congo River mouth to the northern Angola Basin. In the surface waters, river‐borne particle desorption or dissolution and shelf inputs lead to non‐conservative additions of both dBa and dSi to the Congo‐shelf‐zone, with the Ba flux increasing more strongly than that of Si across the shelf. In the epipelagic and mesopelagic layers, Ba and Si are decoupled likely due to different depths of in situ barite precipitation and biogenic silica production. In the deep waters of the northern Angola Basin, we observe large enrichment of dBa, likely originating from high benthic inputs from the Congo deep‐sea fan sediments. Our results reveal different mechanisms controlling the biogeochemical cycling of Ba and Si and highlight a strong margin influence on marine Ba cycling. Their close association across the global ocean must therefore mainly be a consequence of the large‐scale ocean circulation.
Simulated Rupture Dynamics and Radiated Energy on Heterogeneously Damaged Faults
Authors: Lyakhovsky Vladimir , Sagy Amir , Kurzon Ittai 
Published: April 2023

AbstractRupture dynamics along a heterogeneous fault is studied in the framework of the recently developed damage‐breakage rheological model. The model utilizes a fault structure accommodating shear and wear in a heterogeneous weak fault‐core a few centimeters thick, separating two blocks with damage level gradually decreasing toward the host rock. We first demonstrate the similarity of the static stress field around a fault, represented by a three‐body tribosystem, to the one developed around a rough frictional interface. We show that both models predict heterogeneous stress field pattern. We then apply simulations of rupture on heterogeneously damaged faults. We show that increasing initial heterogeneity amplitudes is associated with smaller events with lower slip rates. The simulations further allow us to quantify the amount of accumulated damage correlative with wearing. During the propagating rupture, the strength of the fault‐core evolves, leading to higher wear generation along relatively strong zones or barriers. The total wear production in a given event is strongly dominated by the initial damage heterogeneity. Processing of synthetic seismograms shows excess energy radiation of high‐frequency seismic waves comparing to the expected radiation from planar faults. This radiation is enhanced with the increase in the variability of fault strength heterogeneity. Further calculations of the scaled energy show good fit with previous seismological and laboratory observations and demonstrate the impact of fault heterogeneity on radiated energy during dynamic rupture. Therefore, initial fault heterogeneity, manifested by fault‐core strength or by geometrical irregularity controls many aspects of earthquake rupture, including slip displacement and velocity.
A Specific Earthquake Processing Workflow for Studying Long‐Lived, Explosive Volcanic Eruptions With Application to the 2008 Okmok Volcano, Alaska, Eruption
Authors: Garza‐Girón Ricardo , Brodsky Emily E. , Spica Zack J. , Haney Matthew M. , Webley Peter W. 
Published: April 2023

AbstractBy providing unrivaled resolution in both time and space, volcano seismicity helps to chronicle and interpret eruptions. Standard earthquake detection methods are often insufficient as the eruption itself produces continuous seismic waves that obscure earthquake signals. We address this problem by developing an earthquake processing workflow specific to a high‐noise volcanic environment and applying it to the explosive 2008 Okmok Volcano eruption. This process includes applying single‐channel template matching combined with machine‐learning and fingerprint‐based techniques to expand the existing earthquake catalog of the eruption. We detected an order of magnitude more earthquakes, then located, relocated, determined locally calibrated magnitudes, and classified the events in the enhanced catalog. This new high‐resolution earthquake catalog increases the number of observations by about a factor of 10 and enables the detailed spatiotemporal seismic analysis during a large eruption.
Carbon Monoxide Cycling in the Eastern Indian Ocean
Authors: Xu Gao‐Bin , Xu Feng , Ji Xuan , Zhang Jing , Yan Shi‐Bo , Mao Shi‐Hai , Yang Gui‐Peng 
Published: April 2023

AbstractCarbon monoxide (CO) is an indirect greenhouse gas and a crucial component of the global carbon cycle. The ocean is an important source of atmospheric CO, but the factors controlling the sources and sinks of CO in the marine environment remain uncertain. For the first time, the spatial distribution, production, and removal pathways of seawater CO (including photoproduction, dark production, microbial consumption, and sea‐to‐air exchange) were systematically investigated in the Eastern Indian Ocean. The abundance and component source of chromophoric dissolved organic matter (CDOM) dominated the photoproduction and dark production rates of CO, and the net CO production rate was 2.19 ± 0.71 nmol L−1 d−1 (mean ± SD). The ratio of CO photoproduction efficiencies (normalized by solar radiation) for ultraviolet B radiation (UVB): ultraviolet A radiation (UVA): photosynthetically active radiation (PAR) was 854: 39: 1, while UVA was the most significant contributor to surface CO photoproduction due to the stronger radiation intensity. In the mixed layer, UVB, UVA, and PAR were estimated to contribute 12% ± 5%, 38% ± 8%, and 50% ± 8% (mean ± SD) to CO photoproduction, respectively. The total removal of CO by microbial consumption (96%) and sea‐to‐air exchange (1%) accounted for 97% of the total production. Overall, the sources and sinks of CO kept a dynamic balance in the mixed layer. Our comprehensive approach provides in‐detail insights into the understanding of CO cycling processes in the Eastern Indian Ocean, which is scientifically important for understanding the bio‐geochemical cycling and climate effects of CO in the Eastern Indian Ocean, and also provides additional data support for the CO global cycle modeling studies.
Convection Initiation During the Meiyu Environment in the Yangtze‐Huai River Basin of China
Authors: Zhang Fan , Zhang Qinghong , Sun Juanzhen , Xu Jun 
Published: April 2023

AbstractConvection is the main contributor to heavy rainfall over China's Yangtze‐Huai River Basin (YHRB) during Meiyu season; however, the mechanisms of convection initiation (CI) associated with the Meiyu front are still not well understood. In this study, a large set of 86,099 CI events, identified from composite reflectivity data in YHRB over six Meiyu seasons, were used to investigate the characteristics of the spatiotemporal distribution of CI in YHRB. The result showed that the overwhelming majority of CI events (∼90%) occurred in the region of existing stratiform clouds. Meanwhile, CI tended to concentrate in mountainous areas and exhibited two triggering modes. By relating the CI events with an objective analysis of ERA5 reanalysis data, it was also revealed that the characteristics of CI occurrence varied with patterns of Meiyu circulation and their interactions with local topography, and the warm air advection pattern dominated the Meiyu CI. We further illustrated that CI on the plains occurred with a morning peak corresponding to environments of high 0–3 km shear (SHR3) and low most unstable convective available potential energy (MUCAPE), while the CI near or over mountains had an afternoon peak corresponding to low SHR3 and high MUCAPE environments.
Ambient Conditions of Winter Thunderstorms in Japan to Reproduce Observed Gamma‐Ray Glow Energy Spectra
Authors: Diniz G. S. , Wada Y. , Ohira Y. , Nakazawa K. , Tsurumi M. , Enoto T. 
Published: April 2023

AbstractElectric field of thunderclouds modifies components and energy spectra of the cosmic‐ray air shower. In particular, thunderstorms accelerate charged particles, resulting in an enhancement of gamma‐ray fluxes on the ground, known as a gamma‐ray glow. This phenomenon has been observed in recent years by the Gamma‐Ray Observation of Winter THunderclouds collaboration from winter thunderstorms in the Hokuriku area of Japan. The present work examines the ambient conditions required to produce spectral features of the previously detected gamma‐ray glows, by using Monte Carlo simulations of particle interactions in the atmosphere. We focus on three parameters, the strength and length of the electric field, and the length of a null‐field attenuation region below the electrified region. The average spectrum of observed gamma‐ray glows in winter thunderstorms of Japan requires an electric field intensity close to 0.31 MV/m, slightly exceeding the Relativistic Runaway Electron Avalanche threshold of 0.284 MV/m. The vertical size of the electric field region should be comparable to 1 km. The estimated attenuation region size is 300–500 m, necessary to reduce the low‐energy photon flux of the average gamma‐ray glows. There is still a wide range of acceptable parameter sets with degeneracy to make a similar spectrum.
Unsupervised Clustering and Supervised Machine Learning for Lightning Classification: Application to Identifying EIPs for Ground‐Based TGF Detection
Authors: Pu Yunjiao , Cummer Steven A. , Lyu Fanchao , Zheng Yu , Briggs Michael S. , Lesage Stephen , Mailyan Bagrat , Roberts Oliver J. 
Published: April 2023

AbstractWe developed a framework merging unsupervised and supervised machine learning to classify lightning radio signals, and applied it to the possible detection of terrestrial gamma‐ray flashes (TGFs). Recent studies have established a tight connection between energetic in‐cloud pulses (EIPs, >150 kA) and a subset of TGFs, enabling continuous and large‐scale ground‐based TGF detection. However, even with a high peak current threshold, it is time‐consuming to manually search for EIPs in a background of many non‐EIP events, and it becomes even more difficult when a lower peak current threshold is used. Machine learning classifiers are an effective tool. Beginning with unsupervised learning, spectral clustering is performed on the low‐dimensional features extracted by an autoencoder from raw radio waveforms, showing that +EIPs naturally constitute a distinct class of waveform and 6%–7% of the total population. The clustering results are used to form a labeled data set (∼10,000 events) to further train supervised convolutional neural network (CNN) that targets for +EIPs. Our CNN models identify on average 95.2% of true +EIPs with accuracy up to 98.7%, representing a powerful tool for +EIP classification. The pretrained CNN classifier is further applied to identify lower peak current EIPs (LEIPs, >50 kA) from a larger data set (∼30,000 events). Among 10 LEIPs coincident with Fermi TGF observations, 2 previously reported TGFs and 2 unreported but suspected TGFs are found, while the majority are not associated with detectable TGFs. In addition, unsupervised clustering is found to reflect characteristics of the ionosphere reflection height and its effect on radio wave propagation.