Density Variations Research Articles

Densification behavior in compaction for Cu/TiB2 composite under electromagnetic impact

Cu/TiB2 composite combines electrical conductivity and wear resistance, leading to its wide application prospect in electrical contact. In this study, electromagnetic impact was applied to compact Cu/TiB2 powder. The interaction between powder and die under electromagnetic impact was analyzed by observing the surface quality, density, microstructure, and hardness. The results showed that when the energy was less than 21 kJ, the increase in energy could enhance density and tensile strength of the compact. However, when the energy exceeded 21 kJ, the state of compact hardly changed and burr near the edge would be worsened. Besides, the hardness of the upper surface increased gradually from the center to the edge, while the opposite was true for the lower surface, reflecting the spatial distribution of density. With the aid of simulation, it was found that the stress wave propagation influenced the densification behavior and led to the variation of density.

Diffusion kurtosis imaging, MAP-MRI and NODDI can selectively track gray matter myelin density in the primate cerebral cortex

Abstract Diffusion magnetic resonance imaging (dMRI) has been widely used to model the trajectory of myelinated fiber bundles in the white matter. Increasingly, it is also used to evaluate the microstructure of the cerebral cortex gray matter. For example, in diffusion tensor imaging (DTI) of the cortex, fractional anisotropy (FA) correlates strongly with the anisotropy of cellular anatomy, while radial diffusivity (RD) tracks the anisotropy of myelinated fibers. However, no DTI parameter shows specificity to gray matter myelin density. Here we show that three higher order diffusion parameters - the mean diffusion kurtosis (MK), the Neurite Density Index (NDI) from NODDI, and the Non-Gaussian (NG) parameter from MAP-MRI— each track the laminar and regional myelin density of the primate cerebral cortex in fine detail. We carried out ultra-high resolution, multi-shelled dMRI in ex-vivo marmoset monkey brains. We compared the spatial mapping of the MK, NDI, and ND diffusion parameters to the cortical myelin distribution of these brains, with the latter obtained in two ways: First, using histological sections finely co-registered to the MRI, and second using magnetization transfer ratio MRI scans (MTR), an established non-diffusion method for imaging myelin density. We found that, in contrast to DTI parameters, each of these higher order diffusion measures captured the spatial variation of myelin density in the cortex. The demonstration that diffusion parameters exhibit both sensitivity and specificity for gray matter myelin density will allow dMRI to more effectively track human disease, in which myelinated and non-myelinated tissue compartments are affected differentially.

Classifying Seismic Events Linked to Solar Activity: A Retrospective LSTM Approach Using Proton Density

The influence of solar activity on seismic activity is a subject of debate. Previous studies have shown that there is sometimes a correlation and sometimes a contradiction between solar activity maxima and large earthquakes. Long-term memory neural network is used to study the relationship between solar activity and seismic activity. This study emphasizes retrospective classification rather than direct prediction, refining the LSTM architecture to maximize classification accuracy and processing data from the Solar and Heliospheric Observatory and the U.S. Geological Survey earthquake catalogs. A declustering technique is used to select large seismic events and weighted learning corrects for class imbalances. The LSTM model accurately classified earthquakes (84.47%) and proton density variations. The results support the theory that solar activity, in particular proton density, can anticipate earthquake events.

Sustainable lightweight self-compacting concrete with coal bottom ash as aggregate and cement replacement

This study aims to examine the impact of utilizing coal bottom ash (CBA) sourced from an inactive power plant on the fresh and hardened properties of lightweight self-compacting concrete (LWSCC). To evaluate the workability of the LWSCC mixtures, slump flow, L-box, and sieve segregation tests were performed. The mechanical and physical properties were assessed through dry density, compressive strength and ultrasonic pulse velocity (UPV) tests. A total of five concrete mixes were developed: a control mix and four additional mixtures in which natural coarse aggregate was fully substituted with coal bottom ash aggregate (CCBA). Additionally, Portland cement was partially replaced with coal bottom ash powder (CBAP) at levels of 15%, 20%, and 25%. The results indicated that the use of CBA as a coarse aggregate enhanced the workability of LWSCC, though workability decreased as the proportion of CBAP increased. Nonetheless, the workability of all mixes remained compliant with the standards specified by the French Association of Civil Engineering (AFGC). Minimal variations in dry density and compressive strength were observed with the incorporation of CBA; however, these values remained within the acceptable limits for structural lightweight concrete. Furthermore, the UPV test demonstrated favorable durability for all LWSCC mixtures. Strong linear correlations were identified among the various measured properties, reinforcing the conclusion that CBA serves as an effective replacement for natural coarse aggregate. Moreover, the use of 15% CBAP as a partial substitute for Portland cement proved to be a feasible option for producing sustainable LWSCC.

Study of pollution status in urban environment caused by attitude and waste littering behavior of citizens

Littering behavior is one of the main challenges in waste management, especially in big cities. In this study, density and composition of litter in Tehran was investigated and the urban pollution status was calculated by using the clean environment index. The results showed that the average density of litter in the studied locations was 0.182 number/m2. Spatial variation in the litter density showed that the average density in commercial and residential land-uses was 0.22 and 0.14 number/m2, respectively. Cigarette butts (29%), bottle caps (18%), and paper receipts (10%) consisted the largest ratio of the litter composition in the studied locations. Clean environment index for the studied locations was calculated in the range of 2.34 to 10.42. Based on this, 30% of the studied locations were in a clean status and 70% were in a polluted and worse status. The litter density in the city, interviews with citizens, and the results of previous studies showed insufficient awareness of citizens about the consequences of littering in the urban environment, which led to the prevalence of littering behavior. So, the littering behavior by citizens has caused the urban environment pollution and increased health risk. Educating citizens and modifying their behavior to avoid littering is necessary to improve the observed status.

Discovering and Ranking Urban Social Clusters Out of Streaming Social Media Datasets

ABSTRACTUrban social media mining is the process of discovering urban patterns from spatio‐temporal social media datasets. Urban social clusters are the clusters formed by the social media posts of users living in cities at a certain time and place. Discovering and identifying urban social clusters is of great importance for urban and regional planning, target audience identification, a better understanding of city dynamics and so forth. Discovering and ranking urban social clusters out of streaming social media datasets require efficient filtering approaches and mining algorithms. In the literature, there are several studies performed that address the discovery of the importance of urban clusters. Most of these studies take into account the spatial expansions over time and the changes in the numbers of elements within clusters when identifying the significance of urban clusters. However, in contrast to these studies, we have also considered cluster temporal formation stability, spatial density variation, and the impact of meta‐information on urban social clusters. In this study, Temporal, Spatial, and Meta Important Urban Social Clusters Miner (TSMIUSC‐Miner) algorithm is proposed. In the proposed algorithm, urban social clusters are discovered, and their importance relative to each other are compared and ranked. The temporal, spatial and meta importance scores of the clusters are calculated and then, the clusters that satisfy predefined score thresholds are discovered. The performance of the proposed TSMIUSC‐Miner algorithm compared with that of a naive approach using real‐life streaming Twitter/X dataset. The results showed that the proposed TSMIUSC‐Miner algorithm outperforms the naive approach in terms of execution time.

The primacy of density‐mediated indirect effects in a community of wolves, elk, and aspen

AbstractThe removal or addition of a predator in an ecosystem can trigger a trophic cascade, whereby the predator indirectly influences plants and/or abiotic processes via direct effects on its herbivore prey. A trophic cascade can operate through a density‐mediated indirect effect (DMIE), where the predator reduces herbivore density via predation, and/or through a trait‐mediated indirect effect (TMIE), where the predator induces an herbivore trait response that modifies the herbivore's effect on plants. Manipulative experiments suggest that TMIEs are an equivalent or more important driver of trophic cascades than are DMIEs. Whether this applies generally in nature is uncertain because few studies have directly compared the magnitudes of TMIEs and DMIEs on natural unmanipulated field patterns. A TMIE is often invoked to explain the textbook trophic cascade involving wolves (Canis lupus), elk (Cervus canadensis), and aspen (Populus tremuloides) in northern Yellowstone National Park. This hypothesis posits that wolves indirectly increase recruitment of young aspen into the overstory primarily through reduced elk browsing in response to spatial variation in wolf predation risk rather than through reduced elk population density. To test this hypothesis, we compared the effects of spatiotemporal variation in wolf predation risk and temporal variation in elk population density on unmanipulated patterns of browsing and recruitment of young aspen across 113 aspen stands over a 21‐year period (1999–2019) in northern Yellowstone National Park. Only 2 of 10 indices of wolf predation risk had statistically meaningful effects on browsing and recruitment of young aspen, and these effects were 8–28 times weaker than the effect of elk density. To the extent that temporal variation in elk density was attributable to wolf predation, our results suggest that the wolf–elk–aspen trophic cascade was primarily density‐mediated rather than trait‐mediated. This aligns with the alternative hypothesis that wolves and other actively hunting predators with broad habitat domains cause DMIEs to dominate whenever prey, such as elk, also have a broad habitat domain. For at least this type of predator–prey community, our study suggests that risk‐induced trait responses can be abstracted or ignored while still achieving an accurate understanding of trophic cascades.

Copper-based photocatalysts with natural organic ligands for efficient removal of tetracycline under visible light

The excessive use of broad-spectrum antibiotics, such as tetracycline, presents a significant challenge to human survival and development. Oxygen vacancies (OVs) metal-organic framework (MOF) materials were synthesized using natural organic acids (L-malic acid, L-aspartic acid, and L-asparagine) with similar structures but different charge densities, along with copper as the metal linking agent. The presence of oxygen vacancies in the catalyst provides abundant active sites for photocatalytic reactions. The employment of flexible straight-chain organic ligands devoid of rigid polycyclic rings, combined with the incorporation of different substituents to induce variations in charge density, the resulting catalysts exhibit distinct photocatalytic activities under visible light. Density functional theory calculations confirm that L-asparagine exhibits the largest electron density difference, and the Cu-based MOF (Cu-ASU) synthesized as an organic ligand exhibits the highest photocatalytic activity under visible light excitation. The catalyst displayed remarkable photocatalytic activity against tetracycline antibiotics under identical conditions (with removal rates of 93.5 % for tetracycline, 81.4 % for terramycin, and 95.6 % for chloramphenicol hydrochloride). This provides a novel approach for the design and synthesis of photocatalysts for the removal of antibiotics from water.

Discharge characteristics of silicon-based DC Helium microplasmas: comparison between Through Silicon Via and closed cavity type micro-reactors

Abstract This paper explores the microfabrication process of plasma reactors, from the production of micro-reactors to the addition of Through Silicon Vias (TSV) using proprietary etching techniques and on site cleanroom facilities. The investigation focuses on both newly fabricated closed and open-cavity Micro-Hollow Cathode Discharge (MHCD) reactors, with particular emphasis on electrical and optical diagnostics to characterize their plasma properties. All experiments, including those on closed-cavity reactors, were specifically conducted for this study under identical conditions to ensure a rigorous comparison between reactor types. When electrical diagnostics were carried out in helium at various pressures, interesting phenomena such as the evolution of voltage breakdown were observed. These diagnostics also provided new insights into the impact of surface electrode and overall geometry properties and pressure on reactor performance. 
Additional investigation into instability mechanisms revealed self-pulsing oscillations, with different reactor types having different oscillation amplitudes for similar operating conditions. Optical emission spectroscopy (OES) emerged as a strong tool for spatial plasma analysis, revealing consistent gas temperature distributions across reactor diameters. The Inglis-Teller series provided an estimate of the upper limit of electron density, while the Stark broadening analysis of Hα offered valuable insights into electron density variations, particularly in the self-pulsing regime.

Extracting the Heterogeneous 3D Structure of Molecular Films Using Higher Dimensional SFG Microscopy.

Ultrathin molecular films are widespread in both natural and industrial settings, where details of the molecular structure such as density, out-of-plane tilt angles, and in-plane directionality determine their physicochemical properties. Many of these films possess important molecular-to-macroscopic heterogeneity in these structural parameters, which have traditionally been difficult to characterize. Here, we show how extending sum-frequency generation (SFG) microscopy measurements to higher dimensionality by azimuthal-scanning can extract the spatial variation in the three-dimensional molecular structure at an interface. We extend the commonly applied theoretical assumptions used to analyze SFG signals to the study of systems possessing in-plane anisotropy. This theoretical framework is then applied to a phase-separated mixed lipid monolayer to investigate the variation in molecular density and 3D orientation across the chirally packed lipid domains. The results show little variation in out-of-plane structure but a distinct micron-scale region at the domain boundaries with a reduction in both density and in-plane ordering.

Theoretical analysis of water-droplet condensation and evaporation in prescribed environment with radiation

Abstract A quasi-explicit algebraic solution of the problem of quasi-steady water droplet heat- and mass-transfer with condensation or evaporation in a prescribed environment including radiation has been obtained. The solution is based on the mass-fraction form of Fick’s law of species diffusive mass transport, which allows accounting for variation in gas density through the diffusion layer surrounding the droplet. A new term, the average vapor supersaturation through the diffusion layer, has been included that allows the model to be extended to environmental conditions significantly outside those for which it is theoretically derived (dilute or near-saturation), even to conditions of relatively high temperature, low relative humidity, and low pressure. The latter two have atmospheric significance because evaporative cooling can induce significant decrease in droplet temperature and possibly induce freezing. Basic modeling assumptions are confirmed by comparison with time-dependent temperature and evaporation-rate measurements of a laboratory laser-heated droplet. Accuracy of the new model is assessed by comparison with exact equations. For temperatures between −30 and 0°C and negligible radiation, relative-error magnitudes in droplet temperature and evaporation rate are less than 5%, even for relative humidity down to 10% and pressure down to 60 kPa. This represents an improvement in accuracy over the conventional model, in which property variations of density and supersaturation through the diffusion layer are not taken into account. Differences in predicted evaporation rates between the mass-fraction and the more common partial-density Fick’s laws, which can be significant (>10% above 70% relative humidity) in coupled, implicit equations, are shown to be made negligibly small (<0.5%) by linearizing about the saturation state.

Heterogeneous Hierarchical Fusion Network for Multimodal Sentiment Analysis in Real-World Environments

Multimodal sentiment analysis models can determine users’ sentiments by utilizing rich information from various sources (e.g., textual, visual, and audio). However, there are two key challenges when deploying the model in real-world environments: (1) the limitations of relying on the performance of automatic speech recognition (ASR) models can lead to errors in recognizing sentiment words, which may mislead the sentiment analysis of the textual modality, and (2) variations in information density across modalities complicate the development of a high-quality fusion framework. To address these challenges, this paper proposes a novel Multimodal Sentiment Word Optimization Module and a heterogeneous hierarchical fusion (MSWOHHF) framework. Specifically, the proposed Multimodal Sentiment Word Optimization Module optimizes the sentiment words extracted from the textual modality by the ASR model, thereby reducing sentiment word recognition errors. In the multimodal fusion phase, a heterogeneous hierarchical fusion network architecture is introduced, which first utilizes a Transformer Aggregation Module to fuse the visual and audio modalities, enhancing the high-level semantic features of each modality. A Cross-Attention Fusion Module then integrates the textual modality with the audiovisual fusion. Next, a Feature-Based Attention Fusion Module is proposed that enables fusion by dynamically tuning the weights of both the combined and unimodal representations. It then predicts sentiment polarity using a nonlinear neural network. Finally, the experimental results on the MOSI-SpeechBrain, MOSI-IBM, and MOSI-iFlytek datasets show that the MSWOHHF outperforms several baselines, demonstrating better performance.

Experimental analysis of pressure deviation-induced film cooling effectiveness deviation in pressure sensitive paint measurement

The use of the Pressure Sensitive Paint (PSP) measurement method allows for obtaining dense, completely adiabatic film cooling effectiveness (η) results. In order to adapt to the increasing trend of high-speed and complexity in the application scenarios of film cooling, the pressure deviation-induced film cooling effectiveness deviation in PSP measurements needs to be quantified and corrected. Initially, two different jets of specific materials were injected into a dual-hole flat plate test piece. The pressure difference at corresponding positions of the two jet flows was measured to demonstrate that, compared to air, a ternary oxygen mixed gas better simulated the pressure field of the foreign gas. This illustrates the feasibility of the Test3-OM correction method. Building upon this, the influence of pressure deviation on the accuracy of measurement results was quantitatively evaluated in various scenarios, including a flat circular hole, film holes with a compound angle, a converging slot hole, and film holes at three positions on the blade. The results indicate that the pressure deviation-induced η deviation is non-negligible under conditions of high blowing ratios in most positions. Particularly in the low η region, there is a higher sensitivity to pressure deviation. Generally, there is a negative deviation region near the hole downstream, and a positive deviation region slightly downstream. The generation of deviations stems from variations in density, resulting in different flow patterns of the jet near the wall surface.

Numerical Simulations of Impact River Morphology Evolution Mechanism Under the Influence of Floodplain Vegetation

Shallow floodplains play a crucial role in river basins by providing essential ecological, hydrological, and geomorphic functions. During floods, intricate hydrodynamic conditions arise as flow exits and re-enters the river channel, interacting with the shallow vegetation. The influence and mechanism of shoal vegetation on channel hydrodynamics, bed topography, and sediment transport remain poorly understood. This study employs numerical simulations to address this gap, focusing on the Xiaolangdi–Taochengpu river section downstream of the Yellow River. Sinusoidal-derived curves are applied to represent the meandering river channel to simulate the river’s evolutionary process at a true scale. The study simulated the conditions of bare and vegetated shallow areas using rigid water-supported vegetation with the same diameter but varying spacing. The riverbed substrate was composed of non-cohesive sand and gravel. The analysis examined alterations in in-channel sediments, bed morphology, and bed heterogeneity in relation to variations in vegetation density. Findings indicated a positive correlation between vegetation density and bed heterogeneity, implying that the ecological complexity of river habitats can be enhanced under natural hydrological conditions in shallow plain vegetation and riparian diffuse flow. Therefore, for biological river restoration, vegetation planting in shallow plain regions can provide greater effectiveness.

An Integrated Approach for Development of a Decision Support System for Early Surveillance of Japanese Encephalitis: a Remote Sensing-based study in Conjunction with Epidemiological Scenario in Assam, India

Abstract. Japanese Encephalitis (JE) is one of the serious mosquito borne viral diseases mostly prevalent in India and many of the South Asian countries. A comprehensive study was conducted in Dibrugarh, a JE endemic district of Assam taking into the account of the environmental and social factors that determine the outbreak of the disease. Effect of environmental and climatic factors on mosquito vector densities was investigated using geospatial tools and techniques. Wetlands were found to be the most preferred habitat type for most of the JE-transmitting mosquitoes followed by paddy-growing areas and ponds. The villages abundant with these habitats or located nearby were found to be more vulnerable to JE risk. Based on the spatial distribution of habitat types, villages in the district were divided into three categories of vector abundance and hence the risk of JE. It is found that rainfall (intensity as well as duration) is the most significant determinant in the modulation of mosquito density followed by temperature. It is also observed that the derived models could explain about 61% to 73% variation in mosquito density due to change in weather variables as indicated by R2 values of the Models. Among socio-economic determinants, pig rearing habit of the villagers was found to be most significant. JE risk map was prepared by integrating vector abundance map with the map of host abundance in GIS domain. Attempts were also made to forecast the intensity of JE cases based on a time series analysis of historical morbidity pattern, whereas forecast for onset of the disease was based on the modulation of weather variables in the study area. A web-based decision support system (DSS) developed by integrating the forecasts of JE onset, intensity and JE prone villages has been provided to the concerned health authorities to take timely intervention measures. The JE early warning system developed for Dibrugarh district was extended to two more JE prone districts viz, Sibsagar and Tinsukia as validation sites and observed that the accuracy of forecast was within the range of 69% to 77%. DSS on JE has opened up new opportunities to study similar vector-borne diseases of the JE serogroup viz., West Nile.

Engineering quantitative stomatal trait variation and local adaptation potential by cis-regulatory editing.

Cis-regulatory element editing can generate quantitative trait variation that mitigates extreme phenotypes and harmful pleiotropy associated with coding sequence mutations. Here, we applied a multiplexed CRISPR/Cas9 approach, informed by bioinformatic datasets, to generate genotypic variation in the promoter of OsSTOMAGEN, a positive regulator of rice stomatal density. Engineered genotypic variation corresponded to broad and continuous variation in stomatal density, ranging from 70% to 120% of wild-type stomatal density. This panel of stomatal variants was leveraged in physiological assays to establish discrete relationships between stomatal morphological variation and stomatal conductance, carbon assimilation and intrinsic water use efficiency in steady-state and fluctuating light conditions. Additionally, promoter alleles were subjected to vegetative drought regimes to assay the effects of the edited alleles on developmental response to drought. Notably, the capacity for drought-responsive stomatal density reprogramming in stomagen and two cis-regulatory edited alleles was reduced. Collectively our data demonstrate that cis-regulatory element editing can generate near-isogenic trait variation that can be leveraged for establishing relationships between anatomy and physiology, providing a basis for optimizing traits across diverse environments.

The global distribution and drivers of wood density and their impact on forest carbon stocks.

The density of wood is a key indicator of the carbon investment strategies of trees, impacting productivity and carbon storage. Despite its importance, the global variation in wood density and its environmental controls remain poorly understood, preventing accurate predictions of global forest carbon stocks. Here we analyse information from 1.1 million forest inventory plots alongside wood density data from 10,703 tree species to create a spatially explicit understanding of the global wood density distribution and its drivers. Our findings reveal a pronounced latitudinal gradient, with wood in tropical forests being up to 30% denser than that in boreal forests. In both angiosperms and gymnosperms, hydrothermal conditions represented by annual mean temperature and soil moisture emerged as the primary factors influencing the variation in wood density globally. This indicates similar environmental filters and evolutionary adaptations among distinct plant groups, underscoring the essential role of abiotic factors in determining wood density in forest ecosystems. Additionally, our study highlights the prominent role of disturbance, such as human modification and fire risk, in influencing wood density at more local scales. Factoring in the spatial variation of wood density notably changes the estimates of forest carbon stocks, leading to differences of up to 21% within biomes. Therefore, our research contributes to a deeper understanding of terrestrial biomass distribution and how environmental changes and disturbances impact forest ecosystems.

Effect of Partition Temperature on Microstructure and Mechanical Properties of Cold‐Rolled Medium‐Manganese Steel

The influence of different heat‐treatment conditions on the microstructure evolution and mechanical properties of cold‐rolled Fe–7.69Mn–2.76Al–0.12C (wt%) steel is investigated using various techniques, including scanning electron microscopy, transmission electron microscopy (TEM), and X‐ray diffraction. In the results, it is shown that the stability of austenite is significantly affected by different partitioning temperatures. It is found that factors such as dislocation density, temperature, and grain size collectively influence partitioning behavior. In the observations, it is revealed that when the partitioning temperature is raised from 120 to 180 °C, the dislocation density of the face centered cubic phase within the test steel decreases from 10.38 × 1016 to 7.67 × 1016 m−2. Additionally, within specimens exhibiting higher dislocation densities, carbon element diffusion is more uniform. During the experiment, the poor stability of the austenite is found to be susceptible to stress‐inducing the phase of the martensitic transformation, and the type of the martensite transformation affects the deformation process and the performance of submission behavior. In the observations under TEM, a phenomenon where variations in dislocation density within individual austenite grains may lead to differing stability across grain regions is revealed, thereby triggering localized martensitic phase transformations.

Copy Number Analysis of Whole Exome Sequencing Data

Abstract Background Genetic alterations implicated in constitutional disorders range in size from single nucleotide substitutions to losses and gains of millions of bases. Whole exome sequencing (WES) captures many clinically relevant variants, but an analytic pipeline optimized for detection of small substitutions, insertions, and deletions may miss pathogenic copy number variants (CNVs). Historically, our laboratory performed separate cytogenetic tests to evaluate for larger genomic imbalances. Addition of copy number analysis to our WES pipeline could improve the assay’s diagnostic yield. This study describes part of our strategy to validate copy number analysis of our constitutional whole exome sequencing data. Methods We identified 18 CNV specimens that underwent both chromosomal microarray analysis (CMA) and WES between 2017 and 2023. Variants included 13 deletions (size range 14Kb-2Mb) and 5 duplications (size range 325Kb-10Mb). The standard method of constitutional copy number analysis in our clinical laboratory is CMA using the Affymetrix Cytoscan HD Array and analysis software. CNVs identified on CMA were reviewed with reference to the Database of Genomic Variants and multiple academic hospital databases. WES was performed on an Illumina sequencing system following capture-based library enrichment with the Integrated DNA Technologies xGen Exome Research, CNV Backbone, Human mtDNA Research, and Human ID Research Panel probes. CNVs identified on WES were called using the Broad Institute’s Genomic Analysis Toolkit (GATK) with a quality score cutoff of 120. Whole exome sequencing data was visualized on the Integrative Genomics Viewer. Results 14 out of 18 CNVs reported on CMA were also detected on WES with a quality score at or above 120 (78%). This includes 10 out of 13 deletions (77%) and 3 out of 5 duplications (60%). For both deletions and duplications, the size ranges of detected variants at or above the cutoff score and undetected variants overlapped. One duplication reported on CMA was detected on WES with a quality score below 120. Visualization of whole exome sequencing data showed variation in probe density that may have affected CNV calling. Conclusions The majority of CNVs reported after CMA (14/18, 78%) were also detected on WES data using a GATK quality score cutoff of 120. The data does not suggest an association between CNV size and detection by WES, although interpretation is limited by the small number of cases. Data visualization may be required to identify some CNVs. Future steps to characterize the limitations of copy number analysis of WES data include assessments of variance in probe density and read depth across the exome. Copy number analysis of WES data from cases with normal CMA results will also be performed for assay validation.

Giant Kelvin‐Helmholtz (KH) Waves at the Boundary Layer of the Coronal Mass Ejections (CMEs) Responsible for the Largest Geomagnetic Storm in 20 Years

AbstractStarting in the evening of 10 May 2024 the Earth's magnetosphere was hit by the coronal mass ejections (CMEs) creating the largest geomagnetic storm in 20 years. The CME encounter was characterized by variations of plasma number density and magnetic field. Here, I present the ARTEMIS observations at the lunar orbit during this event. The IMF ranged from −60 to +40 nT both with hour to minutes periodicity with plasma jets propagating in ‐direction within multi‐scale wave structures. Similar signature has been recently reported at the magnetopause by MMS spacecraft (Li et al., 2023, https://doi.org/10.1029/2023GL105539; Nykyri, 2024, https://doi.org/10.1029/2024GL108605) during a strongly southward IMF. Here, I show that the CME boundaries were KH unstable leading to multi‐scale density and magnetic field fluctuations including reconnection jets. The wavelengths varied from 60 to 270 , suggesting that the magnetosphere was periodically exposed to successive intervals of strongly northward and southward IMF leading to enhanced mass and magnetic flux loading.