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Impact dynamics of a heterogeneous droplet striking cylindrical surfaces
No full textHeterogeneous (or compound) fluids consist of multiple immiscible components and are commonly encountered in industrial applications. Due to the diversity of their constituents and the complex structure of their interfaces, their fluid dynamics behavior differs significantly from that of typical homogeneous fluids. This paper systematically investigates the impact dynamics of heterogeneous droplets (HTDs) on cylindrical surfaces with different surface temperatures, providing a deep understanding of how temperature and cylindrical scale influence the impact dynamics of HTD. Firstly, microinjectors were employed to produce HTDs, comprising an inner core of deionized water (ICDW) and an outer shell of silicone oil (OSSO), which were then allowed to free-fall onto cylindrical surfaces maintained at various temperatures. The impact dynamics of HTDs striking cylindrical surfaces were quantitatively compared to those of HTDs impacting a horizontal metallic flat plate where Weber number was observed to produce differing effects on the inner and outer spreading diameters across the two surface geometries. Furthermore, temperature affects the degree of retraction of ICDW where cryogenic cylinders inhibit the suspension of ICDW and alter the bounce-separation behavior, regardless of the eccentricity. Utilizing a pressure transducer to measure the freezing strength of ICDW, it was found that its freezing behavior differs significantly from that of homogeneous water, providing important guidance for engineering design. Finally, an analytical expression for the maximum spreading diameter of ICDW as a function of temperature was derived by simultaneously applying the first law of thermodynamics and Fourier\u27s law
A Mitochondrion- targetable Ratiometric Fluorescent Probe for Viscosity in Non-alcoholic Fatty Liver Disease Cells
No full textNon-alcoholic fatty liver disease (NAFLD) is an increasingly prevalent global health concern, yet its early diagnosis remains challenging due to limitations in detection methods and sensitivity. Additionally, the scarcity of genetic studies on NAFLD hinders a deeper understanding of its pathogenesis. Viscosity, a key component of the cellular microenvironment, plays a crucial role in NAFLD progression. Here, we report a dual-channel ratiometric fluorescent probe (Probe A) based on Förster resonance energy transfer (FRET) and molecular rotor mechanisms for viscosity sensing. Probe A exhibits ratiometric fluorescence at 594 nm and 472 nm in response to viscosity changes and selectively localizes to mitochondria. It successfully detected cellular viscosity alterations induced by nystatin and provided rapid, sensitive monitoring of viscosity changes in free fatty acid (FFA)-induced NAFLD cell models, offering a promising tool for early NAFLD diagnosis. This represents the first fluorescent probe to investigate the relationship between complement C1q/tumor necrosis factor-related protein 6 (CTRP6) and NAFLD, providing new insights genetic studies into NAFLD pathogenesis
Microbial Communities in Glacial Lakes of Glacier National Park, MT, USA
Get PDFGlaciers are retreating, altering alpine ecosystems and creating new proglacial lakes. Compared to lakes fed by snowpack, glacial lakes are often enriched in nutrients and suspended solids that decrease light penetration. However, the microorganisms and biogeochemical conditions within these newly formed lakes are not well characterized. We describe the microbial communities in 14 glacial lakes in Glacier National Park, MT, USA using 16S rRNA gene amplicon sequencing and measurements of nutrient concentrations, water clarity, and other environmental properties. Microbial communities were distinct between lakes, including those connected to the same glacier, indicating the importance of site-specific biogeochemical and physical dynamics on these systems. Microbial community composition correlated with lake age (formation before or after the Little Ice Age) and conductivity but not with whether a lake was connected to a contemporaneous glacier \u3e 0.1 km2. Heterotrophic lineages found in other glacial systems were abundant and widespread, while cyanobacteria only reached appreciable abundances in shallow lakes where light reached the benthos. Relative abundances of ammonia and nitrite oxidizers correlated with concentrations of nitrate and nitrite, suggesting nitrification may help control nitrogen forms and concentrations in glacial lakes. We show that as glaciers recede, unique glacial lake microbial communities will be formed and lost with them
COGNITIVE WORKLOAD ANALYSIS IN COLLABORATIVE ROBOTIC PROGRAMMING OF MANUFACTURING ASSEMBLIES USING TEACH PENDANTS
No full textIn automated manufacturing assembly operations, collaborative robots (cobots) conduct the task of automating repetitive and precise activities to allow human workers to concentrate on more intricate and decision-oriented tasks.Programming cobots for assembly tasks often requires manually setting and recording specific positions throughout the assembly process utilizing the native teach pendant, which is referred to as teach point programming. However, teach point programming can be laborious and introduce physical strain and cognitive challenges due to ergonomic issues and complex, non-intuitive teach pendant interfaces. This study aims to assess how cognitive workload in teach point programming impacts user task performance. To achieve this, we recruited 28 participants to perform standardized assembly tasks on a designated task board. During the experiment, the task success rates, completion times, and cognitive workload (using the NASA Task Load Index (NASA-TLX), a widely recognized tool for assessing perceived workload) were recorded. Correlation analysis revealed significant relationships between cognitive workload and task success. Specifically, higher task success was associated with lower physical and cognitive workload and reduced perceived effort. Additionally, participants who achieved greater task success reported higher levels of perceived performance. In contrast, cognitive workload factors including perceived mental demand, temporal demand, and frustration did not exhibit a direct correlation with task success. None of the cognitive workload responses were correlated with task completion times, indicating that cognitive stress may not influence task speed. The results were further supported by analyzing physical robot-only ratings, gender distributions, and pre-task experience. These findings underscore the importance of cognitive load in achieving successful task outcomes and established a human-centric understanding of teach point programming in cobots. In addition, this study provides an avenue of future research where addressing the relevant cognitive factors could potentially enhance overall task performance in manufacturing environments andleadtomoreeffectivehuman-robotcollaborationinassembly tasks
Questions of How, Not if: An Analysis of Secondary Belief Discourse in Agricultural Policy
No full textOur knowledge of the innerworkings of coalitions in policy debates is wanting, particularly those that focus on instrumental questions of how policies will be implemented after initial questions and conflicts regarding if they will be passed are settled. A deeper and more frequent focus on if questions in studies of coalitions may have resulted in blind spots to the importance of secondary-belief conflict. To begin to construct a better understanding of the innerworkings of political groups, this study utilizes the Advocacy Coalition Framework (ACF). It also incorporates the role of emotions in belief formation via the Emotion Belief Analysis (EBA). This study examines three legislative policies in the state of Colorado debated by the Colorado General Assembly during the 2021 legislative session that implicate rural–urban conflict in agricultural policy and the discourses and emotional expressions used therein
Integrating complete bond dissociation in Class II force fields
No full textPredicting the physical and mechanical properties of organic materials from purely chemical understandings remains a significant challenge due to the limitations of conventional force fields in molecular dynamics (MD). In this work, we present a novel reformulation of Class II force fields that integrates Morse bond potentials with newly derived cross-term interactions, explicitly capturing complete bond dissociation while maintaining computational efficiency. This reformulated functional form combines the stability of fixed-bond models with the reactive capabilities of bond-breaking force fields, achieving accurate and robust MD predictions across crystalline, semi-crystalline, and amorphous organic systems. Extensive benchmarking confirms its predictive accuracy and speed, enabling high-throughput structure–property mapping for integrated computational materials engineering. Reparameterization methods have been implemented in the LUNAR software, which provides a user-friendly interface for rapid MD model development and accelerates materials discovery for composite applications
Detecting the Onset of Drizzle with Joint Lidar and Radar Measurements
No full textSimultaneous lidar and radar observations of clouds and precipitation are becoming more common, and to take advantage of such opportunities, radar–lidar-estimated diameter (RLED) was introduced in earlier work to describe relevant droplet size. Here, we use in situ cloud and drizzle probe measurements of drop size distribution (DSD) to show that RLED, the ratio of the sixth to the second moment of DSD (D62), is sensitive to collision–coalescence, making it a potential indicator of drizzle onset. Remarkably, despite differences in sample volumes, in situ estimates of RLED agree closely with those based solely on lidar–radar measurements. Additionally, RLED is sensitive to DSD dispersion as we demonstrate on remote sensing observations. This is significant as DSD dispersion plays an essential role in numerical weather prediction models and terrestrial radiative transfer
Identification of Novel TAT-I24-Related Peptides with Antiviral Activities
No full textTo identify novel peptides with potential antiviral activities, a database search was performed based on the primary sequence of the peptide I24 (CLAFYACFC), the effective part of the antiviral peptide TAT-I24 consisting of peptide I24 and the cell penetrating TAT-peptide (amino-acids 48–60, GRKKRRQRRRPPQ). A Protein BLAST search identified several sequences with high similarity to I24 in diverse proteins, some of which are known to be involved in the interaction with nucleic acids. Selected sequences and newly designed variants of I24 were synthesized as TAT fusion peptides and tested for antiviral activity in two well-established models: baculovirus transduction of HEK293 cells and mouse cytomegalovirus (MCMV) infection of NIH/3T3 cells. Several of the TAT-fusion peptides exhibited antiviral activities with a potency comparable to TAT-I24. The ability of these peptides to bind double-stranded DNA suggested the same mode of action. Several peptides caused swelling of red blood cells (RBC) but with only one peptide clearly inducing haemolysis. With two exceptions, RBC swelling was observed with antivirally active peptides but not with less active peptides, indicating that antiviral activities are linked to an effect on membrane integrity of target cells. Structural prediction of the TAT-fusion peptides indicated formation of two α-helical elements, with several of these peptides showing remarkable similarity when subjected to structural alignment
MIMAR-Net: Multiscale Inception-Based Manhattan Attention Residual Network and Its Application to Underwater Image Super-Resolution
No full textIn recent years, Single-Image Super-Resolution (SISR) has gained significant attention in the geoscience and remote sensing community for its potential to improve the resolution of low-quality underwater imagery. This paper introduces MIMAR-Net (Multiscale Inception-based Manhattan Attention Residual Network), a new deep learning architecture designed to increase the spatial resolution of input color images. MIMAR-Net integrates a multiscale inception module, cascaded residue learning, and advanced attention mechanisms, such as the MaSA layer, to capture both local and global contextual information effectively. By utilizing multiscale processing and advanced attention strategies, MIMAR-Net allows us to handle the complexities of underwater environments with precision and robustness. We evaluate the model on three popular underwater image datasets, namely UFO-120, USR-248, and EUVP, and perform extensive comparisons against state-of-the-art methods. Experimental results demonstrate that MIMAR-Net consistently outperforms existing approaches, achieving superior qualitative and quantitative improvements in image quality, making it a reliable solution for underwater image enhancement in various challenging scenarios
Improving MuSES EO/IR target and background scene simulation accuracy with the RapidFlow fluid solver
No full textThe ability to accurately predict spectral signatures of targets in outdoor scenes is an important capability for defense agencies, for both target acquisition and detection avoidance efforts. In thermal infrared (IR) wavebands, temperature is a primary contributor to remotely-sensed radiance. Consequently, enhancements to the accuracy of thermal predictions are valuable, with estimates of convective heat transfer being a source of uncertainty where improvements can be realized. One standard method of convective heat transfer calculation employs a global speed-dependent convection coefficient for all exterior surfaces. This approach efficiently predicts convection coefficients for large scenes with dynamically changing wind conditions. Frequent changes in wind speed and direction tend to negate the weaknesses of this simplistic approach (e.g., lack of localized wake and flow acceleration), and numerous thermal and IR validations of MuSES performed in the past bear this out. For some simulation applications however, additional thermal fidelity is required. Traditional computational fluid dynamics (CFD) codes provide ample spatial fidelity, typically with a significant computational cost. A fully-transient diurnal analysis of an outdoor scene would likely be infeasible given the large spatial and temporal scales typically of interest. We present here a flow solver designed specifically for infrared prediction applications. This flow solver represents air flow at an appropriate spatial resolution level for accurate simulation of convective heat transfer in the context of IR scenes. Predicting the geometry-induced location of flow accelerations, wakes, and advective flow is sufficient for calculating convection with acceptable accuracy for thermal EO-IR predictions. The accuracy of this new flow solver greatly improves upon the use of a universal wind speed-dependent (but directionless) convection correlation for entire scenes without all the drawbacks of traditional CFD solutions. Application-specific simplification of the flow equations allows this flow solver to be fast and robust, requiring minimal user effort and fluid domain expertise