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In this study, the behavior of permeate flux decline due to scale precipitation of calcium sulfate on reverse osmosis membranes was investigated. The proposed scaling-based flux model is able to explain that permeate fluxes attributed to three mechanisms of scale precipitation—cake formation, surface blockage, and mixed crystallization—converge to the same newly defined scaling-based critical flux. In addition, a scaling index is defined, which determines whether scale precipitates on the membrane. The experimental results were analyzed based on this index. The mass-transfer coefficients of flat membrane cells used in the experiments were measured and, although the coefficients differed, they could be summarized in the same form as the Leveque equation. Considering the results of the scale precipitation experiments, where the operating conditions of pressure, solute concentration, temperature, and Reynolds number were varied, the convergent values of the permeate fluxes are explained by the scaling-based critical fluxes and the scale precipitation zones by the scaling indexes.

Biomechanical analysis is increasingly being undertaken in field-based settings, often using inertial sensors or video-based pose estimation. These advancements necessitate more practical and accessible scaling methods as alternatives to traditional laboratory-based techniques like optical marker-based scaling. LiDAR scanning is a technique that could provide a reliable and efficient means of scaling biomechanical models. This study tested a scaling method for OpenSim models and comparing outcomes with those of traditional marker-based scaling in healthy adult participants. An anatomical skeleton was inferred from a LiDAR scan taken with an iPad. Key skeletal landmarks were then used to generate scaling factors using statistical shape models. The scaling factors of the pelvis, femur and tibia body segments derived from the LiDAR-based method demonstrated excellent reliability, with repeated scans of seven subjects producing an ICC value of 0.961. When comparing the scaling factors of eight additional subjects with the current gold standard technique of marker-based optical motion capture, a Bland-Altman analysis revealed differences of −0.5% ± 5.3 (95CI = [−10.8, 10]). Joint kinematics calculated using LiDAR scaled models had an average RMSD of 3.7° ± 0.1°when compared with those calculated with a marker-scaled model. These results indicate that a LiDAR-based scaling method can address the challenge of accurate and reliable scaling methods that are practical for use in the field. Future work with larger cohorts and diverse populations, and scaling of other body segments will provide further validation and enhance the generalizability and robustness of this approach.

Rapid and simple estimation of the mass eruption rate (MER) from column height is essential for real‐time volcanic hazard management and reconstruction of past explosive eruptions. Using 134 eruptive events from the new Independent Volcanic Eruption Source Parameter Archive (IVESPA, v1.0), we explore empirical MER‐height relationships for four measures of column height: spreading level, sulfur dioxide height, and top height from direct observations and as reconstructed from deposits. These relationships show significant differences and highlight limitations of empirical models currently used in operational and research applications. The roles of atmospheric stratification, wind, and humidity remain challenging to detect across the wide range of eruptive conditions spanned in IVESPA, ultimately resulting in empirical relationships outperforming analytical models that account for atmospheric conditions. This finding highlights challenges in constraining the MER‐height relation using heterogeneous observations and empirical models, which reinforces the need for improved eruption source parameter data sets and physics‐based models. Plain Language Summary Explosive volcanic eruptions expel gas and tephra in the form of a volcanic column (or plume) that rises into the atmosphere. Two important metrics characterizing these eruptions are the maximum rise height and the eruptive intensity, that is, the rate at which material is emitted from the eruptive vent. Understanding the relationship between these parameters is critical for reconstructing past volcanic events and managing hazards during volcanic crises. In this study, we use a new database of well‐characterized eruptions to constrain simple relationships between column height and eruptive intensity. We distinguish four different measurements of column height: the maximum height reached by tephra from observations and from analysis of deposits, the height at which ash spreads in the atmosphere, and the height reached by volcanic sulfur gases. We show that each height category has a distinct relationship with the eruption intensity, enabling volcanologists and risk managers to use the relationship most appropriate to the measurements available to them. Despite the improved level of detail, our data set cannot resolve any systematic influence of atmospheric conditions such as wind and humidity on eruption column height, highlighting difficulties in measuring volcanic eruption characteristics and understanding their dynamics. Key Points We provide empirical scaling relationships between mass eruption rate (MER) and column height using a new database with 134 volcanic events We constrain bespoke relationships and their uncertainties for four height metrics to support ash dispersion forecasters and researchers We detect no clear atmospheric influence on scaling relationships, highlighting required improvements of scaling models and the database

This study evaluated the between-session reliability of creating subject-specific musculoskeletal models with optoelectronic motion capture data, and using them to estimate spine loading. Nineteen healthy participants aged 24–74 years underwent the same set of measurements on two separate occasions. Retroreflective markers were placed on anatomical regions, including C7, T1, T4, T5, T8, T9, T12 and L1 spinous processes, pelvis, upper and lower limbs, and head. We created full-body musculoskeletal models with detailed thoracolumbar spines, and scaled these to create subject-specific models for each individual and each session. Models were scaled from distances between markers, and spine curvature was adjusted according to marker-estimated measurements. Using these models, we estimated vertebral compressive loading for five different standardized postures: neutral standing, 45˚ trunk flexion, 15˚ trunk extension, 20˚ lateral bend to the right, and 45˚ axial rotation to the right. Intraclass correlation coefficients (ICCs) and standard error of measurement were calculated as measures of between-session reliability and measurement error, respectively. Spine curvature measures showed excellent reliability (ICC = 0.79–0.91) and body scaling segments showed fair to excellent reliability (ICC = 0.46–0.95). We found that musculoskeletal models showed mostly excellent between-session reliability to estimate spine loading, with 91% of ICC values > 0.75 for all activities. This information is a necessary precursor for using motion capture data to estimate spine loading from subject-specific musculoskeletal models, and suggests that marker data will deliver reproducible subject-specific models and estimates of spine loading.

Cosmogenic nuclide dating is an essential component of studying Earth surface processes, but it requires knowledge of how nuclide production rates vary in time and space. Typically, production rates are calibrated at sites with independently well‐constrained exposure histories and then scaled to other sites of interest using scaling frameworks that account for spatial and temporal variations in the secondary cosmic‐ray flux at Earth's surface. To date, scaling schemes for terrestrial cosmogenic nuclide production rates have been developed for the Quaternary, yet cosmogenic nuclide applications that extend beyond the Quaternary are becoming more prevalent. For these deeper time applications, production rate calculations using scaling models optimized for the latest Quaternary neglect longer term spatiotemporal variations in geomagnetic field intensity, paleogeography, and paleoatmospheric depth. We present a production rate scaling scheme for the past 70 million years, SPRITE (Scaling Production Rates In deep TimE). This framework extends existing scaling schemes into deeper time by (a) accounting for site‐specific changes in paleolatitude, (b) integrating a geomagnetic field intensity model rooted in data from a global paleomagnetic database, and (c) incorporating climate‐driven, time‐varying atmospheric depths. We evaluate the efficacy of our model by applying it to existing data sets from paleoexposure sites, and from sites with apparent continuous million‐year exposure histories. This scaling model can be applied with measurements of stable cosmogenic nuclides to research questions such as constraining hiatus durations between ancient lava flows and calculating the formation timescales of stable landforms in arid environments over millions of years. Plain Language Summary Dating geologically old surfaces (older than a few million years) is useful for understanding climatic, tectonic, and volcanic processes. To quantify these timescales, we can use cosmogenic nuclides, which are produced in rocks at the Earth's surface via interactions with high energy particles ultimately originating from the galaxy called cosmic rays. Earth's magnetic field deflects these charged particles, so as the magnetic field intensity changes over time and space, the production rate of cosmogenic nuclides changes as well. Over millions of years, the positions of geologic surfaces relative to the Earth's magnetic field also change due to the motion of tectonic plates. Changes in climate will also affect how thick the atmosphere is locally and therefore the interactions of cosmic rays with the atmosphere before they reach Earth's surface. In this work, we create a new model framework to calculate cosmogenic nuclide production rates over the last 70 million years that accounts for the effects of changes in the magnetic field strength, the location of tectonic plates, and atmospheric thickness over this timespan. Incorporating these effects impacts calculated exposure ages of ancient geologic surfaces and therefore our interpretations of the geologic history of these surfaces from cosmogenic nuclide measurements. Key Points Paleointensity, paleolatitude, and atmospheric changes are included in cosmogenic nuclide production rate calculations extending to 70 Ma Paleointensity variations from 0 to 70 Ma affect production rates more than paleolatitude or climate‐driven changes in atmospheric depth Changes in production rates using this model framework will be most significant for pre‐Quaternary paleoexposures

Intervertebral kinematics can affect model-predicted loads and strains in the spine; therefore knowledge of expected vertebral kinematics error is important for understanding the limitations of model predictions. This study addressed how different kinematic models of the neck affect the prediction of intervertebral kinematics from markers on the head and trunk. Eight subjects executed head and neck extension-flexion motion with simultaneous motion capture and biplanar dynamic stereo-radiography (DSX) of vertebrae C1-C7. A generic head and neck model in OpenSim was scaled by marker data, and three versions of the model were used with an inverse kinematics solver. The models differed according to the number of independent degrees of freedom (DOF) between the head and trunk: 3 rotational DOF with constraints defining intervertebral kinematics as a function of overall head-trunk motion; 24DOF with 3 independent rotational DOF at each level, skull-T1; 48DOF with 3 rotational and 3 translational DOF at each level. Marker tracking error was lower for scaled models compared to generic models and decreased as model DOF increased. The largest mean absolute error (MAE) was found in extension-flexion angle and anterior-posterior translation at C1-C2, and superior-inferior translation at C2-C3. Model scaling and complexity did not have a statistically significant effect on most error metrics when corrected for multiple comparisons, but ranges of motion were significantly different from DSX in some cases. This study evaluated model kinematics in comparison to gold standard radiographic data and provides important information about intervertebral kinematics error that are foundational to model validity.

There is an increasing demand for lightweight multi-person pose estimation for many emerging smart IoT applications. However, the existing algorithms tend to have large model sizes and intense computational requirements, making them ill-suited for real-time applications and deployment on resource-constrained hardware. Lightweight and real-time approaches are exceedingly rare and come at the cost of inferior accuracy. In this paper, we present EfficientHRNet, a family of lightweight multi-person human pose estimators that are able to perform in real-time on resource-constrained devices. By unifying recent advances in model scaling with high-resolution feature representations, EfficientHRNet creates highly accurate models while reducing computation enough to achieve real-time performance. The largest model is able to come within 4.4% accuracy of the current state-of-the-art, while having 1/3 the model size and 1/6 the computation, achieving 23 FPS on Nvidia Jetson Xavier. Compared to the top real-time approach, EfficientHRNet increases accuracy by 22% while achieving similar FPS with 1 3 the power. At every level, EfficientHRNet proves to be more computationally efficient than other bottom-up 2D human pose estimation approaches, while achieving highly competitive accuracy.

In two prior articles, I demonstrated from extensive simulational studies by myself and others that the Rouse model of polymer dynamics is invalid in polymer melts and in dilute solution. However, the Rouse model is the foundational basis for most modern theories of polymeric fluid dynamics, such as reptation/scaling models. One therefore rationally asks whether there is a replacement. There is, namely by extending the Kirkwood–Riseman model. Here, I present a comprehensive review of one such set of extensions, namely the hydrodynamic scaling model. This model assumes that polymer dynamics in dilute and concentrated solution is dominated by solvent-mediated hydrodynamic interactions; chain crossing constraints are taken to create only secondary corrections. Many other models assume, contrariwise, that in concentrated solutions, the chain crossing constraints dominate the dynamics. An extended Kirkwood–Riseman model incorporating interchain hydrodynamic interactions is developed. It yields pseudovirial series for the concentration and molecular weight dependencies of the self-diffusion coefficient Ds and the low-shear viscosity η. To extrapolate to large concentrations, rationales based on self-similarity and on the Altenberger–Dahler positive-function renormalization group are presented. The rationales correctly predict how Ds and η depend on polymer concentration and molecular weight. The renormalization group approach leads to a two-parameter ansatz that correctly predicts the functional forms of the frequency dependencies of the storage and loss moduli. A short description is given of each of the papers that led to the hydrodynamic scaling model. Experiments supporting the aspects of the model are noted.

A large number of articles have documented that as population density of cities increases, car use declines and public transit use rises. These articles had a significant impact of promoting high-density compact urban development to mitigate traffic congestion. Another approach followed by other researchers used the urban scaling model to indicate that traffic congestion increases as population size of cities increases, thus generating a possible contradictory result. Therefore, this study examines the role of both density and population size on traffic congestion in 164 global cities by the use of Stochastic Impacts by Regression on Population, Affluence and Technology model. We divide 164 cities into the two subgroups of 66 low density cities and 98 high density cities for analysis. The findings from the subgroups analysis indicated a clear-cut difference on the critical role of density in low-density cities and the exclusive role of population size in high-density cities. Furthermore, using threshold regression model, 164 cities are divided into the two regions of large and small population cities to determine population scale advantage of traffic congestion. Our findings highlight the importance of including analysis of subgroups based on density and/or population size in future studies of traffic congestion.

The number of social enterprises has grown exponentially in recent times. International research regarding how social enterprises scale is starting to emerge and is becoming an area of increased focus. Due to their hybridity, social enterprises experience unique scaling challenges, and research has started to examine these experiences. This theoretical paper reviews existing literature on social enterprise scaling and, based on this, proposes a conceptual model for understanding the interdependent factors and elements social enterprises must navigate when scaling. The proposed conceptual model will provide a base for further empirical research. When validated, it will also provide a practical tool for social enterprises exploring scaling possibilities and inform future enterprise and policy supports in this area.