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Abstract Investigating the preferential flow path of a debris flow is crucial for quantifying the risk and developing mitigation strategies. Here, we examined 66 debris flows from the Western Ghats in India employing Rapid Mass Movement Simulation (RAMMS)::Debris Flow software to understand the kinematics of run-out. Our analysis revealed that the debris flow run-out in the study area follow two main routes: 60 along the existing stream channels (SC) and six following the steepest hill slope (SH). We further simulated these debris flows to identify their drivers, and derived a threshold that distinguishes between SC and SH-type debris flows. Our results indicate that the debris flow volumes greater than 7072 cu. m is SH-type, whereas those with smaller volumes are more likely to follow SC paths. The model’s accuracy was validated against field observations, achieving a success rate of 93% for SH-type flows and 85% for SC.

Abstract The long run-out debris flows caused by oversaturated soil systems during the aggravated monsoon period in the Western Ghats raise questions about the hill community’s future. Here, we report the catastrophic long run-out Wayanad debris flow that occurred on 30th July 2024, which resulted in 231 fatalities and 128 people missing, and caused widespread destruction to infrastructure. This involved a maximum flow height of 10.66 m and maximum flow velocity of 18.7 m/s, simulated using RApid Mass Movement Simulation.

We developed a rainfall threshold model with the objective of limiting the effects of uncertainties typically associated with them, such as a lack of robust landslide database, the selection of the contributing rain gauge, seasonal variations in rainfall patterns, and the effect of extreme rainfall conditions. With the aid of gauge-corrected satellite precipitation data and a landslide database compiled from various sources, separate rainfall thresholds were developed for two waves of the monsoon season in the Western Ghats, India. The daily vs. antecedent rainfall distributions for different scenarios of antecedent rainfall were analyzed for landslide occurrence. The different scenarios considered included 1, 2, 3, 5, 10-, 20-, 30- and 40-day antecedent rainfalls along with the monsoon antecedent defined as the cumulative rainfall from the start of the monsoon to the day prior to landslide occurrence, and the event antecedent defined as the cumulative rainfall from the start of a rainfall event to the day prior to landslide occurrence. A statistically defined critical value was used to define the thresholds for extreme rainfall conditions, while ordinary least squares and quantile regression models were compared to identify the best-fit model for the non-extreme rainfall threshold. Receiver Operating Characteristic (ROC) analysis was performed on all these models and the best model was chosen based on the efficiency values. The daily vs. monsoon antecedent threshold was the best model for the first monsoon wave, and the daily vs. event antecedent model was the best model for the second monsoon wave. A separate rainfall threshold was defined for the entire monsoon without subdivision into separate waves, and corresponding ROC statistics were compared with the former approach to analyze the efficacy of intra-seasonal variations in rainfall threshold development. The results suggest that cumulative rainfall makes a significant contribution towards landslide initiation and that intra-seasonal variations should be necessarily considered in rainfall threshold modeling.

OBJECTIVES:. Although clinicians may use methylene blue (MB) in refractory septic shock, the effect of MB on patient-important outcomes remains uncertain. We conducted a systematic review and meta-analysis to investigate the benefits and harms of MB administration in patients with septic shock. DATA SOURCES:. We searched six databases (including PubMed, Embase, and Medline) from inception to January 10, 2024. STUDY SELECTION:. We included randomized clinical trials (RCTs) of critically ill adults comparing MB with placebo or usual care without MB administration. DATA EXTRACTION:. Two reviewers performed screening, full-text review, and data extraction. We pooled data using a random-effects model, assessed the risk of bias using the modified Cochrane tool, and used Grading of Recommendations Assessment, Development, and Evaluation to rate certainty of effect estimates. DATA SYNTHESIS:. We included six RCTs (302 patients). Compared with placebo or no MB administration, MB may reduce short-term mortality (RR [risk ratio] 0.66 [95% CI, 0.47–0.94], low certainty) and hospital length of stay (mean difference [MD] –2.1 d [95% CI, –1.4 to –2.8], low certainty). MB may also reduce duration of vasopressors (MD –31.1 hr [95% CI, –16.5 to –45.6], low certainty), and increase mean arterial pressure at 6 hours (MD 10.2 mm Hg [95% CI, 6.1–14.2], low certainty) compared with no MB administration. The effect of MB on serum methemoglobin concentration was uncertain (MD 0.9% [95% CI, –0.2% to 2.0%], very low certainty). We did not find any differences in adverse events. CONCLUSIONS:. Among critically ill adults with septic shock, based on low-certainty evidence, MB may reduce short-term mortality, duration of vasopressors, and hospital length of stay, with no evidence of increased adverse events. Rigorous randomized trials evaluating the efficacy of MB in septic shock are needed. REGISTRATION:. Center for Open Science (https://osf.io/hpy4j).

Ground hazards are a significant problem in the global economy, costing millions of dollars in damage each year. Railroad tracks are vulnerable to ground hazards like flooding since they traverse multiple terrains with complex environmental factors and diverse human developments. Traditionally, flood-hazard assessments are generated using models like the Hydrological Engineering Center–River Analysis System (HEC-RAS). However, these maps are typically created for design flood events (10, 50, 100, 500 years) and are not available for any specific storm event, as they are not designed for individual flood predictions. Remotely sensed methods, on the other hand, offer precise flood extents only during the flooding, which means the actual flood extents cannot be determined beforehand. Railroad agencies need daily flood extent maps before rainfall events to manage and plan for the parts of the railroad network that will be impacted during each rainfall event. A new approach would involve using traditional flood-modeling layers and remotely sensed flood model outputs such as flood maps created using the Google Earth Engine. These new approaches will use machine-learning tools in flood prediction and extent mapping. This new approach will allow for determining the extent of flood for each rainfall event on a daily basis using rainfall forecast; therefore, flooding extents will be modeled before the actual flood, allowing railroad managers to plan for flood events pre-emptively. Two approaches were used: support vector machines and deep neural networks. Both methods were fine-tuned using grid-search cross-validation; the deep neural network model was chosen as the best model since it was computationally less expensive in training the model and had fewer type II errors or false negatives, which were the priorities for the flood modeling and would be suitable for developing the automated system for the entire railway corridor. The best deep neural network was then deployed and used to assess the extent of flooding for two floods in 2020 and 2022. The results indicate that the model accurately approximates the actual flooding extent and can predict flooding on a daily temporal basis using rainfall forecasts.

Abstract We employ path integral methods to calculate the Casimir energy and force densities in a chiral extension of QED. Manifestly gauge invariant perfect electromagnetic boundary conditions, a natural generalization of perfect electric and perfect magnetic conditions, are implemented directly in the action by the usage of auxiliary fields. The chiral properties of the vacuum are modelled using a background θ field, and we introduce techniques to efficiently calculate the path integral in this chiral medium. The flexibility of our method allows us to naturally obtain results for a variety of configurations, and where comparison is possible our results are in perfect agreement with existing literature. Among these are multiple situations where a repulsive Casimir force is possible.

We determine the Casimir energies and forces in a variety of potentially experimentally viable setups, consisting of parallel plates made of perfect electromagnetic conductors (PEMCs), which generalize perfect electric conductors (PECs) and perfect magnetic conductors (PMCs), and Weyl semimetals (WSMs). Where comparison is possible, our results agree with the Casimir forces calculated elsewhere in the literature, albeit with different methods. We find a multitude of known but also new cases where repulsive Casimir forces are in principle possible, but restricting the setup to PECs combined with the aforementioned WSM geometry, results in purely attractive Casimir forces.

In this article, which is based on data obtained from the annual AMPS projects of the SAARF, media trends in South Africa is commented on. It is observed that while the media use among the Coloureds, Whites and Asians is declining (all media except television), blacks show an overall increase in media use. This trend is observed in daily newspaper reading (although there was a slight decrease for blacks in the last year), radio listening, television viewing and cinema attendance, even though it is slight in instances. These trends suggest that reasons have to be determined for the increasing alienation between die media and sectors of the population. In a country where most media are primarily aimed at the whites, these trends also stress the need for media which will serve the interests of blacks.

Die chronologiese verloop van wetenskaplike perspektiewe oor die aard van die kommunikasieproses verleen prominensie aan die gebruike- en beloningsbenadering. Van die aanvanklike veronderstellings aangaande die mag van die massamedia Is gevorder tot die erkenning van die aktiewe Jol van die bestemming en beperkings op die mag van die media. In hierdie artikel word 'n oorsig gegee van die onstaan sowel as problematiek van die gebruike- en belon- Ingsbenadering in die Kommunikasiekun- de. Die bydrae van hierdie teorie vir me dlanavorsing word beklemtoon.

Rapid slope instabilities (i.e., rockfalls) involving highway networks in mountainous areas pose a threat to facilities, settlements and life, thus representing a challenge for asset management plans. To identify different morphological expressions of degradation processes that lead to rock mass destabilization, we combined satellite and uncrewed aircraft system (UAS)-based products over two study sites along the State Highway 133 sector near Paonia Reservoir, Colorado (USA). Along with a PS-InSAR analysis covering the 2017–2021 interval, a high-resolution dataset composed of optical, thermal and multi-spectral imagery was systematically acquired during two UAS surveys in September 2021 and June 2022. After a pre-processing step including georeferencing and orthorectification, the final products were processed through object-based multispectral classification and change detection analysis for highlighting moisture or lithological variations and for identifying areas more susceptible to deterioration and detachments at the small and micro-scale. The PS-InSAR analysis, on the other hand, provided multi-temporal information at the catchment scale and assisted in understanding the large-scale morpho-evolution of the displacements. This synergic combination offered a multiscale perspective of the superimposed imprints of denudation and mass-wasting processes occurring on the study site, leading to the detection of evidence and/or early precursors of rock collapses, and effectively supporting asset management maintenance practices.

Terrain traversability is critical for developing Go/No-Go maps for ground vehicles, which significantly impact a mission’s success. To predict the mobility of terrain, one must understand the soil characteristics. In-situ measurements performed in the field are the current method of collecting this information, which is time-consuming, costly, and can be lethal for military operations. This paper investigates an alternative approach using thermal, multispectral, and hyperspectral remote sensing from an unmanned aerial vehicle (UAV) platform. Remotely sensed data combined with machine learning (linear, ridge, lasso, partial least squares (PLS), support vector machines (SVM), and k nearest neighbors (KNN)) and deep learning (multi-layer perceptron (MLP) and convolutional neural network (CNN)) are used to perform a comparative study to estimate the soil properties, such as the soil moisture and terrain strength, used to generate prediction maps of these terrain characteristics. This study found that deep learning outperformed machine learning. Specifically, a multi-layer perceptron performed the best for predicting the percent moisture content (R2/RMSE = 0.97/1.55) and the soil strength (in PSI), as measured by a cone penetrometer for the averaged 0–6” (CP06) (R2/RMSE = 0.95/67) and 0–12” depth (CP12) (R2/RMSE = 0.92/94). A Polaris MRZR vehicle was used to test the application of these prediction maps for mobility purposes, and correlations were observed between the CP06 and the rear wheel slip and the CP12 and the vehicle speed. Thus, this study demonstrates the potential of a more rapid, cost-efficient, and safer approach to predict terrain properties for mobility mapping using remote sensing data with machine and deep learning algorithms.

BackgroundIt has been suggested that Bayesian dosing apps can assist in the therapeutic drug monitoring of patients receiving vancomycin. Unfortunately, Bayesian dosing tools are often unaffordable to resource-limited hospitals. Our aim was to improve vancomycin dosing in adults. We created a free and open-source dose adjustment app, VancoCalc, which uses Bayesian inference to aid clinicians in dosing and monitoring of vancomycin. ObjectiveThe aim of this paper is to describe the design, development, usability, and evaluation of a free open-source Bayesian vancomycin dosing app, VancoCalc. MethodsThe app build and model fitting process were described. Previously published pharmacokinetic models were used as priors. The ability of the app to predict vancomycin concentrations was performed using a small data set comprising of 52 patients, aged 18 years and over, who received at least 1 dose of intravenous vancomycin and had at least 2 vancomycin concentrations drawn between July 2018 and January 2021 at Lakeridge Health Corporation Ontario, Canada. With these estimated and actual concentrations, median prediction error (bias), median absolute error (accuracy), and root mean square error (precision) were calculated to evaluate the accuracy of the Bayesian estimated pharmacokinetic parameters. ResultsA total of 52 unique patients’ initial vancomycin concentrations were used to predict subsequent concentration; 104 total vancomycin concentrations were assessed. The median prediction error was –0.600 ug/mL (IQR –3.06, 2.95), the median absolute error was 3.05 ug/mL (IQR 1.44, 4.50), and the root mean square error was 5.34. ConclusionsWe described a free, open-source Bayesian vancomycin dosing calculator based on revisions of currently available calculators. Based on this small retrospective preliminary sample of patients, the app offers reasonable accuracy and bias, which may be used in everyday practice. By offering this free, open-source app, further prospective validation could be implemented in the near future.

This position paper looks at the 1964 AIA -ACSA Teacher conference, one that offers us a window into the current anxieties of architectural history survey courses. The conference was organized at a time when PhD programs in Architectural History and Theory were emerging, with accompanying mid-century notions of disciplines with clear boundaries, objects of study and hierarchy of experts. The questions that were being asked were fundamental: What is Architectural History? What are its contents? How should it be taught? Who is an Architectural Historian? However, a closer look beneath the masculine bravado of the conference reveals many of the same symptoms that persist today: questions of ‘diversity’ of content, anxiety to be ‘relevant’ to students in professional programs and a tendency to leave unquestioned the tradition of ‘designo’. This paper journeys through these anxieties with the hope of bringing some of those in play today into sharper focus. Perhaps, it concludes, the work of architectural history might be what Spivak termed as a project of “Planetarity”, involving not merely a change in epistemological methods but an undoing of the social order of architectural history.

Besides the dangers of an actively burning wildfire, a plethora of other hazardous consequences can occur afterwards. Debris flows are among the most hazardous of these, being known to cause fatalities and extensive damage to infrastructure. Although debris flows are not exclusive to fire affected areas, a wildfire can increase a location’s susceptibility by stripping its protective covers like vegetation and introducing destabilizing factors such as ash filling soil pores to increase runoff potential. Due to the associated dangers, researchers are developing statistical models to isolate susceptible locations. Existing models predominantly employ the logistic regression algorithm; however, previous studies have shown that the relationship between the predictors and response is likely better predicted using nonlinear modeling. We therefore propose the use of nonlinear C5.0 decision tree algorithm, a simple yet robust algorithm that uses inductive inference for categorical data modelling. It employs a tree-like decision making system that makes conditional statements to split data into homogeneous classes. Our results showed the C5.0 approach to produce stable and higher validation metrics in comparison to the logistic regression. A sensitivity of 81% and specificity of 78% depicts improved predictive capability and gives credence to the hypothesis that data relationships are likely nonlinear.

In this study, we proposed an automated lithological mapping approach by using spectral enhancement techniques and Machine Learning Algorithms (MLAs) using Airborne Visible Infrared Imaging Spectroradiometer-Next Generation (AVIRIS-NG) hyperspectral data in the greenstone belt of the Hutti area, India. We integrated spectral enhancement techniques such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA) transformation and different MLAs for an accurate mapping of rock types. A conjugate utilization of conventional geological map and spectral enhancement products derived from ASTER data were used for the preparation of a high-resolution reference lithology map. Feature selection and extraction methods were applied on the AVIRIS-NG data to derive different input dataset such as (a) all spectral bands, (b) shortwave infrared bands, (c) Joint Mutual Information Maximization (JMIM) based optimum bands, and (d) optimum bands using PCA, to choose optimum input dataset for automated lithological mapping. The comparative analysis of different MLAs shows that the Support Vector Machine (SVM) outperforms other Machine Learning (ML) models. The SVM achieved an Overall Accuracy (OA) and Kappa Coefficient (k) of 85.48% and 0.83, respectively, using JMIM based optimum bands. The JMIM based optimum bands were more suitable than other input datasets to classify most of the lithological units (i.e. metabasalt, amphibolite, granite, acidic intrusive and migmatite) within the study area . The sensitivity analysis performed in this study illustrates that the SVM is less sensitive to the number of samples and mislabeling in the model training than other MLAs. The obtained high-resolution classified map with accurate litho-contacts of amphibolite, metabasalt, and granite can be coupled with an alteration map of the area for targeting the potential zone of gold mineralization.

Soil strength characterization is essential for any problem that deals with geomechanics, including terramechanics/terrain mobility. Presently, the primary method of collecting soil strength parameters through in situ measurements but sending a team of people out to a site to collect data this has significant cost implications and accessing the location with the necessary equipment can be difficult. Remote sensing provides an alternate approach to in situ measurements. In this lab study, we compare the use of Apparent Thermal Inertia (ATI) against a GeoGauge for the direct testing of soil stiffness. ATI correlates with stiffness, so it allows one to predict the soil strength remotely using machine-learning algorithms. The best performing regression algorithm among the ones tested with different predictor variable combinations was found to be KNN with an R2 of 0.824 and a RMSE of 0.141. This study demonstrates the potential for using remote sensing to acquire thermal images that characterize terrain strength for mobility utilizing different machine-learning algorithms.

Degrading permafrost conditions around the world are posing stability issues for infrastructure constructed on them. Railway lines have exceptionally low tolerances for differential settlements associated with permafrost degradation due to the potential for train derailments. Railway owners with tracks in permafrost regions therefore make it a priority to identify potential settlement locations so that proper maintenance or embankment stabilization measures can be applied to ensure smooth and safe operations. The extensive discontinuous permafrost zone along the Hudson Bay Railway (HBR) in Northern Manitoba, Canada, has been experiencing accelerated deterioration, resulting in differential settlements that necessitate continuous annual maintenance to avoid slow orders and operational interruptions. This paper seeks to characterize the different permafrost degradation susceptibilities present at the study site. Track geometry exceptions were compared against remotely sensed vegetation indices to establish a relationship between track quality and vegetation density. This relationship was used as a proxy for subsurface condition verified by electrical resistivity tomography. The established relationship was then used to develop a three-level degradation susceptibility chart to indicate low, moderate and high susceptibility regions. The defined susceptibility regions can be used to better allocate the limited maintenance resources and also help inform potentially long-term stabilization measures for the severely affected sections.

Soil gradation is an important characteristic for soil mechanics. Traditionally soil gradation is performed by sieve analysis using a sample from the field. In this research, we are interested in the application of hyperspectral remote sensing to characterize soil gradation. The specific objective of this work is to explore the application of hyperspectral remote sensing to be used as an alternative to traditional soil gradation estimation. The advantage of such an approach is that it would provide the soil gradation without having to obtain a field sample. This work will examine five different soil types from the Keweenaw Research Center within a laboratory-controlled environment for testing. Our study demonstrates a correlation between hyperspectral data, the percent gravel and sand composition of the soil. Using this correlation, one can predict the percent gravel and sand within a soil and, in turn, calculate the remaining percent of fine particles. This information can be vital to help identify the soil type, soil strength, permeability/hydraulic conductivity, and other properties that are correlated to the gradation of the soil.

Velocity dictates the destructive potential of a landslide. A combination of synthetic aperture radar (SAR), optical, and GPS data were used to maximize spatial and temporal coverage to monitor continuously-moving portions of the Portuguese Bend landslide complex on the Palos Verdes Peninsula in Southern California. Forty SAR images from the COSMO-SkyMed satellite, acquired between 19 July 2012 and 27 September 2014, were processed using Persistent Scatterer Interferometry (PSI). Eight optical images from the WorldView-2 satellite, acquired between 20 February 2011 and 16 February 2016, were processed using the Co-registration of Optically Sensed Images and Correlation (COSI-Corr) technique. Displacement measurements were taken at GPS monuments between September 2007 and May 2017. Incremental and average deformations across the landslide complex were measured using all three techniques. Velocity measured within the landslide complex ranges from slow (> 1.6 m/year) to extremely slow (< 16 mm/year). COSI-Corr and GPS provide detailed coverage of m/year-scale deformation while PSI can measure extremely slow deformation rates (mm/year-scale), which COSI-Corr and GPS cannot do reliably. This case study demonstrates the applicability of SAR, optical, and GPS data synthesis as a complimentary approach to repeat field monitoring and mapping to changes in landslide activity through time.

Structure from Motion (SfM)/Photogrammetry is a powerful mapping tool in extracting three-dimensional (3D) models from photographs. This method has been applied to a range of applications, including monitoring of infrastructure systems. This technique could potentially become a substitute, or at least a complement, for costlier approaches such as laser scanning for infrastructure monitoring. This study expands on previous investigations, which utilize photogrammetry point cloud data to measure failure mode behavior of a retaining wall model, emphasizing further robust spatial testing. In this study, a comparison of two commonly used photogrammetry software packages was implemented to assess the computing performance of the method and the significance of control points in this approach. The impact of control point selection, as part of the photogrammetric modeling processes, was also evaluated. Comparisons between the two software tools reveal similar performances in capturing quantitative changes of a retaining wall structure. Results also demonstrate that increasing the number of control points above a certain number does not, necessarily, increase 3D modeling accuracies, but, in some cases, their spatial distribution can be more critical. Furthermore, errors in model reproducibility, when compared with total station measurements, were found to be spatially correlated with the arrangement of control points.

Transportation infrastructure is critical for the advancement of society. Bridges are vital for an efficient transportation network. Bridges across the world undergo variable deformation/displacement due to the Earth’s dynamic processes. This displacement is caused by ground motion, which occurs from many natural and anthropogenic events. Events causing deformation include temperature fluctuation, subsidence, landslides, earthquakes, water/sea level variation, subsurface resource extraction, etc. Continual deformation may cause bridge failure, putting civilians at risk, if not managed properly. Monitoring bridge displacement, large and small, provides evidence of the state and health of the bridge. Traditionally, bridge monitoring has been executed through on-site surveys. Although this method of bridge monitoring is systematic and successful, it is not the most efficient and cost-effective. Through technological advances, satellite-based Persistent Scatterer Interferometry (PSI) and Geographic Information Systems (GIS) have provided a system for analyzing ground deformation over time. This method is applied to distinguish bridges that are more at risk than others by generating models that display the displacement at various locations along each bridge. A bridge’s health and its potential risk can be estimated upon analysis of measured displacement rates. In return, this process of monitoring bridges can be done at much faster rates; saving time, money and resources. PSI data covering Oxnard, California, revealed both bridge displacement and regional ground displacement. Although each bridge maintained different patterns of displacement, many of the bridges within the Oxnard area displayed an overall downward movement matching regional subsidence trends observed in the area. Patterns in displacement-time series plots provide evidence for two types of deformation mechanisms. Long-term downward movements correlate with the relatively large regional subsidence observed using PSI in Oxnard. Thermal dilation from seasonal temperature changes may cause short-term variabilities unique to each bridge. Overall, it may be said that linking geologic, weather, and groundwater patterns with bridge displacement has shown promise for monitoring transportation infrastructure and more importantly differentiating between regional subsidence and site-specific displacements.

Retaining walls are critical geotechnical assets and their performance needs to be monitored in accordance to transportation asset management principles. Current practices for retaining wall monitoring consist mostly of qualitative approaches that provide limited engineering information or the methods include traditional geodetic surveying, which may provide high accuracy and reliability, but is costly and time-consuming. This study focuses on evaluating failure modes of a 2.43 m × 2.43 m retaining wall model using three-dimensional (3D) photogrammetry as a cost-effective quantitative alternative for retaining wall monitoring. As a remote sensing technique, photogrammetry integrates images collected from a camera and creates a 3D model from the measured data points commonly referred to as a point cloud. The results from this photogrammetric approach were compared to ground control points surveyed with a total station. The analysis indicates that the accuracy of the displacement measurements between the traditional total station survey and photogrammetry were within 1–3 cm. The results are encouraging for the adoption of photogrammetry as a cost-effective monitoring tool for the observation of spatial changes and failure modes for retaining wall condition assessment.

Automated classification of earthquake damage in remotely-sensed imagery using machine learning techniques depends on training data, or data examples that are labeled correctly by a human expert as containing damage or not. Mislabeled training data are a major source of classifier error due to the use of imprecise digital labeling tools and crowdsourced volunteers who are not adequately trained on or invested in the task. The spatial nature of remote sensing classification leads to the consistent mislabeling of classes that occur in close proximity to rubble, which is a major byproduct of earthquake damage in urban areas. In this study, we look at how mislabeled training data, or label noise, impact the quality of rubble classifiers operating on high-resolution remotely-sensed images. We first study how label noise dependent on geospatial proximity, or geospatial label noise, compares to standard random noise. Our study shows that classifiers that are robust to random noise are more susceptible to geospatial label noise. We then compare the effects of label noise on both pixel- and object-based remote sensing classification paradigms. While object-based classifiers are known to outperform their pixel-based counterparts, this study demonstrates that they are more susceptible to geospatial label noise. We also introduce a new labeling tool to enhance precision and image coverage. This work has important implications for the Sendai framework as autonomous damage classification will ensure rapid disaster assessment and contribute to the minimization of disaster risk.

Pacaya volcano is a persistently active basaltic cone complex located in the Central American Volcanic Arc in Guatemala. In May of 2010, violent Volcanic Explosivity Index-3 (VEI-3) eruptions caused significant topographic changes to the edifice, including a linear collapse feature 600 m long originating from the summit, the dispersion of ~20 cm of tephra and ash on the cone, the emplacement of a 5.4 km long lava flow, and ~3 m of co-eruptive movement of the southwest flank. For this study, Interferometric Synthetic Aperture Radar (InSAR) images (interferograms) processed from both spaceborne Advanced Land Observing Satellite-1 (ALOS-1) and aerial Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data acquired between 31 May 2010 and 10 April 2014 were used to measure post-eruptive deformation events. Interferograms suggest three distinct deformation processes after the May 2010 eruptions, including: (1) subsidence of the area involved in the co-eruptive slope movement; (2) localized deformation near the summit; and (3) emplacement and subsequent subsidence of about a 5.4 km lava flow. The detection of several different geophysical signals emphasizes the utility of measuring volcanic deformation using remote sensing techniques with broad spatial coverage. Additionally, the high spatial resolution of UAVSAR has proven to be an excellent compliment to satellite data, particularly for constraining motion components. Measuring the rapid initiation and cessation of flank instability, followed by stabilization and subsequent influence on eruptive features, provides a rare glimpse into volcanic slope stability processes. Observing these and other deformation events contributes both to hazard assessment at Pacaya and to the study of the stability of stratovolcanoes.

Geometric morphometrics is used in the biological sciences to quantify morphological traits. However, the need for manual landmark placement hampers scalability, which is both time-consuming, labor-intensive, and open to human error. The selected landmarks embody a specific hypothesis regarding the critical geometry relevant to the biological question. Any adjustment to this hypothesis necessitates acquiring a new set of landmarks or revising them significantly, which can be impractical for large datasets. There is a pressing need for more efficient and flexible methods for landmark placement that can adapt to different hypotheses without requiring extensive human effort. This study investigates the precision and accuracy of landmarks derived from functional correspondences obtained through the functional map framework of geometry processing. We utilize a deep functional map network to learn shape descriptors, which enable us to achieve functional map-based and point-to-point correspondences between specimens in our dataset. Our methodology involves automating the landmarking process by interrogating these maps to identify corresponding landmarks, using manually placed landmarks from the entire dataset as a reference. We apply our method to a dataset of rodent mandibles and compare its performance to MALPACA's, a standard tool for automatic landmark placement. Our model demonstrates a speed improvement compared to MALPACA while maintaining a competitive level of accuracy. Although MALPACA typically shows the lowest RMSE, our models perform comparably well, particularly with smaller training datasets, indicating strong generalizability. Visual assessments confirm the precision of our automated landmark placements, with deviations consistently falling within an acceptable range for MALPACA estimates. Our results underscore the potential of unsupervised learning models in anatomical landmark placement, presenting a practical and efficient alternative to traditional methods. Our approach saves significant time and effort and provides the flexibility to adapt to different hypotheses about critical geometrical features without the need for manual re-acquisition of landmarks. This advancement can significantly enhance the scalability and applicability of geometric morphometrics, making it more feasible for large datasets and diverse biological studies., (© 2025 Anatomical Society.)

Geometric morphometrics is widely employed across the biological sciences for the quantification of morphological traits. However, the scalability of these methods to large datasets is hampered by the requisite placement of landmarks, which can be laborious and time consuming if done manually. Additionally, the selected landmarks embody a particular hypothesis regarding the critical geometry pertinent to the biological inquiry at hand. Modifying this hypothesis lacks flexibility, necessitating the acquisition of an entirely new set of landmarks on the entire dataset to reflect any theoretical adjustments. In our research, we investigate the precision and accuracy of landmarks derived from the comprehensive set of functional correspondences acquired through the functional map framework of geometry processing. We use a deep functional map network to learn shape descriptors that effectively yield functional map-based and point-to-point correspondences between the specimens in our dataset. We then interrogate these maps to identify corresponding landmarks given manually placed landmarks from the entire dataset. We assess our method by automating the landmarking process on a dataset comprising mandibles from various rodent species, comparing its efficacy against MALPACA, a cutting-edge technique for automatic landmark placement. Compared to MALPACA, our model is notably faster and maintains competitive accuracy. The Root Mean Square Error (RMSE) analysis reveals that while MALPACA generally exhibits the lowest RMSE, our models perform comparably, especially with smaller training datasets, suggesting strong generalizability. Visual evaluations confirm the precision of our landmark placements, with deviations remaining within an acceptable range. These findings underscore the potential of unsupervised learning models in anatomical landmark placement, providing a viable and efficient alternative to traditional methods.

The methods of geometric morphometrics are commonly used to quantify morphology in a broad range of biological sciences. The application of these methods to large datasets is constrained by manual landmark placement limiting the number of landmarks and introducing observer bias. To move the field forward, we need to automate morphological phenotyping in ways that capture comprehensive representations of morphological variation with minimal observer bias. Here, we present Morphological Variation Quantifier (morphVQ), a shape analysis pipeline for quantifying, analyzing, and exploring shape variation in the functional domain. morphVQ uses descriptor learning to estimate the functional correspondence between whole triangular meshes in lieu of landmark configurations. With functional maps between pairs of specimens in a dataset we can analyze and explore shape variation. morphVQ uses Consistent ZoomOut refinement to improve these functional maps and produce a new representation of shape variation, area-based and conformal (angular) latent shape space differences (LSSDs). We compare this new representation of shape variation to shape variables obtained via manual digitization and auto3DGM, an existing approach to automated morphological phenotyping. We find that LSSDs compare favorably to modern 3DGM and auto3DGM while being more computationally efficient. By characterizing whole surfaces, our method incorporates more morphological detail in shape analysis. We can classify known biological groupings, such as Genus affiliation with comparable accuracy. The shape spaces produced by our method are similar to those produced by modern 3DGM and to auto3DGM, and distinctiveness functions derived from LSSDs show us how shape variation differs between groups. morphVQ can capture shape in an automated fashion while avoiding the limitations of manually digitized landmarks, and thus represents a novel and computationally efficient addition to the geometric morphometrics toolkit., Competing Interests: All authors declare that they have no conflicts of interest., (Copyright: © 2023 Thomas et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Introduction: Total Knee replacement (TKR) is performed to relieve pain and restore both the mechanical axis and joint line whenever indicated. Most of prostheses being used for TKR were manufactured using dimensions from Caucasians' measurements. This study documented the dimensions of distal femora of Nigerians and correlated the dimensions with different TKR prostheses., Materials and Methods: Fifty-six matured femora were measured. Dimensions of distal femora from other regions were retrieved from published articles. The dimensions of TKR prosthesis were extracted from product monographs. Analyses were done with Microsoft excel 2010 (Microsoft Corporation, Redmond, Washington, United States) and STATA version 13 (StataCorp, Texas. USA). Statistical significance was set at p ≤ 0.05., Results: The average Mediolateral dimension was 79.3 ± 4.4 mm. The anterioposterior dimensions of the medial and lateral condyles were 63.7 ± 3.6 mm and 64.9 ± 3.3 mm respectively. There were no significant differences between the left and right distal femur. The average aspect ratio calculated was1.23 ± 0.05. There was a mismatch of aspect ratio of the distal femora and those of the prostheses. Equations that can be useful both clinically and in forensic medicine were generated., Conclusion: This study has provided measurements that can be considered when the designing of a suitable femoral component of total knee prosthesis for Nigerians. This study also provided equations that can be used to estimate the dimensions of the medial and the lateral condyles and femoral length from parts of distal femur in forensic medicine., Funding: Self-funded., Competing Interests: Conflict of interest: None declared, (Copyright © The Author(s).)

Species from the section Miltantha are reviewed in the context of their contained alkaloids. Thebaine was isolated as the major alkaloid from a sample of P. fugax together with narcotine, pronuciferine, alpinigenine, O-ethylalpinigenine, amurensinine, N-methyl-crotonosine, armepavine, isocorydine and salutaridine as minor alkaloids. A second sample of P. fugax contained glaucamine and glaudine as major alkaloids with rhoeadine, oreogenine, oreodine and O-ethyl-glaucamine as minor alkaloids. Pronuciferine and armepavine were isolated as the major alkaloids from a sample of P. tauricola with narcotine, roemerine, nuciferine, nantenine and protopine as minor alkaloids. Another sample of P. tauricola yielded pronuciferine and mecambrine as major alkaloids with armepavine, lirinidine, thebaine and cryptopine as minor alkaloids. A sample of P. armeniacum contained rhoeadine and rhoeagenine as major alkaloids with lirinidine, cryptopine, glaudine and O-ethylrhoeagenine as minor alkaloids. Some 25 alkaloids representing 9 different alkaloidal-types were obtained from extracts of the three Miltantha species. The results show that at least three different chemical races of P. fugax and P. armeniacum exist in which either 1-benzyltetrahydroisoquinoline-, proaporphine-, aporphine- or morphinane- or rhoeadine- types are the major alkaloids. There are at least two different chemical strains of P. tauricola which contain either 1-beuzyltetrahydroisoquinoline-, proaporphine-, aporphine- or rhoeadine- types as the major alkaloids.