3v Geomatics (Canada)

Satellite Interferometric Synthetic Aperture Radar (InSAR) is a monitoring system using radar images to measure displacement of the ground and structures. While InSAR can be performed using ground-based systems, this chapter is focused on satellite-based systems because they are more applicable to network-wide pipeline monitoring. Satellite InSAR (hereafter simply referred to as InSAR) can cover wide areas and requires no specialized equipment on the ground. InSAR can complement other monitoring methods or even function as the sole monitoring method for more remote areas. In addition to identifying and measuring displacement associated with slow-moving landslides or subsidence, the amplitude and/or coherence information can also be used to detect hazards and other changes along a pipeline network.

As climate-driven disasters such as wildfires become more frequent and unpredictable, there is an increasing need for sensing systems that can adapt to dynamic, uncertain environments. This paper presents the Universal Sensor, a cognitive-inspired, software-defined architecture grounded in predictive processing. The system operates as a continuous loop of prediction, sensing, comparison, and control, enabling real-time adaptation to changing conditions. It combines predictive modeling, adaptive multi-sensor data collection, and sensor fusion to interpret context and respond efficiently. Feedback-driven control and a modular policy memory support proactive resource management and lay the groundwork for future analogical transfer. We evaluate the system using a wildfire simulation based on data from the 2016 Fort McMurray event, demonstrating improved adaptability and resource use compared to static sensing strategies. In an 8-day simulation with 2,000 sensors, Universal Sensors reduced transmissions, energy use, and data volume by 36%, while preserving over 90% of critical hotspot events. These findings illustrate how predictive adaptation can enable efficient sensing at scale without sacrificing critical event coverage.

ABSTRACT Introduction This study investigates the relationships among mobility, pain, and socket comfort for people with lower-limb amputations who use prostheses over specific treatment milestones. Methods This is a retrospective chart analysis from January 1, 2015, to July 15, 2021. Outcome measures included are the Socket Comfort Score, Pain Scale, 2-Minute Walk Test, and PLUS-M (Prosthetic Limb Users Survey of Mobility). Data were collected at 4 milestones: Baseline and Discharge from Rehabilitation for those receiving their first prosthesis in rehabilitation and Initial Evaluation and Definitive Fitting for experienced prosthesis users receiving a replacement socket. Demographic data were analyzed using descriptive statistics, and the strength of the correlations was determined using Pearson’s correlations. Results Measures were included from 59 individuals in rehabilitation and 165 individuals receiving a replacement socket. The sample was in general older males with transtibial amputations due to a variety of etiologies. The Pain Scale was negatively correlated with the Socket Comfort Score (−0.49 < r < −0.60), and the PLUS-M was correlated with the 2-minute walk test (0.41 < r < 0.61) at all milestones. Additionally, there were positive correlations between the PLUS-M and Socket Comfort Score (0.30 < r < 0.32), and negative correlations between the PLUS-M and Pain Scale (−0.46 < r < −0.32) for those receiving a replacement socket. Conclusions The relationship among comfort, pain, and mobility changes over the treatment timeline. There is evidence that, by addressing comfort and pain for those with experience using a prosthesis, clinicians can support the goal of increasing self-reported functional mobility. Clinical Relevance This study provides novel information about the relationships among pain, comfort, and mobility and the importance of considering the impact of treatment milestones when taking outcome measures and providing interpretative values as scores and relationships differ over the course of treatment.

If my love is the circle's circumference And your life is the diameter, then My missing you is π Which starts with the whole number 3, including every day Every night, & every minute Between day and night After the separating (decimal) point, it is As indefinite as the neural network of the universe Yuan Changming,

The Stelidium group is readily distinguished from all other members of the subgenus Stelis Panzer, 1806 by the combination of small body size ( 6 mm), pale maculations on the head adjacent to the inner margins of the compound eyes and laterally on the vertex in both sexes, and females with sternum 6 extended beyond tergum 6, the former with the dorsal lip trowel-shaped with the apex broadly rounded or subtruncate to more narrowly pointed. This monophyletic clade, which is endemic to North America, currently consists of members previously placed into two species groups: the permaculata group containing S. anasazi Parker & Griswold, 2013, S. ashmeadiellae Timberlake, 1941, S. permaculata Cockerell, 1898, and S. robertsoni Timberlake, 1941, and the palmarum group containing S. broemelingi Parker & Griswold, 2013, S. elongativentris Parker, 1987, and S. palmarum Timberlake, 1941; two additional species, S. herberti (Cockerell, 1916) from Mexico, and S. nyssonoides (Brues, 1903) from Texas, United States, have not been definitively placed in either species group. Two new species are herein described, one from southcentral British Columbia, Canada, the other from New Mexico, United States. A preliminary molecular phylogeny places both new species in the permaculata species group. In addition, S. herberti is also placed within the permaculata species group based on morphological similarity, sharing the multi-spotted maculation pattern on the terga. Based on molecular affinity, S. broemelingi also belongs to the permaculata species group. Because no type specimen for S. nyssonoides is seemingly available for examination, it is hereby considered nomen dubium until the specimen is found and its taxonomic status clarified in relation to the more recently described species in the permaculata species group. A key to females and diagnoses are provided for all known taxa.

<p>LiDAR (Light Detection and Ranging) is a technology that provides three-dimensional point cloud data with high spatial accuracy. It is increasingly used in various applications and disciplines, including engineering. Along with other capabilities, LiDAR systems are able to record reflected backscattered energy as intensity data and measured distances to targets as range data. As the laser signal wavelength that operates in LiDAR sensors is typically in the near-infrared (NIR) spectrum, high spectral reflectance separability can be observed and a number of different materials distinguished. These capabilities have motivated researchers to study the applicability of using LiDAR range and intensity data for pavement crack extraction.</p> <p>The main goal of the present research is to examine the potential use of LiDAR range and intensity data for automated pavement crack detection. The study explores the following: a) crack detection using the two image classification logics of pixel-based and object-based image classification; b) crack detection using the Maximum Likelihood Classifier (MLC), the Random Trees Classifier (RTC), and the Support Vector Machine (SVM) classifier; c) crack detection by conducting image classification of four scenarios of multi-layer images generated from LiDAR data; and d) crack detection using LiDAR data with different point spacing.</p> <p>The experimental results indicate that the use of LiDAR data independently is effective for pavement crack detection. In addition, the results show that object-based image classification logic optimizes classification accuracy and crack detection results, and that employing SVM enhances the classification process and improves crack detection accuracy. It is also observed that using range data separately produces high accuracy for the crack detection results while the accuracy is slightly reduced when intensity data are combined with range data compared with using range data only. Furthermore, in the classification process, adding slope and aspect layers to range and intensity data decreases the accuracy of crack detection results, while increasing LiDAR data point spacing reduces crack extraction accuracy.</p>