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Abstract Forecasting floods encompasses significant complexity due to the nonlinear nature of hydrological systems, which involve intricate interactions among precipitation, landscapes, river systems, and hydrological networks. Recent efforts in hydrology have aimed at predicting water flow, floods, and quality, yet most methodologies overlook the influence of adjacent areas and lack advanced visualization for water level assessment. Our contribution is two-fold: firstly, we introduce a graph neural network model (LocalFLoodNet) equipped with a graph learning module to capture the interconnections of water systems and the connectivity between stations to predict future water levels. Secondly, we develop a simulation prototype offering visual insights for decision-making in disaster prevention and policy-making. This prototype visualizes predicted water levels and facilitates data analysis using decades of historical information. Focusing on the Greater Montreal Area (GMA), particularly Terrebonne, Quebec, Canada, we apply LocalFLoodNet and prototype to demonstrate a comprehensive method for assessing flood impacts. By utilizing a digital twin of Terrebonne, our simulation tool allows users to interactively modify the landscape and simulate various flood scenarios, thereby providing valuable insights into preventive strategies. This research aims to enhance water level prediction and evaluation of preventive measures, setting a benchmark for similar applications across different geographic areas.

Introduction: Intraductal papilloma is an exceptionally rare condition in the pediatric population, particularly among males in infancy and early childhood, typically most prevalent among women aged between 35 and 55 years presenting as sanguineous or serosanguineous nipple discharge and a discrete breast mass. Despite its historical classification as benign, emerging research indicates a potential for malignant transformation, prompting a reevaluation of its clinical significance. Case presentation: We present the case of a healthy 8-month-old male infant with a right subareolar breast swelling that appeared non tender, well-circumscribed, smooth, mobile, rubbery, without any associated nipple discharge or regional lymphadenopathy, clinically measuring 10 × 10 mm in diameter, and exhibited a bluish green hue with no associated changes in overlying skin. Ultrasound confirmed presence of a right retro areolar breast mass, characterized as a multiseptated complex cyst-like structure with tubular anechoic formations within, measuring 21 x 20 × 6 mm. The findings were suggestive of possible duct ectasia classified as Bi-Rads Category 3 with a benign probability. Due to parental concern for increase in size over 2 months, the mass was resected under general anesthesia through a subareolar incision. Microscopic analysis confirmed dilated cystic ducts and an intraductal papilloma with calcifications. The patient recovered well from the operation, and follow-up at nine months, he has experienced no recurrences. Conclusion: Although rare, this case underscores the importance of including intraductal papilloma in the differential diagnosis of breast nodules in male infants.

The application of large field-of-view (FoV) cameras equipped with fish-eye lenses brings notable advantages to various real-world computer vision applications, including autonomous driving. While deep learning has proven successful in conventional computer vision applications using regular perspective images, its potential in fish-eye camera contexts remains largely unexplored due to limited datasets for fully supervised learning. Semi-supervised learning comes as a potential solution to manage this challenge. In this study, we explore and benchmark two popular semi-supervised methods from the perspective image domain for fish-eye image segmentation. We further introduce FishSegSSL, a novel fish-eye image segmentation framework featuring three semi-supervised components: pseudo-label filtering, dynamic confidence thresholding, and robust strong augmentation. Evaluation on the WoodScape dataset, collected from vehicle-mounted fish-eye cameras, demonstrates that our proposed method enhances the model’s performance by up to 10.49% over fully supervised methods using the same amount of labeled data. Our method also improves the existing image segmentation methods by 2.34%. To the best of our knowledge, this is the first work on semi-supervised semantic segmentation on fish-eye images. Additionally, we conduct a comprehensive ablation study and sensitivity analysis to showcase the efficacy of each proposed method in this research.

Introduction: The COVID-19 pandemic has had wide economic, social, and health impacts, and has disproportionately affected individuals who were already vulnerable. Individuals who use opioids have dealt with evolving public health measures and disruptions while also dealing with the ongoing opioid epidemic. Opioid-related mortalities in Canada increased throughout the COVID-19 pandemic, but it is unclear to what extent public health measures and the progression of the pandemic contributed to opioid-related harms.Methods: To address this gap, we used emergency room (ER) visits recorded in the National Ambulatory Care Reporting System (NACRS) between 1 April 2017, and 31 December 2021, to investigate trends of opioid-related harms throughout the pandemic. This study also included semi-structured interviews with service providers in the field of opioid use treatment, to help contextualize the trends seen in ER visits and offer perspectives on how opioid use and services have changed throughout the COVID-19 pandemic.Results: Overall, the number of hospitalizations related to an opioid use disorder (OUD) decreased with progressing waves of the pandemic and with increasing severity of public health measures in Ontario. The rate of hospitalizations related to opioid poisonings (e.g., central nervous system and respiratory system depression caused by opioids) significantly increased with the progressing waves of the pandemic, as well as with increasing severity of public health measures in Ontario.Discussion: The increase in opioid-related poisonings is reflected in the existing literature whereas the decrease in OUDs is not. Moreover, the increase in opioid-related poisonings aligns with the observations of service providers, whereas the decrease in OUD contradicts the trends that service providers described. This discrepancy could be explained by factors identified by service providers, including the pressures on ERs during the pandemic, hesitancy to seek treatment, and drug toxicity.

Recent advances in communication technologies have enabled researchers to collect travel data based on ubiquitous and location-aware smartphones. These advances hold out the promise of allowing the automatic detection of the critical aspects (mode of transport, purpose, etc.) of people’s trips. Until now, efforts have concentrated on one aspect of trips (e.g. mode) at a time. Such methods have typically been developed on small data sets, often with data collected by researchers themselves and not in large-scale real-world data collection initiatives. This research develops a machine learning-based framework to identify complete trip information based on smartphone location data as well as online data from GTFS (General Transit Feed Specification) and Foursquare data. The framework has the potential to be integrated with smartphone travel surveys to produce all trip characteristics traditionally collected through household travel surveys. We use data from a recent, large-scale smartphone travel survey in Montréal, Canada. The collected smartphone data, augmented with GTFS and Foursquare data are used to train and validate three random forest models to predict mode of transport, transit itinerary as well as trip purpose (activity). According to cross-validation analysis, the random forest models show prediction accuracies of 87%, 81% and 71% for mode, transit itinerary and activity, respectively. The results compare favorably with previous studies, especially when taking the large, real-world nature of the dataset into account. Furthermore, the cross validation results show that the machine learning-based framework is an effective and automated tool to support trip information extraction for large-scale smartphone travel surveys, which have the potential to be a reliable and efficient (in terms of cost and human resources) data collection technique. Keywords: Smartphone, Household travel survey, Machine learning, Social network, Activity pattern

Shape memory alloys (SMAs) are popular as actuators for use in soft robots due to their high work density and compatibility with miniaturized on-board batteries and power electronics. However, because SMA actuators are activated through electrical Joule heating, they exhibit poor energy efficiency and low actuator frequencies that arise from long cool-down times. Moreover, in the case of SMA wires that are subject to flexural loading, their load capacity and mechanical work output decrease exponentially with decreasing cross-sectional area. In this study, we perform analytic and numerical analyses to examine the thermal and structural design space around a particular class of flexural SMA wire actuators with the intention of increasing actuator operating frequency and actuation forces. Measurements obtained through experimental testing are consistent with theoretical studies of actuator force output and provide additional insight into the efficiency of electrical-to-mechanical energy conversion. Together, the theoretical and experimental studies provide insights that have the potential to inform SMA wire design and usage in soft robotic applications.

With the advent of smartphones and their ability to know their own location, there is an enormous potential to collect location data for many purposes, including travel-related research. While the ability to create smartphone travel survey applications is potentially revolutionary, the development of such applications remains sufficiently difficult to be beyond a typical transportation researcher’s ability. Of course, development of an app is only the first challenge associated with using such tools; information also needs to be inferred from collected data. The Itinerum platform was created to overcome the barriers facing the use of smartphones for transportation research. The Itinerum platform is a smartphone travel survey platform that allows researchers to customize the Itinerum app with their own questions and prompts, distribute these surveys, monitor, visualize and increasingly process collected data without a background in programming. With the platform, a customized study can be created in 10 min. Keywords: Smartphones, Travel surveys, Web platform

Circadian clocks in many brain regions and peripheral tissues are entrained by the daily rhythm of food intake. Clocks in one or more of these locations generate a daily rhythm of locomotor activity that anticipates a regular mealtime. Rats and mice can also anticipate two daily meals. Whether this involves 1 or 2 circadian clocks is unknown. To gain insight into how the circadian system adjusts to 2 daily mealtimes, male rats in a 12∶12 light-dark cycle were fed a 2 h meal either 4 h after lights-on or 4 h after lights-off, or a 1 h meal at both times. After 30 days, brain, blood, adrenal and stomach tissue were collected at 6 time points. Multiple clock genes from adrenals and stomachs were assayed by RT-PCR. Blood was assayed for corticosterone and ghrelin. Bmal1 expression was quantified in 14 brain regions by in situ hybridization. Clock gene rhythms in adrenal and stomach from day-fed rats oscillated in antiphase with the rhythms in night-fed rats, and at an intermediate phase in rats fed twice daily. Corticosterone and ghrelin in 1-meal rats peaked at or prior to the expected mealtime. In 2-meal rats, corticosterone peaked only prior the nighttime meal, while ghrelin peaked prior to the daytime meal and then remained elevated. The olfactory bulb, nucleus accumbens, dorsal striatum, cerebellum and arcuate nucleus exhibited significant daily rhythms of Bmal1 in the night-fed groups that were approximately in antiphase in the day-fed groups, and at intermediate levels (arrhythmic) in rats anticipating 2 daily meals. The dissociations between anticipatory activity and the peripheral clocks and hormones in rats anticipating 2 daily meals argue against a role for these signals in the timing of behavioral rhythms. The absence of rhythmicity at the tissue level in brain regions from rats anticipating 2 daily meals support behavioral evidence that circadian clock cells in these tissues may reorganize into two populations coupled to different meals.

Metropolitan regions around the world are looking for sustainable strategies to battle energy consumption and emissions. These strategies include land use policies and the desire to decrease the externalities from transportation and therefore increase the share of public transit use. The goal of this research is to better understand the relationships between Land-use (LU), public transit accessibility (PT), parking policies, and mode choice for suburban Montreal commuters. In this regard we evaluate the potential impact of land-use, transit accessibility and parking policies on the mode choice of the households dwelling in commuter rail line catchments in the region of Montreal, Canada. The approach adopted here is to consider mode choice as a joint decision with neighborhood type choice, based on neighborhood typologies derived using a cluster analysis characterized by land use variables. Also, the effect of parking fees on mode choice is investigated and quantified. The method used in this study is a simultaneous modeling approach to account for the endogeneity of mode and household location choice. The model system is made up of two models – a neighborhood type choice model and a mode choice model. The results demonstrate the robustness of the models and methodology implemented thus providing important innovations for transportation and land-use planning.

UrbanSim has significant data requirements. In particular, it requires disaggregate data (traditionally at the 150 meter by 150 meter gridcell level) for employment, households, and buildings. While such data are not always easily available, most regions have readily available data in a more aggregate form, often at the level of traffic analysis zone (TAZ) or other municipal divisions. This paper describes two UrbanSim applications for the cities of Brussels, Belgium and Lyon, France that adopted different approaches of using aggregate data. In Brussels, aggregate zonal data were disaggregated to the gridcell level. In the Lyon application, the zone was used as the unit of analysis and as such, each zone corresponds to one gridcell. The objectives of this paper are: 1) establish whether an UrbanSim model can be developed using aggregate data; 2) describe two different approaches to using aggregate data with UrbanSim and evaluate; and 3) evaluate the advantages and disadvantages of using aggregate data, as well as the two different approaches described. In doing so, it advances knowledge in the field of transportation and land use modeling by helping modelers evaluate the use of an increasingly popular integrated transportation land use modeling option. Several conclusions flow from this work. First, aggregate data can be used to develop UrbanSim models. Second, only a limited amount of disaggregate information can be drawn from aggregate data. In the context of UrbanSim, this is manifested in models with relatively few variables and dubious simulation results—in other words, while it is possible to develop an UrbanSim application with aggregate data, it should not be used for applied analysis. Finally, the development of such models can be a relatively low-cost exercise to gain familiarity with UrbanSim’s functioning and data requirements. As a result, it can also be seen as an important first step to developing or evaluating UrbanSim for application in a new region.

Social scientists appear to be divided into two camps: those who use models and those who do not. In order to understand this phenomenon, a clear understanding of what a model is is required. Unfortunately, this is more complicated than one might think. To be sure, few social scientists would have trouble identifying what they consider to be a model: defining what a model is, however, is more difficult. To echo Associate Justice Potter Stewart's famous quote about pornography, most social ...