Polytechnique Montréal

BACKGROUND AND OBJECTIVES Degenerative cervical myelopathy (DCM) is a progressive and disabling condition resulting from chronic compression of the spinal cord, leading to functional impairments that can severely affect quality of life. Traditional methods for assessing spinal cord injury and morphometrics rely on subjective visualization of contrast changes and manual segmentation, which are nonstandardized, time-consuming, and inconsistent across patients. This variability limits understanding of DCM pathology and hampers timely clinical intervention. METHODS We introduce a semiautomated pipeline using the Spinal Cord Toolbox, an open-source platform that uses advanced algorithms, including optimization and computational efficiency algorithms, support vector machine, and convolutional neural networks, to streamline the assessment of spinal cord shape, microstructural changes, and gray and white matter integrity. By integrating spinal cord segmentation, anatomical labeling, and registration to a standardized template, the pipeline extracts normalized morphometric measures, providing efficient and reliable analysis of spinal cord pathology in DCM. RESULTS We extracted normalized spinal cord morphometrics, including cross-sectional area (CSA), anterior-posterior diameter, right-left diameter, eccentricity, solidity, gray matter CSA, white matter CSA, and regional and tract-based magnetization transfer ratio measures. Our analysis demonstrates that DCM patients exhibit significant reductions in these morphometrics compared with healthy controls, even in regions without visible compression. Furthermore, CSA reductions across the spinal cord highlight areas of severe compression, including at the intervertebral disks, which may not be apparent on standard imaging. CONCLUSION These quantitative measures give clinicians easily interpretable data on the extent of spinal cord injury, even in regions without obvious compression. This enables a comprehensive understanding of DCM pathophysiology. By eliminating the subjectivity of manual segmentation and accounting for intersubject and intrasubject variability, this approach supports consistent cross-subject comparisons and is poised to reshape how clinicians assess and manage DCM.

Background Functional Magnetic Resonance Imaging (fMRI) is based on the Blood Oxygenation Level Dependent contrast and has been exploited for the indirect study of the neuronal activity within both the brain and the spinal cord. However, the interpretation of spinal cord fMRI (scfMRI) is still controversial and its adoption is rather restricted because of technical limitations. Overcoming these limitations would have a beneficial effect for the assessment and follow-up of spinal injuries and neurodegenerative diseases. Purpose This study was aimed at systematically verifying whether sagittal scanning in scfMRI using EPI readout is a viable alternative to the more common axial scanning, and at optimizing a pipeline for EPI-based scfMRI data analysis, based on Spinal Cord Toolbox (SCT). Methods Forty-five healthy subjects underwent MRI acquisition in a Philips Achieva 3T MRI scanner. T2*-weighted fMRI data were acquired using a GE-EPI sequence along sagittal and axial planes during an isometric motor task. Differences on benchmarks were assessed via paired two-sample t-test at p < 0.05. Results We investigated the impact of the acquisition strategy by means of various metrics such as Temporal Signal to Noise Ratio (tSNR), Dice Coefficient to assess geometric distortions, Reproducibility and BOLD signal sensitivity to the stimulus. tSNR was higher in axial than in sagittal scans, as well as reproducibility within the whole cord mask (t = 7.4, p < 0.01) and within the GM mask (t = 4.2, p < 0.01). The other benchmarks, associated with distortion and functional response, showed no difference between images obtained along the axial and sagittal planes. Conclusions Quantitative metrics of data quality suggest that axial scanning would be the optimal choice. We conclude that axial acquisition is advantageous specially to mitigate the effects of physiological noise and to minimize inter-subject variance.

Clinical research emphasizes the implementation of rigorous and reproducible study designs that rely on between-group matching or controlling for sources of biological variation such as subject’s sex and age. However, corrections for body size (i.e., height and weight) are mostly lacking in clinical neuroimaging designs. This study investigates the importance of body size parameters in their relationship with spinal cord (SC) and brain magnetic resonance imaging (MRI) metrics. Data were derived from a cosmopolitan population of 267 healthy human adults (age 30.1 ± 6.6 years old, 125 females). We show that body height correlates with brain gray matter (GM) volume, cortical GM volume, total cerebellar volume, brainstem volume, and cross-sectional area (CSA) of cervical SC white matter (CSA-WM; 0.44 ≤ r ≤ 0.62). Intracranial volume (ICV) correlates with body height (r = 0.46) and the brain volumes and CSA-WM (0.37 ≤ r ≤ 0.77). In comparison, age correlates with cortical GM volume, precentral GM volume, and cortical thickness (-0.21 ≥ r ≥ -0.27). Body weight correlates with magnetization transfer ratio in the SC WM, dorsal columns, and lateral corticospinal tracts (-0.20 ≥ r ≥ -0.23). Body weight further correlates with the mean diffusivity derived from diffusion tensor imaging (DTI) in SC WM (r = -0.20) and dorsal columns (-0.21), but only in males. CSA-WM correlates with brain volumes (0.39 ≤ r ≤ 0.64), and with precentral gyrus thickness and DTI-based fractional anisotropy in SC dorsal columns and SC lateral corticospinal tracts (-0.22 ≥ r ≥ -0.25). Linear mixture of age, sex, or sex and age, explained 2 ± 2%, 24 ± 10%, or 26 ± 10%, of data variance in brain volumetry and SC CSA. The amount of explained variance increased to 33 ± 11%, 41 ± 17%, or 46 ± 17%, when body height, ICV, or body height and ICV were added into the mixture model. In females, the explained variances halved suggesting another unidentified biological factor(s) determining females’ central nervous system (CNS) morphology. In conclusion, body size and ICV are significant biological variables. Along with sex and age, body size should therefore be included as a mandatory variable in the design of clinical neuroimaging studies examining SC and brain structure; and body size and ICV should be considered as covariates in statistical analyses. Normalization of different brain regions with ICV diminishes their correlations with body size, but simultaneously amplifies ICV-related variance (r = 0.72 ± 0.07) and suppresses volume variance of the different brain regions (r = 0.12 ± 0.19) in the normalized measurements.

As the energy sector transitions toward decarbonization, the integration of variable renewable energy (VRE) sources presents challenges in grid reliability, operational flexibility, and cost optimization. This article explores the practical approaches and advanced optimization methods for look-ahead coordinated generation scheduling in grids with a high share of VREs. Using Republic of Korea as a case study, we discuss the implementation of real-time markets, renewable energy bidding systems, and bulk energy storage solutions to mitigate the intermittency of renewables and ensure secure power system operation. Emphasis is placed on the role of accurate forecasting, coordinated operation of dispatchable and nondispatchable resources, and the use of enabling technologies to achieve a sustainable and robust power grid. The findings offer actionable insights into addressing the technical and economic challenges of transitioning to a low-carbon energy system.

Purpose This study investigates the effectiveness of through‐slice gradient optimization in dynamic slice‐wise B0 shimming of the cervico‐thoracic spinal cord to enhance signal recovery in gradient‐echo (GRE) EPI sequences commonly used in functional MRI studies. Methods Six volunteers underwent MRI acquisitions with dynamic shim updating (DSU) using a custom‐built 15‐channel AC/DC coil at 3 T. A magnetization‐prepared rapid gradient echo was acquired to segment the spine and to provide a clear image of the anatomical region of interest in the figures. GRE B0 field maps were used to measure field homogeneity before and after shimming; the pre‐shimming field map was used for optimization. Shimmed fields were dynamically applied to GRE–echo planar imaging acquisitions simulating functional MRI acquisitions under two shimming conditions: DSU with and without through‐slice gradient consideration. Results DSU with through‐slice gradient optimization increased the temporal signal‐to‐noise ratio at the T2 vertebral level by 201% compared with volume‐wise shim and by 28% compared with DSU without through‐slice. The residual geometric distortions were similar between DSU with and without through‐slice gradient optimization. A high signal loss penalty parameter was effective in simulations for reducing through‐slice gradient‐induced signal loss but led to instability and reduced image quality in actual acquisitions due to excessive in‐plane B0 inhomogeneities. Conclusion Introducing a carefully balanced through‐slice gradient parameter in slice‐wise shimming substantially improves signal recovery in axial GRE images of the spinal cord, without compromising in‐plane homogeneity. This effective approach can advance spinal cord functional MRI applications at high field strengths.

E‐commerce operations are essentially online, with customer orders arriving dynamically. However, very little is known about the performance of online policies for warehousing with respect to optimality, particularly for order picking and batching operations, which constitute a substantial portion of the total operating costs in warehouses. We aim to close this gap for one of the most prominent dynamic algorithms, namely reoptimization (Reopt) , which reoptimizes the current solution each time a new order arrives. We examine Reopt in the Online Order Batching, Sequencing, and Routing Problem (OOBSRP) , in both cases when the picker uses either a manual pushcart or a robotic cart. Moreover, we examine the non‐interventionist Reopt in the case of a manual pushcart, wherein picking instructions are provided exclusively at the depot. We establish analytical performance bounds employing worst‐case and probabilistic analysis . We demonstrate that, under generic stochastic assumptions, Reopt is almost surely asymptotically optimal and, notably, we validate its near‐optimal performance in computational experiments across a broad range of warehouse settings. These results underscore Reopt 's relevance as a method for online warehousing applications.

Passenger transport is an important contributor to unsustainable urban systems. To achieve the necessary socio-ecological transition will require overcoming the entrenched system of automobility. Composed of several mutually reinforcing components, this system has conferred psychosocial dimensions to car ownership and use that leads to important institutional, political, and individual resistance to change both car-centric transportation infrastructure and individual travel behaviour. For this reason, a growing consensus suggests that transitioning to a sustainable mobility system requires a more holistic approach that applies a synergistic integration of “hard” supply-side measures and “soft” demand-side solutions. This means increasing non-automobile accessibility and supporting such change with soft travel behaviour change solutions that target social-psychological barriers to change. While such approaches have demonstrated their effectiveness around the world, this second category of interventions remains underutilized, particularly in North America. Drawing from social psychology and a North American case study, this chapter proposes a theory-to-practice guide for practitioners to designing effective voluntary travel behaviour change interventions based on the Stage Model of Self-Regulated Behaviour Change (SSBC). A four-level integration framework for intervention design based on the SSBC is proposed. The framework proposes intervention approaches from using the model as a simple diagnostic tool to a complete integration to deliver a fully individualized and stage-tailored intervention. Stage-specific messages and strategies are described to shift people away from car use towards active, collective, and shared mobility options. The chapter concludes on suggestions for collaborative efforts between researchers and practitioners to design, evaluate, and enhance the effectiveness of these interventions, thus moving beyond infrastructure-only solutions to foster a successful transition to sustainable mobility in Québec.

Content Addressable Memories (CAMs) are pivotal in high-speed packet processing systems, enabling rapid data lookup operations essential for applications such as routing, switching, and network security. While traditional Register-Transfer Level (RTL) methodologies have been extensively used to implement CAM architectures on Field-Programmable Gate Arrays (FPGAs), they often involve complex, time-consuming design processes with limited flexibility. In this paper, we propose a novel templated High-Level Synthesis (HLS)-based approach for the design and implementation of CAM architectures such as Binary CAMs (BCAMs) and Ternary CAMs (TCAMs) optimized for data plane packet processing. Our HLS-based methodology leverages the parallel processing capabilities of FPGAs through employing various design parameters and optimization directives while significantly reducing development time and enhancing design portability. This paper also presents architectural design and optimization strategies to offer a fine-tuned CAM solution for networking-related arbitrary use cases. Experimental results demonstrate that HLSCAM achieves a high throughput, reaching up to 31.18 Gbps, 9.04 Gbps, and 33.04 Gbps in the 256×128, 512×36, and 1024×150 CAM sizes, making it a competitive solution for high-speed packet processing on FPGAs.

Objective: Transcranial ultrasound imaging is currently limited by attenuation and aberration induced by the skull. First used in contrast-enhanced ultrasound (CEUS), highly echoic microbubbles allowed for the development of novel imaging modalities such as ultrasound localization microscopy (ULM). Herein, we develop an inverse problem approach to aberration correction (IPAC) that leverages the sparsity of microbubble signals. Methods: We propose to use the a priori knowledge of the medium based upon microbubble localization and wave propagation to build a forward model to link the measured signals directly to the aberration function. A standard least-squares inversion is then used to retrieve the aberration function. We first validated IPAC on simulated data of a vascular network using plane wave as well as divergent wave emissions. We then evaluated the reproducibility of IPAC in vivo in 5 mouse brains. Results: We showed that aberration correction improved the contrast of CEUS images by 4.6 dB. For ULM images, IPAC yielded sharper vessels, reduced vessel duplications, and improved the resolution from 21.1 µm to 18.3 µm. Aberration correction also improved hemodynamic quantification for velocity magnitude and flow direction. Conclusion: We showed that IPAC can perform skull-induced aberration correction and improved Power Doppler as well as ULM images acquired on the mouse brain. Significance: This technique is promising for more reliable transcranial imaging of the brain vasculature with potential non-invasive clinical applications. Index Terms-Phase aberration correction, inverse problem, gradient method, contrast-enhanced ultrasound, ultrafast ultrasound imaging, ultrasound localization mi-croscopy.

In recent years, advancements in air quality monitoring have been driven by the development of various sensor technologies, each with distinct advantages and limitations. Among these, polymer-based Distributed Bragg Reflectors (DBRs) have garnered significant interest for use in cost-effective, portable colorimetric sensors for detecting volatile organic compounds (VOCs). However, a key challenge in the fabrication of polymer-based DBRs lies in achieving an adequate refractive index contrast between the individual polymer layers. In this work, we fabricate plasmonic DBR sensors by a combination of low-temperature plasma-based techniques with reduced environmental footprint, investigate their potential as VOC sensors, and propose an optical model that links the sensors’ optical properties and microstructure. Plasmonic nanoparticles of silver (Ag) are synthesized by gas aggregation and embedded into thermally evaporated poly(lactic acid) (PLA) layers to create nanocomposites with an enhanced refractive index (∼2.0). A 6-bilayer plasmonic DBR sensor is then produced by alternating depositions of plain PLA and nanocomposite layers as low and high refractive index materials, respectively. The resulting DBR achieves a 77% reflectance at 570 nm. The potential use-case of such a DBR as a VOC sensor is highlighted by its optical response upon exposure to ethanol (a model VOC) vapors as well as other VOCs (water, propanol, acetone, hexane). In an ethanol atmosphere, swelling of the polymer layers occurs, resulting in a red-shift of the reflection peak to 640 nm and a change in the DBR color. We take advantage of a generalized Maxwell-Garnett approach to create an advanced model that accurately reproduces the DBR spectra and captures swelling and degradation by accounting for structural changes and the behavior of isolated and coalesced Ag NPs within individual layers. Despite a decrease in the sensing performance with the number of swelling cycles, these plasmonic DBRs offer a promising solution for low-cost real-time VOC sensing.

We study, in an exploratory manner, the potential impact of service contracts on the cash flows and creditworthiness of two transport cooperatives in the Philippines. Annual audited financial statements for two periods (before service contracting [SC] and during SC) are examined using four financial metrics. The analysis reveals that the cash flows stabilized or improved in 2020-2022 when the two transport cooperatives participated in the SC program. The cash flow stability/improvement is a potential result of SC. We then presented the cash flow analysis to six credit officers and asked them to evaluate the credit-worthiness of the transport cooperatives based on the cash flows. In general, the bank credit officers confirmed that cash flow improved after 2020 and that SC was a contributing factor to this improvement in cash flow, which has somehow enhanced the creditworthiness of the transport cooperatives as credit borrowers. The results of the study contribute to the field of financing for informal public transport reform, which is understudied in the literature. Moreover, this research is relevant for global policymakers and transport scholars, including those in regions such as North America and Europe, offering insights into how SC can enhance the financial sustainability of smaller transport operators.