Showing papers by "University of Winnipeg published in 2022"

Advancing local energy transitions: A global review of government instruments supporting community energy

Abstract: The adoption and encouragement of community energy, that is, the incentive to develop renewable energy projects with community participation and ownership, is a key ingredient of energy transition. Government policies and other instruments can pose both barriers and opportunities for community energy development; however, there has been little analysis of the state of research on the range of government tools to facilitate energy transition and the implications of these instruments for community energy. This paper analyses the current scholarly research on government instruments for community energy, focusing on the multiple scales of governance. Our analysis identified 108 articles addressing government instruments and community energy. Research addressing government instruments and community energy has increased substantially in recent years, with most of the emphasis on national or state instruments, situated in the European context, and focused on grid-connected communities. We identified four global categories of government tools designed to support community energy: payment-based, grid access, environmental protection and community planning and capacity. Within these categories, nineteen different government instruments emerged with tools for financial support, feed-in-tariffs, grid services, and fiscal incentives receiving the most attention. Findings emphasize the need for further research on community-focused instruments for renewable energy, the importance of coordination between levels of government to support such instruments, and analysis of the suitability of current instruments for community-appropriate energy solutions in remote and off-grid communities.

Amplification of primordial perturbations from the rise or fall of the inflaton

Abstract: Abstract The next generation of cosmic microwave background, gravitational wave, and large scale structure, experiments will provide an unprecedented opportunity to probe the primordial power spectrum on small scales. An exciting possibility for what lurks on small scales is a sharp rise in the primordial power spectrum: this can lead to the formation of primordial black holes, providing a dark matter candidate or the black holes observed by the LIGO-Virgo collaboration. In this work we develop a mechanism for the amplification of the small-scale primordial power spectrum, in the context of single-field inflation with a step-like feature in the inflaton potential. Specifically, we consider both the upward and the downward step in the potential. We also discuss the possibility of the strong coupling between perturbations because the rapid changes of the potential derivatives with the time-dependent field value, caused by the step-like feature, could make the coupling stronger. As a result, we find that the perturbations can remain weakly coupled yet sufficiently enhanced if the step realizes the rapid changes of the potential derivatives in some fraction of an e-fold, 𝒪(𝒫 ℛ 1/2 ) ≲ Δ N < 1, where 𝒫 ℛ is the power spectrum of the curvature perturbation at that time. We also discuss the PBH formation rate from the inflaton trapping at the local minimum, which can occur in the potential with an upward step.

Flooding‐induced inorganic phosphorus transformations in two soils, with and without gypsum amendment

Abstract: Anaerobic conditions developed during flooding can increase phosphorus (P) losses from soils to waterways. Soil amendment with gypsum (CaSO4 ·2H2 O) can effectively reduce flooding-induced P release, but its effectiveness is soil dependent, and the reasons are poorly understood. The objectives of this study were to reveal the possible inorganic P transformations during flooding of two soils (acidic-Neuenberg sandy loam [NBG-SL] and alkaline-Fyala clay [FYL-Cl]), with and without gypsum amendment prior to flooding. Porewater samples collected at 0, 35, and 70 d after flooding (DAF) from soils incubated in vessels were analyzed for dissolved reactive P (DRP); pH; and concentrations of calcium (Ca), magnesium, iron (Fe), manganese, chloride, nitrate, sulfate, and fluoride. Thermodynamic modeling using Visual MINTEQ software and chemical fractionation of soil P were used to infer P transformations. Soil redox potential (Eh) decreased with flooding and favored reductive dissolution of Fe-associated P increasing porewater DRP concentrations. Greater solubility of Ca-P under acidic pH maintained a higher DRP concentration in NBG-SL during early stages of flooding. A subsequent increase in pH with flooding and higher Ca concentration with added gypsum enhanced the stability of Ca-P (β-tricalcium phosphate and octacalcium phosphate), reducing the DRP concentration in gypsum-amended NBG-SL. Stability of Ca-P was less affected with flooding and gypsum amendment in FYL-Cl soil because it had an alkaline pH and inherently higher Ca concentration. The FYL-Cl, with a more rapid decrease in Eh than NBG-SL, became severely reduced, releasing more P and Fe by 70 DAF. These conditions favored vivianite formation in FYL-Cl but not in NBG-SL.

Fully automated 2D and 3D convolutional neural networks pipeline for video segmentation and myocardial infarction detection in echocardiography

Abstract: Cardiac imaging known as echocardiography is a non-invasive tool utilized to produce data including images and videos, which cardiologists use to diagnose cardiac abnormalities in general and myocardial infarction (MI) in particular. Echocardiography machines can deliver abundant amounts of data that need to be quickly analyzed by cardiologists to help them make a diagnosis and treat cardiac conditions. However, the acquired data quality varies depending on the acquisition conditions and the patient's responsiveness to the setup instructions. These constraints are challenging to doctors especially when patients are facing MI and their lives are at stake. In this paper, we propose an innovative real-time end-to-end fully automated model based on convolutional neural networks (CNN) to detect MI depending on regional wall motion abnormalities (RWMA) of the left ventricle (LV) from videos produced by echocardiography. Our model is implemented as a pipeline consisting of a 2D CNN that performs data preprocessing by segmenting the LV chamber from the apical four-chamber (A4C) view, followed by a 3D CNN that performs a binary classification to detect if the segmented echocardiography shows signs of MI. We trained both CNNs on a dataset composed of 165 echocardiography videos each acquired from a distinct patient. The 2D CNN achieved an accuracy of 97.18% on data segmentation while the 3D CNN achieved 90.9% of accuracy, 100% of precision and 95% of recall on MI detection. Our results demonstrate that creating a fully automated system for MI detection is feasible and propitious.

1-Dimensional Polynomial Neural Networks for audio signal related problems

Abstract: In addition to being extremely non-linear, modern problems require millions if not billions of parameters to solve or at least to get a good approximation of the solution, and neural networks are known to assimilate that complexity by deepening and widening their topology in order to increase the level of non-linearity needed for a better approximation. However, compact topologies are always preferred to deeper ones as they offer the advantage of using less computational units and less parameters. This compacity comes at the price of reduced non-linearity and thus, of limited solution search space. We propose the 1-Dimensional Polynomial Neural Network (1DPNN) model that uses automatic polynomial kernel estimation for 1-Dimensional Convolutional Neural Networks (1DCNNs) and that introduces a high degree of non-linearity from the first layer which can compensate the need for deep and/or wide topologies. We show that this non-linearity enables the model to yield better results with less computational and spatial complexity than a regular 1DCNN on various classification and regression problems related to audio signals, even though it introduces more computational and spatial complexity on a neuronal level. The experiments were conducted on three publicly available datasets and demonstrate that, on the problems that were tackled, the proposed model can extract more relevant information from the data than a 1DCNN in less time and with less memory.

Effects of biofouling on the uptake of perfluorinated alkyl acids by organic-diffusive gradients in thin films passive samplers

Abstract: While organic-diffusive gradients in thin films (o-DGT) passive samplers have been used to assess organic contaminants in water, the effects of biofouling on accurate analyte quantification by o-DGT are poorly understood. We evaluated the effects of biofouling on the uptake of six common perfluoroalkyl substances (PFAS) using a previously developed polyacrylamide-WAX (weak anion exchange) o-DGT without a filter membrane. Linear uptake (R2 > 0.91) over 21 days was observed in fouled samplers. The measured sampling rates (Rs) and accumulated masses of PFAS in pre-fouled o-DGT were significantly lower (p < 0.05, 20-39% relative error) than in control-fouled samplers. However, compared to clean o-DGT (no biofouling), the Rs of most PFAS in control-fouled samplers (i.e., those with clean diffusive and binding gels initially) were not affected by biofouling. Under flowing (∼5.8 cm s-1) and static conditions, the measured diffusive boundary layer (DBL) thicknesses for clean o-DGT were 0.016 and 0.082 cm, respectively, whereas the effective in situ biofilm thicknesses for fouled o-DGT were 0.018 and 0.14 cm, respectively. These results suggest that biofilm growth does not have significant effects on target PFAS sampling by o-DGT under typical flowing conditions (≥2 cm s-1). However, rapid surface growth of biofilm on o-DGT deployed in quiescent waters over long periods of time may exacerbate the adverse effects of biofilms, necessitating the estimation of biofilm thickness in situ. This study provides new insights for evaluating the capability of o-DGT samplers when biofilm growth can be significant.

Maximal state complexity and generalized de Bruijn words

Abstract: We compute the exact maximum state complexity for the language consisting of m words of length N , and characterize languages achieving the maximum. We also consider a special case, namely languages C ( w ) consisting of the conjugates of a single word w . The words for which the maximum state complexity of C ( w ) is achieved turn out to be a natural generalization of de Bruijn words. We show that generalized de Bruijn words exist for each length and consider the number of them.

Pygmalion and Medusa as Cixous’ writing women in <i>InSEXts</i>: ‘I Write Woman’

Abstract: Abstract Set in nineteenth century Europe, InSEXts tells the story of Lady Bertram and Mariah, two women in a romantic relationship who can transform into insects in order to defend their nascent family. In the second arc of the series, The Necropolis, Lady Bertram, and Mariah are joined by Phoebe (a Black, transgender artist) whose friends are being transformed into art by members of the patriarchal art community, who value women only as silent and beautiful Muses. Allying with ‘Medusa’, a misidentified Javanese goddess who has been forcibly brought to Europe, Lady Bertram and Mariah thus enter a narrative replete with Greek and Roman mythology. In this paper, the author sets out to demonstrate that, through the receptions of Pygmalion and Medusa, InSEXts: The Necropolis effectively reflects Cixous’ ‘Laugh of the Medusa’, replicating the empowerment of female creativity which Cixous suggests. By mirroring these Ovidian intertexts and becoming ‘writing women’ themselves, comics creators Bennett and Kristantina allow their own characters to re-write and subvert traditional narratives, to tell their own stories that represent elements of Ovidian narrative to respond to and embody the kind of female creativity advocated by Cixous.

Blood-Red Relations In and Out of Place: Women’s Self-Harm and Supernatural Crime in<i>The Moth Diaries</i>

Abstract: In Canadian filmmaker Mary Harron’s The Moth Diaries (a Canadian/American/Irish co-production), exploring adolescent girls’ friendships and self-harm in a boarding school setting, blood is out of place. It drips from the protagonist’s father’s wrist artery, willingly shed in suicide; involuntarily tarnishes her nightgown as menstrual blood; falls on the school director’s china figurines as nosebleed; and pours in the school library as a vampire-invoked rain. Moth uses blood to manifest the suicide contagion that Rebecca fears she has inherited from her artist father. Blood also signifies her resistance and recovery, enabled by her difficult relationship with her schoolmates, erstwhile best friend Lucy, and vampire Ernessa. Blood functions as a material marker of transition from girls’ childhood relationships that mainstream Anglo-American films often render passive and vulnerable, and marks same-sex attractions of different types of friendship and love. It symbolizes and draws attention to harms and crimes in interpersonal violence, paternal abandonment, and self-damage. Our focus on relationships between so called “blood kin” and the idea of blood relations weaves into our discussion of female agency, woman identification, and queer affinities through Moth’s out-of-place ontologies for blood as not only conventionally abject, but also a sacralized substance and symbol.

Implementing Corporate Equality through Québec Inc.

Abstract: Québec has been a leader in Canada for the promotion of women on corporate boards with the 2006 law that established gender parity on the boards of public enterprises. In the rest of Canada, at the federal and provincial levels, a self-regulatory comply-or-explain approach to monitoring and encouraging board diversity has been preferred. While the parity law in Québec is a potential feminist way forward in advancing the representation of women on boards, the policy fell short. Although the number of women on state-owned boards increased and parity was achieved on targeted state-owned company boards, the equality initiative has not diffused into the corporate world. Like all of Canada, there is no concerted interest in legislating or self-regulating gender parity on boards in corporate Québec. Moreover, the impact of the quota law was limited to numerical increases in women on the corporate boards of state-owned enterprises in Québec without strong evidence of significant gender transformation.

Asymptotic bounds for the number of closed and privileged words

Abstract: A word $w$ has a border $u$ if $u$ is a non-empty proper prefix and suffix of $u$. A word $w$ is said to be \emph{closed} if $w$ is of length at most $1$ or if $w$ has a border that occurs exactly twice in $w$. A word $w$ is said to be \emph{privileged} if $w$ is of length at most $1$ or if $w$ has a privileged border that occurs exactly twice in $w$. Let $C_k(n)$ (resp. $P_k(n)$) be the number of length-$n$ closed (resp. privileged) words over a $k$-letter alphabet. In this paper, we improve existing upper and lower bounds on $C_k(n)$ and $P_k(n)$. We prove that $C_k(n) \in \Theta(\frac{k^n}{n})$. Let $\log_k^{\circ 0}(n) = n$ and $\log_k^{\circ j}(n) = \log_k(\log_k^{\circ j-1}(n))$ for $j\geq 1$. We also prove that for all $j\geq 0$ there exist constants $N_j$, $c_j$, and $c_j'$ such that \[c_j\frac{k^n}{n\log_k^{\circ j}(n)\prod_{i=1}^j\log_k^{\circ i}(n)}\leq P_k(n) \leq c_j'\frac{k^n}{n\prod_{i=1}^j\log_k^{\circ i}(n)}\] for all $n>N_j$.

How should we proxy for race/ethnicity? Comparing Bayesian improved surname geocoding to machine learning methods

Abstract: Bayesian Improved Surname Geocoding (BISG) is the most popular method for proxying race/ethnicity in voter registration files that do not contain it. This paper benchmarks BISG against a range of previously untested machine learning alternatives, using voter files with self-reported race/ethnicity from California, Florida, North Carolina, and Georgia. This analysis yields three key findings. First, machine learning consistently outperforms BISG at individual classification of race/ethnicity. Second, BISG and machine learning methods exhibit divergent biases for estimating regional racial composition. Third, the performance of all methods varies substantially across states. These results suggest that pre-trained machine learning models are preferable to BISG for individual classification. Furthermore, mixed results across states underscore the need for researchers to empirically validate their chosen race/ethnicity proxy in their populations of interest.