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The Online Pivot: Student Reflections on Virtual Peer Mentorship in the Context of the Global Covid-19 Pandemic
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A Review of Anna-Leah King, Kathleen O’Reilly, and Patrick J. Lewis’ (Eds.) (2024) <i>Unsettling Education: Decolonizing and Indigenizing</i> <i>the Land</i>
No full textUnsettling Education: Decolonizing and Indigenizing the Land (King, O’Reilly, & Lewis, 2024) focuses on decolonization, Indigenization and reconciliation for educators and students through addressing colonialism within the education system and academia. The authors each bring forth an abundance of knowledge and experience in the field of Indigenous education through the stories and words of Indigenous communities, Elders, Indigenous leaders, and Knowledge Keepers. Further, the authors provide an abundance of examples/teachings regarding Indigenous perspectives, theories, and teachings for current and future teachers to implement in the classroom. In addition, this book serves as a guide for non-Indigenous peoples to practice self-reflexivity, by reflecting on their own positionality and privilege while engaging with truth and reconciliation strategies through the stories and experiences of Indigenous scholars and educators. The book is comprised of eighteen chapters by twenty-nine authors (both Indigenous and non-Indigenous) and grouped into three sections
Commentary on: M. Kagan’s “Informal Logic and Critical Thinking (ILCT) and Secret Writing”
No full textBattyCoda: A novel open-source software for bat call annotation and classification
No full textThe field of acoustic communication needs tools that facilitate the annotation and labeling of animal calls. Bat acoustic libraries gathered over the past few decades have primarily focused on compiling echolocation calls, which have been leveraged to develop machine learning algorithms capable of classifying bat species. However, because these classification methods require large training datasets, they have not yet been generalized to classify types of bat communication calls. Communication call repertoires in bats are wide, and distinct syllables occur with varying frequency, with some call types being recorded only rarely. Furthermore, collecting communication calls poses greater technical challenges, making these calls more difficult to capture reliably. Here, we present BattyCoda, an open-access, customizable tool to categorize and label bat communication call types within the repertoire of a species using small training datasets (tens to hundreds of labeled calls). In this work, we compiled an initial training dataset of 11 types of big brown bat (Eptesicus fuscus) calls, tested the performance of various candidate classifiers, and assessed the final classifier's training sample size sensitivity. We found that the best performing classifier achieved a balanced accuracy of ∼50 %, with common call types achieving classification accuracies over 70 %. Our tool can greatly facilitate annotating bat calls in recordings by providing accurate labels for common call types, while also assisting researchers in categorizing rarer communication calls. BattyCoda has the potential to build research capacity in the field of acoustic communication by expanding the availability of libraries including a wider range of bat calls and species, thereby enabling the exploration of new hypotheses
A Framework to Unify the Relationship Between Numerical Abundance, Biomass, and Environmental DNA
No full textDoes environmental DNA (eDNA) concentration correlate with numerical abundance (N) or biomass in aquatic organisms? We hypothesize that eDNA can be adjusted to simultaneously reflect both. Building on frameworks developed from the Metabolic Theory of Ecology, we derive two equations to adjust eDNA data to simultaneously reflect both N and biomass using population size structure data and allometric scaling coefficients. We also demonstrate that these equations share model parameters, necessitating the joint estimation of regressions between adjusted eDNA, N, and biomass. Furthermore, our framework can be extended to model how other variables (temperature, taxa, diet, trophic level, etc.) might impact relationships between eDNA, N, and biomass in natural ecosystems. We applied our framework to data from two previously published studies correlating eDNA to Brook Trout (Salvelinus fontinalis) N and biomass. In both case studies, point estimates of the scaling coefficient (b) reflected allometric processes (b = 0.51 and 0.37 for Case Study 1 and 2, respectively), with credible intervals indicating that b likely differed from zero (i.e., eDNA scales with N) and one (i.e., eDNA scales with biomass). Directly estimating the value of b improved estimates of N and biomass relative to assuming b equals 0, which particularly affected the capacity to estimate biomass. However, models assuming eDNA production scaled with biomass (i.e., b = 1) were largely similar to estimating b, implying that assuming eDNA scales linearly with biomass might be a sufficient approximation for some systems. Nevertheless, the framework demonstrates that correlating eDNA directly with either N or biomass (as is commonly done in many studies) inherently necessitates an adjustment to infer the other metric if populations exhibit size structure variation. Collectively, we demonstrate that quantitative eDNA data is unlikely to correspond exactly to either population N or biomass but can be adjusted to simultaneously reflect both
The Lance: School Year 2012-2013
No full textSchool Year 2012-2013 Vol. 85: no. 1 (2012: May 2) 16p.Vol. 85: no. 2 (2012: May 16) 16p.Vol. 85: no. 3 (2012: May 30) 16p.Vol. 85: no. 4 (2012: June 13) 16p.Vol. 85: no. 5 (2012: June 27) 16p.Vol. 85: no. 6 (2012: July 11) 16p.Vol. 85: no. 7 (2012: July 25) 16p.Vol. 85: no. 8 (2012: Aug. 8) 16p.Vol. 85: no. 9 (2012: Aug. 22) 20p.Vol. 85: no. 10 (2012: Sept. 5) 24p.Vol. 85: no. 11 (2012: Sept. 12) 20p.Vol. 85: no. 12 (2012: Sept. 19) 16p.Vol. 85: no. 13 (2012: Sept. 26) 16p.Vol. 85: no. 14 (2012: Oct. 3) 16p.Vol. 85: no. 15 (2012: Oct. 10) 16p.Vol. 85: no. 16 (2012: Oct. 17) 16p.Vol. 85: no. 17 (2012: Oct. 24) 16p.Vol. 85: no. 18 (2012: Oct. 31) 16p.Vol. 85: no. 19 (2012: Nov. 7) 16p.Vol. 85: no. 20 (2012: Nov. 14) 16p.Vol. 85: no. 21 (2012: Nov. 21) 16p.Vol. 85: no. 22 (2012: Nov. 28) 12p.Vol. 85: no. 23 (2012: Dec. 5) 16p.Vol. 85: no. 24 (2012: Dec. 12) 16p. Pages 9-16 are an unpaginated supplement: Holiday GuideVol. 85: no. 25 (2012: Dec. 19) 20p. Pages 13-20 are an unpaginated supplement: NYE SpecialVol. 85: no. 26 (2013: Jan. 9) 12p.Vol. 85: no. 27 (2013: Jan. 16) 12p.Vol. 85: no. 28 (2013: Jan. 23) 16p.Vol. 85: no. 29 (2013: Jan. 30) 16p.Vol. 85: no. 30 (2013: Feb. 6) 16p.Vol. 85: no. 31 (2013: Feb. 13) 16p.Vol. 85: no. 32 (2013: Feb. 27) 16p.Vol. 85: no. 33 (2013: Mar. 6) 16p.Vol. 85: no. 34 (2013: Mar. 13) 16p.Vol. 85: no. 35 (2013: Mar. 20) 16p.Vol. 85: no. 36 (2013: Mar. 27) 16p.Vol. 85: no. 37 (2013: Apr. 3) 16p.Vol. 85: no. 38 (2013: Apr. 17) 16p
Raw-Data Driven Functional Data Analysis with Multi-Adaptive Functional Neural Networks for Ergonomic Risk Classification Using Facial and Bio-Signal Time-Series Data
No full textErgonomic risk classification during manual lifting tasks is crucial for the prevention of workplace injuries. This study addresses the challenge of classifying lifting task risk levels (low, medium, and high risk, labeled as 0, 1, and 2) using multi-modal time-series data comprising raw facial landmarks and bio-signals (electrocardiography [ECG] and electrodermal activity [EDA]). Classifying such data presents inherent challenges due to multi-source information, temporal dynamics, and class imbalance. To overcome these challenges, this paper proposes a Multi-Adaptive Functional Neural Network (Multi-AdaFNN), a novel method that integrates functional data analysis with deep learning techniques. The proposed model introduces a novel adaptive basis layer composed of micro-networks tailored to each individual time-series feature, enabling end-to-end learning of discriminative temporal patterns directly from raw data. The Multi-AdaFNN approach was evaluated across five distinct dataset configurations: (1) facial landmarks only, (2) bio-signals only, (3) full fusion of all available features, (4) a reduced-dimensionality set of 12 selected facial landmark trajectories, and (5) the same reduced set combined with bio-signals. Performance was rigorously assessed using 100 independent stratified splits (70% training and 30% testing) and optimized via a weighted cross-entropy loss function to manage class imbalance effectively. The results demonstrated that the integrated approach, fusing facial landmarks and bio-signals, achieved the highest classification accuracy and robustness. Furthermore, the adaptive basis functions revealed specific phases within lifting tasks critical for risk prediction. These findings underscore the efficacy and transparency of the Multi-AdaFNN framework for multi-modal ergonomic risk assessment, highlighting its potential for real-time monitoring and proactive injury prevention in industrial environments
