Douglas College

Real-time imaging sonar is crucial for underwater monitoring where optical sensing fails, but its use is limited by low uplink bandwidth and severe sonar-specific artifacts (speckle, motion blur, reverberation, acoustic shadows) affecting up to 98% of frames. We present SCOPE, a selfsupervised framework that jointly performs compression and artifact correction without clean-noise pairs or synthetic assumptions. SCOPE combines (i) Adaptive Codebook Compression (ACC), which learns frequency-encoded latent representations tailored to imaging sonar, with (ii) Frequency-Aware Multiscale Segmentation (FAMS), which decomposes frames into low-frequency structure and sparse high-frequency dynamics while suppressing rapidly fluctuating artifacts. A hedging training strategy further guides frequency-aware learning using low-pass proxy pairs generated without labels. Evaluated on months of in-situ ARIS sonar data, SCOPE achieves a structural similarity index (SSIM) of 0.77, representing a 40% improvement over prior self-supervised denoising baselines, at bitrates down to = 0.0118 bpp. It reduces uplink bandwidth by more than 80% while improving downstream detection. The system runs in real time, with 3.1 ms encoding on an embedded GPU and 97 ms full multi-layer decoding on the server end. SCOPE has been deployed for months in three Pacific Northwest rivers to support real-time salmon enumeration and environmental monitoring in the wild. Results demonstrate that learning frequency-structured latents enables practical, low-bitrate sonar streaming with preserved signal details under real-world deployment conditions.

This research study examined the experiences of racialized social workers in Canada through semi-structured interviews with five participants. Grounded theory analysis revealed six themes: participants' experience with racism; bearing the burden: professional and personal responsibility; progress, regression, and stagnation: addressing racism; participants' personal response to racism; participants' experiences reporting racism; and identity, role and resilience in social work. Across all themes, participants described pervasive racism. The findings highlight the emotional labor of racialized practitioners who must advocate for racialized clients, shoulder microaggressions, and challenge racism without organizational support. Implications for social work and policy include the need for culturally responsive leadership, formal reporting mechanisms, and anti-racism training developed by racialized professionals. Future researchers should expand the sample size and compare experiences across settings. The findings from this study reinforce the importance of an anti-racist frameworks in social work education and practice.

Wi-Fi-based passive indoor localization has gained prominence owing to its high accuracy and ease of deployment in global positioning system (GPS)-denied environments. However, channel state information (CSI)-based systems face challenges, including high data acquisition requirements, significant computational overhead, and limited transferability. In this article, we introduce DBLoc, a Wi-Fi localization system that leverages beamforming feedback information (BFI), a novel attribute provided by modern Wi-Fi hardware. BFI's clear-text transmission and stable characteristics make it an ideal choice for localization tasks. We prove that BFI provides a lightweight alternative to CSI, significantly reducing both data acquisition and storage requirements. Compared with traditional deep learning frameworks using convolutional networks, DBLoc employs a pruning-based residual architecture to reduce computational overhead, achieving an inference cost of only 175.7 MFLOPs, thus optimizing performance within an edge-deployment budget. To enable transferability that surpasses current meta-learning approaches, DBLoc incorporates a virtual-domain-based meta-learning algorithm, ensuring robust performance with minimal target-domain data. In addition, a spatial-encryption mechanism is proposed to safeguard the BFI-based model from eavesdropping. Extensive evaluations demonstrate that DBLoc achieves a median localization error of approximately 0.5 m while significantly reducing localization accuracy for unauthorized attackers.

Given the rapid integration of genomics into health care, educational innovation is necessary to equip the nursing workforce with the knowledge and skills required to deliver safe and equitable genomics-informed care. This commentary presents an online, open-access genomics education resource for nursing students, outlining the curriculum structure, pedagogical approach, core content, and strategies to support nurse educator implementation. By offering a customizable, credit-based curriculum aligned with existing competencies, this resource addresses the high-priority need for a harmonized genomic nursing curriculum that can be disseminated internationally. As such, this course helps to overcome some of the global challenges to integrating genomics into nursing education and practice.

Adolescence can be a difficult time for many, and avoiding unwanted attention such as teasing, bullying, and unwanted sexual attention can often prevent negative downstream effects. In study 1, we used act nomination to develop a taxonomy of protective behaviors that participants (N = 164) remembered using or observing during middle/high school to avoid unwanted attention. Participants also described the social risks these behaviors were used to avoid. The acts were consolidated into a taxonomy yielding 53 distinct behaviors. Thematic analysis of the social risks being managed produced four themes consistent with fundamental motives: status/esteem, affiliation, mating, and self-protection (Griskevicius & Kenrick, 2013). Study 2 (N = 249) examined whether individual differences in social support, HEXACO emotionality, vulnerability, coolness, socioeconomic status (SES), and formidability predicted the frequency of use, perceived cost, and perceived effectiveness associated with the retrospective use of the 53 protective behaviors. Use of protective behaviors was positively related to emotionality, vulnerability, and perceived costs, while social support, SES, coolness, and formidability were negatively related to behavior use. Qualitative responses revealed that while some protective behaviors may provide short-term relief, they can also facilitate social withdrawal, suggesting that supportive environments and strong social networks may help reduce adolescents’ reliance on costly avoidance strategies.