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The Artificial Intelligence-Enhanced Echocardiographic Detection of Congenital Heart Defects in the Fetus: A Mini-Review
Get PDFArtificial intelligence (AI) is rapidly gaining attention in radiology and cardiology for accurately diagnosing structural heart disease. In this review paper, we first outline the technical background of AI and echocardiography and then present an array of clinical applications, including image quality control, cardiac function measurements, defect detection, and classifications. Collectively, we answer how integrating AI technologies and echocardiography can help improve the detection of congenital heart defects. Particularly, the superior sensitivity of AI-based congenital heart defect (CHD) detection in the fetus (\u3e90%) allows it to be potentially translated into the clinical workflow as an effective screening tool in an obstetric setting. However, the current AI technologies still have many limitations, and more technological developments are required to enable these AI technologies to reach their full potential. Also, integrating diagnostic AI technologies into the clinical workflow should resolve ethical concerns. Otherwise, deploying diagnostic AI may not address low-resource populations’ healthcare access disadvantages. Instead, it will further exacerbate the access disparities. We envision that, through the combination of tele-echocardiography and AI, low-resource medical facilities may gain access to the effective detection of CHD at the prenatal stage
Evaluation of L- and S-Band Polarimetric Data for Monitoring Great Lakes Coastal Wetland Health in Preparation for NISAR
No full textHighlights: What are the main findings? Inundation extent mapping demonstrated high accuracy (79–83%) at C-, S- and L-band areas with limitations related to stand structure and frequency, while du-al-frequency SAR was found to have high accuracy (~92%) for wetland type mapping. Misattribution of dominant double-bounce scatter (characteristic of wetlands) to single-bounce scatter occurs at certain vegetation moisture and SAR geometries for C-, S- and L-band areas. What are the implications of the main findings? Multi-frequency polarimetric SAR provides high-accuracy wetland mapping capabilities, regardless of cloud cover. The misattribution of double-bounce to single-bounce scattering results in errors in wetland extent mapping, but it may also be useful in monitoring wetland health since it has larger anomalies with low vegetation moisture. Coastal wetlands are a critical buffer between land and water that are threatened by land use and climate change, necessitating improved monitoring for management and resilience planning. The recently launched NASA-ISRO L- and S-band SAR satellite (NISAR) will provide regular collections of fully polarimetric SAR imagery over the Great Lakes, allowing for unprecedented remote monitoring of the large expanses of coastal wetlands in the region. Prior research with polarimetric C-band SAR showed inconsistencies with common polarimetric analysis techniques, including the erroneous misattribution of double-bounce scattering in three-component scattering models. To prepare for NISAR and determine whether SAR-based coastal wetland analysis methods established with the C-band are applicable to the L- and S-bands, the NASA-ISRO airborne system (ASAR) collected imagery over western Lake Erie and Lake St. Clair coincident with a field data collection campaign. ASAR data were analyzed to identify common Great Lakes coastal wetland vegetation species, assess the extent of inundation, and derive biomass retrieval algorithms. Co-polarized phase difference histograms were also analyzed to assess the validity of three-component scattering decompositions. The L- and S-bands allowed for the production of wetland type maps with high accuracies (92%), comparable to those produced using a fusion of optical and SAR data. Both frequencies could assess the extent of flooded vegetation, with the S-band correctly identifying inundated vegetation at a slightly higher rate than the L-band (83% to 78%). Marsh vegetation biomass retrieval algorithms derived from L-band data had the best correlation with field data (R2 = 0.71). Three component scattering models were found to misattribute double-bounce scattering at incidence angles shallower than 35°. The L- and S-band results were compared with satellite RADARSAT-2 imagery collected close to the ASAR acquisitions. This study provides an advanced understanding of polarimetric SAR for monitoring wetlands and provides a framework for utilizing forthcoming NISAR data for effective monitoring
Adapting Design Workshops for Autistic Adults
No full textAutism is a neurodevelopmental disability that impacts one\u27s social communication and interaction. When left unsupported, this can increase the amount of loneliness felt by autistic people. Communication technology, such as AAC, can be helpful in supporting social communication, especially when co-designed with autistic people. We conducted a series of design workshops to co-design a new AAC system specifically supporting social communication. In this paper, we focus on the accessibility issues that were identified when running our workshops and provide recommendations on how to improve the process. We found that it is critical to build support for information processing time into the workshops, include a variety of AAC stakeholders, and create a shared vocabulary between the workshop participants to make design workshops more accessible to autistic adults
Aeolia: A Fast and Secure Userspace Interrupt-Based Storage Stack
No full textPolling-based userspace storage stacks achieve great I/O performance. However, they cannot efficiently and securely share disks and CPUs among multiple tasks. In contrast, interrupt-based kernel stacks inherently suffer from subpar I/O performance but achieve advantages in resource sharing.We present Aeolia, a novel storage stack that achieves great I/O performance while offering efficient and secure resource sharing. Aeolia is an interrupt-based userspace storage stack, representing a new point in the design space previously considered unfeasible. Our main observation is that, contrary to conventional wisdom, polling offers only marginal disk performance improvements over interrupts. Aeolia exploits user interrupt, an emerging hardware feature commonly used for userspace IPIs, in a novel way to deliver storage interrupts directly to userspace, thereby achieving high I/O performance with direct access. Aeolia leverages the hardware intra-process isolation features and sched_ext, an eBPF-based userspace scheduling framework, to efficiently and securely share CPUs and disks among multiple tasks, challenging the common belief that these are inherent disadvantages of userspace storage stacks. The above design enables Aeolia to realize AeoFS, a high-performance library file system that securely and directly accesses disks. Our evaluation shows that Aeolia outperforms Linux by 2× and AeoFS outperforms ext4 by up to 19.1×, respectively
Analysis of a Hybrid Light Tactical Vehicle Demonstrator: Considering Performance, Functionality, and Weight
No full textHybrid powertrain technology serves to improve performance, enable new functional capabilities, decrease fuel consumption, increase operational reach, and increase lethality by supporting advanced weapons systems. Several demonstrators have been developed for the Army, including those recently commissioned and tested by numerous programs over the last decade. This work examines the results of one of these demonstrators for a Light Tactical Vehicle (LTV) and analyzes tradeoffs in the components\u27 characteristics, including the battery size, energy, and power capabilities, specifically regarding the system\u27s ability to meet key performance and power generation requirements. This work was completed through test data analysis coupled with a vehicle 1D simulation. Results show design implementation impacts and tradeoffs between vehicle weight, performance, EV-only range, and fuel consumption that can be utilized for system-level optimization
Observation of the Time-Invariant Nature of Evaporating Thin Films: Insights from SPR Imaging
No full textThe evaporating thin film (ETF) plays a critical role in heat dissipation due to the substantially enhanced evaporation flux. Despite its fundamental importance in interfacial evaporation dynamics, direct experimental characterization of the ETF [1-3] has remained a significant challenge, resulting in a limited understanding of its temporal evolution. This presentation provides the first direct experimental evidence demonstrating the time-invariant nature of ETF profiles during the pinning stage of droplet evaporation, offering novel insights into interfacial evaporation mechanisms using the surface plasmon resonance (SPR) imaging [4-5]. The ultra-thin liquid film profiles are measured under varying surface wettability conditions through a self-assembled monolayer (SAM) on a gold substrate. A theoretical model incorporating kinetic theory and the augmented Young-Laplace equation is also employed to quantitatively examine the finite evaporation flux in the ETF region. The findings reveal that the ETF profile remains time-invariant during the pinned stage, exhibiting local equilibrium characteristics, and theoretical predictions show good agreement with experimental observations. Notably, it is found that surface wettability has a minor influence on variations in ETF profiles, and the disjoining pressure is found to be higher on hydrophilic substrates, contributing to further thinning of the ETF. Furthermore, these findings indicate that higher surface wettability enhances ETF thinning, thereby improving heat dissipation via maximum evaporation flux
THERMAL PROCESSING SIMULATIONS OF MULTI-CONSTITUENT AUSTENITE-CONTAINING STEELS FOR EXPLOSIVE CONFINEMENT VESSEL CONSTRUCTION
No full textExplosive confinement vessels (ECVs) require materials that exhibit high strength and toughness that are also amenable to manufacturing and vessel fabrication. There have been considerable challenges identifying materials that exhibit suitable mechanical performance while also being manufacturable in thick sections and weldable without post-weld heat treatment. Multi-Constituent Austenite-Containing (MCA) steels are candidate alloys for ECV construction that are anticipated to exhibit greater performance and increased manufacturability relative to currently available materials. The fine-scale microstructures associated with MCA steels predominantly consist of ferrite, bainite, and/or martensite, with retained austenite. Austenite retention is accomplished through chemical stabilization by partitioning of carbon (C), manganese (Mn), and/or nickel (Ni) during thermal processing. The resulting composite microstructure can enable ductility at low temperatures while maintaining high strength. Due to the amount of Mn and/or Ni in MCA alloys, there is sufficient hardenability to achieve uniform microstructures in thick-section ECV components. The relatively low C content is anticipated to enable good weldability. The present work discusses progress toward developing MCA steels for ECVs. Understanding the effects of austenite characteristics, such as volume fraction, solute enrichment, and morphology, on mechanical performance, are of particular interest. Toward this goal, twelve experimental alloys have been developed with a range of Mn and Ni concentrations as the predominant austenite stabilizing alloy. Thermo-Calc® was used to calculate austenite characteristics for multiple austenite re-forming heat treatments that are intended to develop MCA microstructures. Heat treatments considered here include intercritical annealing (IA), quenchlamellarize-temper (QLT), and double-soaking (DS)
Adsorptive Removal of Arsenite and Cobalt by Commercial Sorbents
No full textDespite the prevalence and toxicity of heavy metals in the environment, arsenic and cobalt are of particular concern due to their high mobility and bioaccumulation potential, particularly in contaminated groundwater. Herein, we studied the adsorption behavior of commercially available sorbents, including Fluorosorb-100 (FS-100), Fluorosorb-200 (FS-200), and Filtrasorb-400 (F-400), for the removal of arsenite (As(III)) and cobalt (Co(II)), aiming at the selection of filter media in terms of future groundwater remediation. Kinetic analysis revealed that As(III) adsorption followed a pseudo-second-order model, while Co(II) showed mixed first- and second-order behavior, reflecting sorbent-dependent mechanisms. Equilibrium isotherm modeling revealed strong correlations with both Langmuir and Freundlich models, confirming heterogeneous adsorption sites and multilayer interactions. FS-100 demonstrated the highest affinity for As(III) (qₘ = 0.46 mg/g) and F-400 exhibited the greatest adsorption capacity for Co(II) (qₘ = 1.00 mg/g), while FS-200 consistently showed relatively weaker adsorption for both metals. Desorption studies indicated predominantly irreversible binding, with minimal release of As(III) from F-400 and Co(II) from FS-200 and F-400, even at high concentrations. Overall, these findings highlight that commercially available sorbents can effectively capture arsenite and cobalt, offering cost-effective and scalable options for heavy-metal removal in groundwater remediation systems under realistic environmental conditions
Innovation Curse: The Wastefulness of Technologies Believed to Mitigate Climate Change
No full textTechnological innovations are increasingly promoted as solutions to climate change. However, many innovations, including Carbon Capture and Storage, bioplastics, and glacier geo-engineering, suffer significant limitations from high costs, speculative efficacy, and adverse ecological consequences. Using Granular Interaction Thinking Theory (GITT), a transdisciplinary framework grounded in information theory, quantum mechanics, and mindsponge theory, in this study, we explain how such technological innovations become systemically favored despite their flaws. We introduce the concept of the “innovation curse,” which arises when institutional and cognitive filtering systems, operating under high informational entropy, default to familiar but ineffective techno-solutions while marginalizing Indigenous and Local Knowledges and nature-based approaches. To address this dysfunction, we propose the Eco-Surplus Transformation Framework, a new model for environmental decision-making designed to foster an eco-surplus culture. Guided by a core semiconducting principle that prohibits offsetting environmental harm with monetary value, the framework establishes a rational hierarchy for climate action that prioritizes harm prevention and proven ecological strategies. We provide the Eco-Surplus Governance Matrix as a toolkit for implementing this paradigm shift through institutional reform. Ultimately, our study argues for a fundamental reorientation of climate strategy away from technological solutionism and toward regenerative, community-driven practices rooted in Indigenous and Local Knowledges to foster long-term environmental resilience
BOARD # 461: The Husky PAWS (Pathways for Academic Wellness and Success) S-STEM Program
No full textThe Husky PAWS (Pathways for Academic Wellness and Success) NSF S-STEM program at Michigan Tech was awarded in 2023. Our team reviewed initial applications in Spring 2024 and launched the primer 3-week Husky PAWS Summer Bridge in 2024. The inaugural cohort included 6 students at the 4-year scholarship level and 6 students receiving one-year finishing scholarships. The Husky PAWS S-STEM program is utilizing Yosso’s Cultural Wealth Model [1] to leverage scholar’s cultural wealth assets for their academic success. The overarching program goals are increasing retention and graduation rates of these Pell-eligible scholars to those of non-Pell students.
Centering the Husky PAWS S-STEM scholars as experts in their own lived experience, the Husky PAWS S-STEM program takes a participatory action research (PAR) approach to improving our program. We have included funding for one of the Husky PAWS S-STEM scholars to serve as a PAR co-researcher alongside our project team. At this point, we have identified our first PAR researcher, who is a co-author on this poster and paper.
This paper will highlight progress, and offer key takeaways of the Husky PAWS S-STEM program through its first year. Efforts include developing applicant screening materials, summer bridge metacognition programming, cohort activities to build community throughout the academic year, and our PAR approach to improving these activities for the second project year