An official website of the United States government
During flash flooding, quick and effective rescue operations are crucial to minimizing harm to vulnerable communities. While much research focused on emergency response and evacuation, few studies address how overhead powerline obstructions impact rescue operations. Additionally, existing research on vulnerable communities often emphasizes long-term flood mitigation and recovery but less so on immediate responses. To ensure rapid and equitable flood rescue operations, this study derives an integrated metric to quantify rescue demands that incorporate rescue efficiency, community flood severity, and social vulnerability. In detail, rescue efficiency is calculated by analyzing a network that captures the geospatial interdependencies between the residential buildings' road networks and overhead power lines; community flood severity is quantified as the percentage of building damage resulting from flood impacts; and social vulnerability is an integrated indication of key household composition factors (e.g., elders, single parents, and minorities). Based on this metric, a systematic step is designed to suggest the sequence of rescue operations and the strategies for distributing rescue lifeboats at emergency facilities. The applicability and feasibility of the proposed approach were demonstrated using lifeboat rescue operations in Manville, New Jersey, during Hurricane Ida. This study calculates dynamic changes in rescue loads of all emergency facilities and then finds the optimal strategies for distributing lifeboats. The results highlight the significant impact of overhead power line obstructions on the optimal rescue lifeboat distribution. Additionally, the results suggest prioritizing emergency evacuation for socially vulnerable households in Manville township. Practically, the generated rescue sequence and rescue lifeboat distribution are expected to help emergency response agencies perform effective and rapid rescue operations.
more »
« less
Comprehensive survey of body weight estimation: techniques, datasets, and applications
Accurate weight measurement is critical in emergency medicine, particularly for the precise administration of medications and treatments. However, traditional methods of weight estimation can be unreliable, especially in time-sensitive or resource-limited environments. This study provides a comprehensive review of the advancements and techniques in body weight estimation, with a focus on modern approaches leveraging contactless sensors, such as 3D cameras, and AI-powered computational models. The research evaluates the accuracy, reliability, and practical applicability of these methods across different contexts, including healthcare, forensic sciences, and emergency response. Additionally, this study identifies the limitations of current methodologies and uncovers gaps in the literature that warrant further investigation. Our findings aim to guide future research efforts and the development of more precise and scalable weight estimation solutions, ultimately enhancing their applicability in a variety of sectors.
more »
« less
- Award ID(s):
- 2231200
- PAR ID:
- 10571786
- Editor(s):
- Furht, Borko
- Publisher / Repository:
- Springer Publisher
- Date Published:
- Journal Name:
- Multimedia Tools and Applications
- ISSN:
- 1573-7721
- Subject(s) / Keyword(s):
- Weight estimation 3d camera Depth Emergency medicine Drug delivery
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
A major challenge in the practical use of Machine Translation (MT) is that users lack information on translation quality to make informed decisions about how to rely on outputs. Progress in quality estimation research provides techniques to automatically assess MT quality, but these techniques have primarily been evaluated in vitro by comparison against human judgments outside of a specific context of use. This paper evaluates quality estimation feedback in vivo with a human study in realistic high-stakes medical settings. Using Emergency Department discharge instructions, we study how interventions based on quality estimation versus backtranslation assist physicians in deciding whether to show MT outputs to a patient. We find that quality estimation improves appropriate reliance on MT, but backtranslation helps physicians detect more clinically harmful errors that QE alone often misses.more » « less
-
Abstract ObjectivesPreviously developed methods in subadult body mass estimation have not been tested in populations other than European–American or African–American. This study uses a contemporary Taiwanese sample to test these methods. Through evaluating their accuracy and bias, we addressed whether the allometric relationships between body mass and skeletal traits commonly used in subadult body mass estimation are conserved among different populations. Materials and MethodsComputed tomography scans of lower limbs from individuals aged 0–17 years old of both sexes were collected from National Taiwan University Hospital along with documented body weight. Polar second moment of area, distal femoral metaphyseal breadth, and maximum superior/inferior femoral head diameter were collected either directly from the scans or from reconstructed 3D models. Estimated body mass was compared with documented body mass to assess the performance of the equations. ResultsCurrent methods provided good body mass estimates in Taiwanese individuals, with accuracy and bias similar to those reported in other validation studies. A tendency for increasing error with increasing age was observed for all methods. Reduced major axis regression showed the allometric relationships between different skeletal traits and body mass across different age categories can all be summarized using a common fitted line. A revised, maximum likelihood‐based approach was proposed for all skeletal traits. DiscussionThe results suggested that the allometric relationships between body mass and different skeletal traits are largely conserved among populations. The revised method provided improved applicability with strong underlying theoretical justifications, and potential for future improvements.more » « less
-
We study the theory of neural network (NN) from the lens of classical nonparametric regression problems with a focus on NN's ability to adaptively estimate functions with heterogeneous smoothness -- a property of functions in Besov or Bounded Variation (BV) classes. Existing work on this problem requires tuning the NN architecture based on the function spaces and sample sizes. We consider a "Parallel NN" variant of deep ReLU networks and show that the standard weight decay is equivalent to promoting the ℓp-sparsity (0<1) of the coefficient vector of an end-to-end learned function bases, i.e., a dictionary. Using this equivalence, we further establish that by tuning only the weight decay, such Parallel NN achieves an estimation error arbitrarily close to the minimax rates for both the Besov and BV classes. Notably, it gets exponentially closer to minimax optimal as the NN gets deeper. Our research sheds new lights on why depth matters and how NNs are more powerful than kernel methods.more » « less
-
Building occupancy information is significant for a variety of reasons, from allocation of resources in smart buildings to responding during emergency situations. As most people spend more than 90% of their time indoors, a comfortable indoor environment is crucial. To ensure comfort, traditional HVAC systems condition rooms assuming maximum occupancy, accounting for more than 50% of buildings’ energy budgets in the US. Occupancy level is a key factor in ensuring energy efficiency, as occupancy-controlled HVAC systems can reduce energy waste by conditioning rooms based on actual usage. Numerous studies have focused on developing occupancy estimation models leveraging existing sensors, with camera-based methods gaining popularity due to their high precision and widespread availability. However, the main concern with using cameras for occupancy estimation is the potential violation of occupants’ privacy. Unlike previous video-/image-based occupancy estimation methods, we addressed the issue of occupants’ privacy in this work by proposing and investigating both motion-based and motion-independent occupancy counting methods on intentionally blurred video frames. Our proposed approach included the development of a motion-based technique that inherently preserves privacy, as well as motion-independent techniques such as detection-based and density-estimation-based methods. To improve the accuracy of the motion-independent approaches, we utilized deblurring methods: an iterative statistical technique and a deep-learning-based method. Furthermore, we conducted an analysis of the privacy implications of our motion-independent occupancy counting system by comparing the original, blurred, and deblurred frames using different image quality assessment metrics. This analysis provided insights into the trade-off between occupancy estimation accuracy and the preservation of occupants’ visual privacy. The combination of iterative statistical deblurring and density estimation achieved a 16.29% counting error, outperforming our other proposed approaches while preserving occupants’ visual privacy to a certain extent. Our multifaceted approach aims to contribute to the field of occupancy estimation by proposing a solution that seeks to balance the trade-off between accuracy and privacy. While further research is needed to fully address this complex issue, our work provides insights and a step towards a more privacy-aware occupancy estimation system.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s11042-024-20318-4
