Search for: All records

Abstract Background and AimsA comprehensive standardized evaluation tool was needed to assess community awareness and preparedness when the pandemic hit the United States. This study aimed to develop and validate a new Coronavirus Awareness and Preparedness Scale (CAPS) through psychometric testing. MethodsThis study unfolded in two phases. Phase 1 (conducted in March and April 2020) focused on the development of the scale. Phase 2 (conducted in June and July 2020) measured the reliability and validity of the scale. Psychometric testing, including exploratory factor analysis and reliability testing, was performed with a convenience sample of 1237 faculty, staff, and students at a southern university in the United States. ResultsThe final CAPS model consists of four factors with 26 items: threat (seven items), confidence (11 items), individual precautions (three items), and public precautions (five items). The scale demonstrated satisfactory internal consistency (Cronbach'sα = 0.75). Strong and statistically significant item correlations were observed within the subscales through item analysis. ConclusionThe CAPS is a reliable and valid comprehensive evaluation instrument designed to gauge community awareness and preparedness during the early stages of the COVID‐19 pandemic. Its adaptability makes it suitable for measuring readiness and preparedness concerning any novel airborne disease or future airborne pandemic within a community. 

Abstract The quantification of urban impervious area has important implications for the design and management of urban water and environmental infrastructure systems. This study proposes a deep learning model to classify 15‐cm aerial imagery of urban landscapes, coupled with a vector‐oriented post‐classification processing algorithm for automatically retrieving canopy‐covered impervious surfaces. In a case study in Corpus Christi, TX, deep learning classification covered an area of approximately 312 km²(or 14.86 billion 0.15‐m pixels), and the post‐classification effort led to the retrieval of over 4 km²(or 0.18 billion pixels) of additional impervious area. The results also suggest the underestimation of urban impervious area by existing methods that cannot consider the canopy‐covered impervious surfaces. By improving the identification and quantification of various impervious surfaces at the city scale, this study could directly benefit a variety of environmental and infrastructure management practices and enhance the reliability and accuracy of processed‐based models for urban hydrology and water infrastructure. 

Abstract This study uses a small unmanned aircraft system equipped with a multispectral sensor to assess various vegetation indices (VIs) for their potential to monitor iron deficiency chlorosis (IDC) in a grain sorghum (Sorghum bicolorL.) crop. IDC is a nutritional disorder that stunts a plants’ growth and causes its leaves to yellow due to an iron deficit. The objective of this project is to find the best VI to detect and monitor IDC. A series of flights were completed over the course of the growing season and processed using Structure‐from‐Motion photogrammetry to create orthorectified, multispectral reflectance maps in the red, green, red‐edge, and near‐infrared wavelengths. Ground data collection methods were used to analyze stress, chlorophyll levels, and grain yield, correlating them to the multispectral imagery for ground control and precise crop examination. The reflectance maps and soil‐removed reflectance maps were used to calculate 25 VIs whose separability was then calculated using a two‐class distance measure, determining which contained the largest separation between the pixels representing IDC and healthy vegetation. The field‐acquired data were used to conclude which VIs achieved the best results for the dataset as a whole and at each level of IDC (low, moderate, and severe). It was concluded that the MERIS terrestrial chlorophyll index, normalized difference red‐edge, and normalized green (NG) indices achieved the highest amount of separation between plants with IDC and healthy vegetation, with the NG reaching the highest levels of separability for both soil‐included and soil‐removed VIs. 

Abstract Scarce and unreliable urban water supply in many countries has caused municipal users to rely on transfers from rural wells via unregulated markets. Assessments of this pervasive water re-allocation institution and its impacts on aquifers, consumer equity and affordability are lacking. We present a rigorous coupled human–natural system analysis of rural-to-urban tanker water market supply and demand in Jordan, a quintessential example of a nation relying heavily on such markets, fed by predominantly illegal water abstractions. Employing a shadow-economic approach validated using multiple data types, we estimate that unregulated water sales exceed government licences 10.7-fold, equalling 27% of the groundwater abstracted above sustainable yields. These markets supply 15% of all drinking water at high prices, account for 52% of all urban water revenue and constrain the public supply system’s ability to recover costs. We project that household reliance on tanker water will grow 2.6-fold by 2050 under population growth and climate change. Our analysis suggests that improving the efficiency and equity of public water supply is needed to ensure water security while avoiding uncontrolled groundwater depletion by growing tanker markets. 

Machine unlearning is becoming increasingly important as deep models become more prevalent, particularly when there are frequent requests to remove the influence of specific training data due to privacy concerns or erroneous sensing signals. Spatial-temporal Graph Neural Networks, in particular, have been widely adopted in real-world applications that demand efficient unlearning, yet research in this area remains in its early stages. In this paper, we introduce STEPS, a framework specifically designed to address the challenges of spatio-temporal graph unlearning. Our results demonstrate that STEPS not only ensures data continuity and integrity but also significantly reduces the time required for unlearning, while minimizing the accuracy loss in the new model compared to a model with 0% unlearning.