Search Results (5)

By definition, a smart city must improve its readiness for extreme events in order to confront the growing unpredictability of natural disasters. Doing this implies planning for resilience. That is, to enhance our capacity to cope, mitigate, adapt, and rebuild human settlements after a catastrophic event. Although scholars have argued that biodiversity can enhance resilience, there is a dearth of empirical research that specifically addresses this crucial issue. This research analyzes Nature’s Contributions to People related to tsunami resilience. Then, the relationship between biodiversity and community resilience indexes is examined for 50 coastal Chilean cities that are prone to tsunamis, using biodiversity data from an open access database. The resilience index “population living in the first kilometer from the shoreline” was found to be correlated with species richness (p = 0.48) and the evenness biodiversity index, Pielou (p = −0.47). These results suggest that biodiversity data availability is crucial for understanding nature’s contribution to human settlement resilience. Although this study was hindered by limited data availability, the potential use in other contexts remains valuable for the development of smart cities. The study highlights the need for increased biodiversity data collection on a national scale and emphasizes the use of ecoinformatics to create smart cities that can effectively respond to climate uncertainty in coastal urban areas. Full article

“Tsunami Alert Efficiency” is the rapid, accurate and reliable conduct of tsunami warning messaging, from the detection of potential tsunamigenic earthquakes to dissemination to all people under threat, and the successful survival of every person at risk on the basis of prior awareness and preparedness. Full article

Background: The COVID-19 pandemic has swept the world like a gigantic tsunami, turning social and economic activities upside down. Methods: This paper presents some of the innovative response strategies implemented by the public health system, healthcare facilities, and government in South Korea, which has been hailed as the model country for its success in containing COVID-19. Korea reinvented its public health infrastructure with a sense of urgency. Results: Korea’s success rests on its readiness, with the capacity for massive testing and obtaining prompt test results, effective contact tracing based on its world-leading mobile technologies, timely provision of personal protective equipment (PPE) to first responders, effective treatment of infected patients, and invoking citizens’ community and civic conscience for the shared goal of defeating the pandemic. The lessons learned from Korea’s response in countering the onslaught of COVID-19 provide unique implications for public healthcare administrators and operations management practitioners. Conclusion: Since many epidemic experts warn of a second wave of COVID-19, the lessons learned from the first wave will be a valuable resource for responding to the resurgence of the virus. Full article

Ready-to-eat (RTE) spinach is considered a high-risk food, susceptible to colonization by foodborne pathogens; however, other microbial populations present on the vegetable surface may interact with foodborne pathogens by inhibiting/inactivating their growth. In addition, sanitizers applied to minimally processed salad leaves should not disrupt this autochthonous barrier and should be maintained throughout the shelf life of the product. This investigation aimed at comparing the effects of a pH neutral electrochemically activated solution (ECAS), a peroxyacetic acid (PAA)-based commercial sanitizer (Ecolab Tsunami^® 100), and tap water wash on the minimally processed spinach leaf microbiome profile for 10 days after washing. The bacterial microbiota composition on spinach samples was assessed by 16S rRNA pyrosequencing and downstream analyses. Predominant phyla observed in decreasing order of abundance were Proteobacteria, Bacteroidetes, Actinobacteria and Firmicutes corresponding with the dominant families Micrococcaceae, Clostridiales Family XII, Flavobacteriaceae, Pseudomonadaceae, and Burkholderiaceae. Bacterial species richness and evenness (alpha diversity) and bacterial community composition among all wash types were not significantly different. However, a significant difference was apparent between sampling days, corresponding to a loss of overall heterogeneity over time. Analysis of composition of microbiome (ANCOM) did not identify any amplicon sequence variants (ASVs) or families having significantly different abundance in wash types; however, differences (17 ASVs and five families) were found depending on sampling day. This was the first bacterial microbiome composition study focused on ECAS and PAA-based wash solutions. These wash alternatives do not significantly alter microbial community composition of RTE spinach leaves; however, storage at refrigerated temperature reduces bacterial species heterogeneity. Full article

Accurate and high resolution bathymetric data is a necessity for a wide range of coastal oceanographic research topics. Active sensing methods, such as ship-based soundings and Light Detection and Ranging (LiDAR), are expensive and time consuming solutions. Therefore, the significance of Satellite-Derived Bathymetry (SDB) has increased in the last ten years due to the availability of multi-constellation, multi-temporal, and multi-resolution remote sensing data as Open Data. Effective SDB algorithms have been proposed by many authors, but there is no ready-to-use software module available in the Geographical Information System (GIS) environment as yet. Hence, this study implements a Geographically Weighted Regression (GWR) based SDB workflow as a Geographic Resources Analysis Support System (GRASS) GIS module (i.image.bathymetry). Several case studies were carried out to examine the performance of the module in multi-constellation and multi-resolution satellite imageries for different study areas. The results indicate a strong correlation between SDB and reference depth. For instance, case study 1 (Puerto Rico, Northeastern Caribbean Sea) has shown an coefficient of determination (R²⁾ of 0.98 and an Root Mean Square Error (RMSE) of 0.61 m, case study 2 (Iwate, Japan) has shown an R² of 0.94 and an RMSE of 1.50 m, and case study 3 (Miyagi, Japan) has shown an R² of 0.93 and an RMSE of 1.65 m. The reference depths were acquired by using LiDAR for case study 1 and an echo-sounder for case studies 2 and 3. Further, the estimated SDB has been used as one of the inputs for the Australian National University and Geoscience Australia (ANUGA) tsunami simulation model. The tsunami simulation results also show close agreement with post-tsunami survey data. The i.mage.bathymetry module developed as a part of this study is made available as an extension for the Open Source GRASS GIS to facilitate wide use and future improvements. Full article