Sensing by wireless reading Ag/AgCl redox conversion on RFID tag : universal, battery-less biosensor design

Massive integration of biosensors into design of Internet-of-Things (IoT) is vital for progress of healthcare. However, the integration of biosensors is challenging due to limited availability of battery-less biosensor designs. In this work, a combination of nanomaterials for wireless sensing of biological redox reactions is described. The design exploits silver nanoparticles (AgNPs) as part of the RFID tag antenna. We demonstrate that a redox enzyme, particularly, horseradish peroxidase (HRP), can convert AgNPs into AgCl in the presence of its substrate, hydrogen peroxide. This strongly changes the impedance of the tag. The presented example exploits gold nanoparticle (AuNP)-assisted electron transfer (ET) between AgNPs and HRP. We show that AuNP is a vital intermediate for establishing rapid ET between the enzyme and AgNPs. As an example, battery-less biosensor-RFID tag designs for H2O2 and glucose are demonstrated. Similar battery-less sensors can be constructed to sense redox reactions catalysed by other oxidoreductase enzymes, their combinations, bacteria or other biological and even non-biological catalysts. In this work, a fast and general route for converting a high number of redox reaction based sensors into battery-less sensor-RFID tags is described

Sensing by wireless reading Ag/AgCl redox conversion on RFID tag : universal, battery-less biosensor design

Massive integration of biosensors into design of Internet-of-Things (IoT) is vital for progress of healthcare. However, the integration of biosensors is challenging due to limited availability of battery-less biosensor designs. In this work, a combination of nanomaterials for wireless sensing of biological redox reactions is described. The design exploits silver nanoparticles (AgNPs) as part of the RFID tag antenna. We demonstrate that a redox enzyme, particularly, horseradish peroxidase (HRP), can convert AgNPs into AgCl in the presence of its substrate, hydrogen peroxide. This strongly changes the impedance of the tag. The presented example exploits gold nanoparticle (AuNP)-assisted electron transfer (ET) between AgNPs and HRP. We show that AuNP is a vital intermediate for establishing rapid ET between the enzyme and AgNPs. As an example, battery-less biosensor-RFID tag designs for H2O2 and glucose are demonstrated. Similar battery-less sensors can be constructed to sense redox reactions catalysed by other oxidoreductase enzymes, their combinations, bacteria or other biological and even non-biological catalysts. In this work, a fast and general route for converting a high number of redox reaction based sensors into battery-less sensor-RFID tags is described

Development of a novel hybrid multi-boosting neural network model for spatial prediction of urban flood

Abstract In this study, a new hybridized machine learning algorithm for urban flood susceptibility mapping, named MultiB-MLPNN, was developed using a multi-boosting technique and MLPNN. The model was tested in Amol City, Iran, a data-scarce city in an ungauged area which is prone to severe flood inundation events and currently lacks flood prevention infrastructure. Performance of the hybridized model was compared with that of a standalone MLPNN model, random forest and boosted regression trees. Area under the curve, efficiency, true skill statistic, Matthews correlation coefficient, misclassification rate, sensitivity and specificity were used to evaluate model performance. In validation, the MultiB-MLPNN model showed the best predictive performance. The hybridized MultiB-MLPNN model is thus useful for generating realistic flood susceptibility maps for data-scarce urban areas. The maps can be used to develop risk-reduction measures to protect urban areas from devastating floods, particularly where available data are insufficient to support physically based hydrological or hydraulic models

Spatiotemporal changes in Iranian rivers’ discharge

Abstract Trends in river flow at national scale in Iran remain largely unclear, despite good coverage of river flow at multiple monitoring stations. To address this gap, this study explores the changes in Iranian rivers’ discharge using regression and analysis of variance methods to historically rich data measured at hydrometric stations. Our assessment is performed for 139 selected hydrometric stations located in Iranian data-rich basins that cover around 97% of the country’s rivers with more than 30 years of observations. Our findings show that most of the studied Iran’s rivers (>56%) have undergone a downward trend (P value < 0.1) in mean annual flow that is 2.5 times bigger than that obtained for the large world’s rivers, resulting in a change from permanent to intermittent for around 20% of rivers in Iran’s subbasins. Given no significant change observed in the main natural drivers of Iranian rivers’ discharge, these findings reveal the country’s surface fresh-water shortage was caused dominantly by anthropogenic disturbances rather than variability in climate parameters. It may even indicate the development of new river regimes with deep implications for future surface fresh-water storage in the country. This research’s findings improve our understanding of changes in Iranian rivers’ discharge and provide beneficial insights for sustainable management of water resources in the country

Development of novel hybridized models for urban flood susceptibility mapping

Abstract Floods in urban environments often result in loss of life and destruction of property, with many negative socio-economic effects. However, the application of most flood prediction models still remains challenging due to data scarcity. This creates a need to develop novel hybridized models based on historical urban flood events, using, e.g., metaheuristic optimization algorithms and wavelet analysis. The hybridized models examined in this study (Wavelet-SVR-Bat and Wavelet-SVR-GWO), designed as intelligent systems, consist of a support vector regression (SVR), integrated with a combination of wavelet transform and metaheuristic optimization algorithms, including the grey wolf optimizer (GWO), and the bat optimizer (Bat). The efficiency of the novel hybridized and standalone SVR models for spatial modeling of urban flood inundation was evaluated using different cutoff-dependent and cutoff-independent evaluation criteria, including area under the receiver operating characteristic curve (AUC), Accuracy (A), Matthews Correlation Coefficient (MCC), Misclassification Rate (MR), and F-score. The results demonstrated that both hybridized models had very high performance (Wavelet-SVR-GWO: AUC = 0.981, A = 0.92, MCC = 0.86, MR = 0.07; Wavelet-SVR-Bat: AUC = 0.972, A = 0.88, MCC = 0.76, MR = 0.11) compared with the standalone SVR (AUC = 0.917, A = 0.85, MCC = 0.7, MR = 0.15). Therefore, these hybridized models are a promising, cost-effective method for spatial modeling of urban flood susceptibility and for providing in-depth insights to guide flood preparedness and emergency response services