FLOOD SUSCEPTIBILITY MODELLING USING GEOSPATIAL-BASED MULTI-CRITERIA DECISION MAKING IN LARGE SCALE AREAS

Flood is one of the most hazardous natural disasters that cause damages and poses a major threat to human lives and infrastructures worldwide, and its prevention is almost unfeasible. Thus, the detection of flood susceptible areas can be a key to lessen the amount of destruction and mortality. This study aims to implement a framework to identify flood potential zones in an ungauged large-scale area with frequent flood events in recent years. We used two Multi-Criteria Decision Making (MCDM) approaches combined with geospatial analysis, and remote sensing observations for this susceptibility analysis. Nine geomorphological and environmental factors that have an impact on flood behaviour were selected and used for susceptibility modelling. At first, the criteria’s weights were estimated using two MCDM approaches and based on experts’ knowledge. The resultant weights revealed that Flow Accumulation, Topographic wetness index, and Distance to River were the most influential flood susceptibility criteria. After calculating these weights, the criteria’s layers were aggregated through geospatial analysis, which resulted in generating flood susceptibility map. The area under the curve (AUC) and statistical measures such as the Kappa index were used to evaluate the proposed method's efficiency. The validation results illustrate that hybrid FAHP, with AUC= 96.68 and Kappa = 81.36 performed more efficiently than standard AHP, with AUC= 94.53 and Kappa=76.35. Overlaying these maps with the historical flood inventory dataset revealed that 86.43% of flooded areas were categorized as “high” and “very high”. Therefore, the flood susceptibility maps generated through the proposed approach can help the decision-makers and managers allocate the mitigation equipment and facility in data-scarce and ungauged large-scale areas

IDENTIFYING SUITABLE LOCATIONS FOR MANGROVE PLANTATION USING GEOSPATIAL INFORMATION SYSTEM AND REMOTE SENSING

Mangroves provide numerous environmental benefits, such as carbon sequestration, water purification, climate change mitigation, and flood and Tsunami impact reduction. Despite these unique advantages, mangroves are threatened by the combined adverse impacts of human activities and climate change. Therefore, it is essential to implement reasonable practices to avoid further degradation of mangroves and provide efficient workflows to increase their extent. Accordingly, better plantation policies are principally required for their conservation and rehabilitation. In this study, we desired to detect suitable locations for mangrove plantation in coastal areas of Hormozgan Province, Iran. We considered a relatively new Multi Criteria Decision Making (MCDM) technique to combine ten criteria derived from remote sensing in a GIS environment. The Best Worst Method (BWM), as an MDCM technique, was implemented to determine the relative importance of each criterion. Afterward, all criteria were aggregated using the Weighted Linear Combination (WLC) method to produce a mangrove plantation suitability map. Statistical measures, including Overall Accuracy (OA = 95%), Kappa Coefficient (KC = 87.9%), and Area Under Curve (AUC = 98.79%), indicated the high applicability of the implemented method for mangrove plantation site allocation. The produced map could give managers a profound insight into finding optimal spots to plant mangroves

Collaborative development of the Arrowsmith two node search interface designed for laboratory investigators.

Arrowsmith is a unique computer-assisted strategy designed to assist investigators in detecting biologically-relevant connections between two disparate sets of articles in Medline. This paper describes how an inter-institutional consortium of neuroscientists used the UIC Arrowsmith web interface http://arrowsmith.psych.uic.edu in their daily work and guided the development, refinement and expansion of the system into a suite of tools intended for use by the wider scientific community

CemOrange2 fusions facilitate multifluorophore subcellular imaging in C. elegans

Due to its ease of genetic manipulation and transparency, Caenorhabditis elegans (C. elegans) has become a preferred model system to study gene function by microscopy. The use of Aequorea victoria green fluorescent protein (GFP) fused to proteins or targeting sequences of interest, further expanded upon the utility of C. elegans by labeling subcellular structures, which enables following their disposition during development or in the presence of genetic mutations. Fluorescent proteins with excitation and emission spectra different from that of GFP accelerated the use of multifluorophore imaging in real time. We have expanded the repertoire of fluorescent proteins for use in C. elegans by developing a codon-optimized version of Orange2 (CemOrange2). Proteins or targeting motifs fused to CemOrange2 were distinguishable from the more common fluorophores used in the nematode; such as GFP, YFP, and mKate2. We generated a panel of CemOrange2 fusion constructs, and confirmed they were targeted to their correct subcellular addresses by colocalization with independent markers. To demonstrate the potential usefulness of this new panel of fluorescent protein markers, we showed that CemOrange2 fusion proteins could be used to: 1) monitor biological pathways, 2) multiplex with other fluorescent proteins to determine colocalization and 3) gain phenotypic knowledge of a human ABCA3 orthologue, ABT-4, trafficking variant in the C. elegans model organism

Reprocessing Models for the Optical Light Curves of Hypervariable Quasars from the Sloan Digital Sky Survey Reverberation Mapping Project

We explore reprocessing models for a sample of 17 hypervariable quasars, taken from the Sloan Digital Sky Survey Reverberation Mapping (SDSS-RM) project, which all show coordinated optical luminosity hypervariability with amplitudes of factors $\gtrsim 2$ between 2014 and 2020. We develop and apply reprocessing models for quasar light curves in simple geometries that are likely to be representative of quasar inner environments. In addition to the commonly investigated thin-disk model, we include the thick-disk and hemisphere geometries. The thick-disk geometry could, for instance, represent a magnetically-elevated disk, whereas the hemisphere model can be interpreted as a first-order approximation for any optically-thick out-of-plane material caused by outflows/winds, warped/tilted disks, etc. Of the 17 quasars in our sample, eleven are best-fit by a hemisphere geometry, five are classified as thick disks, and both models fail for just one object. We highlight the successes and shortcomings of our thermal reprocessing models in case studies of four quasars that are representative of the sample. While reprocessing is unlikely to explain all of the variability we observe in quasars, we present our classification scheme as a starting point for revealing the likely geometries of reprocessing for quasars in our sample and hypervariable quasars in general.Comment: 23 pages, 8 figures, submitted to Ap

Semantic distillation: a method for clustering objects by their contextual specificity

Techniques for data-mining, latent semantic analysis, contextual search of databases, etc. have long ago been developed by computer scientists working on information retrieval (IR). Experimental scientists, from all disciplines, having to analyse large collections of raw experimental data (astronomical, physical, biological, etc.) have developed powerful methods for their statistical analysis and for clustering, categorising, and classifying objects. Finally, physicists have developed a theory of quantum measurement, unifying the logical, algebraic, and probabilistic aspects of queries into a single formalism. The purpose of this paper is twofold: first to show that when formulated at an abstract level, problems from IR, from statistical data analysis, and from physical measurement theories are very similar and hence can profitably be cross-fertilised, and, secondly, to propose a novel method of fuzzy hierarchical clustering, termed \textit{semantic distillation} -- strongly inspired from the theory of quantum measurement --, we developed to analyse raw data coming from various types of experiments on DNA arrays. We illustrate the method by analysing DNA arrays experiments and clustering the genes of the array according to their specificity.Comment: Accepted for publication in Studies in Computational Intelligence, Springer-Verla

Impact of linear dimensionality reduction methods on the performance of anomaly detection algorithms in hyperspectral images

Anomaly Detection (AD) has recently become an important application of hyperspectral images analysis. The goal of these algorithms is to find the objects in the image scene which are anomalous in comparison to their surrounding background. One way to improve the performance and runtime of these algorithms is to use Dimensionality Reduction (DR) techniques. This paper evaluates the effect of three popular linear dimensionality reduction methods on the performance of three benchmark anomaly detection algorithms. The Principal Component Analysis (PCA), Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) as DR methods, act as pre-processing step for AD algorithms. The assessed AD algorithms are Reed-Xiaoli (RX), Kernel-based versions of the RX (Kernel-RX) and Dual Window-Based Eigen Separation Transform (DWEST). The AD methods have been applied to two hyperspectral datasets acquired by both the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Hyperspectral Mapper (HyMap) sensors. The evaluation of experiments has been done using Receiver Operation Characteristic (ROC) curve, visual investigation and runtime of the algorithms. Experimental results show that the DR methods can significantly improve the detection performance of the RX method. The detection performance of neither the Kernel-RX method nor the DWEST method changes when using the proposed methods. Moreover, these DR methods increase the runtime of the RX and DWEST significantly and make them suitable to be implemented in real time applications

IDENTIFYING SUITABLE LOCATIONS FOR MANGROVE PLANTATION USING GEOSPATIAL INFORMATION SYSTEM AND REMOTE SENSING

Mangroves provide numerous environmental benefits, such as carbon sequestration, water purification, climate change mitigation, and flood and Tsunami impact reduction. Despite these unique advantages, mangroves are threatened by the combined adverse impacts of human activities and climate change. Therefore, it is essential to implement reasonable practices to avoid further degradation of mangroves and provide efficient workflows to increase their extent. Accordingly, better plantation policies are principally required for their conservation and rehabilitation. In this study, we desired to detect suitable locations for mangrove plantation in coastal areas of Hormozgan Province, Iran. We considered a relatively new Multi Criteria Decision Making (MCDM) technique to combine ten criteria derived from remote sensing in a GIS environment. The Best Worst Method (BWM), as an MDCM technique, was implemented to determine the relative importance of each criterion. Afterward, all criteria were aggregated using the Weighted Linear Combination (WLC) method to produce a mangrove plantation suitability map. Statistical measures, including Overall Accuracy (OA = 95%), Kappa Coefficient (KC = 87.9%), and Area Under Curve (AUC = 98.79%), indicated the high applicability of the implemented method for mangrove plantation site allocation. The produced map could give managers a profound insight into finding optimal spots to plant mangroves

The Sloan Digital Sky Survey Reverberation Mapping Project : UV–optical accretion disk measurements with the Hubble Space Telescope

Funding: Y.H., J.R.T., and G.F.A. acknowledge support from NASA grants HST-GO-15650 and 18-2ADAP18-0177 and NSF grant CAREER-1945546. K.H. acknowledges support from STFC grant ST/R000824/1. C.J.G. acknowledges support from NSF grant AST-2009949. Y.S. acknowledges support from NSF grants AST-1715579 and AST-2009947. P.H. acknowledges support from the Natural Sciences and Engineering Research Council of Canada (NSERC), funding reference number 2017-05983. L.C.H. was supported by the National Science Foundation of China (11721303, 11991052) and the National Key R&D Program of China (2016YFA0400702).We present accretion-disk structure measurements from UV–optical reverberation mapping (RM) observations of a sample of eight quasars at 0.24 < z < 0.85. Ultraviolet photometry comes from two cycles of Hubble Space Telescope monitoring, accompanied by multiband optical monitoring by the Las Cumbres Observatory network and Liverpool Telescopes. The targets were selected from the Sloan Digital Sky Survey Reverberation Mapping project sample with reliable black hole mass measurements from Hβ RM results. We measure significant lags between the UV and various optical griz bands using JAVELIN and CREAM methods. We use the significant lag results from both methods to fit the accretion-disk structure using a Markov Chain Monte Carlo approach. We study the accretion disk as a function of disk normalization, temperature scaling, and efficiency. We find direct evidence for diffuse nebular emission from Balmer and Fe ii lines over discrete wavelength ranges. We also find that our best-fit disk color profile is broadly consistent with the Shakura & Sunyaev disk model. We compare our UV–optical lags to the disk sizes inferred from optical–optical lags of the same quasars and find that our results are consistent with these quasars being drawn from a limited high-lag subset of the broader population. Our results are therefore broadly consistent with models that suggest longer disk lags in a subset of quasars, for example, due to a nonzero size of the ionizing corona and/or magnetic heating contributing to the disk response.Publisher PDFPeer reviewe