Exploratory analysis of excitation-emission matrix fluorescence spectra with self-organizing maps as a basis for determination of organic matter removal efficiency at water treatment works

In the paper, the self-organizing map (SOM) was employed for the exploratory analysis of fluorescence excitation-emission data characterizing organic matter removal efficiency at 16 water treatment works in the UK. Fluorescence spectroscopy was used to assess organic matter removal efficiency between raw and partially treated (clarified) water to provide an indication of the potential for disinfection by-products formation. Fluorescence spectroscopy was utilized to evaluate quantitative and qualitative properties of organic matter removal. However, the substantial amount of fluorescence data generated impeded the interpretation process. Therefore a robust SOM technique was used to examine the fluorescence data and to reveal patterns in data distribution and correlations between organic matter properties and fluorescence variables. It was found that the SOM provided a good discrimination between water treatment sites on the base of spectral properties of organic matter. The distances between the units of the SOM map were indicative of the similarity of the fluorescence samples and thus demonstrated the relative changes in organic matter content between raw and clarified water. The higher efficiency of organic matter removal was demonstrated for the larger distances between raw and clarified samples on the map. It was also shown that organic matter removal was highly dependent on the raw water fluorescence properties, with higher efficiencies for higher emission wavelengths in visible and UV humic-like fluorescence centers

Air pollution data classification by SOM Neural Network

Over the last ten years, Salamanca has been considered among the most polluted cities in México. This paper presents a Self-Organizing Maps (SOM) Neural Network application to classify pollution data and automatize the air pollution level determination for Sulphur Dioxide (SO2) in Salamanca. Meteorological parameters are well known to be important factors contributing to air quality estimation and prediction. In order to observe the behavior and clarify the influence of wind parameters on the SO2 concentrations a SOM Neural Network have been implemented along a year. The main advantages of the SOM is that it allows to integrate data from different sensors and provide readily interpretation results. Especially, it is powerful mapping and classification tool, which others information in an easier way and facilitates the task of establishing an order of priority between the distinguished groups of concentrations depending on their need for further research or remediation actions in subsequent management steps. The results show a significative correlation between pollutant concentrations and some environmental variables

Visual and computational analysis of structure-activity relationships in high-throughput screening data

Novel analytic methods are required to assimilate the large volumes of structural and bioassay data generated by combinatorial chemistry and high-throughput screening programmes in the pharmaceutical and agrochemical industries. This paper reviews recent work in visualisation and data mining that can be used to develop structure-activity relationships from such chemical/biological datasets

Unsupervised system to classify SO2 pollutant concentrations in Salamanca, Mexico

Salamanca is cataloged as one of the most polluted cities in Mexico. In order to observe the behavior and clarify the influence of wind parameters on the Sulphur Dioxide (SO2) concentrations a Self-Organizing Maps (SOM) Neural Network have been implemented at three monitoring locations for the period from January 1 to December 31, 2006. The maximum and minimum daily values of SO2 concentrations measured during the year of 2006 were correlated with the wind parameters of the same period. The main advantages of the SOM Neural Network is that it allows to integrate data from different sensors and provide readily interpretation results. Especially, it is powerful mapping and classification tool, which others information in an easier way and facilitates the task of establishing an order of priority between the distinguished groups of concentrations depending on their need for further research or remediation actions in subsequent management steps. For each monitoring location, SOM classifications were evaluated with respect to pollution levels established by Health Authorities. The classification system can help to establish a better air quality monitoring methodology that is essential for assessing the effectiveness of imposed pollution controls, strategies, and facilitate the pollutants reduction

Trace element contamination in the arms of the Danube Delta (Romania/Ukraine): Current state of knowledge and future needs

This paper provides the first critical synopsis of contamination by selected trace elements in the whole Danube Delta (Romania/Ukraine) to: identify general patterns of contamination by trace elements across the Delta, provide recommendations to refine existing monitoring networks and discuss the potential toxicity of trace elements in the whole Delta. Sediment samples were collected between 2004 and 2007 in the three main branches of the Delta (Chilia, Sulina and Sfantu Gheorghe) and in the secondary delta of the Chilia branch. Samples were analyzed for trace elements (Cd, Co, Cr, Cu, Ni, Pb, V, and Zn) and TiO2, Fe2O3, MnO, CaCO3 and total organic carbon. Cluster analysis (CA) and Principal Component Analysis (PCA) showed that levels of Cd, Cu, Pb, and Zn were influenced by anthropogenic activities. At the opposite, concentrations of Cr and Ni largely originated from the weathering of rocks located in the Romanian part of the Danube catchment and naturally rich in these elements. Data analysis using Self- Organizing Maps confirmed the conclusions of CA/PCA and further detected that the contamination tended to be higher in the Chilia and Sulina arms than in the Sfantu Gheorghe arm. The potential ecological risks due to trace element contamination in the Danube Delta could be identified as moderate and localized, provided that the presence of the natural sources of Cr and Ni was properly considered. The available results suggest that monitoring sediment quality at the mouths of Sulina and Sfantu Gheorghe arms is probably enough to get a picture of the sediment quality along their entire lengths. However, a larger network of monitoring points is necessary in the Chilia and secondary Chilia delta to account for the presence of local point sources and for the more complex hydrodynamic of this part of the Danube Delta

An unsupervised neuromorphic clustering algorithm

Brains perform complex tasks using a fraction of the power that would be required to do the same on a conventional computer. New neuromorphic hardware systems are now becoming widely available that are intended to emulate the more power efficient, highly parallel operation of brains. However, to use these systems in applications, we need “neuromorphic algorithms” that can run on them. Here we develop a spiking neural network model for neuromorphic hardware that uses spike timing-dependent plasticity and lateral inhibition to perform unsupervised clustering. With this model, time-invariant, rate-coded datasets can be mapped into a feature space with a specified resolution, i.e., number of clusters, using exclusively neuromorphic hardware. We developed and tested implementations on the SpiNNaker neuromorphic system and on GPUs using the GeNN framework. We show that our neuromorphic clustering algorithm achieves results comparable to those of conventional clustering algorithms such as self-organizing maps, neural gas or k-means clustering. We then combine it with a previously reported supervised neuromorphic classifier network to demonstrate its practical use as a neuromorphic preprocessing module

Using self-organizing maps to investigate environmental factors regulating colony size and breeding success of the White Stork (Ciconia ciconia)

We studied variations in the size of breeding colonies and in breeding performance of White Storks Ciconia ciconia in 2006–2008 in north-east Algeria. Each colony site was characterized using 12 environmental variables describing the physical environment, land-cover categories, and human activities, and by three demographic parameters: the number of breeding pairs, the number of pairs with chicks, and the number of fledged chicks per pair. Generalized linear mixed models and the self-organizing map algorithm (SOM, neural network) were used to investigate effects of biotic, abiotic, and anthropogenic factors on demographic parameters and on their relationships. Numbers of breeding pairs and of pairs with chicks were affected by the same environmental factors, mainly anthropogenic, which differed from those affecting the number of fledged chicks per pair. Numbers of fledged chicks per pair was not affected by colony size or by the number of nests with chicks. The categorization of the environmental variables into natural and anthropogenic, in connection with demographic parameters, was relevant to detect factors explaining variation in colony size and breeding parameters. The SOM proved a relevant tool to help determine actual dynamics in White Stork colonies, and thus to support effective conservation decisions at a regional scale

GUASOM: An Adaptive Visualization Tool for Unsupervised Clustering in Spectrophotometric Astronomical Surveys

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] We present an adaptive visualization tool for unsupervised classification of astronomical objects in a Big Data context such as the one found in the increasingly popular large spectrophotometric sky surveys. This tool is based on an artificial intelligence technique, Kohonen’s self-organizing maps, and our goal is to facilitate the analysis work of the experts by means of oriented domain visualizations, which is impossible to achieve by using a generic tool. We designed a client-server that handles the data treatment and computational tasks to give responses as quickly as possible, and we used JavaScript Object Notation to pack the data between server and client. We optimized, parallelized, and evenly distributed the necessary calculations in a cluster of machines. By applying our clustering tool to several databases, we demonstrated the main advantages of an unsupervised approach: the classification is not based on pre-established models, thus allowing the “natural classes” present in the sample to be discovered, and it is suited to isolate atypical cases, with the important potential for discovery that this entails. Gaia Utility for the Analysis of self-organizing maps is an analysis tool that has been developed in the context of the Data Processing and Analysis Consortium, which processes and analyzes the observations made by ESA’s Gaia satellite (European Space Agency) and prepares the mission archive that is presented to the international community in sequential periodic publications. Our tool is useful not only in the context of the Gaia mission, but also allows segmenting the information present in any other massive spectroscopic or spectrophotometric database.This work made use of the infrastructures acquired with grants provided by the State Research Agency (AEI) of the Spanish Government and the European Regional Development Fund (FEDER), RTI2018-095076-B-C22. We acknowledge support from CIGUS-CITIC, funded by Xunta de Galicia and the European Union (FEDER Galicia 2014-2020 Program) through grant ED431G 2019/01 and research consolidation grant ED431B 2021/36. This work has made use of data from the European Space Agency (ESA) mission Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC), https://www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. Funding for the Sloan Digital Sky Survey IV has been provided by the Alfred P. Sloan Foundation, the U.S. Department of Energy Office of Science, and the Participating Institutions. SDSS-IV acknowledges support and resources from the Center for High Performance Computing at the University of Utah. The SDSS website is www.sdss.org. SDSS-IV is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS Collaboration. We also want to acknowledge Alhambra survey funded by the Spanish Goverment under Grant AYA2006-14056. Open Access funding provided thanks to the Universidade da Coruña/CISUG agreement with Springer NatureXunta de Galicia; ED431G 2019/01Xunta de Galicia; ED431B 2021/3

Sub-Saharan Africa at a crossroads: a quantitative analysis of regional development

This repository item contains a single issue of The Pardee Papers, a series papers that began publishing in 2008 by the Boston University Frederick S. Pardee Center for the Study of the Longer-Range Future. The Pardee Papers series features working papers by Pardee Center Fellows and other invited authors. Papers in this series explore current and future challenges by anticipating the pathways to human progress, human development, and human well-being. This series includes papers on a wide range of topics, with a special emphasis on interdisciplinary perspectives and a development orientation.Sub-Saharan Africa is at a crossroads of development. Despite a quarter of a century of economic reforms propagated by national policies and international financial agencies and institutions, sub-Saharan Africa is still lagging in development. In this paper, the authors adopt two techniques using both qualitative (e.g. governance) and quantitative factors (e.g., GDP) to examine regional patterns of development in sub-Saharan Africa. More specifically, they examine and analyze similarities and differences among the countries in this region using a multivariate statistical technique, Principal Component Analysis (PCA), and a unsupervised neural network called Kohonen’s Self-Organizing Map (SOM) to cluster levels of development. PCA serves as a tool for determining regional patterns while SOM is more useful for determining continental patterns in development. Both PCA and SOM results show a “developed” cluster in Southern Africa (South Africa, Namibia, Botswana, and Gabon). SOM exhibits a cluster of least developed countries in southern Western Africa and western Central Africa. The results demonstrate that the applied techniques are highly effective to compress multidimensional qualitative and quantitative data sets to extract relevant information about development from a policy perspective. Our analysis indicates the significance of governance variables in some clusters while a combination of variables explains other regional clusters. Zachary Tyler works for a consulting firm in Massachusetts that conducts program evaluations for energy efficiency programs, and he continues to work on statistical and geospatial analyses of human development issues. In 2010, he will receive a master’s degree in energy and environmental analysis from Boston University. Sucharita Gopal is Professor and Director of Graduate Studies in the Department of Geography and Environment and a member of the Cognitive & Neural Systems (CNS) Technology Lab at Boston University. She teaches and conducts research in geographical information systems (GIS), spatial analysis and modeling, and remote sensing for environmental and public health applications. Her recent research includes the development of a marin integrated decision analysis system (MIDAS) for Belize, Panama, and Massachusetts, and a post-disaster geospatial risk model for Haiti. This paper is part of the Africa 2060 Project, a Pardee Center program of research, publications, and symposia exploring African futures in various aspects related to development on continental and regional scales. For more information, visit www-staging.bu.edu/pardee/research/