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Credit scoring is a mathematical and statistical tool that aids financial institutions in deciding suitable candidates for the issuance of loans, based on the analysis of the borrower’s financial history. Distinct groups of borrowers have unique characteristics that must be identified and trained on to increase the accuracy of classification models for all credit borrowers that financial institutions serve. Numerous studies have shown that models based on diverse base-classifier models outperform other statistical and AI-based techniques for related classification problems. This paper proposes a novel multi-layer clustering and soft-voting-based ensemble classification model, aptly named Self Organizing Map Clustering with Metaheuristic Voting Ensembles (SCMVE) which uses a self-organizing map for clustering the data into distinct clusters with their unique characteristics and then trains a sailfish optimizer powered ensemble of SVM-KNN base classifiers for classification of each distinct identified cluster. We train and evaluate our model on the standard public credit scoring datasets—namely the German, Australian and Taiwan datasets and use multiple evaluation scores such as precision, F1 score, recall to compare the results of our model with other prominent works in the field. On evaluation, SCMVE shows outstanding results (95% accuracy on standard datasets) when compared with popular works in the field of credit scoring. [ABSTRACT FROM AUTHOR]

This study quantifies the influences of anthropogenic forcing to date on precipitation over Aotearoa New Zealand (ANZ). Large ensembles of simulations from the weather@home regional climate model experiments are analysed under two scenarios, a natural (NAT) or counter-factual scenario which excludes human-induced changes to the climate system and an anthropogenic (ANT) or factual scenario. The impacts of anthropogenic forcing on precipitation are analysed in the context of large-scale circulation types characterized using an existing Self Organizing Map classification. The combined effect of both thermodynamics and dynamics are compared with values expected from the Clausius–Clapeyron (C–C) relation. Changes in the precipitation intensity due to greenhouse gas-forced temperature rise are lower than the expected C–C value. However extreme precipitation changes approach the C–C value for some circulation types. Specifically westerly flows enhance precipitation change across ANZ relative to the C–C rate, particularly over the West Coast. Conversely, northwesterly flows reduce the change over the North Island relative to the C–C value. Moreover, the wet day frequency generally reduces in the ANT scenario relative to NAT, reductions are largest on the West Coast of the South Island for westerly flows. Additionally, the frequency of days with extreme precipitation rises over ANZ for most circulation patterns, except in Northland and for northwesterly flows. This underscores the combined influence of dynamics and thermodynamics in shaping both precipitation intensity and frequency patterns across ANZ.

Earthquakes are hazardous natural disasters, and they may cause severe damage and losses where they occur. In addition to their devastating effects, they may trigger following disasters like tsunamis and fires. Post-earthquake fires are known as the most dangerous secondary disasters and generally cause much more damage than the damage caused by the earthquake itself. The difficulty in determining and responding to ignition sources, the lack of equipment and workforce, and obstacles like collapsed buildings that block the ways to reach fires may cause catastrophic disasters after an earthquake. In recent years, Unmanned Aerial Vehicle technologies (UAVs) have shown promising performance in post-disaster response operations. Parallel to technological improvements, they have been used for many purposes, like fire-fighting, victim location detection, base station support, and material distribution in disaster areas. To manage a possible response and improve the performance of UAVs in post-earthquake fire areas, it is crucial to be prepared in advance. This study proposes an artificial neural network-based clustering approach for unmanned aerial vehicle use in post-earthquake fire areas. After conducting a detailed literature review covering post-earthquake fires, usage of UAVs in disasters, and some aspects of Self Organizing Maps, the methodology used for clustering the neighborhoods regarding their post-earthquake fire risk similarities is introduced. A real-life application is carried out to identify and cluster the regions and provide preliminary information to the decision-makers on possible interventions. Neighborhoods of Tuzla district, one of the riskiest districts in terms of post-earthquake fires in Istanbul, are clustered with Self-Organizing Maps (SOM). In a possible post-earthquake fire disaster, the Tuzla district can be divided into three areas, and UAVs can be organized more efficiently and quickly based on this cluster information. The results of this real-life application can guide decision-makers by showing which regions have similarities for UAV response in possible post-earthquake fires and where they can be intervened together. The authorities can benefit from the findings of this study while preparing disaster plans, intervention actions, and post-disaster humanitarian activities. [ABSTRACT FROM AUTHOR]

The East Australian Current (EAC), as the western boundary current of the South Pacific Gyre, governs the mesoscale circulation dynamics along the East Coast of Australia. It also exhibits meandering and instabilities that generate fine‐scale structures. Understanding the interplay between these fine‐scale processes and the larger‐scale EAC patterns is crucial in regards to chlorophyll‐a distribution, a key indicator of phytoplankton abundance and an essential component of marine ecosystems. However, the link between fine‐scale and large‐scale variability in the EAC's separation dynamics and its impact on chlorophyll‐a distribution remains poorly understood. In this study, we investigate this relationship using a clustering of high‐frequency radar data near the EAC's coherent jet and separation point, combined with satellite data on chlorophyll‐a concentration, sea surface temperature, and altimetry. Our findings reveal that the EAC acts as a frontal barrier for water‐mass and chlorophyll‐a concentration. The EAC separation enhances shelf‐offshore exchange and off‐shelf chlorophyll‐a concentration. In contrast, the extension of the current is characterized by fine‐scale structures with cyclonic vorticity and elevated strain, consequently contributing to an elevation of chlorophyll‐a levels in the region. In conclusion, the impact of separation dynamics on chlorophyll‐a distribution involves a complex interplay between large‐scale and fine‐scale processes, which may possibly compete under certain scenarios. Considering the implications of our findings in the context of future climate conditions, it becomes evident that purely dynamical changes in the separation latitude of the East Australian Current may hold consequences for chlorophyll‐a concentration distribution off the east coast of Australia. Plain Language Summary: The study focuses on the East Australian Current (EAC), a powerful ocean current flowing along Australia's East Coast. We investigate how small‐scale processes of the order of kilometers, and larger EAC patterns of the order of 100s kilometers affect chlorophyll‐a distribution, an indicator of phytoplankton abundance that forms the foundation of the marine food web. We used remote high‐frequency radar and satellite observations of surface currents, temperature and ocean color to investigate these interactions. We found that, when the current separates from the coast, it allows shelf and open ocean waters to interact, affecting chlorophyll‐a concentrations. Additionally, the EAC's twists and turns generate eddies (circular water movements) and fronts (boundary zones between different water masses), which have significant effects on chlorophyll‐a abundance in different areas. In conclusion, we highlight that the impact of the EAC dynamics involves a complex interplay between large‐scale and fine‐scale processes. Understanding these interactions is crucial for predicting how future climate changes may impact chlorophyll‐a distribution and marine life off the east coast of Australia. Key Points: The East Australian Current acts as a frontal barrier for water‐mass and chlorophyll‐a concentrationShelf‐offshore exchange of chlorophyll‐a concentration is enhanced when the East Australian Current separates from the coastThe strongest chlorophyll‐a anomalies are associated with cyclonic sub‐mesoscale strain inshore of the East Australian Current [ABSTRACT FROM AUTHOR]

Introduction: The escalating challenge of climate change has underscored the critical need to understand cold defense mechanisms in cultivated grapevine Vitis vinifera. Temperature variations can affect the growth and overall health of vine. Methods: We used Self Organizing Maps machine learning method to analyze gene expression data from leaves of five Vitis vinifera cultivars each treated by four different temperature conditions. The algorithm generated sample-specific "portraits" of the normalized gene expression data, revealing distinct patterns related to the temperature conditions applied. Results: Our analysis unveiled a connection with vitamin B1 (thiamine) biosynthesis, suggesting a link between temperature regulation and thiamine metabolism, in agreement with thiamine related stress response established in Arabidopsis before. Furthermore, we found that epigenetic mechanisms play a crucial role in regulating the expression of stress-responsive genes at low temperatures in grapevines. Discussion: Application of Self Organizing Maps portrayal to vine transcriptomics identified modules of coregulated genes triggered under cold stress. Our machine learning approach provides a promising option for transcriptomics studies in plants. [ABSTRACT FROM AUTHOR]

IntroductionThe escalating challenge of climate change has underscored the critical need to understand cold defense mechanisms in cultivated grapevine Vitis vinifera. Temperature variations can affect the growth and overall health of vine.MethodsWe used Self Organizing Maps machine learning method to analyze gene expression data from leaves of five Vitis vinifera cultivars each treated by four different temperature conditions. The algorithm generated sample-specific “portraits” of the normalized gene expression data, revealing distinct patterns related to the temperature conditions applied.ResultsOur analysis unveiled a connection with vitamin B1 (thiamine) biosynthesis, suggesting a link between temperature regulation and thiamine metabolism, in agreement with thiamine related stress response established in Arabidopsis before. Furthermore, we found that epigenetic mechanisms play a crucial role in regulating the expression of stress-responsive genes at low temperatures in grapevines.DiscussionApplication of Self Organizing Maps portrayal to vine transcriptomics identified modules of coregulated genes triggered under cold stress. Our machine learning approach provides a promising option for transcriptomics studies in plants.

Education is a provision to develop self-potential. The role of education is a condition for the progress of a nation. Choosing a department must be adjusted to the skills/expertise possessed, talents, and interests and supported by information to prevent students from choosing majors. The selection of majors in Vocational High Schools still uses a manual system. Information and data from prospective students are processed with a manual system, allowing majors taken by prospective students not in accordance with the desired. Current technological advancements make it easier for computer systems to group data, one method of grouping data is clustering. Experts widely develop clustering or cluster analysis methods. Clustering partitions many data sets into many groups based on their similarity. Clustering methods include K-Means, DBSCAN, Self Organizing Map, and Fuzzy C-Means. This study compared 2 methods, namely Self Organizing Map and Fuzzy C-Means. In this study, the grouping has been done using the Self Organizing Maps and Fuzzy CMeans methods to help prospective students make decisions based on calculating skills, talents, and interests. The grouping is expected to help determine the desired direction. The results of these calculations are in the form of clusters or grouping results as recommendations to prospective students. Accuracy testing on both methods showed that the SOM method had better accuracy with 100% accuracy than the FCM method which has an accuracy value of 80% and 20% is an accuracy data that is not appropriate. Based on the test results it can be explained that testing has succeeded well in classifying majors.

Due to the everchanging and evergrowing nature of programming technologies, the gap between the programming industry's needs and the educational capabilities of both formal and informal educational environments has never been wider. However, the need to learn computer programming has never been greater, regardless of the motivation behind it. The number of programming concepts to be taught is increasing over time, while the amount of time available for education and training usually remains the same. The objective of this research was to analyze the source codes used in many educational systems to teach fundamental programming concepts to learners, regardless of their prior experience in programming. A total of 25 repositories containing 3882 Python modules were collected for the analysis. Through self-organization of the collected content, we obtained very compelling results about code structure, distribution, and differences. Based on those results, we concluded that Self-Organizing Maps are a powerful tool for both content and knowledge management, because they can highlight problems in the curriculum's density as well as transparently indicate which programming concepts it has successfully observed and learned to recognize. Based on the level of transparency exhibited by Self-Organizing Maps, it is safe to say that they could be used in future research to enhance both human and machine learning of computer programming. By achieving this level of transparency, such an Artificial Intelligence system would be able to assist in overall computer programming education by communicating what should be taught, what needs to be learned, and what is known. [ABSTRACT FROM AUTHOR]

In the past years, high entropy alloys (HEAs) witnessed great interest because of their superior properties. Phase prediction using machine learning (ML) methods was one of the main research themes in HEAs in the past three years. Although various ML-based phase prediction works exhibited high accuracy, only a few studied the variables that drive the phase formation in HEAs. Those (the previously mentioned work) did that by incorporating domain knowledge in the feature engineering part of the ML framework. In this work, we tackle this problem from a different direction by predicting the phase of HEAs, based only on the concentration of the alloy constituent elements. Then, pruned tree models and linear correlation are used to develop simple primitive prediction rules that are used with self-organizing maps (SOMs) and constructed Euclidean spaces to formulate the problem of discovering the phase formation drivers as an optimization problem. In addition, genetic algorithm (GA) optimization results reveal that the phase formation is affected by the electron affinity, molar volume, and resistivity of the constituent elements. Moreover, one of the primitive prediction rules reveals that the FCC phase formation in the AlCoCrFeNiTiCu family of high entropy alloys can be predicted with 87% accuracy by only knowing the concentration of Al and Cu.

Tailored analytics play a key role in the successful delivery of cultural content to huge and diverse groups. Primarily the latter depends on a number of information retrieval factors determining user experience quality, most prominently precision, recall, and timing. These imply that cultural analytics should be designed with strong predictive power. In turn, the latter relies heavily on the clustering of the system user base. A self organizing map is a neural network architecture trained in an unsupervised way through a modified Hebbian rule to couple distances between two distinct spaces such that a manifold in the high dimensional space is projected smoothly to the lower dimensional one. The twofold focus of this work is the development of a tensor user distance metric for SOMs as well as the inclusion of behavioral attributes therein, both aiming at additional descriptive power and clustering flexibility. As a concrete example, the proposed SOMs are applied to data taken from a cultural content delivery system. The proposed methodology is evaluated based on a scoring method assessing both complexity and clustering quality criteria, including the number of epochs, the average cluster distance, and the topological error, with encouraging results. [ABSTRACT FROM AUTHOR]

Due to the everchanging and evergrowing nature of programming technologies, the gap between the programming industry’s needs and the educational capabilities of both formal and informal educational environments has never been wider. However, the need to learn computer programming has never been greater, regardless of the motivation behind it. The number of programming concepts to be taught is increasing over time, while the amount of time available for education and training usually remains the same. The objective of this research was to analyze the source codes used in many educational systems to teach fundamental programming concepts to learners, regardless of their prior experience in programming. A total of 25 repositories containing 3882 Python modules were collected for the analysis. Through self-organization of the collected content, we obtained very compelling results about code structure, distribution, and differences. Based on those results, we concluded that Self-Organizing Maps are a powerful tool for both content and knowledge management, because they can highlight problems in the curriculum’s density as well as transparently indicate which programming concepts it has successfully observed and learned to recognize. Based on the level of transparency exhibited by Self-Organizing Maps, it is safe to say that they could be used in future research to enhance both human and machine learning of computer programming. By achieving this level of transparency, such an Artificial Intelligence system would be able to assist in overall computer programming education by communicating what should be taught, what needs to be learned, and what is known.

The current situation of COVID-19 highlights the paramount importance of infectious disease surveillance, which necessitates early monitoring for effective response. Policymakers are interested in data insights identifying high-risk areas as well as individuals to be quarantined, especially as the public gets back to their normal routine. We investigate both requirements by the implementation of disease outbreak modeling and exploring its induced dynamic spatial risk in form of risk assessment, along with its real-time integration back into the disease model. This paper implements a contact tracing-based stochastic compartment model as a baseline, to further modify the existing setup to include the spatial risk. This modification of each individual-level contact's intensity to be dependent on its spatial location has been termed as Contextual Contact Tracing. The results highlight that the inclusion of spatial context tends to send more individuals into quarantine which reduces the overall spread of infection. With a simulated example of an induced spatial high-risk, it is highlighted that the new spatio-SIR model can act as a tool to empower the analyst with a capability to explore disease dynamics from a spatial perspective. We conclude that the proposed spatio-SIR tool can be of great help for policymakers to know the consequences of their decision prior to their implementation. [ABSTRACT FROM AUTHOR]

Hypertension and diabetes have become a global health and economic issue, being among the major chronic conditions worldwide, particularly in developed countries. To face this global problem, a better knowledge about these diseases becomes crucial to characterize chronic patients. Our aim is two-fold: (1) to provide an efficient visual tool for identifying clinical patterns in high-dimensional data; and (2) to characterize the patient health-status through a data-driven approach using electronic health records of healthy, hypertensive and diabetic populations. We propose a two-stage methodology that uses diagnosis and drug codes of healthy and chronic patients associated to the University Hospital of Fuenlabrada in Spain. The first stage applies the Self-Organizing Map on the aforementioned data to get a set of prototype patients which are projected onto a grid of nodes. Each node has associated a prototype patient that captures relationships among clinical characteristics. In the second stage, clustering methods are applied on the prototype patients to find groups of patients with a similar health-status. Clusters with distinctive patterns linked to chronic conditions were found, being the most remarkable highlights: a cluster of pregnant women emerged among the hypertensive population, and two clusters of diabetic individuals with significant differences in drug-therapy (insulin and non-insulin dependant). The proposed methodology showed to be effective to explore relationships within clinical data and to find patterns related to diabetes and hypertension in a visual way. Our methodology raises as a suitable alternative for building appropriate clinical groups, becoming a promising approach to be applied to any population due to its data-driven philosophy. A thorough analysis of these groups could spawn new and fruitful findings.

Beer production and consumption has increased worldwide during the past years. In this growing market, consumers have opted for products endowed with greater quality and diversity. In this respect, malted beers offer a more pleasant sensory experience. From a practical point of view, the high cost of production, when compared to the unmalted beer, passed on to the added value of the final product, then is common to encounter unmalted beers labeled as malted in the market. So, the characterization of beers into groups of malted and unmalted beers is of great importance for food control agencies. The present work reports a good alternative classification procedure that is fast, efficient, with no sample preparation using fluorescence spectroscopy associated with SOM (Self Organizing Map) and it is compared with the negative results (no pattern recognition) obtained with a FTIR and UV-VIS spectroscopy associated with PCA also performed with no sample preparation process.

Beer production and consumption has increased worldwide during the past years. In this growing market, consumers have opted for products endowed with greater quality and diversity. In this respect, malted beers offer a more pleasant sensory experience. From a practical point of view, the high cost of production, when compared to the unmalted beer, passed on to the added value of the final product, then is common to encounter unmalted beers labeled as malted in the market. So, the characterization of beers into groups of malted and unmalted beers is of great importance for food control agencies. The present work reports a good alternative classification procedure that is fast, efficient, with no sample preparation using fluorescence spectroscopy associated with SOM (Self Organizing Map) and it is compared with the negative results (no pattern recognition) obtained with a FTIR and UV-VIS spectroscopy associated with PCA also performed with no sample preparation process. [ABSTRACT FROM AUTHOR]

Self Organizing Maps (SOM) are unsupervised neural networks suited for visualisation purposes and clustering analysis. This study uses SOM to solve a software engineering problem: detecting the most important (key) classes from software projects. Key classes are meant to link the most valuable concepts of a software system and in general these are found in the solution documentation. UML models created in the design phase become deprecated in time and tend to be a source of confusion for large legacy software. Therefore, developers try to reconstruct class diagrams from the source code using reverse engineering. However, the resulting diagram is often very cluttered and difficult to understand. There is an interest for automatic tools for building concise class diagrams, but the machine learning possibilities are not fully explored at the moment. This paper proposes two possible algorithms to transform SOM in a classification algorithm to solve this task, which involves separating the important classes - that should be on the diagrams - from the others, less important ones. Moreover, SOM is a reliable visualization tool which able to provide an insight about the structure of the analysed projects. [ABSTRACT FROM AUTHOR]

The boundary layer (BL) profile over the coastal plain of Israel, Eastern Mediterranean (EM), varies considerably during winter. Although in the context of air pollution, the characteristics of the BL height (BLH) was intensively investigated, a quantitative classification of the BL profile regimes has not been performed. Here, we seek to reveal the dominant, recurring regimes of the BL profiles, their quantitative characteristics and links to regional synoptic‐scale patterns. An objective unsupervised classification of winter BL radiosonde profiles is performed for the first time by multi‐parameter self‐organizing map (SOM) analysis. The analysis uses high‐resolution, 12:00‐UTC data of wind, temperature, humidity and pressure measurements during December to February 2007–2018, and yields 30 distinct profile regimes. Composite analysis using ERA5 reanalysis suggests strong association between the profile regimes and synoptic weather systems and highlights four groups: (a) Deep winter cyclones with strong westerly wind and precipitation; (b) Strong surface anticyclones and Red Sea troughs (RST) with a mid‐tropospheric ridge, moderate dry easterly wind and extreme temperatures. (c) Moderate pressure gradients under shallow cyclones, anticyclone to the west and RST to the east of Israel. (d) Active RSTs, accompanied by upper‐tropospheric trough/cutoff low and heavy precipitation. For the first time, general objective classification observes the active RST without requiring specific criteria. Consistent with previous knowledge, the new classification exhibits distinct categories of thermal stability, BLH and turbulence. Importantly, we show that the automatic objective classification of profile data from a single station can be a sensitive discriminator of winter synoptic regimes in the EM, and therefore explains the variability of the BL profile. It facilitates the study of the interaction between the BL and the free troposphere and may improve the prediction of air pollution or future BL profile regimes based on long time series from historical data or climate models. [ABSTRACT FROM AUTHOR]

Background This study postulates that underlying environmental conditions and a susceptible population’s socio-economic status should be explored simultaneously to adequately understand a vector borne disease infection risk. Here we focus on West Nile Virus (WNV), a mosquito borne pathogen, as a case study for spatial data visualization of environmental characteristics of a vector’s habitat alongside human demographic composition for understanding potential public health risks of infectious disease. Multiple efforts have attempted to predict WNV environmental risk, while others have documented factors related to human vulnerability to the disease. However, analytical modeling that combines the two is difficult due to the number of potential explanatory variables, varying spatial resolutions of available data, and differing research questions that drove the initial data collection. We propose that the use of geovisualization may provide a glimpse into the large number of potential variables influencing the disease and help distill them into a smaller number that might reveal hidden and unknown patterns. This geovisual look at the data might then guide development of analytical models that can combine environmental and socio-economic data. Methods Geovisualization was used to integrate an environmental model of the disease vector’s habitat alongside human risk factors derived from socio-economic variables. County level WNV incidence rates from California, USA, were used to define a geographically constrained study area where environmental and socio-economic data were extracted from 1,133 census tracts. A previously developed mosquito habitat model that was significantly related to WNV infected dead birds was used to describe the environmental components of the study area. Self-organizing maps found 49 clusters, each of which contained census tracts that were more similar to each other in terms of WNV environmental and socio-economic data. Parallel coordinate plots permitted visualization of each cluster’s data, uncovering patterns that allowed final census tract mapping exposing complex spatial patterns contained within the clusters. Results Our results suggest that simultaneously visualizing environmental and socio-economic data supports a fuller understanding of the underlying spatial processes for risks to vector-borne disease. Unexpected patterns were revealed in our study that would be useful for developing future multilevel analytical models. For example, when the cluster that contained census tracts with the highest median age was examined, it was determined that those census tracts only contained moderate mosquito habitat risk. Likewise, the cluster that contained census tracts with the highest mosquito habitat risk had populations with moderate median age. Finally, the cluster that contained census tracts with the highest WNV human incidence rates had unexpectedly low mosquito habitat risk.

Background and objectives: Dengue is an emerging and re-emerging infectious disease, transmitted by mosquitoes. It is mostly prevalent in tropical and sub-tropical regions of the world, particularly, in Asia-Pacific region. To understand the epidemiology and spatial distribution of dengue, a retrospective surveillance study was conducted in the state of Andhra Pradesh, India during 2011–2013. Material and methods: District-wise disease endemicity levels were mapped through geographical information system (GIS) tools. Spatial statistical analysis such as Getis-Ord Gi* was performed to identify hot spots and cold spots of dengue disease. Similarly self organizing maps (SOM), a datamining tool was also applied to understand the endemicity patterns in study areas. Results: The analysis shows that districts of Warangal, Karimnagar, Khammam and Vizianagaram are reported as hot spot regions whereas Adilabad and Nizamabad reported as cold spots for dengue. The SOM classify 23 districts in 03 major (07 sub) clusters. These SOM clusters were projected in the geographical space and based on the disease/cases intensity the districts were characterized into low, medium and high endemic areas. Conclusion: This visualization approach, SOM-GIS helps the public health officials to identify the disease endemic zones and take real time decisions for disease management.

Background: This study postulates that underlying environmental conditions and a susceptible population's socio-economic status should be explored simultaneously to adequately understand a vector borne disease infection risk. Here we focus on West Nile Virus (WNV), a mosquito borne pathogen, as a case study for spatial data visualization of environmental characteristics of a vector's habitat alongside human demographic composition for understanding potential public health risks of infectious disease. Multiple efforts have attempted to predict WNV environmental risk, while others have documented factors related to human vulnerability to the disease. However, analytical modeling that combines the two is difficult due to the number of potential explanatory variables, varying spatial resolutions of available data, and differing research questions that drove the initial data collection. We propose that the use of geovisualization may provide a glimpse into the large number of potential variables influencing the disease and help distill them into a smaller number that might reveal hidden and unknown patterns. This geovisual look at the data might then guide development of analytical models that can combine environmental and socio-economic data. Methods: Geovisualization was used to integrate an environmental model of the disease vector's habitat alongside human risk factors derived from socio-economic variables. County level WNV incidence rates from California, USA, were used to define a geographically constrained study area where environmental and socio-economic data were extracted from 1,133 census tracts. A previously developed mosquito habitat model that was significantly related to WNV infected dead birds was used to describe the environmental components of the study area. Self-organizing maps found 49 clusters, each of which contained census tracts that were more similar to each other in terms of WNV environmental and socio-economic data. Parallel coordinate plots permitted visualization of each cluster's data, uncovering patterns that allowed final census tract mapping exposing complex spatial patterns contained within the clusters. Results: Our results suggest that simultaneously visualizing environmental and socio-economic data supports a fuller understanding of the underlying spatial processes for risks to vector-borne disease. Unexpected patterns were revealed in our study that would be useful for developing future multilevel analytical models. For example, when the cluster that contained census tracts with the highest median age was examined, it was determined that those census tracts only contained moderate mosquito habitat risk. Likewise, the cluster that contained census tracts with the highest mosquito habitat risk had populations with moderate median age. Finally, the cluster that contained census tracts with the highest WNV human incidence rates had unexpectedly low mosquito habitat risk. [ABSTRACT FROM AUTHOR]

Extractive text summarization yields the sensitive parts of the document by neglecting the irrelevant and redundant information. In this paper, we propose a new strategy for extractive single-document summarization in Malayalam. Initially, entity recognition is done, followed by relevance analysis is made based on some context-aware features. The scored sentences are then clustered using self-organizing maps (SOM) and from these clusters, relevant sentences are extracted out based on the proposed algorithm. Both theoretical and practical evaluations are done to analyze the implemented system. In theoretical evaluation, gradient calculations of relevance equations are used to know that which of these sentence scoring features are contributing more. The relevance equation is optimized with the help of Lagrange's multiplier. The complexity analysis of the proposed algorithms is also performed. In practical evaluation, the system compared with online and offline summarizers upon metrics like precision, recall, and F-measure. The system is tested through a non-clustering approach also in order to analyze the impact of clustering used in our work. Some existing strategies like question game evaluation, sentence rank evaluation, and keyword association are also done to evaluate the different parameters like the relevance of sentences, important entity words, etc. [ABSTRACT FROM AUTHOR]

Study region: Groundwater in the southern part of the Saloum Basin in Senegal. Study focus: The Saloum estuary is a hypersaline and ‘inverse’ estuary where the salinity of river water increases in the upstream direction. This region is problematic in that due to the underlain superficial Continental Terminal aquifer bordered by the hypersaline estuary constitutes the unique fresh groundwater reservoir for water supply for its estimated 466,000 residents living in 18 rural districts (belonging to the regions of Fatick, Kaolack and Kaffrine). This is of high value given that the deep Maastrichtian aquifer (200–300 m depth) is saline. This study aims to describe and understand temporal changes in the chemical and isotopic compositions of groundwater, the geochemical processes and especially the groundwater salinization. New hydrological insights for the region: The analytical data were discriminated into 3 groups on the basis of the water types. Na-Cl, Ca-Cl and Ca-SO4 rich waters derived from saline water intrusion at the vicinity of the Saloum River accompanied by ion exchange reactions and pollution dominate the first group. The second group located mainly in the centre and eastern parts of the region is featured fresh groundwater of Ca-HCO3 derived from calcite dissolution reactions. The third group of Na-HCO3 type and less mineralized indicates freshening processes by recently infiltrating rainwaters. Slight seasonal chemical variations are observed due to new infiltrating water reaching the water table. High variation in rainfall between the 2 reference years (2003 and 2012) also changes chemical patterns in the groundwater. Chemical evolution of the groundwater is geographically observed and is due to a combination of dilution by recharge, anthropic contamination and seawater intrusion. The results of environmental isotopes (δ18O, δ2H) compared with the local meteoric line indicate that the groundwater has been affected by evaporation processes before and during infiltration. The results also clearly indicate mixing with saltwater and an evolution towards relative freshening between 2003 and 2012 in some wells near the Saloum River.