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The scientific work is devoted to the study of dynamic behavior patterns of the prices of DeFi assets as part of the cryptocurrency market. The DeFi segment has shown an unprecedented level of growth over the past 5 years of its existence. The uniqueness of the innovative decentralized approach to the provision of financial services in the ecosystem provides opportunities to provide new high-tech financial products and services. The DeFi ecosystem is becoming stable and has many prospects for expansion and improvement. All these factors determine the relevance of this work and research on this topic. The purpose of this research is to create clusters of asset behavior patterns in the field of decentralized finance (DeFi) on the cryptocurrency market. This includes identifying key innovations, examining market trends in asset dynamics, analyzing the impact of DeFi on traditional financial systems, and assessing possible risks and challenges associated with this rapidly growing segment. During the research, various general scientific and special methods of scientific knowledge were used for a comprehensive approach to solving tasks and achieving the goal. Dynamic patterns of price behavior are revealed using methods of hierarchical clustering and Kohonen maps. The article discusses the technological innovations and functional aspects of the DeFi ecosystem, such as smart contracts, liquid pools, stablecoins, and democratic governance. These innovations form the core of the effective operation of DeFi projects. Aspects of how the DeFi ecosystem affects the financial system have been identified, providing easy access to classic services, but there are threats of fraud and theft, especially among uninformed users. The question of international legal regulation remains open, slowing down the development of DeFi. The study found consistent pricing patterns of DeFi assets, which demonstrates their dynamic patterns and differences from traditional cryptocurrencies. The obtained research results define DeFi as a stable cryptocurrency ecosystem. They can be used to improve and develop new DeFi projects, improving the functionality of the ecosystem. The regularities of the dynamics of DeFi assets can serve as a tool for managing and investing in a volatile market. The findings also point to the need for further development of the regulatory environment, educational programs and research for DeFi tools. [ABSTRACT FROM AUTHOR]

Abstract With widespread cultivation, Cucurbita moschata stands out for the carotenoid content of its fruits such as β and α-carotene, components with pronounced provitamin A function and antioxidant activity. C. moschata seed oil has a high monounsaturated fatty acid content and vitamin E, constituting a lipid source of high chemical–nutritional quality. The present study evaluates the agronomic and chemical–nutritional aspects of 91 accessions of C. moschata kept at the BGH-UFV and propose the establishment of a core collection based on multivariate approaches and on the implementation of Artificial Neural Networks (ANNs). ANNs was more efficient in identifying similarity patterns and in organizing the distance between the genotypes in the groups. The averages and variances of traits in the CC formed using a 15% sampling of accessions, were closer to those of the complete collection, particularly for accumulated degree days for flowering, the mass of seeds per fruit, and seed and oil productivity. Establishing the 15% CC, based on the broad characterization of this germplasm, will be crucial to optimize the evaluation and use of promising accessions from this collection in C. moschata breeding programs, especially for traits of high chemical–nutritional importance such as the carotenoid content and the fatty acid profile.

This article examines the peculiarities of the management of assets and liabilities of Ukrainian banks in the conditions of significant structural transformations of the resource base during the period of martial law. The analysis is carried out at the level of homogeneous structural and functional groups of banks (SFGBs), which are formed using published reporting data and the application of Kohonen's self-organizing map (SOM). Accumulation of statistical data has been carried out for 5 years, special attention is paid to structural changes in the resource base and directions of placement of bank assets over the past two years. Over the past two years, the bank has been under the influence of shock factors affecting assets and liabilities. At the beginning of 2022, there was an outflow of funds from bank accounts, which was gradually compensated by the inflow of current funds from corporations and the population of individuals. In 2023, the National Bank of Ukraine actively stimulated the development of the term resource base, the basis for ensuring the growth of credit operations. Transactions with state securities continue to grow in the structure of bank assets. The priority task of the banking system remains the financial support of business, but in the conditions of a full-scale war, such development of credit operations is limited. It is expedient to study the strategy of banks by combining the structure of assets and liabilities according to similar characteristics and analyzing the dynamics of groups. Observation of homogeneous groups confirms their stable nature, features of strategy, risk profile and development priorities. It has been proven that banks within homogeneous SFGBs demonstrate similar behaviour in the formation of management strategies and reactions to internal and external shocks. At the macro level, the SOM structure allows you to quantitatively assess the main processes taking place in the banking system, conduct comparisons with maps, and identify problems and priorities in the management of bank assets and liabilities. The SFGB method allows you to evaluate the trajectory of individual banks on the map and develop recommendations for improving the strategy of managing assets and liabilities. [ABSTRACT FROM AUTHOR]

The goal of the paper was to assess safety of passenger cars sold in the Czech Republic in terms of the development of both active and passive safety features of cars sold between 2020-2022. Using content analysis aimed at collecting secondary data, the sales of passenger cars and their safety ratings were examined. Cluster analysis and neural networks were subsequently used to classify vehicles into selforganizing Kohonen maps, within which the movement between individual clusters was monitored. It was found that more than 25 % of vehicles sold between 2021 and 2022 changed their position compared to the year 2020. When taking into account vehicles newly introduced to the market, the average level of safety of vehicles sold compared to the year 2020. Further research could focus on a more detailed analysis of factors affecting safety on roads and their quantification for making better predictions and prevention of road accidents. It should be considered that vehicle safety ratings are based on a specific methodology and criteria and can vary significantly. [ABSTRACT FROM AUTHOR]

Parkinson's disease is a progressive neurodegenerative condition whose prevalence has significantly increased. This work proposes the development of a severity index to classify patients from symptoms, mainly motor ones, using an Artificial Neuronal Network (ANN) trained by the Self-Organizing Maps (SOMs) algorithm. The FOX Insight database was used, which offers data in the form of questionnaires answered by patients or caregivers from all over the world, with information regarding this pathology. After pre-processing the data, a set of 597 questionnaires containing 28 defined questions was selected. The symptoms were individually analyzed after mapping and divided into four classes. In class 1, most symptoms were not present. In class 2, the presence of certain symptoms demonstrated early milestones of the disease. In class 3, symptoms related to the patient's mobility, in particular pain, stand out among the most reported. In class 4, the intense presence of all symptoms is observed. To test the tool, data were used from some of these patients, who answered the same questionnaire at different times (simulating medical appointments). The presented severity index to classify patients allowed identifying the current stage of the disease allowing the follow-up. This AI-based decision-support tool can help medical professionals to predict the evolution of Parkinson's disease, which can result in longer life quality of patients, in terms of symptoms and medication requirements. [ABSTRACT FROM AUTHOR]

Due to the growing number of data and the growing variety of requirements for their processing, now we have to move away from data processing at the time of the request and increasingly shift the main work on its implementation or the implementation of its main aspects to pre-stored and prepared results. In many ways, DBMS thus try to solve performance problems by increasing memory consumption, but in many ways, it is necessary to think about saving the latter, while preferably preserving the results of methods based on a similar approach – indexing, hashing, neural network algorithms. The article discusses a method for improving the efficiency of solving search problems for large tables. The proposed method is based on partial indexing of elements near convergence centers and the introduction of the concept of metadata for these centers. Such clustering with stored metadata for the centers, near which the next intermediate nodes are formed, allows you to reduce the memory costs for indexing, because, firstly, with this approach there is no need for nested indexing, which can lead to serious spatial costs. Secondly, such an approach can make it possible to use one indexing for different combinations of the presence of columns in the search image, without losing most of the search efficiency during indexing. Such a combination, if used correctly, can make it possible to efficiently process tables with different search needs, for different groups of columns, for which storing indexing for each large type of query or group of queries can naturally lead to serious memory consumption costs as well as loss of performance when working with large arrays of memory, which also increases far from linearly.

The article analyzes changes in the business models of Ukrainian banks using the author's method of structural and functional groups of banks (SFGB). The method’s basis is the processing, systematization, and visualization of the system’s values of banks’ financial indicators using Kohonen’s self-organizing map (SOM). Depending on the level distribution of a large number of indicators that characterize the structure of assets, liabilities, income, expenses, and other qualitative indicators that describe the business models of each bank on successive reporting dates, homogeneous groups of banks are formed. The purpose of this study is to compare the key features of the banking system as of January 1 and September 1, 2022, and the corresponding changes in business models. Over the eight months of 2022, the number of banks with corporate lending increased slightly, but the resource base of these banks gradually changed. The number of banks with retail financing decreased at the expense of banks with current resources. During an increase in the discount rate and in the price of refinancing loans, banks’ business model that attracts resources on the interbank market and places them in securities has shrunk. At the same time, the number of banks with an increased level of securities in assets and corporate financing increased. The quality of the portfolio indicates the increased credit risks of the respective large state banks. The drawback of the proposed method is the dependence of conclusions on official banks 'statements that do not always reflect nuisances of financial position. Within small banks, we can sometimes observe that current changes in clients' account balances affect the position in SFGB. The SFGB method can be applied to analyze trends and estimate the probability of subsequent structural changes. For each bank, one can observe the trajectory change on the map and investigate the reasons for the change in business strategy. [ABSTRACT FROM AUTHOR]

A method of identifying banks’ business models and studying the features of their risk profile, considering the system of indicators featuring the structure of assets, liabilities, income, expenses, and other qualitative indicators based on monthly statistical reporting. Kohonen's self-organizing maps (SOM) are used to process large data sets, revealing objects’ hidden features by forming homogeneous groups according to similar values of a large system of indicators. The choice of the system of indicators that play the most significant role in describing the business models of modern banks is substantiated. The proposed method makes it possible to group banks with homogeneous characteristics into so-called structural-functional groups and studies the change in the characteristics of groups of banks over time to compare their behavior during periods of active development of the system and during a crisis. That approach is useful for studying the banking system at the macro level, as it provides a quantitative measure of its financial stability. The more banks are in groups with negative values of parameters, increased risks, and unprofitable performance, the worse the general state of the system. The method also allows studying the features of each structural and functional group and the business models’ features at the meso-level. The number and composition of banks inherent in any group change dynamically, which characterizes the features of the relevant business model in a particular period. The averages of each group reflect the objective changes in the banking system structure. In addition, the SOM trajectory can be built for each individual bank determining the development of its strategy, features of a particular business model, and risk profile. At the micro-level, it allows comparing the features of individual banks within the SFGB and models’ ways to improve efficiency and financial stability by forecast values for SOM. An extensive system of indicators used to form structural and functional groups of banks allows to quickly respond to changes in the banking system, identify areas of increased risk and explore the adequacy and effectiveness of banks’ business models. [ABSTRACT FROM AUTHOR]

Parkinson’s disease is a progressive neurodegenerative condition whose prevalence has significantly increased. This work proposes the development of a severity index to classify patients from symptoms, mainly motor ones, using an Artificial Neuronal Network (ANN) trained by the Self-Organizing Maps (SOMs) algorithm. The FOX Insight database was used, which offers data in the form of questionnaires answered by patients or caregivers from all over the world, with information regarding this pathology. After pre-processing the data, a set of 597 questionnaires containing 28 defined questions was selected. The symptoms were individually analyzed after mapping and divided into four classes. In class 1, most symptoms were not present. In class 2, the presence of certain symptoms demonstrated early milestones of the disease. In class 3, symptoms related to the patient’s mobility, in particular pain, stand out among the most reported. In class 4, the intense presence of all symptoms is observed. To test the tool, data were used from some of these patients, who answered the same questionnaire at different times (simulating medical appointments). The presented severity index to classify patients allowed identifying the current stage of the disease allowing the follow-up. This AI-based decision-support tool can help medical professionals to predict the evolution of Parkinson’s disease, which can result in longer life quality of patients, in terms of symptoms and medication requirements.

The article analyzes changes in the business models of Ukrainian banks using the author's method of structural and functional groups of banks (SFGB). The method’s basis is the processing, systematization, and visualization of the system’s values of banks’ financial indicators using Kohonen’s self-organizing map (SOM). Depending on the level distribution of a large number of indicators that characterize the structure of assets, liabilities, income, expenses, and other qualitative indicators that describe the business models of each bank on successive reporting dates, homogeneous groups of banks are formed. The purpose of this study is to compare the key features of the banking system as of January 1 and September 1, 2022, and the corresponding changes in business models. Over the eight months of 2022, the number of banks with corporate lending increased slightly, but the resource base of these banks gradually changed. The number of banks with retail financing decreased at the expense of banks with current resources. During an increase in the discount rate and in the price of refinancing loans, banks’ business model that attracts resources on the interbank market and places them in securities has shrunk. At the same time, the number of banks with an increased level of securities in assets and corporate financing increased. The quality of the portfolio indicates the increased credit risks of the respective large state banks. The drawback of the proposed method is the dependence of conclusions on official banks 'statements that do not always reflect nuisances of financial position. Within small banks, we can sometimes observe that current changes in clients' account balances affect the position in SFGB. The SFGB method can be applied to analyze trends and estimate the probability of subsequent structural changes. For each bank, one can observe the trajectory change on the map and investigate the reasons for the change in business strategy.

A method of identifying banks’ business models and studying the features of their risk profile, considering the system of indicators featuring the structure of assets, liabilities, income, expenses, and other qualitative indicators based on monthly statistical reporting. Kohonen's self-organizing maps (SOM) are used to process large data sets, revealing objects’ hidden features by forming homogeneous groups according to similar values of a large system of indicators. The choice of the system of indicators that play the most significant role in describing the business models of modern banks is substantiated. The proposed method makes it possible to group banks with homogeneous characteristics into so-called structural-functional groups and studies the change in the characteristics of groups of banks over time to compare their behavior during periods of active development of the system and during a crisis. That approach is useful for studying the banking system at the macro level, as it provides a quantitative measure of its financial stability. The more banks are in groups with negative values of parameters, increased risks, and unprofitable performance, the worse the general state of the system. The method also allows studying the features of each structural and functional group and the business models’ features at the meso-level. The number and composition of banks inherent in any group change dynamically, which characterizes the features of the relevant business model in a particular period. The averages of each group reflect the objective changes in the banking system structure. In addition, the SOM trajectory can be built for each individual bank determining the development of its strategy, features of a particular business model, and risk profile. At the micro-level, it allows comparing the features of individual banks within the SFGB and models ways to improve efficiency and financial stability by forecast values for SOM. An extensive system of indicators used to form structural and functional groups of banks allows to quickly respond to changes in the banking system, identify areas of increased risk and explore the adequacy and effectiveness of banks’ business models.

To select a rational method for cutting of rolled stocks made from materials with different physical and mechanical properties, an engineer must have the tools to make such a choice. In order to find the most informative system of fracture criteria allowing to classify materials authentically by their fracture sensitivity, the software was developed. It allows to solve the clustering problem in a multidimensional space of parameters and present the obtained information in a visual form using self-organizing Kohonen maps. The cluster analysis of the material fracture criteria was carried out. The most informative criterion among the ones characterizing the mechanical properties of steels is the «offset yield strength», among the synergistic ones – «scale criterion». At the same time, among the traditional mechanical features of steels, a set allowing to classify materials with a given integrity by their cutting sensitivity has not been found. It was established that the fracture criteria: «crack growth criterion» and «brittleness criterion» are basic informative features. When adding any of the remaining complex criteria («crack nucleation criterion» or «scale criterion»), they form the most informative sets of minimal power, providing the classification of materials by their cutting sensitivity with a given integrity. In order to obtain high-quality workpieces at a minimum conversion cost, recommendations were developed for choice of the method for cutting of rolled stocks in magnitude of criteria values: «crack growth criterion» and «brittleness criterion». To cut materials «in plastic state», the cutting by shear should be used, specifically—a closed cutoff scheme or an incompletely closed cutoff scheme with an active transverse clamp or a cutoff scheme with a differentiated clamp of rolled stocks. If increased requirements are imposed on the geometric accuracy of the workpieces, it is recommended to use complex blanking and cutting processes. For cutting materials «in elastoplastic state» there are good reasons to use the cutting by shear, and in particular—an incompletely closed cutoff scheme with an active and passive transverse clamp. Closer to the class interval, it is possible to apply breaking by bending with the application of an effective stress concentrator. Breaking by bending should be used to cut «brittle» materials. The carried out experiments confirmed the adequacy of theoretical conclusions and recommendations on the choice of a rational method for cutting of the rolled stocks to obtain high-quality workpieces. [ABSTRACT FROM AUTHOR]

Solving inverse problems, where we find the input values that result in desired values of outputs, can be challenging. The solution process is often computationally expensive and it can be difficult to interpret the solution in high-dimensional input spaces. In this paper, we use a problem from additive manufacturing to address these two issues with the intent of making it easier to solve inverse problems and exploit their results. First, focusing on Gaussian process surrogates that are used to solve inverse problems, we describe how a simple modification to the idea of tapering can substantially speed up the surrogate without losing accuracy in prediction. Unlike block tapering, which approximates the covariance matrix by diagonal blocks, our approach divides the data itself into blocks. Both approaches reduce the computational cost by replacing the Cholesky decomposition of the full matrix by the decomposition of multiple smaller matrices, but our approach gives accurate predictions despite the approximation as we identify hyperparameters optimal for each block. Second, we demonstrate that Kohonen self-organizing maps can be used to visualize and interpret the solution to the inverse problem in the high-dimensional input space. For our data set, as not all input dimensions are equally important, we show that using weighted distances results in a better organized map that not only makes the relationships among the inputs obvious, but also indicates the location of the solution in the input space so an additive manufacturing engineer can control the inputs appropriately for a desired output.

Cereals are an essential part of the diet of Homo sapiens. Since late Neolithic times, with the transition to sedentary farming, working with grain (growing, storing, processing, cooking food) has become a traditional type of professional human activity. As part of the accumulated historical experience, numerous technological processes have been developed and optimized for this type of activity. The relevant technologies evolved in close correlation with the changing conditions of life, literally under the pressure of Darwinian natural selection, because they were directly related to the survival of the Homo sapiens. Further development of grain-processing technologies remains invariably urgent today, as evidenced by the report [1] presented by the UN on the state of food security and nutrition in the world - with horrifying figures depicting the need and misery of the wide masses of the population of the planet. An important component of grain processing is the technology associated with the storage of grain products. Part of the stored grain products is used as seed stock for a new cycle of grain sales, the other - a significant part - for processing into food products. At the same time, new developed (optimized, improved) grain storage technologies must be safe, low-cost, maximally compatible with previously developed (available) equipment, and scalable to large volumes of stored material. Of course, the technology must ensure proper efficiency, an indicator of which should be a reduction in the percentage of grain product losses. In this regard, management methods used in the technological processes of grain products storage are substantially important, as well as methods of control over the current state of grain products for the correct organization of the technological processes. In particular, methods using elements of artificial intelligence are of high interest. Among them, neural networks are promising, especially those capable of learning "without a teacher" - Kohonen Maps (KK). Modified KK algorithm [2] implements reduced learning time[3], which is relevant in the implementation of adaptive procedures for processing the results of measurements of controlled parameters. The purpose of this paper is to consider the principles of using modified Kohonen maps to classify situations with applicability to remote quality control of grain products storage.

Introduction. Formation of prerequisites for innovative development of industrial enterprises includes strategic planning, which is one of the components of the management system. The choice of the strategy of innovative development of the portfolio of strategies for innovation-active enterprises depends on their internal and external environment. Purpose. The aim of the study is to develop conceptual approaches to clustering of innovation-active machine-building enterprises, to analyze the clusters in order to identify their significant differences, and on this basis to develop appropriate strategies for each cluster of enterprises. Results. A conceptual approach to the clustering of innovation-active machine-building enterprises in order to identify the degree of their readiness for innovation. Clustering was carried out for thirty innovation-active enterprises on two indicators – the level of the crisis and the indicator of the use of strategic capabilities of enterprises. By means of the correlation analysis the relative dependence of input factors is established and by its results it is shown that the incoming factors are practically independent from each other. The use of cluster analysis on the basis of modern research methods using neural networks – Kohonen maps allowed to differentiate the innovation-active enterprises according to the degree of their readiness for innovation. The method of neural networks – Kohonen maps was used for clustering. Clustering is carried out on the basis of two input indicators – an indicator of the level of crisis and an indicator of the use of strategic opportunities.These clusters are called «0», «1», «2», «3» and «4». The largest cluster «0» included 13 machine-building innovation-active enterprises, the smallest cluster «1» included one innovation-active enterprise. The enterprises of cluster «3» have average strategic opportunities with moderate negative dynamics of the crisis indicator. In General, this is one of the largest crisis groups of enterprises among the thirty studied innovation-active enterprises. Enterprises of clusters «1» and «2» have average strategic opportunities with stable positive dynamics of the crisis state indicator. These two clusters include only three enterprises out of the thirty innovation-active enterprises under study. Conclusions. All thirty studied enterprises are divided into five clusters, which differ significantly in parameters. For machine-building innovative-active machine-building enterprises of each cluster the corresponding innovative strategy is offered.

The qualitative analysis of multidimensional data using their visualization allows to observe some characteristics of data in a way which is the most natural for a human, through the sense of sight. Thanks to such an approach, some characteristics of the analyzed data are simply visible. This allows to avoid using often complex algorithms allowing to examine specific data properties. Visualization of multidimensional data consists in using the representation transforming a multidimensional space into a two-dimensional space representing a computer screen. The important information which can be obtained in this way is the possibility to separate points belonging to different classes in the multidimensional space. Such information can be directly obtained if images of points belonging to different classes occupy other areas of the picture presenting these data. The paper presents the effectiveness of the qualitative analysis of multidimensional data conducted in this way through their visualization with the application of Kohonen maps and autoassociative neural networks. The obtained results were compared with results obtained using the perspective-based observational tunnels method, PCA, multidimensional scaling and relevance maps. Effectiveness tests of the above methods were performed using real seven-dimensional data describing coal samples in terms of their susceptibility to fluidal gasification. The methods' effectiveness was compared using the criterion for the readability of the multidimensional visualization results, introduced in earlier papers. [ABSTRACT FROM AUTHOR]

Artificial neural networks represent an alternative to traditional multivariate techniques, such as principal component and discriminant analysis, which rely on hypotheses regarding the normal distribution of the data and homoscedasticity. They also may be a powerful tool for multivariate modeling of systems that do not present linear correlation between variables, as well as to visualize high-dimensional data in bi- or trivariate structures. One special kind of neural network of interest in archaeometric studies is the Self-Organizing Map (SOM). SOMs can be distinguished from other neural networks for preserving the topological features of the original multivariate space. In this study, the self-organizing maps were applied to concentration data of chemical elements measured in archaeological ceramics from Central Amazon using instrumental neutron activation analysis (INAA). The main objective was testing the chemical patterns previously identified using cluster and principal component analysis, forming groups of ceramics according the multivariate chemical composition. It was verified by statistical tests that the chemical elemental data was not normally distributed and did not present homogeneity of covariance matrices for different groups, as requested by principal component analysis and other multivariate techniques. The maps obtained were consistent with the patterns identified by cluster and principal component analysis, forming two chemical groups of pottery shards for each archaeological site tested. Finally, it was verified the potential of SOMs for testing if failures in underlying hypotheses of traditional multivariate techniques might be critically influencing the results and subsequent archaeological interpretation of archaeometric data.

To interpret and to process the answers to questionnaires with large amount of questions may be not easy task. They are multidimensional data, sometimes with high dimensionality (in the hundreds). Therefore, it is necessary that some data reduction approach should be employed. On the other hand, answers to specific questions in questionnaires are imprecise, and the type and degree of imprecision is determined by the kind of the questions. The authors of the paper consider the imprecise answers to management type questions using a numerical scale as fuzzy degrees, and based on the semantic connections among the individual questions, a hierarchical structure is assumed. The paper suggests the use of factor analysis in order to determine this hierarchical structure, and thus the construction of fuzzy signatures from the tree graph representing the connections among the questions and answers, and the values normalized into membership degrees are assigned to the leaves of this tree. An interesting issue is how to determine the aggregations at the intermediate nodes. This may happen based on management science domain expert knowledge, and validated by the obtained results. Kohonen maps are used to demonstrate the clusters emerging among the overall fuzzy degrees representing the Fuzzy Signatures. The evaluation brings some results that partly confirm soft science based assumptions about employee behavior in the literature, and partly bring some interesting novel recognitions that may be brought in feedback to the original management science related problem, where the new method is illustrated. [ABSTRACT FROM AUTHOR]

Highlights • Brazilian soft drinks could be differenced by chemometric tools. • PCA and ANN were compared in the pattern recognition capacity. • PCA and ANN were able to extract relevant information from soft drink samples. Abstract This study approaches the determination of nine elements from Brazilian carbonated soft drinks of several flavors and manufactures using inductively coupled plasma optical emission spectrometry (ICP OES). The concentrations of the elements varied as follows: (in µg L−1: Cu: 4.00–78.0; Fe: 74.0–506; Mn: 20.0–66.0; Zn: 104–584) and (in mg L−1: Ca: 4.81–16.2; K: 6.73–260; Na: 26.0–175; S: 1.43–5.41; P: 0.186–219). Principal component analysis has shown some tendencies to form two groups according to the drink flavor (orange and cola), but only cola presented a clear and complete separation. Using Kohonen maps, it was observed a tendency to form three flavor groups: (i) cola, (ii) orange and lemon, and (iii) guarana. However, this last tool proved to be more accurate in the groups' formation. [ABSTRACT FROM AUTHOR]

Artificial neural networks represent an alternative to traditional multivariate techniques, such as principal component and discriminant analysis, which rely on hypotheses regarding the normal distribution of the data and homoscedasticity. They also may be a powerful tool for multivariate modeling of systems that do not present linear correlation between variables, as well as to visualize high-dimensional data in bi- or trivariate structures. One special kind of neural network of interest in archaeometric studies is the Self-Organizing Map (SOM). SOMs can be distinguished from other neural networks for preserving the topological features of the original multivariate space. In this study, the self-organizing maps were applied to concentration data of chemical elements measured in archaeological ceramics from Central Amazon using instrumental neutron activation analysis (INAA). The main objective was testing the chemical patterns previously identified using cluster and principal component analysis, forming groups of ceramics according the multivariate chemical composition. It was verified by statistical tests that the chemical elemental data was not normally distributed and did not present homogeneity of covariance matrices for different groups, as requested by principal component analysis and other multivariate techniques. The maps obtained were consistent with the patterns identified by cluster and principal component analysis, forming two chemical groups of pottery shards for each archaeological site tested. Finally, it was verified the potential of SOMs for testing if failures in underlying hypotheses of traditional multivariate techniques might be critically influencing the results and subsequent archaeological interpretation of archaeometric data. [ABSTRACT FROM AUTHOR]

Self-organizing maps, otherwise known as Kohonen -maps, are one form of unsupervised artificial neural networks that can produce two-dimensional plots from multidimensional data. This tool is especially useful in community pattern analyses and has been previously used in spatial pattern analysis with different perspectives. The present study aims to find zooplankton's community pattern in the Bakreswar reservoir ecosystem. Bakreswar reservoir is a freshwater ecosystem in the Birbhum district of West Bengal, India. The reservoir is primarily used to supply freshwater to the Bakreswar thermal power plant. However, the local villages around the reservoir depend on it for drinking water and fishing sustenance. The data used in this study was collected over two years from three different stations. Thus, in addition to describing the spatial pattern of community distribution of zooplankton groups, the temporal variation was also studied. It is observed in the study that the four major groups of zooplankton – Copepoda, Cladocera, Ostracoda, and Rotifera – react differently to the different environmental attributes. Primarily directed by the physical environmental factors, the effect of the chemical factors on the patterning is also evident from the study. Copepods are the dominant group in the system, closely followed by cladocerans and rotifers. But this observation changes at different stations and throughout the study period. The temperature profiles of the reservoir primarily direct the occurrence of ostracods and rotifers, whereas cladocerans and copepods are inclined more towards a chemical factor directive. Rotifers are dominant in the monsoon, whereas the post-monsoon and winter seasons show an increased presence of copepods and cladocerans. The overall observation that the reservoir's water quality is good, and the trophic structure is healthy is in accordance with previous studies as well. • Self-Organizing Map used to analyze Bakreswar reservoirs' zooplankton community. • Spatial and temporal variations in community structure were analyzed. • Community composition varies according to season and area of collection. • Ostracods and rotifers are dominant in summer and monsoon. • Copepods and cladocerans are dominant in post-monsoon and winter. • Important regulating factors are - D O , N PP , S RAD , pH, N IT.N , P HOS.P , T DS. [ABSTRACT FROM AUTHOR]

This paper shows a methodology based on neural networks to prioritize some market areas with a business approach. In this research, we try to resolve the uncertainty that exists in most organizations around the priority of a market area. The research problem is supported by the lack of tools to estimate the priority of a market area and for the lack of an effective interface between logistics and marketing departments. To solve this situation we used a special neural network (Kohonen Maps). These maps are a type of neural network to facilitate the grouping of customers and they allow determining which the most are frequently affecting the previously established criteria dealing prioritization. Finally, three scenarios are propose to validate the behavior of neural networks to prioritize market areas. [ABSTRACT FROM AUTHOR]

Xue-Zhi-Ning (XZN) is a widely used traditional Chinese medicine formula to treat hyperlipidemia. Recently, cyclodextrins (CDs) have been extensively used to minimize problems relative to medicine bioavailability, such as low solubility and poor stability. The objective of this study was to determine the associated-extraction efficiency of various CDs in XZN. Three various type CDs were evaluated, including native CDs (α-CD, β-CD), hydrophilic CD derivatives (HP-β-CD and Me-β-CD), and ionic CD derivatives (SBE-β-CD and CM-β-CD). An ultra high-performance liquid chromatography (UHPLC) fingerprint was applied to determine the components in CD extracts and original aqueous extract (OAE). A counterpropagation artificial neural network (CP-ANN) was used to analyze the components in different extracts and compare the selective extraction of various CDs. Extraction efficiencies of the various CDs in terms of extracted components follow the ranking, ionic CD derivatives > hydrophilic CD derivatives > native CDs > OAE. Besides, different types of CDs have their own selective extraction and ionic CD derivatives present the strongest associated-extraction efficiency. Antioxidant potentials of various extracts were evaluated by determining the inhibition of spontaneous, H 2 O 2 -induced, CCl 4 -induced and Fe 2+ /ascorbic acid-induced lipid peroxidation (LPO) and analyzing the scavenging capacity for DPPH and hydroxyl radicals. The order of extraction efficiencies of the various CDs relative to antioxidant activities is as follows: SBE-β-CD > CM-β-CD > HP-β-CD > Me-β-CD > β-CD > α-CD. It can be demonstrated that all of the CDs studied increase the extraction efficiency and that ionic CD derivatives (SBE-β-CD and CM-β-CD) present the highest extraction capability in terms of amount extracted and antioxidant activities of extracts. [ABSTRACT FROM AUTHOR]

Methods of multi-parameter data visualization through the transformation of multidimensional space into two-dimensional one allow to present multidimensional data on computer screen, thus making it possible to conduct a qualitative analysis of this data in the most natural way for human - by a sense of sight. In the paper a comparison was made to show the efficiency of selected seven methods of multidimensional visualization and further, to analyze data describing various coal type samples. Each of the methods was verified by checking how precisely a coal type can be classified when a given method is applied. For this purpose, a special criterion was designed to allow an evaluation of the results obtained by means of each of these methods. Detailed information included presentation of methods, elaborated algorithms, accepted parameters for best results as well the results. The framework for the comparison of the analyzed multi-parameter visualization methods includes: observational tunnels method multidimensional scaling MDS, principal component analysis PCA, relevance maps, autoassociative neural networks, Kohonen maps and parallel coordinates method. [ABSTRACT FROM AUTHOR]

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This article is an extended version of a paper presented in the WSOM׳2012 conference (Bourgeois et al., 2012 [1] ). We display a combination of factorial projections, SOM algorithm and graph techniques applied to a text mining problem. The corpus contains eight medieval manuscripts which were used to teach arithmetic techniques to merchants. Among the techniques for Data Analysis, those used for Lexicometry (such as Factorial Analysis) highlight the discrepancies between manuscripts. The reason for this is that they focus on the deviation from the independence between words and manuscripts. Still, we also want to discover and characterize the common vocabulary among the whole corpus. Using the properties of stochastic Kohonen maps, which define neighborhood between inputs in a non-deterministic way, we highlight the words which seem to play a special role in the vocabulary. We call them fickle and use them to improve both Kohonen map robustness and significance of FCA visualization. Finally we use graph algorithmic to exploit this fickleness for classification of words. [ABSTRACT FROM AUTHOR]

Low level atmospheric circulations downscaled by regional climate model are used to identify austral summer (December-January-February) weather regimes (WRs) over central Brazil. For the period 1989-2006, the Kohonen's self-organizing maps method is applied to provide simulated weather patterns and their relationship with daily rainfall. Six WRs were defined: two related to the active phase of the South American monsoon system (SAMS), with the large occurrence of days with extreme rainfall; two related to the break phase and few occurrences of extreme rainfall; and two others resembling transient WRs, those with fewer extreme rainfall. The WRs formed a cycle obtained from their transition probabilities, which suggests alternating phases of the convergence zone and their association with extreme rainfall. Thus, the variability of summer precipitation, related to the behavior of the simulated WRs enables the verification of the regional dynamic model on intraseasonal time scale. [ABSTRACT FROM AUTHOR]

The primary goal of this study was to describe and compare the criteria used to assess carcinogenic activity. The statistically-based predictive quantitative structure–activity relationship (QSAR) models based on the counter propagation artificial neural network (CPANN) algorithm, and knowledge-based expert systems based on a decision tree structural alert (SA) approach (Toxtree application), were considered. The integration of the QSAR (CPANN models) and SAR (Toxtree SA application) approach contributed to the mechanistic understanding of the QSAR model considered. The mapping technique inherent to CPANN Kohonen enables us to relate the similarities or dissimilarities within a congeneric set of chemicals with particular SAs for carcinogenicity. The focus of our investigations was the similarities and dissimilarities of the features used in the QSAR and SAR methods. Due to the complexity of the carcinogenic endpoint, the integration of different approaches allows the models to be improved and provides a valuable technique for evaluating the safety of chemicals. [ABSTRACT FROM AUTHOR]