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The transition towards a sustainable and low-carbon future requires significant changes in energy behaviour among energy consumers. However, the question remains about how, by whom, and what changes are necessary to achieve this transition. While many policies have been implemented to overcome these barriers, current policies focus on technology market development and market failures. Policies that address behavioural anomalies are limited, leading to a lack of engagement and awareness among many energy consumers. This issue is particularly prominent among hard-to-reach (HTR) consumer groups, which can be found in both the commercial and residential sectors (Rotmann, Sea, 2019). The impact of HTR energy consumers on energy efficiency measures and the achievement of climate neutrality remains largely unexplored. It is necessary to identify and understand these groups to address this gap. By doing so, it is possible to help them change their energy consumption behaviour towards a more sustainable and low-carbon future (Rotmann, Sea, 2019).This project aims to provide insights into HTR energy consumers in Sweden and assess their impact on achieving climate neutrality. The primary objective is to identify the most common HTR groups, their main barriers, and the potential drivers that could help change their energy consumption behaviour. A detailed literature review is conducted to gather information on practical and theoretical cases globally to achieve these objectives. After the literature review, a semi-quantitative data analysis has identified and classified HTR energy user in Sweden, by using modelling software like Mental Modeler. A qualitative analysis was carried out to analyze the HTR groups, identify their main barriers and potential drivers, and to understand their energy behaviour better. From the literature review, it was found that high income, low income, and elderly are hard to reach, and there is a research gap of their behavior, needs and how to addres, Övergången till en hållbar framtid med låga koldioxidutsläpp kräver betydande förändringar i energibeteendet bland energikonsumenter. Frågan kvarstår dock om hur, av vem och vilka förändringar som krävs för att uppnå denna övergång. Även om ett stort antal policyer har implementerats för att övervinna dessa barriärer, fokuserar nuvarande policyer främst på teknikmarknadsutveckling och marknadsmisslyckanden. Policyer som tar itu med beteendeavvikelser är begränsade, vilket leder till bristande engagemang och medvetenhet bland många energikonsumenter. Denna fråga är särskilt framträdande bland svåråtkomliga konsumentgrupper, som finns i både kommersiella och bostadssektorer (Rotmann, Sea, 2019). De svåråtkomliga energikonsumenters inverkan på energieffektivitetsåtgärder och uppnåendet av klimatneutralitet är fortfarande i stort sett outforskad. För att komma till rätta med denna klyfta är det nödvändigt att identifiera och förstå dessa grupper bättre. Genom att göra det kommer det att vara möjligt att hjälpa dem att ändra sitt energikonsumtionsbeteende mot en mer hållbar framtid med låga koldioxidutsläpp (Rotmann, Sea, 2019). Detta projekt syftar till att ge insikter om HTR energikonsumenter i Sverige och bedöma deras inverkan på att uppnå klimatneutralitet. Det primära målet är att identifiera de vanligaste HTR-grupperna, de huvudsakliga hindren de möter och de potentiella drivkrafterna som kan bidra till att förändra deras energiförbrukningsbeteende.En noggrann litteraturöversikt genomförs för att samla information om praktiska och teoretiska fall globalt för att uppnå dessa mål. Efter litteraturöversikten har en semi-kvantitativ dataanalys identifierat och klassificerat HTR-energianvändare i Sverige genom att använda modelleringsprogramvara som Mental Modeler. En kvalitativ analys utfördes för att analysera HTR-grupperna, identifiera deras huvudsakliga hinder och potentiella drivkrafter och för att bättre förstå deras energibeteende. Från litteraturgenomgången fram

The transition towards a sustainable and low-carbon future requires significant changes in energy behaviour among energy consumers. However, the question remains about how, by whom, and what changes are necessary to achieve this transition. While many policies have been implemented to overcome these barriers, current policies focus on technology market development and market failures. Policies that address behavioural anomalies are limited, leading to a lack of engagement and awareness among many energy consumers. This issue is particularly prominent among hard-to-reach (HTR) consumer groups, which can be found in both the commercial and residential sectors (Rotmann, Sea, 2019). The impact of HTR energy consumers on energy efficiency measures and the achievement of climate neutrality remains largely unexplored. It is necessary to identify and understand these groups to address this gap. By doing so, it is possible to help them change their energy consumption behaviour towards a more sustainable and low-carbon future (Rotmann, Sea, 2019).This project aims to provide insights into HTR energy consumers in Sweden and assess their impact on achieving climate neutrality. The primary objective is to identify the most common HTR groups, their main barriers, and the potential drivers that could help change their energy consumption behaviour. A detailed literature review is conducted to gather information on practical and theoretical cases globally to achieve these objectives. After the literature review, a semi-quantitative data analysis has identified and classified HTR energy user in Sweden, by using modelling software like Mental Modeler. A qualitative analysis was carried out to analyze the HTR groups, identify their main barriers and potential drivers, and to understand their energy behaviour better. From the literature review, it was found that high income, low income, and elderly are hard to reach, and there is a research gap of their behavior, needs and how to addres, Övergången till en hållbar framtid med låga koldioxidutsläpp kräver betydande förändringar i energibeteendet bland energikonsumenter. Frågan kvarstår dock om hur, av vem och vilka förändringar som krävs för att uppnå denna övergång. Även om ett stort antal policyer har implementerats för att övervinna dessa barriärer, fokuserar nuvarande policyer främst på teknikmarknadsutveckling och marknadsmisslyckanden. Policyer som tar itu med beteendeavvikelser är begränsade, vilket leder till bristande engagemang och medvetenhet bland många energikonsumenter. Denna fråga är särskilt framträdande bland svåråtkomliga konsumentgrupper, som finns i både kommersiella och bostadssektorer (Rotmann, Sea, 2019). De svåråtkomliga energikonsumenters inverkan på energieffektivitetsåtgärder och uppnåendet av klimatneutralitet är fortfarande i stort sett outforskad. För att komma till rätta med denna klyfta är det nödvändigt att identifiera och förstå dessa grupper bättre. Genom att göra det kommer det att vara möjligt att hjälpa dem att ändra sitt energikonsumtionsbeteende mot en mer hållbar framtid med låga koldioxidutsläpp (Rotmann, Sea, 2019). Detta projekt syftar till att ge insikter om HTR energikonsumenter i Sverige och bedöma deras inverkan på att uppnå klimatneutralitet. Det primära målet är att identifiera de vanligaste HTR-grupperna, de huvudsakliga hindren de möter och de potentiella drivkrafterna som kan bidra till att förändra deras energiförbrukningsbeteende.En noggrann litteraturöversikt genomförs för att samla information om praktiska och teoretiska fall globalt för att uppnå dessa mål. Efter litteraturöversikten har en semi-kvantitativ dataanalys identifierat och klassificerat HTR-energianvändare i Sverige genom att använda modelleringsprogramvara som Mental Modeler. En kvalitativ analys utfördes för att analysera HTR-grupperna, identifiera deras huvudsakliga hinder och potentiella drivkrafter och för att bättre förstå deras energibeteende. Från litteraturgenomgången fram

FCMpy is an open-source Python module for building and analyzing Fuzzy Cognitive Maps (FCMs). The module provides tools for end-to-end projects involving FCMs. It is able to derive fuzzy causal weights from qualitative data or simulating the system behavior. Additionally, it includes machine learning algorithms (e.g., Nonlinear Hebbian Learning, Active Hebbian Learning, Genetic Algorithms, and Deterministic Learning) to adjust the FCM causal weight matrix and to solve classification problems. Finally, users can easily implement scenario analysis by simulating hypothetical interventions (i.e., analyzing what-if scenarios). FCMpy is the first open-source module that contains all the functionalities necessary for FCM oriented projects. This work aims to enable researchers from different areas, such as psychology, cognitive science, or engineering, to easily and efficiently develop and test their FCM models without the need for extensive programming knowledge., QC 20221024

Actualment, existeixen tecnologies que fan la funció de proporcionar informació de l'estat de la mar via satèl·lit, la qual cosa fa que la informació no sigui en temps real i no sigui del tot correcta. Amb la condició d'aconseguir que aquesta informació es transmeti de manera precisa i en temps real, sorgeix la idea de fer un sistema amb boies intel·ligents que permetin conèixer l'estat de la mar. L'objectiu del projecte és el disseny i programació d'una aplicació mòbil que permeti la visualització i gestió de boies costaneres que realitzen informació respecte a l'onatge, la posició GPS de la boia en temps real, temperatura i nivell de bateria. El projecte està format per 3 grans blocs, en el primer bloc tracta la part relacionada amb la base de dades, el segon bloc tracta sobre el web service, el seu desenvolupament i funcionalitat, i finalment, el tercer bloc tracta sobre les diferents pantalles del mòbil, el seu funcionament intern i la manera de rebre les notificacions. En la memòria, respecte a la base de dades, s'ha explicat l'estructura actual presentada per l'equip de SARTI i els canvis i addicions que s'han hagut de fer per a l'ús de l'aplicació, Respecte al web service s'ha detallat que és aquest tipus de servei, com està estructurat, el desenvolupament de les diferents rutes d'accés i enviament de notificacions. Finalment, respecte a l'aplicació, s'han presentat les diferents pantalles que la formen, detallant, per a cadascuna com s'obté la informació necessària, com es construeix, el seu funcionament i finalment com es reben notificacions sobre les boies., Actualmente, existen tecnologías que hacen la función de proporcionar información del estado del mar vía satélite, lo cual hace que la información no sea en tiempo real y no sea del todo correcta. Con tal de conseguir que esta información se transmita de manera precisa y en tiempo real, surge la idea de hacer un sistema con boyas inteligentes que permitan conocer el estado del mar. El objetivo del proyecto es el diseño y programación de una aplicación móvil que permita la visualización y gestión de boyas costeras que realizan información respecto al oleaje, la posición GPS de la boya en tiempo real, temperatura y nivel de batería. El proyecto está formado por 3 grandes bloques, en el primer bloque trata la parte relacionada con la base de datos, el segundo bloque trata sobre el web service, su desarrollo y funcionalidad, y por último, el tercer bloque trata sobre las diferentes pantallas del móvil, su funcionamiento interno y la forma de recibir las notificaciones. En la memoria, respecto a la base de datos, se ha explicado la estructura actual presentada por el equipo de SARTI y los cambios y adiciones que se han tenido que hacer para el uso de la aplicación, Respecto al web service se ha detallado que es este tipo de servicio, como está estructurado, el desarrollo de las diferentes rutas de acceso y envío de notificaciones. Finalmente, respeto a la aplicación, se han presentado las diferentes pantallas que la forman, detallando, para cada una como se obtiene la información necesaria, como se construye, su funcionamiento y finalmente como se reciben notificaciones sobre las boyas., Currently, there are technologies that provide information on the state of the sea via satellite, which means that the information is not in real time and is not entirely correct. In order to ensure that this information is transmitted accurately and in real time, the idea of making a system with intelligent buoys that allow knowing the state of the sea arises. The objective of the project is the design and programming of a mobile application that allows the visualization and management of coastal buoys that provide information regarding the waves, the GPS position of the buoy in real time, temperature and battery level. The project is made up of 3 large blocks, the first block deals with the part related to the database, the second block deals with the web service, its development and functionality, and finally, the third block deals with the different mobile screens, its internal operation and how to receive notifications. In the paper, regarding the database, the current structure presented by the SARTI team and the changes and additions that have had to be made for the use of the application have been explained. Regarding the web service, it has been detailed what this type of service is, how it is structured, the development of the different access routes and the sending of notifications. Finally, regarding the application, the different screens that form it have been presented, detailing, for each one, how the necessary information is obtained, how it is built, its operation and finally how notifications about the buoys are received.

Fuzzy document representationinvolves transforming the unstructured data into numerical vectors. Such a representation is more useful for text classification and document clustering. The proposed Fuzzy Conceptualization Model (FCM) performs conceptualization and provides a better data representation model on the basis of semantic relatedness and similarity between terms in a word corpus. Word embedding is used to hold the semantically related words in a concept cluster. The concept clusters are inferred and vectored forthe given corpus to hold the data in a multidimensional space. FCM determines the fuzzy membership value of a base term by calculating the affinity score between its corresponding word embedding and other word embeddings. A weighing scheme isused to distinguish between exact and approximate matches. The greatest bound for the distribution of base set over the documents gives the best matched documents for a search query. The exact and approximate matches are differentiated by considering the normalized term frequency of a term in the specified concept cluster along with its actual presence. The resultant matrix gives a lower dimensional and discriminated representation of data

Fuzzy document representationinvolves transforming the unstructured data into numerical vectors. Such a representation is more useful for text classification and document clustering. The proposed Fuzzy Conceptualization Model (FCM) performs conceptualization and provides a better data representation model on the basis of semantic relatedness and similarity between terms in a word corpus. Word embedding is used to hold the semantically related words in a concept cluster. The concept clusters are inferred and vectored forthe given corpus to hold the data in a multidimensional space. FCM determines the fuzzy membership value of a base term by calculating the affinity score between its corresponding word embedding and other word embeddings. A weighing scheme isused to distinguish between exact and approximate matches. The greatest bound for the distribution of base set over the documents gives the best matched documents for a search query. The exact and approximate matches are differentiated by considering the normalized term frequency of a term in the specified concept cluster along with its actual presence. The resultant matrix gives a lower dimensional and discriminated representation of data

Fuzzy document representationinvolves transforming the unstructured data into numerical vectors. Such a representation is more useful for text classification and document clustering. The proposed Fuzzy Conceptualization Model (FCM) performs conceptualization and provides a better data representation model on the basis of semantic relatedness and similarity between terms in a word corpus. Word embedding is used to hold the semantically related words in a concept cluster. The concept clusters are inferred and vectored forthe given corpus to hold the data in a multidimensional space. FCM determines the fuzzy membership value of a base term by calculating the affinity score between its corresponding word embedding and other word embeddings. A weighing scheme isused to distinguish between exact and approximate matches. The greatest bound for the distribution of base set over the documents gives the best matched documents for a search query. The exact and approximate matches are differentiated by considering the normalized term frequency of a term in the specified concept cluster along with its actual presence. The resultant matrix gives a lower dimensional and discriminated representation of data

Fuzzy document representationinvolves transforming the unstructured data into numerical vectors. Such a representation is more useful for text classification and document clustering. The proposed Fuzzy Conceptualization Model (FCM) performs conceptualization and provides a better data representation model on the basis of semantic relatedness and similarity between terms in a word corpus. Word embedding is used to hold the semantically related words in a concept cluster. The concept clusters are inferred and vectored forthe given corpus to hold the data in a multidimensional space. FCM determines the fuzzy membership value of a base term by calculating the affinity score between its corresponding word embedding and other word embeddings. A weighing scheme isused to distinguish between exact and approximate matches. The greatest bound for the distribution of base set over the documents gives the best matched documents for a search query. The exact and approximate matches are differentiated by considering the normalized term frequency of a term in the specified concept cluster along with its actual presence. The resultant matrix gives a lower dimensional and discriminated representation of data

Fuzzy document representationinvolves transforming the unstructured data into numerical vectors. Such a representation is more useful for text classification and document clustering. The proposed Fuzzy Conceptualization Model (FCM) performs conceptualization and provides a better data representation model on the basis of semantic relatedness and similarity between terms in a word corpus. Word embedding is used to hold the semantically related words in a concept cluster. The concept clusters are inferred and vectored forthe given corpus to hold the data in a multidimensional space. FCM determines the fuzzy membership value of a base term by calculating the affinity score between its corresponding word embedding and other word embeddings. A weighing scheme isused to distinguish between exact and approximate matches. The greatest bound for the distribution of base set over the documents gives the best matched documents for a search query. The exact and approximate matches are differentiated by considering the normalized term frequency of a term in the specified concept cluster along with its actual presence. The resultant matrix gives a lower dimensional and discriminated representation of data

Fuzzy document representationinvolves transforming the unstructured data into numerical vectors. Such a representation is more useful for text classification and document clustering. The proposed Fuzzy Conceptualization Model (FCM) performs conceptualization and provides a better data representation model on the basis of semantic relatedness and similarity between terms in a word corpus. Word embedding is used to hold the semantically related words in a concept cluster. The concept clusters are inferred and vectored forthe given corpus to hold the data in a multidimensional space. FCM determines the fuzzy membership value of a base term by calculating the affinity score between its corresponding word embedding and other word embeddings. A weighing scheme isused to distinguish between exact and approximate matches. The greatest bound for the distribution of base set over the documents gives the best matched documents for a search query. The exact and approximate matches are differentiated by considering the normalized term frequency of a term in the specified concept cluster along with its actual presence. The resultant matrix gives a lower dimensional and discriminated representation of data

Fuzzy document representationinvolves transforming the unstructured data into numerical vectors. Such a representation is more useful for text classification and document clustering. The proposed Fuzzy Conceptualization Model (FCM) performs conceptualization and provides a better data representation model on the basis of semantic relatedness and similarity between terms in a word corpus. Word embedding is used to hold the semantically related words in a concept cluster. The concept clusters are inferred and vectored forthe given corpus to hold the data in a multidimensional space. FCM determines the fuzzy membership value of a base term by calculating the affinity score between its corresponding word embedding and other word embeddings. A weighing scheme isused to distinguish between exact and approximate matches. The greatest bound for the distribution of base set over the documents gives the best matched documents for a search query. The exact and approximate matches are differentiated by considering the normalized term frequency of a term in the specified concept cluster along with its actual presence. The resultant matrix gives a lower dimensional and discriminated representation of data

Organisations need for an effective supplier selection system towards attaining the competition advantages in the market. In this paper, we propose an alternative supplier selection framework integrated by fuzzy c-means, belief-based quality function deployment, fuzzy weighted average, and a proposed fuzzy decision measure. This framework consists of three phases. In the first phase, we present the potential assessments for a supplier selection and their uncertainty descriptions. We divide supplier assessments into two parts: 1) expert assessments using combined belief-based quality function deployment and fuzzy weighted average; 2) system assessments comprised of a multi input-single output system. In the second phase, we analyse uncertainties of both expert and current systems using the well-known fuzzy c-means. In the third phase, we propose a fuzzy decision measure to complete the supplier selection procedure with finding the most appropriate supplier. We apply this framework to supplier selection process of a Turkish Machinery Corporation. © 2017 Inderscience Enterprises Ltd.

[EN] The cosmic microwave background (CMB) radiation was emitted at very early times, shortly after the Big Bang. It reaches the earth from every direction and, even though it has become weak and has suffered some changes on its way, it gives important information on how the universe was and how it is evolving. The radiation from the CMB is polarized, and different information can be extracted from each polarization mode. In articular, the detection of primordial B-modes would imply the existence of a primordial background of gravitational waves, which is predicted by inflation, and would in turn greatly reinforce this theory. The B- mode of polarization is very weak, and its observations are very contaminated from Galactic diffuse emission. This project has focused on creating a component separation method using neural networks (NNs) to recover the signal of the CMB from multi-frequency observations of the full sky containing thermal dust and synchrotron foregrounds. The NN models have been optimized for observations of the satellite LiteBIRD, due to be launched in 2027 and whose goal is precisely to obtain measurements of the primordial B-modes, training the models with simulations following specifications of that instrument. Promising results have been obtained, with relative errors of the predictions around 11% in the region of the sky with less intense foregrounds, better than with a standard technique such as ILC. This study suggests that a robust component separation method could be obtained with NNs; still, more has to be analyzed regarding the models and the residuals from foregrounds and noise that are produced, as well as the response of the networks to different foregrounds., [ES] La radiación del fondo cósmico de microondas (FCM) fue emitida poco después del Big Bang. Llega a la tierra desde todas las direcciones y, aunque se ha hecho más débil y ha sufrido algunos cambios por el camino, da información muy valiosa sobre como era el universo y como esta evolucionando. La radiación del FCM está polarizada, y distinta información se puede obtener de cada modo. En particular, la detección de los modos B primordiales implicaría la existencia de un fondo primordial de ondas gravitacionales, el cual esta predicho por inflación, y reforzaría fuertemente esta teoría. El modo B de polarización es muy débil, y sus observaciones vienen muy contaminadas por emisión galáctica difusa. Este proyecto se ha centrado en crear un método de separación de componentes usando redes neuronales (RNs) para recuperar la señal del FCM de observaciones a todo el cielo a distintas frecuencias que contienen componentes de emisión de polvo térmico y synchrotron. Las RNs han sido optimizadas para observaciones del satélite LiteBIRD, programado para ser lanzado en 2027 y cuyo objetivo es precisamente la medición de los modos B primordiales, entrenando los modelos con simulaciones siguiendo las especificaciones de ese instrumento. Se han obtenido resultados prometedores, con errores relativos de las predicciones cerca del 11 % en la región del cielo con contaminación menos intensa, mejor que con una técnica estándar como el ILC. Este estudio sugiere que un método de separación de componentes robusto se podría obtener con RNs; aun así, los modelos y los residuos de contaminantes y ruido que se producen deben de ser analizados en más profundidad, así como la respuesta de las redes a distintos tipos de contaminantes.

In this paper, an adapted unsupervised segmentation approach is proposed to fully automate the segmentation of white blood cells and their nuclei. Segmentation and counting of white blood cells from microscope images are challenging tasks, especially the segmentation of white blood cell nuclei from the cell wall and cytoplasm because of the need to consider intraclass variations arising from non-uniform illumination, stage of maturity, colour distribution, scale, and overlapped cells with other components of the blood. We propose the use of the JSEG algorithm based on colour-texture distribution, and adapted region growing using the Fuzzy C Mean to segment and count WBCs and their nuclei. First, colours in the image are quantized to represent differentiated regions in the image. Image pixel colours are then replaced by their corresponding colour class labels, thus forming a class-map of the image. A criterion for “good” segmentation using this spatial class-map is applied to local image windows resulting in J-images, which can be segmented using adapted region growing based on the Fuzzy C Mean algorithm. The Fuzzy C Mean is also employed for counting each white blood cell in images. Performance of the proposed method is evaluated on a combined dataset of 10 types of white blood cell with 200 digital images collected from 3 datasets. It achieves an average segmentation accuracy using four indices for WBC segmentation: Jaccard distance, rand index, boundary detection error and F-value indices, 0.002, 0.93, 10.11, 0.93, respectively, while for WBC nuclei segmentation, it achieves indices values, 0.015, 0.88, 14.11, 0.90, respectively. The segmentation accuracy of the proposed method is also compared and benchmarked with the other existing techniques for segmentation of white blood cells over the same datasets and the results show that the proposed method is superior to other approaches.

Electroencephalographic (EEG) signals are very closely related to psychophysiological. The EEG signals displayed few responses which can be categorized. This article discussed the use of statistics over the EEG features which confirm the different mental characteristics. Two different type of stimulus was given named as relaxed state and non-relaxed state. Asymmetry index was computed as the EEG features via the alpha waves and was extracted during the relaxed state and the non-relaxed state. The EEG features were clustered to a group of three, four and five using Fuzzy C-Means. During the relaxed state, the alpha wave showed a higher response as compared to the non-relaxed state. This is observed by using the mean relative energy between the relaxed state and non-relaxed state. To ensure which EEG features in the clusters showed a significant difference, p < .05, a statistical test was used. Wilcoxon Signed Ranks test is the best-statistical test to verify the selected clusters as it is suitable to analyze the small sample of data. Wilcoxon Signed Ranks test used a hypothesis testing which using the same method as paired sample t-test. The advantage in using Wilcoxon Signed Ranks test is that, it uses the median to get the difference between two samples of data. Analytical results showed that the data features of four clusters and three clusters give a significant difference, thus the obtained results can be used to further up the study. The Wilcoxon Signed Ranks test results confirmed that the proposed technique has potential in establishing the calmness index.

The shortage of data on non-intentionally added substances (NIAS) present in food contact material (FCM) limits the ability to ensure food safety. Recent strategies in analytical method development allow investigating NIAS by using chemical exploration; but this has not been sufficiently investigated in risk assessment context. Here, exploration is applied on two paperboard FCM samples followed by risk prioritization for chemicals that can potentially migrate to food. Concentration estimates from exploration are converted into a tentative exposure assessment, while predicted chemical structures are assessed using quantitative structure-activity relationships (QSAR) models for carcinogenicity, mutagenicity, and reproductive toxicity. A selection of 60 chemical compounds from two FCMs is assessed by four risk assessors to classify chemical compounds based on probable risk. For 60% of cases, the assessors classified compounds as either high priority or low priority. Unclassified compounds are due to disagreements between experts or due to a lack of data. Among the high priority substances were high concentration compounds, benzophenone derivatives, and dyes. The low priority compounds contained e.g. oligomers from plasticizers and linear alkane amides. The classification scheme was demonstrated to provide valuable information based on tentative data, able to prioritize discovered chemical compounds for pending risk assessment.

Tato diplomová práce je věnována stanovení kofeinu a izomerů kyseliny chlorogenové v kávě pomocí kapalinové chromatografie se spektrofotometrickou detekcí. Optimalizovaná chromatografická separace byla použita pro analýzu pražených, zelených a bezkofeinových kávových infuzí. Dále byla pozornost věnována metodám pro odstranění kofeinu z kávových zrn. Nejprve byla využita extrakce různými organickými rozpouštědly, z nichž nejlepších výsledků bylo dosaženo pomocí dichlormethanu. Dále byla testována superkritická fluidní extrakce s oxidem uhličitým. V neposlední řadě byla měřena antioxidační aktivita a celkový obsah fenolických látek pomocí spektrofotometrických technik., The aim of this thesis was the determination of caffeine and chlorogenic acid isomers in coffee by liquid chromatography with spectrophotometric detection. Optimized chromatographic separation was used for analysis of roasted, green and non-caffeine coffee infusions. Further, the attention was paid to methods for removing caffeine from coffee beans. First, the extraction with various organic solvents was used, from which the best results were obtained with dichloromethane. In addition, supercritical fluid extraction with carbon dioxide was tested. Last but not least, the antioxidant activity and total phenolic content were measured by spectrophotometric techniques., Fakulta chemicko-technologická, Studentka úspěšně obhájila diplomovou práci a zodpověděla otázky komise.

Tiivistelmä Kuorinnan tavoitteena on valmistaa tasalaatuista raaka-ainetta haketukseen ja edelleen keittämölle. Puuhäviöitä syntyy, kun esikäsittelylinjaston kuorimarummussa kuoren irtoamisen lisäksi myös osa puusta silppuuntuu ja poistuu kuoren mukana kuoriaukkojen kautta. Kuorinnan tavoitteena on mahdollisimman suuri kuorintapuhtaus samalla kun puuhäviöt pysyvät pienenä. Optimaaliseen kuorinta-asteeseen päästään säätämällä kuorintarummun nopeutta ja täyttöastetta puulaadun ja rummun mitoituksen mukaan. Työssä tavoitteena ollut kuorinnan optimointi perustuu Valmet Woodhandlingin VisiBark-mittariin, joka soveltuu havupuun erivärisen kuoren ja puuaineksen erottamiseen toisistaan. Klusterointimenetelmänä työssä on käytetty Matlab FuzzyEqu Toolboxin FCM-funktiota (sumeaa keskiarvoistettua klusterointia) kunkin klusterivaihtoehdon kehittämiseen. Klusteroinnilla pyritään jakamaan todellinen rummulta saatu mittausdata eri puulaatujen mukaisiin klustereihin. Puun laatu vaihtelee varsin voimakkaasti, kun rummulle tulee tuoretta puuta suoraan hakkuutyömaalta ja väliin taas puukentällä jo pitempään kuivanutta ja osin jopa lahonnutta puutavaraa. Puulaatujen vaihteluväli voi olla erittäinkin lyhyt, jos samaan aikaan linjalle ajetaan puuta eri kohteista. Koska puun laadullisesta määrityksestä ei ole vielä saatavissa mittausdataa, joka voitaisiin liittää yhdeksi muuttujaksi, päädyttyyn jakamaan data vain kolmeen klusteriin. Tällöin muodostuivat mallit normaaleille ajotilanteille, jossa puuhäviöt pysyvät kohtuullisina sekä häiriötilanteille. Häiriötilanteissa puuhäviöt joko kohosivat huomattavasti normaalia suuremmiksi tai jäivät erittäin pieniksi tilanteissa, joissa rummun kierrosnopeus jouduttiin ajamaan alas. Työn tuloksena todettiin klusterien pohjalta tehtyjen mallien erottuvan toisistaan. Raportti on osa projektia "Keiton, pesun ja happidelignifioinnin älykäs ohjaus ja kuitulinjan eri prosessien yhteistoiminnan tarkempi hallinta" (KUHA), joka kuuluu osana TEKES:n rahoittamaa

A method is described for identifying tumor tissue by means of FTIR microspectroscopy and positron emission tomography (PET). Thin tissue sections of human squamous carcinoma from hypopharynx (FaDu) and human colon adenocarcinoma (HT-29) grown in nude mice were investigated. FTIR spectroscopic maps of the thin tissue sections were generated and evaluated by Fuzzy C-Means (FCM) clustering and principal component analysis (PCA). The processed data were reassembled into images and compared to stained tissue samples and to PET. Tumor tissue could successfully be identified by this FTIR microspectroscopic method, while it was not possible to accomplish this with PET alone. On the other hand, PET permitted the non-invasive screening for suspicious tissue inside the body, which could not be achieved by FTIR.

Синюк В. Г. Об одном гибридном алгоритме нечёткой кластеризации / В. Г. Синюк, В. М. Поляков, А. И. Анищенко // Вестник БГТУ им. В. Г. Шухова. - 2013. - №2. - С. 173-176., Нечѐткая кластеризация является важнейшей проблемой и предметом активных исследований в различных областях. Нечѐткий алгоритм С-среднего один из самых популярных методов нечѐткой кластеризации (FCM). Однако FCM чувствителен к инициализации и легко попадает в локальные оптимумы. Во многих задачах оптимизации используется Particle Swarm Optimization (PSO) – инструмент глобальной стохастической оптимизации. В данной статье предлагается гибридный нечѐткий метод оптимизации, базируемый на FCM и нечѐткой PSO (FPSO), который использует достоинств обоих алгоритмов. Эксперименты показывают, что предлагаемый метод эффективен и может показывать хорошие результаты.

Mohammed, H., Clarke, N., & Li, F. (2018, August). Evidence identification in heterogeneous data using clustering. In Proceedings of the 13th International Conference on Availability, Reliability and Security (p. 35). ACM. Available here

Cílem této práce je vytvořit mobilní aplikaci pro operační systém Android, která propojí především drobné vinaře se zákazníky. V aplikaci si potenciální zákazník zadá oblast hledání a zprávu pro vinaře. Na základě této poptávky se rozešlou zprávy vinařům v dané oblasti. Přijmutím poptávky vinařem dostane zákazník informaci, že je dostupný. Případné upřesnění poptávky či nabídky lze uskutečnit pomocí soukromých zpráv v aplikaci. Aplikace je vytvořena s důrazem na jednoduchost použití a respektuje Material Design. Data jsou ukládána pomocí technologie Firebase od Google, notifikace jsou zasílány pomocí služby Firebase Cloud Messaging. S ohledem na jednoduchost jsou výsledkem dvě aplikace, první Mám víno! je určena pro vinaře. Druhá aplikace Chcu víno! slouží výhradně pro zákazníky, kteří pomocí ní vyhledají vinaře., The aim of this thesis is to create a mobile application for Android that will connect small wineries with their customers. The result is two applications, the first of which is for wineries and the second one for customers, who can search wineries by simple criteria, save them as favorites, make notes and much more. The applications respects Material Design rules. Google Firebase is used for work with data and Firebase Cloud Messaging handles notifications between devices.

Cílem této práce je vytvořit mobilní aplikaci pro operační systém Android, která propojí především drobné vinaře se zákazníky. V aplikaci si potenciální zákazník zadá oblast hledání a zprávu pro vinaře. Na základě této poptávky se rozešlou zprávy vinařům v dané oblasti. Přijmutím poptávky vinařem dostane zákazník informaci, že je dostupný. Případné upřesnění poptávky či nabídky lze uskutečnit pomocí soukromých zpráv v aplikaci. Aplikace je vytvořena s důrazem na jednoduchost použití a respektuje Material Design. Data jsou ukládána pomocí technologie Firebase od Google, notifikace jsou zasílány pomocí služby Firebase Cloud Messaging. S ohledem na jednoduchost jsou výsledkem dvě aplikace, první Mám víno! je určena pro vinaře. Druhá aplikace Chcu víno! slouží výhradně pro zákazníky, kteří pomocí ní vyhledají vinaře., The aim of this thesis is to create a mobile application for Android that will connect small wineries with their customers. The result is two applications, the first of which is for wineries and the second one for customers, who can search wineries by simple criteria, save them as favorites, make notes and much more. The applications respects Material Design rules. Google Firebase is used for work with data and Firebase Cloud Messaging handles notifications between devices.

Cílem této práce je vytvořit mobilní aplikaci pro operační systém Android, která propojí především drobné vinaře se zákazníky. V aplikaci si potenciální zákazník zadá oblast hledání a zprávu pro vinaře. Na základě této poptávky se rozešlou zprávy vinařům v dané oblasti. Přijmutím poptávky vinařem dostane zákazník informaci, že je dostupný. Případné upřesnění poptávky či nabídky lze uskutečnit pomocí soukromých zpráv v aplikaci. Aplikace je vytvořena s důrazem na jednoduchost použití a respektuje Material Design. Data jsou ukládána pomocí technologie Firebase od Google, notifikace jsou zasílány pomocí služby Firebase Cloud Messaging. S ohledem na jednoduchost jsou výsledkem dvě aplikace, první Mám víno! je určena pro vinaře. Druhá aplikace Chcu víno! slouží výhradně pro zákazníky, kteří pomocí ní vyhledají vinaře., The aim of this thesis is to create a mobile application for Android that will connect small wineries with their customers. The result is two applications, the first of which is for wineries and the second one for customers, who can search wineries by simple criteria, save them as favorites, make notes and much more. The applications respects Material Design rules. Google Firebase is used for work with data and Firebase Cloud Messaging handles notifications between devices.

Cílem této práce je vytvořit mobilní aplikaci pro operační systém Android, která propojí především drobné vinaře se zákazníky. V aplikaci si potenciální zákazník zadá oblast hledání a zprávu pro vinaře. Na základě této poptávky se rozešlou zprávy vinařům v dané oblasti. Přijmutím poptávky vinařem dostane zákazník informaci, že je dostupný. Případné upřesnění poptávky či nabídky lze uskutečnit pomocí soukromých zpráv v aplikaci. Aplikace je vytvořena s důrazem na jednoduchost použití a respektuje Material Design. Data jsou ukládána pomocí technologie Firebase od Google, notifikace jsou zasílány pomocí služby Firebase Cloud Messaging. S ohledem na jednoduchost jsou výsledkem dvě aplikace, první Mám víno! je určena pro vinaře. Druhá aplikace Chcu víno! slouží výhradně pro zákazníky, kteří pomocí ní vyhledají vinaře., The aim of this thesis is to create a mobile application for Android that will connect small wineries with their customers. The result is two applications, the first of which is for wineries and the second one for customers, who can search wineries by simple criteria, save them as favorites, make notes and much more. The applications respects Material Design rules. Google Firebase is used for work with data and Firebase Cloud Messaging handles notifications between devices.