During the past thirty years several models for lifelong education after retirement have been developed worldwide, one of them being the University of the Third Age (U3A). This study explored the contributions of the U3A to the educational needs of older adults and evaluated the benefits they perceived from their participation in U3A by means of a literature study and an empirical investigation. The latter used a survey to explore the experiences of U3A members of two U 3As and presidents of 68 U3As in Victoria, Australia by means of two different questionnaires. As only 1.47 percent of the over-55 population of Victoria are U3A members, the survey also investigated barriers to U3A participation in general and with special reference to the male population. The findings indicated that member respondents were very satisfied with their U3A experiences which had made substantial differences in their lives. Both male and female respondents saw personal, mental, social, and physical improvement as a result of U3A participation. The majority indicated that participation had improved their intellectual development. Significant differences in the perceptions of male and female participants emerged: female members outnumbered males by three to one. Both the presidents and the members expressed some programmatic concerns, primarily obtaining tutors and classroom availability. The subject areas covered by courses presented were extensive. There was a difference in the subjects desired by males and female respondents; very few courses are offered in science and economics. Some barriers to participation identified are a lack of awareness of U3A, the stereotypical attitudinal barrier of `I am too old' and negative past educational experiences. Moreover, U3As should increase marketing endeavours. Although most U3As advertise, almost a third of the respondents indicated that they would have joined earlier if aware of U3As. A contributing factor appears to be a virtual lack of research and information provided in educational academic journals and other media about lifelong education after retirement. Based on the findings, recommendations were made for future research and for improved practice in the U3A environment as a means to enhance the quality of life for older adults. / Educational Studies / D.Ed. (Comparative Education)

During the past thirty years several models for lifelong education after retirement have been developed worldwide, one of them being the University of the Third Age (U3A). This study explored the contributions of the U3A to the educational needs of older adults and evaluated the benefits they perceived from their participation in U3A by means of a literature study and an empirical investigation. The latter used a survey to explore the experiences of U3A members of two U 3As and presidents of 68 U3As in Victoria, Australia by means of two different questionnaires. As only 1.47 percent of the over-55 population of Victoria are U3A members, the survey also investigated barriers to U3A participation in general and with special reference to the male population. The findings indicated that member respondents were very satisfied with their U3A experiences which had made substantial differences in their lives. Both male and female respondents saw personal, mental, social, and physical improvement as a result of U3A participation. The majority indicated that participation had improved their intellectual development. Significant differences in the perceptions of male and female participants emerged: female members outnumbered males by three to one. Both the presidents and the members expressed some programmatic concerns, primarily obtaining tutors and classroom availability. The subject areas covered by courses presented were extensive. There was a difference in the subjects desired by males and female respondents; very few courses are offered in science and economics. Some barriers to participation identified are a lack of awareness of U3A, the stereotypical attitudinal barrier of `I am too old' and negative past educational experiences. Moreover, U3As should increase marketing endeavours. Although most U3As advertise, almost a third of the respondents indicated that they would have joined earlier if aware of U3As. A contributing factor appears to be a virtual lack of research and information provided in educational academic journals and other media about lifelong education after retirement. Based on the findings, recommendations were made for future research and for improved practice in the U3A environment as a means to enhance the quality of life for older adults. / Educational Studies / D.Ed. (Comparative Education)

The growing urgency of climate change has led to growth in the electrification technology field, where batteries have emerged as an essential role in the renewable energy transition, supporting the implementation of environmentally friendly technologies such as smart grids, energy storage systems, and electric vehicles. Battery cell degradation is a common occurrence indicating battery usage. Optimizing lithium-ion battery degradation during operation benefits the prediction of future degradation, minimizing the degradation mechanisms that result in power fade and capacity fade. This degree project aims to investigate battery degradation prediction based on capacity using deep learning methods. Through analysis of battery degradation and health prediction for lithium-ion cells using non-destructive techniques. Such as electrochemical impedance spectroscopy obtaining ECM and three different deep learning models using multi-channel data. Additionally, the AI models were designed and developed using multi-channel data and evaluated performance within MATLAB. The results reveal an increased resistance from EIS measurements as an indicator of ongoing battery aging processes such as loss o active materials, solid-electrolyte interphase thickening, and lithium plating. The AI models demonstrate accurate capacity estimation, with the LSTM model revealing exceptional performance based on the model evaluation with RMSE. These findings highlight the importance of carefully managing battery charging processes and considering factors contributing to degradation. Understanding degradation mechanisms enables the development of strategies to mitigate aging processes and extend battery lifespan, ultimately leading to improved performance.

Image Super-Resolution (ISR) is a technology that aims to increase image resolution while preserving as much content and detail as possible. In this study, we evaluate four different Deep Learning models (EDSR, LapSRN, ESPCN, and FSRCNN) to determine their effectiveness in increasing the resolution of lowresolution images. The study builds on previous research in the field as well as the results of the comparison between the different deep learning models. The problem statement for this study is: “Which of the four Deep Learning-based models, EDSR, LapSRN, ESPCN, and FSRCNN, generates an upscaled image with the best quality from a low-resolution image on a dataset of Abyssinian cats, with a factor of four, based on quantitative results?” The study utilizes a dataset consisting of pictures of Abyssinian cats to evaluate the performance and results of these different models. Based on the quantitative results obtained from RMSE, PSNR, and Structural Similarity (SSIM) measurements, our study concludes that EDSR is the most effective Deep Learning-based model. / Bildsuperupplösning (ISR) är en teknik som syftar till att öka bildupplösningen samtidigt som så mycket innehåll och detaljer som möjligt bevaras. I denna studie utvärderar vi fyra olika Deep Learning modeller (EDSR, LapSRN, ESPCN och FSRCNN) för att bestämma deras effektivitet när det gäller att öka upplösningen på lågupplösta bilder. Studien bygger på tidigare forskning inom området samt resultatjämförelser mellan olika djupinlärningsmodeller. Problemet som studien tar upp är: “Vilken av de fyra Deep Learning-baserade modellerna, EDSR, LapSRN, ESPCN och FSRCNN generarar en uppskalad bild med bäst kvalité, från en lågupplöst bild på ett dataset med abessinierkatter, med skalningsfaktor fyra, baserat på kvantitativa resultat?” Studien använder en dataset av bilder på abyssinierkatter för att utvärdera prestandan och resultaten för dessa olika modeller. Baserat på de kvantitativa resultaten som erhölls från RMSE, PSNR och Structural Similarity (SSIM) mätningar, drar vår studie slutsatsen att EDSR är den mest effektiva djupinlärningsmodellen.

<p dir="ltr">Digital soil mapping (DSM) combines field and laboratory data with environmental factors to predict soil properties. The accuracy of these predictions depends on factors such as model selection, data quality and quantity, and landscape characteristics. In our study, we investigated the impact of sample density and the use of various environmental covariates (ECs) including slope, topographic position index, topographic wetness index, multiresolution valley bottom flatness, and multiresolution ridge top flatness, as well as the spatial resolution of these ECs on the predictive accuracy of four predictive models; Cubist (CB), Random Forest (RF), Regression Kriging (RK), and Ordinary Kriging (OK). Our analysis was conducted at three sites in Indiana: the Purdue Agronomy Center for Research and Education (ACRE), Davis Purdue Agriculture Center (DPAC), and Southeast Purdue Agricultural Center (SEPAC). Each site had its unique soil data sampling designs, management practices, and topographic conditions. The primary focus of this study was to predict the spatial distribution of soil properties, including soil organic matter (SOM), cation exchange capacity (CEC), and clay content, at different depths (0-10cm, 0-15cm, and 10-30cm) by utilizing five environmental covariates and four spatial resolutions for the ECs (1-1.5 m, 5 m, 10 m, and 30 m).</p><p dir="ltr">Various evaluation metrics, including R², root mean square error (RMSE), mean square error (MSE), concordance coefficient (pc), and bias, were used to assess prediction accuracy. Notably, the accuracy of predictions was found to be significantly influenced by the site, sample density, model type, soil property, and their interactions. Sites exhibited the largest source of variation, followed by sampling density and model type for predicted SOM, CEC, and clay spatial distribution across the landscape.</p><p dir="ltr">The study revealed that the RF model consistently outperformed other models, while OK performed poorly across all sites and properties as it only relies on interpolating between the points without incorporating the landscape characteristics (ECs) in the algorithm. Increasing sample density improved predictions up to a certain threshold (e.g., 66 samples at ACRE for both SOM and CEC; 58 samples for SOM and 68 samples for CEC at SEPAC), beyond which the improvements were marginal. Additionally, the study highlighted the importance of spatial resolution, with finer resolutions resulting in better prediction accuracy, especially for SOM and clay content. Overall, comparing data from the two depths (0-10cm vs 10-30cm) for soil properties predications, deeper soil layer data (10-30cm) provided more accurate predictions for SOM and clay while shallower depth data (0-10cm) provided more accurate predictions for CEC. Finally, higher spatial resolution of ECs such as 1-1.5 m and 5 m contributed to more accurate soil properties predictions compared to the coarser data of 10 m and 30 m resolutions.</p><p dir="ltr">In summary, this research underscores the significance of informed decisions regarding sample density, model selection, and spatial resolution in digital soil mapping. It emphasizes that the choice of predictive model is critical, with RF consistently delivering superior performance. These findings have important implications for land management and sustainable land use practices, particularly in heterogeneous landscapes and areas with varying management intensities.</p>

Time series anomaly detection has been a longstanding area of research with applications across various domains. In recent years, there has been a surge of interest in applying deep learning models to this problem domain. This thesis presents a critical examination of the efficacy of deep learning models in comparison to classical approaches for time series anomaly detection. Contrary to the widespread belief in the superiority of deep learning models, our research findings suggest that their performance may be misleading and the progress illusory. Through rigorous experimentation and evaluation, we reveal that classical models outperform deep learning counterparts in various scenarios, challenging the prevailing assumptions. In addition to model performance, our study delves into the intricacies of evaluation metrics commonly employed in time series anomaly detection. We uncover how it inadvertently inflates the performance scores of models, potentially leading to misleading conclusions. By identifying and addressing these issues, our research contributes to providing valuable insights for researchers, practitioners, and decision-makers in the field of time series anomaly detection, encouraging a critical reevaluation of the role of deep learning models and the metrics used to assess their performance. / Tidsperiods avvikelsedetektering har varit ett långvarigt forskningsområde med tillämpningar inom olika områden. Under de senaste åren har det uppstått ett ökat intresse för att tillämpa djupinlärningsmodeller på detta problemområde. Denna avhandling presenterar en kritisk granskning av djupinlärningsmodellers effektivitet jämfört med klassiska metoder för tidsperiods avvikelsedetektering. I motsats till den allmänna övertygelsen om överlägsenheten hos djupinlärningsmodeller tyder våra forskningsresultat på att deras prestanda kan vara vilseledande och framsteg illusoriskt. Genom rigorös experimentell utvärdering avslöjar vi att klassiska modeller överträffar djupinlärningsalternativ i olika scenarier och därmed utmanar de rådande antagandena. Utöver modellprestanda går vår studie in på detaljerna kring utvärderings-metoder som oftast används inom tidsperiods avvikelsedetektering. Vi avslöjar hur dessa oavsiktligt överdriver modellernas prestandapoäng och kan därmed leda till vilseledande slutsatser. Genom att identifiera och åtgärda dessa problem bidrar vår forskning till att erbjuda värdefulla insikter för forskare, praktiker och beslutsfattare inom området tidsperiods avvikelsedetektering, och uppmanar till en kritisk omvärdering av djupinlärningsmodellers roll och de metoder som används för att bedöma deras prestanda.

Reading and mathematics are two essential subjects for academic success and cognitive development. Several studies show a correlation between the reading ability and mathematical ability of pupils (Korpershoek et al., 2015; Ní Ríordáin &amp; O’Donoghue, 2009; Reikerås, 2006; Walker et al., 2008). The didactical part of this thesis presents a study investigating a correlation between reading ability and mathematical ability among pupils in upper secondary schools in Sweden. This study collaborated with Lexplore AB to use machine learning and eye-tracking to measure reading ability. Mathematical ability was measured with Mathematics 1c grades and Stockholmsprovet, which is a diagnostic mathematics test. Although no correlation was found, there are several insights about selection and measures following the result that may improve future studies on the subject. This thesis finds that the result could have been affected by a biased selection of the participants. This thesis also suggests that the measure through machine learning and eye-tracking used in the study may not fully capture the concept of reading ability as defined in previous studies. The technological aspect of this thesis focuses on modifying and improving the model used to calculate users’ reading ability scores. As the model’s estimation tends to plateau after the fifth year of compulsory school, the study aims to maintain the same level of progression observed before this point. Previous research indicates that silent reading, being unconstrained by vocalization, is faster than reading aloud. To address this progression flattening, a grid search algorithm was employed to adjust hyperparameters and assign appropriate weight to silent and aloud reading. The findings emphasize that reading aloud should be prioritized in the weighted average and the corresponding hyperparameters adjusted accordingly. Furthermore, gathering more data for older pupils can improve the machine learning model by accounting for individual reading strategies. Introducing different word complexity factors can also enhance the model’s performance. / Läsning och matematik är två avgörande ämnen för akademisk framgång och kognitiv utveckling. Flera studier visar på ett samband mellan elevers läsförmåga och matematiska förmåga (Korpershoek et al., 2015; Ní Ríordáin &amp; O’Donoghue, 2009; Reikerås, 2006; Walker et al., 2008). Den didaktiska delen av denna rapport presenterar en studie som undersöker sambandet mellan läsförmåga och matematisk förmåga hos elever på gymnasiet i Sverige. Studien samarbetade med Lexplore AB för att använda maskininlärning och ögonspårning för att mäta läsförmåga. Matematisk förmåga mättes genom matematikbetyg och Stockholms provet, som är ett diagnostiskt matematiktest. Trotsatt inget samband hittades uppges insikter om urvalet och åtgärder som kan förbättra framtida studier i ämnet. Rapporten konstaterar att resultatet kan ha påverkats avett sned vridet urval av deltagare. Dessutom föreslår rapporten att mätningen genom maskininlärning och ögonspårning som användes i studien kanske inte helt fångar upp begreppet läsförmåga som används i tidigare studier. Teknikdelen av denna rapport fokuserar på att modifiera och förbättra modellen som används för att beräkna användarnas läsförmågepoäng. Eftersom modellens uppskattning tenderar att avplattas efter femte året i grundskola, syftar studien till att bibehålla samma nivå av progression som observerats före denna punkt. Tidigare forskning indikerar att tyst läsning, som inte begränsas av att uttala orden, är snabbare än högläsning. För att adressera denna avplattning av progression användes en rutnätssöknings-algoritm för att justera hyperparametrar och tilldela rätt viktning åt tyst läsning. Resultaten betonar att högläsning bör prioriteras i viktade medelvärdet och att motsvarande justeringar av hyperparametrar bör implementeras. Dessutom kan insamling av mer data för äldre elever förbättra maskininlärningsmodellen genom att ta hänsyn till individuella lässtrategier. Införandet av olika faktorer för textkomplexitet kan också förbättra modellens prestanda.

This research delves into the application of semantic segmentation in precision agriculture, specifically targeting the automated identification and classification of various irrigation system types within agricultural landscapes using high-resolution aerial imagery. With irrigated agriculture occupying a substantial portion of US land and constituting a major freshwater user, the study's background highlights the critical need for precise water-use estimates in the face of evolving environmental challenges, the study utilizes advanced computer vision for optimal system identification. The outcomes contribute to effective water management, sustainable resource utilization, and informed decision-making for farmers and policymakers, with broader implications for environmental monitoring and land-use planning. In this geospatial evaluation research, we tackle the challenge of intraclass variability and a limited dataset. The research problem centers around optimizing the accuracy in geospatial analyses, particularly when confronted with intricate intraclass variations and constraints posed by a limited dataset. Introducing a novel approach termed "dynamic contrastive learning," this research refines the existing contrastive learning framework. Tailored modifications aim to improve the model's accuracy in classifying and segmenting geographic features accurately. Various deep learning models, including EfficientNetV2L, EfficientNetB7, ConvNeXtXLarge, ResNet-50, and ResNet-101, serve as backbones to assess their performance in the geospatial context. The data used for evaluation consists of high-resolution aerial imagery from the National Agriculture Imagery Program (NAIP) captured in 2015. It includes four bands (red, green, blue, and near-infrared) with a 1-meter ground sampling distance. The dataset covers diverse landscapes in Lonoke County, USA, and is annotated for various irrigation system types. The dataset encompasses diverse geographic features, including urban, agricultural, and natural landscapes, providing a representative and challenging scenario for model assessment. The experimental results underscore the efficacy of the modified contrastive learning approach in mitigating intraclass variability and improving performance metrics. The proposed method achieves an average accuracy of 96.7%, a BER of 0.05, and an mIoU of 88.4%, surpassing the capabilities of existing contrastive learning methods. This research contributes a valuable solution to the specific challenges posed by intraclass variability and limited datasets in the realm of geospatial feature classification. Furthermore, the investigation extends to prominent deep learning architectures such as Segformer, Swin Transformer, Convexnext, and Convolution Vision Transformer, shedding light on their impact on geospatial image analysis. ConvNeXtXLarge emerges as a robust backbone, demonstrating remarkable accuracy (96.02%), minimal BER (0.06), and a high MIOU (85.99%).

Tesis por compendio / [ES] El cáncer de mama es una de las principales causas de muerte en mujeres de todo el mundo. Supone el 18.2% de las muertes por cáncer en la mujer y la primera causa de muerte en mujeres entre 40 y 55 años según la Sociedad Española de Senología y Patología Mamaria (SESPM). Una forma eficiente de disminuir este porcentaje es diagnosticarlo de forma temprana mediante exámenes de rayos x (Mamografía, Tomografía por emisión de positrones, Imagen de resonancia magnética, Tomografía computarizada), Ultrasonido, Tomosíntesis, Histopatología y Termografía. En la actualidad dentro del campo de la radiómica estos datos clínicos están siendo procesados con el uso de algoritmos de inteligencia artificial, especialmente para el preprocesamiento, segmentación y clasificación de lesiones malignas o benignas presentes en las imágenes médicas. Además, el desarrollo de estos sistemas computacionales asistidos para diagnóstico y detección temprana de anomalías presentes en la mama, ayudan al médico con una segunda opinión al diagnóstico manual tradicional. En consecuencia, el objetivo de este estudio es construir modelos de aprendizaje profundo y automático para la detección, segmentación y clasificación de lesiones mamarias en imágenes de mamografía y ultrasonido. Los hallazgos de este estudio brindan diversas herramientas de aumento de datos, super resolución, segmentación y clasificación automática de imágenes de mama para mejorar la precisión en los algoritmos de clasificación de lesiones mamarias. / [CA] El càncer de mama és una de les principals causes de mort en dones de tot el món. La mortalitat relacionada amb esta mena de càncer és més alta en comparación amb altres tipus de càncer. Una forma eficient de disminuir este percentatge és diagnosticar-lo de manera primerenca mitjançant exàmens de raigs x (Mamografia, Tomografía per emissió de positrons, Imatge de ressonància magnètica, Tomografia computada), Ultrasò, Tomosíntesi, Histopatologia i Termografia. En la actualidad dins del camp de la radiómica estes dades clíniques estan sent processados amb l'ús d'algorismes d'intel·ligència artificial, especialment per al preprocesamiento, segmentació i classificació de lesions malignes o benignes presents en les imatges mèdiques. A més, el desenvolupament d'estos sistemes computacionals asistidos per a diagnòstic i detecció precoç d'anomalies presents en la mama, ajuden al metge amb una segona opinió al diagnòstic manual tradicional. En conseqüència, l'objectiu d'este estudi és construir models d'aprenentatge profundo i automàtic per a la detecció, segmentació i classificació de lesions mamàries en imatges de mamografia i ultrasò. Les troballes d'este estudi brinden vaig donar-verses ferramentes d'augment de dades, super resolució, segmentació i classificación automàtica d'imatges de mama per a millorar la precisió en els algorismes de classificació de lesions mamàries. / [EN] Breast cancer is one of the most common causes of death in women worldwide. It accounts for 18.2% of cancer deaths in women and is the leading cause of death in women between 40 and 55 years of age, according to the Spanish Society of Senology and Breast Pathology (SESPM). An effective way to reduce this rate is through early diagnosis using radiological imaging (mammography, positron emission tomography, magnetic resonance imaging, computed tomography), Ultrasound, Tomosynthesis, Histopathology and Thermography. Currently, the field of radiomics is processing these clinical data using artificial intelligence algorithms, for pre-processing, segmentation, and classification of malignant or benign lesions present in medical images. In addition, the development of these computer-aided systems for diagnosis and early detection of breast abnormalities helps the radiologists with a second opinion to the traditional manual diagnosis. Therefore, the aim of this study is to build deep and machine learning models for the detection, segmentation, and classification of breast lesions in mammography and ultrasound images. The results of this study provide several tools for data augmentation, super-resolution, segmentation, and automatic classification of breast images to improve the accuracy of breast lesion classification algorithms. / This research project was co-funded by the Spanish Government Grant PID2019-107790RB-C22, which aimed to develop software for a continuous PET crystal system to be applied in breast cancer treatment. / Jiménez Gaona, YDC. (2024). Breast medical images classification through the application of deep learning processing technologies [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/208435 / Compendio

Объект исследования – процесс разработки архитектуры и реализации модульной системы устного перевода на основе моделей с открытым исходным кодом. Цель работы - реализация модульной системы машинного устного перевода на основе моделей с открытым исходным кодом, устанавливаемая на локальном компьютере и работающая без обращения к внешним серверам и подключения к Интернету. Рассматриваются модели машинного обучения, вспомогательное программное обеспечение и библиотеки для распознавания речи, машинного перевода, преобразования текста в речь и ее воспроизведения с открытым исходным кодом. Результат работы: реализована система устного перевода, удовлетворяющая заданным условиям, в двух вариантах архитектуры и двух версиях для работы с различными операционными системами, и проведено ее тестирование. / The object of research is the process of architecture design and implementation of a modular interpreting system based on open-source models. The aim of the work is to implement a modular system of machine interpretation based on open-source models, installed on a local computer and working without access to external servers and Internet connection. Machine learning models, auxiliary software and libraries for speech recognition, machine translation, text-to-speech conversion and speech output are reviewed. Result of the work: an interpreting system satisfying the given conditions in two architecture variants and two versions for working on different operating systems is implemented and tested.