Abstract The purpose of this study was to clarify the notion of diaconal nursing, to produce a description of diaconal nursing and to outline a model of it. The study was accomplished in three stages in accordance with the hybrid model developed by Schwarz-Barcott and Kim (2000). At the theoretical stage, the literature was searched for information of diaconal nursing. For this purpose, a discretionary sample of references dealing with diaconal work and nursing was collected in 1998–1999. The material was analyzed with methods of inductive content analysis. At the empirical stage, the narrative approach was used to collect experiential data about diaconal nursing. The data were collected in the form of written essays complemented by oral interviews. The narrators were a group of 70 senior nursing students with the orientation towards diaconal care, 36 teachers of diaconal care, two consecrated deaconess nurses from five polytechnics on different locations in Finland and eight nurses without diaconal education and four patients from the Kanta-Häme and Pirkanmaa regions. One of the informants was testing the diaconal nursing practice, and she discussed that in her personal narrative. The data were collected in 1998–2003. The substance of the empirical data was analyzed with holistic methods of narrative content analysis. At the analytical stage, diaconal nursing was described as a synthesis of the theoretical and empirical stages, and a model was constructed based on that description. The results indicated that nurses with and without diaconal education used different terms to describe diaconal nursing. Based on the model developed here, diaconal work and nursing are combined into diaconal nursing via the cultural level of religion. Diaconal nursing is a profession carried out in nursing environments and parishes by nurses with diaconal education. Knowledge of both nursing science and theology is applied. In addition to nursing interventions, caritative and liturgic interventions are also used in diaconal nursing. The term 'professional service' is in diaconal nursing. The interactive relationship is a professional human relationship, where the person being cared for receives care and compassion without an obligation to "pay back". For the nursing professional, actions accordant with the Christian view of humanity may be a resource in everyday nursing. The recipient of care is not expected to have a religious or other conviction. Diaconal nursing can be learnt through education, and being a professing Christian is not enough to make a nurse a professional of diaconal nursing. The knowledge produced is the study can be utilized in diaconal nursing instruction and helps students to encounter people in nursing practice. The model also provides insight for the development of diaconal nursing curricula in polytechnics. / Tiivistelmä Tämän tutkimuksen tarkoituksena oli selkiyttää käsitystä diakonisesta hoitotyöstä ja tuottaa kuvaus diakonisesta hoitotyöstä sekä rakentaa siitä malli. Tutkimus muodostuu kolmesta vaiheesta Schwarz - Barcottin ja Kimin (2000) kuvaaman hybridisen mallin mukaisesti. Teoreettisessa vaiheessa tehtävänä oli etsiä kirjallisuudesta tietoa diakonisesta hoitotyöstä. Tätä varten kerättiin 1998–1999 harkinnanvaraisella otannalla aineisto diakoniaa ja hoitotyötä käsittelevästä kirjallisuudesta. Aineisto analysoitiin induktiivisella sisällönanalyysilla. Empiirisessä vaiheessa koottiin narratiivista lähestymistapaa soveltaen kokemuksellista tietoa diakonisesta hoitotyöstä. Aineisto koottiin kirjoitelmina ja sitä täydennettiin haastatteluin. Kertojina oli 70 diakoniapainotteisen sairaanhoitajakoulutuksen päättävää opiskelijaa, 36 diakonian opettajaa, kaksi diakonian virkaan vihittyä sairaanhoitajaa viiden eri paikkakunnan ammattikorkeakoulusta sekä 8 sairaanhoitajaa ja 4 potilasta Kanta-Hämeestä ja Pirkanmaalta. Yksi kertojista testasi diakonista hoitotyötä hoitokäytännössä, josta hän tuotti sisäisen tarinansa. Aineisto koottiin 1998–2003 välisenä aikana. Aineisto analysoitiin holistis-sisällöllisesti narratiivisen aineiston analyysin mukaisesti. Analyyttisessa vaiheessa kuvattiin diakonista hoitotyötä teoreettisen ja empiirisen vaiheen muodostamana synteesinä, jonka perusteella siitä rakennettiin malli. Tulokset osoittivat, että diakoniaan kouluttautuneet ja kouluttautumattomat sairaanhoitajat puhuvat eri käsittein diakonisesta hoitotyöstä. Kehitetyn mallin mukaan diakonia ja hoitotyö yhdistyvät diakoniseksi hoitotyöksi uskonnon kulttuurisen tason kautta. Diakoninen hoitotyö on professio, jota tekevät diakoniseen hoitotyöhön kouluttautuneet sairaanhoitajat hoitotyön toimintaympäristöissä ja seurakunnissa. Siinä sovelletaan hoitotieteen ja teologian tietoa. Diakonisessa hoitotyössä toteutetaan hoitotyön auttamismenetelmien lisäksi karitatiivisia ja liturgisia auttamismenetelmiä. Diakonisessa hoitotyössä puhutaan ammatillisesta palvelemisesta. Vuorovaikutussuhde on ammatillinen lähimmäissuhde, jossa hoidettava kokee saavansa lahjomatonta hoitamista ja rakkauden tunnetta. Hoitotyöntekijälle kristillisen ihmiskäsityksen mukainen toiminta voi olla voimavara hoitamisen arjessa. Hoidettavana olevalta ihmiseltä ei edellytetä uskonnollista tai muuta vakaumusta. Diakoninen hoitotyö voidaan oppia koulutuksessa ja pelkkä hoitajan kristillinen vakaumus ei anna valmiuksia tähän työhön. Tutkimuksessa tuotettua tietoa voidaan hyödyntää hoitotyön opetuksessa ja sen seurauksena käytännön hoitotyössä ihmisen kohtaamisissa. Lisäksi tuotettu malli lisää ymmärrystä kehittää diakonista hoitotyötä opiskelevan sairaanhoitajan opetussuunnitelmaa ammattikorkeakoulussa.

[pt] Nesta dissertação é proposto um modelo híbrido para previsão de carga de curtíssimo prazo, combinando amortecimento exponencial simples e redes neurais artificiais do topo feed-forward. O modelo fornece previsões pontuais e limites superiores e inferiores para um horizonte de quinze dias. Estes limites formam um intervalo ao qual pode ser associado um nível de confiança empírico, estimado através de um teste fora da amostra. O desempenho do modelo é avaliado ao longo de uma simulação realizada com dados reais de duas concessionárias de energia elétrica brasileiras. / [en] This thesis presents an hibrid short term load forecasting model that mixes simple exponential smoothing with feed- forward neural networks. The model gives point predictions with upper and lower limits for 15-day-ahead horizon. These limits yields an interval with associated empirical confidence level, estimated by an out of sample test. The model's performance is evaluated through a simulation with real data obtained from two Brazilian utilities. / [es] En esta disertación se propone un modelo híbrido para previsión de carga de cortísimo plazo, combinando amortecimiento exponencial simple y redes neurales artificiales tipo feed-forward. EL modelo nos da las previsiones puntuales y los límites superiores e inferiores para un horizonte de quince días. Estos límites forman un intervalo al cual se le puede asociar un nível de confianza empírico, estimado a través de un test out of sample. EL desempeño del modelo se evalúa utilizando datos reales de dos concesionarias de energía eléctrica brasileras.

[pt] Nos últimos anos, as linguagens de script ganharam muita importância em diversas áreas da computação. Uma das áreas onde essas linguagens ainda são pouco exploradas é na área de computação paralela. A computação paralela sempre foi fortemente associada a computação científica, mas recentemente ela ganhou uma nova importância com a popularização de processadores multi-core. Com esse crescimento se torna necessário o surgimento de novos paradigmas de programação paralela para facilitar o desenvolvimento e dinamizar as aplicações, e linguagens de script podem ser usadas para isso, trazendo dinamismo, simplicidade e flexibilidade às aplicações. Esta dissertação visa estudar um modelo híbrido de programação entre duas linguagens de programação, Lua e Charm plus plus. / [en] Recently, scripting languages have become very important in many fields of computer science. One area in which these languages have not been explored is paralel programming. Paralel programming has always been strongly associated with scientific usage, but recently, with the growth in popularity of multi core systems, it has gained a new field of action. With this change, the development of new programming paradigms of paralel programming become necessary in order to make development easier and applications more dynamic. Scripting languages may be used for this, bringing dynamics, flexibility and simplicity to aplications. This dissertation aims to study a hybrid programming model with two programming languages, Charm plus plus and Lua.

Diese Arbeit beschäftigt sich mit der Naturverjüngung von Traubeneiche (Quercus petraea (Matt.) Liebl.) unter Kiefernschirm. Eine erfolgreiche Verjüngung von Traubeneiche ist ohne menschliche Hilfe nur schwer umzusetzen. Als Ursache wird in der Literatur die Konkurrenzsituation von Begleitvegetation um die Ressource Wasser besonders hervorgehoben. Um künftige Managementmaßnahmen von Oberstand und Begleitvegetation bezüglich der Eichenverjüngung bewerten zu können, wurde das individuen-basierte Modell oak-lay entwickelt, das die Konkurrenz um Wasser explizit auf pflanzenphysiologischer Basis berücksichtigt. Die Kombination von individuen-basierten und prozess-basierten Ansätzen wird auch als Hybrid-Modell bezeichnet. In der Verjüngungsmodellierung ist bisher keine Anwendung eines Hybrid-Modells bekannt, so dass es sich vermutlich um das erste Modell seiner Art handelt. Die Arbeit ist in drei große Arbeitsschwerpunkte aufgeteilt. Im ersten Teil wird oak-lay detailliert vorgestellt und analysiert. Dabei kommen standardisierte Verfahren wie eine globale Sensitivitätsanalyse oder eine Analyse der Rechenzeit mittels Landau-Symbole zum Einsatz. Der zweite Teil analysiert mit Hilfe von Simulationsexperimenten eine neue Methode der Mortalitätsbeschreibung in individuen-basierten Modellen auf physiologischer Basis. Die prozess-basierte Wasserhaushaltsberechnung des oak-lay ermöglicht die Festsetzung einer Mortalitätsschwelle über das Druckpotential der Individuen. Für die Analysen wurde auf Ereigniszeitanalysen zurückgegriffen. Konkret angewandt wurden der Kaplan-Meier Schätzer und das semiparametrische Cox-Modell. Der dritte und letzte Teil wendet das Modell beispielhaft in einer Reihe von Simulationsexperimenten an. Dabei werden unterschiedliche Managementmaßnahmen am Oberstand und der Begleitvegetation simuliert und auf Unterschiede im Verjüngungserfolg und der räumlichen Verteilung getestet. Das Modell oak-lay konnte erfolgreich nachweisen, dass Hybrid-Modelle im Bereich der Verjüngungsmodellierung entwickelt und angewendet werden können. Die globale Sensitivitätsanalyse des Wasserhaushaltsmodells konnte die einflussreichsten Parameter identifizieren und das Laufzeitverhalten des Modells konnte ebenso analysiert werden. Die Einhaltung bestimmter Selbstdifferenzierungsmuster wurde über alle Simulationen geprüft. Dabei wurde erfolgreich die Mortalität über den Wassergehalt der Individuen bestimmt. Die Simulationsexperimente der verschiedenen Managementmaßnahmen haben gezeigt, dass eine Anwendung als Managementtool möglich ist. Die Entwicklung eines neuen Modelltyps bei der Verjüngungsmodellierung hat allerdings auch einigen neuen Forschungsbedarf generiert. Um oak-lay weiterzuentwickeln sind weitere Arbeiten nötig.

No description available.

abstract: Behavior-driven obesity has become one of the most challenging global epidemics since the 1990s, and is presently associated with the leading causes of death in the U.S. and worldwide, including diabetes, cardiovascular disease, strokes, and some forms of cancer. The use of system identification and control engineering principles in the design of novel and perpetually adaptive behavioral health interventions for promoting physical activity and healthy eating has been the central theme in many recent contributions. However, the absence of experimental studies specifically designed with the purpose of developing control-oriented behavioral models has restricted prior efforts in this domain to the use of hypothetical simulations to demonstrate the potential viability of these interventions. In this dissertation, the use of first-of-a-kind, real-life experimental results to develop dynamic, participant-validated behavioral models essential for the design and evaluation of optimized and adaptive behavioral interventions is examined. Following an intergenerational approach, the first part of this work aims to develop a dynamical systems model of intrauterine fetal growth with the prime goal of predicting infant birth weight, which has been associated with subsequent childhood and adult-onset obesity. The use of longitudinal input-output data from the “Healthy Mom Zone” intervention study has enabled the estimation and validation of this fetoplacental model. The second part establishes a set of data-driven behavioral models founded on Social Cognitive Theory (SCT). The “Just Walk” intervention experiment, developed at Arizona State University using system identification principles, has lent a unique opportunity to estimate and validate both black-box and semiphysical SCT models for predicting physical activity behavior. Further, this dissertation addresses some of the model estimation challenges arising from the limitations of “Just Walk”, including the need for developing nontraditional modeling approaches for short datasets, as well as delivers a new theoretical and algorithmic framework for structured state-space model estimation that can be used in a broader set of application domains. Finally, adaptive closed-loop intervention simulations of participant-validated SCT models from “Just Walk” are presented using a Hybrid Model Predictive Control (HMPC) control law. A simple HMPC controller reconfiguration strategy for designing both single- and multi-phase intervention designs is proposed. / Dissertation/Thesis / Doctoral Dissertation Chemical Engineering 2021

Time Series data has become ubiquitous thanks to affordable edge devices and sensors. Much of this data is valuable for decision making. In order to use these data for the forecasting task, the conventional centralized approach has shown deficiencies regarding large data communication and data privacy issues. Furthermore, Neural Network models cannot make use of the extra information from the time series, thus they usually fail to provide time series specific results. Both issues expose a challenge to large-scale Time Series Forecasting with Neural Network models. All these limitations lead to our research question:Can we realize decentralized time series forecasting with a Federated Learning mechanism that is comparable to the conventional centralized setup in forecasting performance?In this work, we propose a Federated Series Forecasting framework, resolving the challenge by allowing users to keep the data locally, and learns a shared model by aggregating locally computed updates. Besides, we design a hybrid model to enable Neural Network models utilizing the extra information from the time series to achieve a time series specific learning. In particular, the proposed hybrid outperforms state-of-art baseline data-central models with NN5 and Ericsson KPI data. Meanwhile, the federated settings of purposed model yields comparable results to data-central settings on both NN5 and Ericsson KPI data. These results together answer the research question of this thesis. / Tidseriedata har blivit allmänt förekommande tack vare överkomliga kantenheter och sensorer. Mycket av denna data är värdefull för beslutsfattande. För att kunna använda datan för prognosuppgifter har den konventionella centraliserade metoden visat brister avseende storskalig datakommunikation och integritetsfrågor. Vidare har neurala nätverksmodeller inte klarat av att utnyttja den extra informationen från tidsserierna, vilket leder till misslyckanden med att ge specifikt tidsserierelaterade resultat. Båda frågorna exponerar en utmaning för storskalig tidsserieprognostisering med neurala nätverksmodeller. Alla dessa begränsningar leder till vår forskningsfråga:Kan vi realisera decentraliserad tidsserieprognostisering med en federerad lärningsmekanism som presterar jämförbart med konventionella centrala lösningar i prognostisering?I det här arbetet föreslår vi ett ramverk för federerad tidsserieprognos som löser utmaningen genom att låta användaren behålla data lokalt och lära sig en delad modell genom att aggregera lokalt beräknade uppdateringar. Dessutom utformar vi en hybrid modell för att möjliggöra neurala nätverksmodeller som kan utnyttja den extra informationen från tidsserierna för att uppnå inlärning av specifika tidsserier. Den föreslagna hybrida modellen presterar bättre än state-of-art centraliserade grundläggande modeller med NN5och Ericsson KPIdata. Samtidigt ger den federerade ansatsen jämförbara resultat med de datacentrala ansatserna för både NN5och Ericsson KPI-data. Dessa resultat svarar tillsammans på forskningsfrågan av denna avhandling.

Conventional machine learning (ML) models and algorithms are constantly advancing at a fast pace. Most of this development are due to the implementation of hybrid- and ensemble techniques that are powerful tools to complement and empower the efficiency of the algorithms. At the same time, the development and demand for renewable energy sources are rapidly increasing driven by political and environmental issues in which failure to act fast enough, could lead to an existential crisis. With the phasing of non-renewable to renewable energy sources, new challenges arise due to its intermittent and variable nature. Accurate forecasting techniques plays a crucial role in addressing these challenges. In this thesis, I present a hybrid ensemble machine learning model based upon stacking, utilizing a Gradient Boosted Tree as a meta-learner to predict the energy demand for the energy area SE3 in Sweden. The Hybrid model is based on three composite models: XGBoost, CatBoost and Random Forest (RF); utilizing only features extracted from the timeseries data. For training and testing the proposed Hybrid model, hourly demand load data was gathered from Svenska Kraftnät, measuring energy consumption for the energy area SE3 from year 2016-2021. The forecasting results of the models are measured using a regression score (R-squared, which measures Explained Variance) and Accuracy (measured in terms of Mean Absolute Percentage Error). The result shows that in an experimental setting, the Hybrid model reaches a R-squared score of 0.9785 and an accuracy of 97.85%. When utilized for day-ahead prediction on unseen data outside of the scope of the training dataset, the Hybrid model reaches a R-squared score of 0.9764 and an Accuracy of 93.43%. This thesis concludes that the proposed methodology can be utilized to accurately predict the variance in the energy demand and can serve as a framework to decision makers in order to accurately predict the energy demand in Sweden. / Konventionella maskininlärningsmodeller (ML) och algoritmer utvecklas ständigt i snabb takt. Det mesta av denna utveckling beror på implementeringen av hybrid- och ensembletekniker som är kraftfulla verktyg för att komplettera och stärka effektiviteten hos algoritmer. Samtidigt ökar utvecklingen och efterfrågan på förnybara energikällor snabbt, drivet av politiska och miljömässiga motiv, där underlåtenhet att agera tillräckligt snabbt kan leda till en existentiell kris. Med utfasningen av icke-förnybara till förnybara energikällor uppstår nya utmaningar på grund av dess intermittenta och varierande karaktär. Noggranna prognostekniker spelar en avgörande roll för att hantera dessa utmaningar. I det här examensarbetet presenterar jag en hybrid ensemble maskininlärningsmodell baserad på stacking, med användning av ett Gradient Boosted Decision Tree (GBDT) som en meta-learner för att förutsäga energibehovet för energiområdet SE3 i Sverige. Hybridmodellen är baserad på tre kompositmodeller: XGBoost, CatBoost och Random Forest (RF) och använder endast features extraherade från tidsseriedata. För att utbilda och testa den föreslagna hybridmodellen samlades timbelastningsdata från Svenska Kraftnät, som mäter energiförbrukningen för energiområdet SE3 från år 2016-2021. Modellernas prognosresultat mäts med hjälp av ett regressionsmått (R-kvadrat, som mäter Explained Variance) och Accuracy (mätt i termer av Mean Absolute Percentage Error). Resultatet visar att i en experimentell miljö når hybridmodellen en R-kvadratvärde på 0,9785 och en Accuracy på 97,85%. När hybridmodellen används för att förutsäga energiförbrukningen dagen framåt på data utanför omfattningen av träningsdata, når hybridmodellen ett R-kvadratpoäng på 0,9764 och en Accuracy på 93,43%. Denna avhandling drar slutsatsen att den föreslagna metoden kan användas för att korrekt förutsäga variansen i energibehovet och kan fungera som ett ramverk för beslutsfattare för att korrekt prognostisera energibehovet i Sverige.

Background. This research centers on tackling the serious global problem of trafficaccidents. With more than a million deaths each year and numerous injuries, it’svital to predict and prevent these accidents. By combining satellite images and dataon accidents, this study uses a mix of advanced learning methods to build a modelthat can foresee accidents. This model aims to improve how accurately we predictaccidents and understand what causes them. Ultimately, this could lead to betterroad safety, smoother maintenance, and even benefits for self-driving cars and insurance. Objective.The objective of this thesis is to create a predictive model that improvesthe accuracy of traffic accident severity forecasts by integrating satellite imagery andhistorical accident data and comparing this model with stand-alone data models.Through this hybrid approach, the aim is to enhance prediction precision and gaindeeper insights into the underlying factors contributing to accidents, thereby potentially aiding in the reduction of accidents and their resulting impact. Method.The proposed method involves doing a literature review to find currentimage recognition models and then experimentation by training a Logistic Regression, Random Forest, SVM classifier, VGG19, and the hybrid model using the CNNand VGG19 and then comparing their performance using metrics mentioned in thethesis work. Results.The performance of the proposed method is evaluated using various metrics, including precision, recall, F1 score, and confusion matrix, on a large datasetof labeled images. The results indicate that a high accuracy of 81.7% is achieved indetecting traffic accident severity through our proposed approach where the modelbuilt on individual structural data and image data got an accuracy of 58.4% and72.5%. The potential utilization of our proposed method can detect safe and dangerous locations for accidents. Conclusion.The predictive modeling of Traffic accidents are performed using thethree different types of datasets which are structural data, satellite images, and acombination of both. The finalized architectures are an SVM classifier, VGG19, anda hybrid input model using CNN and VGG19. These models are compared in orderto find the best-performing approach. The results indicate that our hybrid modelhas the best accuracy with 81.7% indicating a strong performance by the model.

The use of Response to Intervention, more commonly referred to as RTI has become more prevalent as school systems look to find ways of bridging the opportunity gap and provide support those students who are not successful in their attempts to access the general education curriculum. More research is needed in order to have a better understanding of not only how schools implement RTI, but also how they utilize data, monitor student progress and help to ensure fidelity of implementation. The purpose of this study was to examine and explain how one elementary school with a high quality RTI program implemented Response to Intervention while keeping all three essential components in consideration. The findings demonstrate that the subject elementary school combined several elements of Response to Intervention and in turn, created their own version of a hybrid RTI model that utilized components from both the standard protocol model and the problem-solving model. In order to monitor student progress, universal screeners were utilized several times throughout the year for both reading and math. Reading was also monitored through running records, PALS Quick Checks, Orton Gillingham assessments, and exit tickets, whereas Math utilized formative assessments, anecdotal notes, and exit tickets to track student progress. Each math and reading CLT met weekly to engage in dialogue around student data. An important finding is that the subject elementary school made RTI implementation decisions around what was best for their students, which allowed for a more flexible and adaptable approach. The system utilized targeted individual student needs and helped to ensure that ALL students had access to the necessary supports that would help to ensure student success. / Doctor of Education / As schools continue to face increasing demands, including how to meet the needs of students with diverse academic backgrounds, they have been charged with exploring new ways and methods of ensuring that students are successful in their attempts to access the general education curriculum. Response to Intervention, more commonly referred to as RTI, has become more widely used in school systems as they continue to work to ensure student success for all. RTI is seen as a tool to help accurately identify students who have a learning disability (Ciolfi and Ryan, 2011), however more research is needed in order to have a better understanding of how schools implement RTI, as well as how they utilize the data collected and monitor student progress. This qualitative case study analyzes how one subject elementary school implemented RTI, how they utilized data, as well as how they monitored the progress of their students.