De Förenta Nationernas Agenda 2030 fastställer som ett delmål att säkerställa utbildning av hög kvalitet och främja livslångt lärande för alla som en del av arbetet för ett mer hållbart samhälle. Vikten av detta delmål blir särskilt tydlig i och med det observerbara sambandet mellan en fullständig gymnasieexamen och allmän hälsa i Sverige; gymnasiestudenter som går ut med en gymnasieexamen tenderar att erhålla bättre allmän hälsa. Learning Analytics är ett relativt nytt område inom utbildningsvetenskaplig forskning som syftar till att förbättra utbildning med hjälp av elevdata. Detta arbete undersökte vilken möjlig påverkan och begränsningar som förekommer vid implementering av en multipel linjär regressionsmodell utvecklad för en matematikkurs i en gymnasieskola. Vid utvecklingen av denna modell fastställdes tre signifikanta indikatorer för att förutsäga elevernas slutbetyg; Diagnos resultat,resultat på nationella proven och frånvaro. Prognosmodellen har utvärderats statistiskt varpå den visade sig vara tillförlitlig i 90% av bedömningarna, vilket inte är tillräckligt säkert för att användas i verkliga bedömningstillfällen eftersom lärare kräver att resultaten är obestridliga. Genom en fokusgruppsintervju med lärare granskas dessa resultat och deltagarna uttrycker sitt intresse för prognosmodeller tillsammans med en reflektion över elevers potentiella negativa reaktioner på en ogynnsam prognos. Utvärdering av modellen visar att den i dagsläget har en rimlig förmåga att förutsäga elevers slutbetyg men att det finns ett starkt behov av insamling av mer nyanserade data för att öka möjligheten till innovation i framtida arbeten / The 2030 Agenda establishes the goal to ensure quality education and promote lifelong learning opportunities for all. The importance of this goal becomes particularly clear when taking into account the link between upper secondary school graduation and general health in Sweden; Upper secondary school graduates tend to have better general health. Learning Analytics is a relatively new area of education research which aims to improve education using student data. This report examines the possible impact and limitations when implementing a multiple linear regression model developed for a mathematics course in an upper secondary school. In developing this model, three major indicators are established to be significant in predicting students' final grade; Diagnosis results, national test results and the amount of student absence. The model was statistically evaluated and found to be reliable in 90% of cases, which is not secure enough to be used in real assessment situations as teachers require the results to be indisputable. Through a focus group interview with teachers these results are evaluated and the participants establish their interest in predictive tools along with concerns for students' negative reactions to poor results. Evaluation of the model shows it has a reasonable ability to anticipate students' final grades but with a strong need for improvement in data collection methods and acquisition of more nuanced data to support greater possibility for innovation in future works.

Under covid-19-pandemin har avloppsvattenbaserad epidemiologi (WBE) använts i stor utsträckning som ett komplement till kliniska tester över många delar av världen. Detta projekt syftade till att detektera och kvantifiera belastningen av Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) i avloppsvattenprover med hjälp av Revers transkriptas kvantitativ polymeraskedjereaktion (RT-qPCR). De analyserade proverna kom från fyra olika avloppsreningsverk i Sverige, under perioden november 2022 till maj 2023. Studien omfattade en översikt över olika provtagnings- och analytiska tekniker och normaliseringsmetoder som används i WBE-studier, vilket betonade vikten av metodval. SARS-CoV-2-RNA upptäcktes i alla analyserade prover och infektionstrender kunde identifieras effektivt, inklusive COVID-19-vågen som observerades under semesterperioden. De dominerande varianterna som upptäcktes under denna övervakningsperiod var omikron variantens undergrupper, BA.2. och BA.2.75. Den veckovisa kvantifierade SARS-CoV-2-belastningen i avloppsvattenproverna visade en signifikant positiv korrelation till de kliniska fall som rapporterats i motsvarande avrinningsområden. Denna associering förstärktes ytterligare genom att normalisera SARS-CoV-2-innehållet med fekal biomarkör peppar milt fläckvirus (PMMoV). Dessutom har två metoder för tidig varning, nämligen medelvärdet plus två standardavvikelser (MSD) och positiv procentuell förändring (PPC), implementerats på avloppsvattendata, vilket pekar på vikten av att tillämpa sådana varningsmetoder för att ge förståeliga och tolkbara resultat. Denna studie ger värdefulla insikter om övervakning och analys av SARS-CoV-2 i avloppsvatten, vilket bidrar till utvecklingen av robusta system för tidig varning och folkhälsostrategier. / During the COVID-19 pandemic, wastewater-based epidemiology (WBE) has been applied extensively as a complementary tool to clinical testing across many parts of the globe. This project aimed to detect and measure the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) load in wastewater samples using Reverse transcriptase quantitative polymerase chain reaction (RT-qPCR). The analyzed samples were from four different wastewater treatment plants (WWTPs) in Sweden, covering the period from November 2022 through May 2023. The study encompassed an overview of various sampling and analytical techniques and normalization approaches employed in WBE studies, highlighting the importance of method selection. SARS-CoV-2 RNA was detected in all the samples analyzed, and infection trends could be identified effectively, including the COVID-19 peak observed during the holiday season. The dominant variants detected during this monitoring period were the omicron variants; omicron BA.2. and omicron BA.2.75. The weekly quantified SARS-CoV-2 load in the wastewater samples showed a significant positive correlation to the clinical cases reported in the corresponding catchment areas. This association was further enhanced by normalizing SARS-CoV-2 content with the fecal biomarker pepper mild mottle virus (PMMoV). Furthermore, two early warning methods, namely the mean plus two standard deviations (MSD) and positive percentage change (PPC), were implemented on the wastewater data pinpointing the importance of applying such warning methods to provide understandable and interpretable results. This study provides valuable insights into the monitoring and analysis of SARS-CoV-2 in wastewater, contributing to the development of robust early warning systems and public health strategies.

It is the intention of this thesis to effectively describe what is known about EI systems to date. Many sources of information are used, beginning with peer reviewed journals such as Justice Quarterly, Police Quarterly, Policing: An International Journal of Police Strategies & Management, Police and Society and The American Journal of Criminal Justice. Also reports from criminal justice research affiliates will be used like the National Institute of Justice and the National Criminal Justice Reference Service. This thesis will work towards creating a comprehensive outline of the history of EI and future possibilities. With the aid of extant research, inquiries into the effectiveness of various EI systems, their shortcomings, or even best practices will be addressed. The aim is to review the existing discussion essentially paving the way for future researchers to conduct primary research studies on EI effectiveness. By accumulating, and subsequently compiling available research, the hope is to identify key arguments and perspectives on these systems and their implementation.

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Med nya utmaningar för att tillgodose behoven av tjänligt dricksvatten hos konsumenter över hela världen krävs innovativa tekniker för övervakning av vattenkvalitet. I det här projektet undersöktes ett nytt instrument som detekterar och klassificerar partiklar i dricksvatten med hjälp av maskininlärda modeller. Målet var att utvärdera dess användbarhet som ett onlinesystem för tidig varning på Norrvattens reningsverket och ledningsnät. Utvärderingen utfördes som två separata delar: (1) en översiktlig analys av data som tidigare samlats in av tre instrument placerade på Norrvattens reningsverk och ledningsnät med målet att hitta trender och definiera tröskelnivåer, och (2) genom att utföra spikningsexperiment i instrumentet med kända föroreningar i laboratoriemiljö. Föroreningarna som undersöktes var E. coli, B. megaterium, humussyror, cyanobakterier av stam Synechocystis PCC 6803, och biofilm. Flödescytometri genomfördes på samma föroreningar för att möjliggöra jämförelser. Dataanalysen visade att instrumentet kan upptäcka säsongsvariationer i partikelnivåer. Dessutom har det partikelklasser som inte varierade med dessa fluktuationer vilket gör dem lovande som oberoende parametrar i ett varningssystem. Det fanns dock indikationer på att instrumentet kan göra oförutsedda klassifikationer av partiklar utifrån skillnader i sammansättningen mellan träningsdatats vatten och vattnet på Norrvatten. De laborativa experimenten visade att instrumentet kunde detektera alla föroreningar som testades, även vid cellantal på några få hundra celler/mL. Det kunde jämföras med flödescytometern, där det nya instrumentet kunde upptäcka halter av cyanobakterier under detektionsgränsen för flödescytometern, vilket indikerar en hög känslighet. Därför drogs slutsatsen att instrumentet har potential som tidigt varningssystem, men dess användbarhet hos Norrvatten är begränsad i dess nuvarande tillstånd på grund av de oförutsedda klassificeringarna av partiklar i deras vatten. / With emerging challenges in ensuring safe supplies of drinking water to consumers worldwide, there is a need for innovative technologies for water quality monitor- ing. In this project, an instrument which can detect and classify particles in drinking water using machine-learned models was investigated. The aim was to assess its usefulness as an online, early warning system in the drinking water treatment plant and distribution system at Norrvatten. The assessment was conducted as two separate parts: (1) an analysis of data previously collected by three instruments located around Norrvatten’s plant and distribution system, with the aim of finding trends and creating baselines, and (2) by conducting spiking experiments in the instrument using known contaminants in a lab environment. The contaminants tested were E. coli, B. megaterium, humic acids, cyanobacteria Synechocystis PCC 6803, and biofilm. Flow cytometry was performed on the same contaminants to enable comparison. It could be concluded from the data analysis that the instrument can detect seasonal trends in particle levels. In addition, there are classes of particles which are not subject to these fluctuations, making them promising as independent parameters in a warning system. There were however indications that the instrument can make unexpected classifications of particles due to differences in composition in the training data water and the water at Norrvatten. The lab experiments showed that the instrument could detect all contaminants tested, even cell numbers of a few hundred cells/mL. Comparing with flow cytometry, the novel instrument could detect concentrations of cyanobacteria below the detection limit of the flow cytometer, indicating a high sensitivity. It was concluded that the instrument has properties desired in an early warning system, but its usefulness at Norrvatten is limited in its current state due to the unexpected classifications of particles in their water.

Practitioners of Forecast-based Action (FbA) argue that a humanitarian response able to utilize forecasts to accurately predict disaster, secure funding, and take action before the onset of a crisis will benefit donors and beneficiaries alike. In search of effective and efficient early-action regimes, a number of major humanitarian actors are developing FbA projects of various designs, predominantly in response to natural disaster and famine. While numerous organizations and institutions have expressed interest in developing FbA mechanisms, the tool has only been applied in a limited capacity to the humanitarian needs generated by armed conflict. This research seeks to understand whether a scalable FbA approach can be developed to stage principled, anticipatory humanitarian action in response to situations in which rigorous evaluations predict the likelihood of imminent armed conflict. The hypothesis is that the application of FbA to armed conflict is possible, but due to the complex political nature of conflict, implementing organizations should try to focus on creating mechanisms managed by humanitarian actors and, in so far as possible, be insulated from outside influence. This research is the first academic work to specifically investigate the application of FbA to armed conflict. Following an extensive review of current FbA mechanisms and conflict early warning practices, this research concludes that a conflict-centered FbA system akin to the automated FbA systems in use today to respond to natural disaster and famine is possible, but that the endeavor presents many practical and conceptual barriers to implementation. In particular, diffuse models such as the Start Fund offer a hopeful glimpse at a type of horizontal, member-driven FbA mechanism that is both highly context-sensitive and relatively insulated from outside influence. Such a design, however, features notable and inherent limitations in its ability to reliably and accurately predict the outbreak of conflict and respond in a manner that minimizes regretful actions.

The purpose of this study was to use first-year test results to develop an early warning system for the identification of freshmen at risk of failing. All students registered between 1989 and 1997 for the six-year programmes chiropractic and homoeopathy were included in this ex post facto study. A descriptive study firstly indicated a serious problem of attrition with on average only 66% of chiropractic and 55% homoeopathy freshmen successfully completing the first year. A relationship was demonstrated between both first and second test results and outcome at the end of the first year of studies. A logistic regression model estimated retrospectively from first test results in physiology, anatomy, biology and chemistry was able to discriminate between successful and non-successful freshmen with an overall predictive accuracy of 80.82%. When this model was validated on a different set of data it was shown to have a very high sensitivity and was thus able to correctly identify >93 % of the potentially at risk freshmen. It also had a low Type II error ( <7%) and thus missed very few of the freshmen at risk of failing. A logistic regression model estimated retrospectively from second test results in physiology, anatomy, biology and chemistry had an overall predictive accuracy of 85.94% . The validated model had a sensitivity of 67% which was too low for the model to be of much use as a management tool for the identification of the freshmen at risk of failing. However, the model was shown to have a high specificity and was able to correctly identify >93% of the potentially successful freshmen. It also had a low Type I error (14.29%). Discriminant analysis models estimated from both first and second test results in physiology, anatomy, biology and chemistry produced strong support for the use of test results for the early identification of those freshmen who would need support in order to be successful. It is suggested that the objective models developed in this research could identify the freshman in need of support at an early enough stage for support measures to still have a positive effect on attrition. / Educational Studies / D. Ed. (Educational management)

L’auteure traite des stratagèmes de propriété occulte et de vote vide orchestrés par l’entremise des instruments dérivés. Dans un premier temps, cette recherche expose les diverses perturbations occasionnées par ces stratagèmes sur le système d’alerte et de notification ainsi que sur le droit corporatif canadien. Pour illustrer ces propos, une revue de la jurisprudence est détaillée. De plus, de nombreuses solutions législatives sont proposées afin de pallier aux utilisations potentiellement abusives des instruments dérivés. La solution la mieux adaptée au contexte des marchés financiers canadiens est finalement sélectionnée. / The research paper herein deals with hidden morphable ownership schemes and empty voting strategies orchestrated through derivatives. Initially, the author exposes the various disturbances caused by these schemes on early warning systems and follows up with repercussions that reverberate through to the Canadian corporate laws framework. Detailed case reviews are also presented to highlight the practical implications of these aforementioned schemes. Legislative solutions to mitigate the potential misuse of derivatives are proposed and ultimately the preferred solution tailored to Canadian financial markets is selected.