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.

The aim of the research project RADimFOKUS was to develop and evaluate a cyclist waming system (CWS) prototype in order to prevent safety critical events (SCEs), such as accidents, for the specifically vulnerable group of cyclists and, in turn, contributing to an enhanced traffic safety for this sustainable and healthy mode of transport. The basic idea of the system was to warn cyclists in case a SCE is detected. Although research about CWS is rather scarce, :first evaluations of such systems are promising [1]. Considering actual developments and trends, the CWS detects SCEs based on connected traffic information and is in a fust step intended for the implementation in electrified bicycles (i.e. pedelecs), where power supply is provided by the integrated battery. In the scope of the project, we performed the following 3 stages, which are described in the current contribution: (1) Development of the waming model and user interface for the CWS prototype, (2) Development of a bicycle simulator and implementation of the CWS interface for user studies, and (3) First evaluation of the CWS prototype in the scope of a bicycle simulator user study.

[pt] A seca é um dos desastres naturais mais críticos que tem efeitos devastadores sobre habitats naturais, ecossistemas e muitos setores econômicos e sociais. Devido a esses graves impactos dos eventos de seca, muitos estudos estão focados no monitoramento, previsão e análise de risco de secas para auxiliar os planos de preparação e medidas de mitigação. Esta tese propõe um sistema de monitoramento e alerta de secas na região do semiárido do Brasil, chamado Drought Risk Assessment Interface (DRAI), que se baseia em um índice composto de risco de seca. O índice de risco possui duas componentes: ameaça e vulnerabilidade. A ameaça considera indicadores meteorológicos, enquanto a vulnerabilidade considera variáveis sociais. Com base na opinião de especialistas de vários países do mundo, com mais de 10 anos de experiência na área, foi definido o peso de cada um desses indicadores usando o processo de hierarquia analítica (AHP - Analytical Hierarchy Process). Os resultados foram comparados com outros índices de seca com o intuito de validar o índice proposto. Em seguida, foram levantados os principais sistemas de monitoramento e alertas em nível nacional e internacional e, então, foi proposto um padrão para geração de alertas no DRAI. Os alertas foram associados à sete medidas de mitigação de risco de seca validadas por técnicos locais. O DRAI tem como usuário final, além de outros pesquisadores, as Defesas Civis que poderão atuar diretamente nas ações de mitigação dos riscos. Como pesquisas futuras, sugere-se a automatização da coleta dos dados que compõem o índice de ameaça bem como a aplicação do estudo para todo o território brasileiro. / [en] Drought is one of the most critical natural disasters that have devastating effects on natural habitats, ecosystems and many economic and social sectors. Due to these severe impacts of drought events, many studies are focused on monitoring, forecasting and analyzing drought risk, to help with drought preparedness plans and mitigation measures. This study presents a drought early warning system in the semiarid region of Brazil, called the Drought Risk Assessment Interface (DRAI), which is based on a composite index of meteorological drought risk. The risk index has two components: hazard and vulnerability. The hazard considers meteorological indicators while the vulnerability considers social variables. Based on the opinion of experts from several countries in the world, with more than ten years of experience in the field, we define the weight of each of these indicators using the analytical hierarchy process (AHP). Then, the main early warning systems at national and international level were raised and then, a standard for generating warnings in the DRAI was proposed. The warnings were associated with seven drought risk mitigation measures validated by local technicians. DRAI has as its end user, in addition to other researchers, Civil Defenses that can act directly in risk mitigation actions. Finally, the system and its main features are presented. As future research, we suggest automating the collection of data that make up the hazard index as well as applying the study to the entire Brazilian territory.

No description available.

Machine learning is when a computer can learn from data and draw its own conclusions without being explicitly programmed to do so. To implement machine learning effectively and correctly, it is important to have a structured framework to follow. Today, there exist several different frameworks but no framework is suited for all purposes of machine learning. This thesis evaluates three chosen frameworks CRISP-DM, SEMMA and KDD for the purpose of imple- menting machine learning in signal processing. This study was conducted at Saab AB in Ja¨rf¨alla. The specific problem area of signal processing that was evaluated in the thesis was radar warn- ing systems. A hypothesis is that they could become more efficient with machine learning. To evaluate the chosen frameworks, it was studied what was demanded from a framework when implementing machine learning in the chosen problem area. The evaluation was done with a theoretical comparison where no implementations of the different frameworks were done. The frameworks were evaluated through an evaluation method created by the authors. The evaluation method was used for the purpose of finding a framework suitable for signal processing when developing the software for a radar warning system. The result is that CRISP-DM is the most well-suited of the three frame- works. This because it originates from a business perspective, is distinct in how to use it and is easy to implement in an agile process like Scrum. / Maskininlärning är när en dator kan lära sig från data och dra egna slutsatser utan att specifikt vara programmerad att göra det. För att lyckas med att implementera maskininlärning på ett effektivt sätt så krävs det att man följer ett tydligt ramverk. Idag finns det många ramverk men inget som är lämpat för alla typer av maskininlärning. Denna rapport utvärderar tre valda ramverk: CRISP- DM, SEMMA och KDD. Detta med syftet att implementera maskininlärn-ing i signalbehandling. Studien utfördes på Saab AB i Järfälla. Det specifika problemområde inom signalbehandling som utvärderades i rapporten var radarvarningssys- tem. En hypotes är att de kan bli mer effektiva med maskininlärning. För att utvärdera de valda ramverken så studerades vad som krävdes av ett ramverk för det valda problemområdet. Utvärderingen skedde genom en teoretisk jämförelse där ingen implementation av de olika ramverken genomfördes. Ramverken utvärderades genom en utvärderingsmetod skapad av förfat-tarna. Utvärderingsmetoden användes med syftet att finna ett ramverk som var lämpligt för signalbehandling vid utveckling av mjukvara för ett radarvarningssystem. Resultatet var att CRISP-DM var den mest lämpade metoden. Detta för att den utgår från ett affärsperspektiv, har tydliga riktlinjer hur den ska användas och att den enkelt kan implementeras i agila processer såsom Scrum.

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.

No description available.

Questa tesi di ricerca si occupa del ruolo rivestito dai parlamenti nazionali italiano, britannico e tedesco. Analizza il modo in cui questi parlamenti partecipano al processo decisionale ed implementano il diritto dell'Unione europea dopo il Trattato di Lisbona. La ricerca si concentra su un'analisi comparata delle leggi, delle procedure e consuetudini parlamentari al fine di esaminare il ruolo rivestito dai parlamenti nazionali nel contesto europeo. Il nuovo quadro giuridico previsto dal Trattato di Lisbona promuove la creazione di un sistema parlamentare integrato, basato sulle istituzioni europee e sui parlamenti nazionali cui è attribuito un ruolo più incisivo nel processo decisionale europeo, nella convinzione che un loro maggiore coinvolgimento possa contribuire a garantire un livello più efficace di democrazia nel funzionamento complessivo dell'Unione. I parlamenti nazionali possono contribuire a rendere l'U.E. più o meno efficiente. Essi sono chiamati a svolgere un ruolo rilevante nel processo legislativo europeo, in particolare nella fase di formazione delle politiche e del diritto dell’Unione europea (c.d. fase ascendente) e nel monitoraggio dell'esecuzione del principio di sussidiarietà. Il Trattato di Lisbona introduce norme di partecipazione diretta dei parlamenti nazionali nel processo legislativo europeo, trasformandoli in "guardians of subsidiarity". Il Trattato di Lisbona e i relativi Protocolli riconoscono il ruolo della cooperazione interparlamentare, affidando ai parlamenti nazionali il compito di promuovere e organizzare la sua realizzazione all'interno dell'Unione europea. In questa prospettiva le competenze delle commissioni specializzate in affari europei e della COSAC (Conference of Parliamentary Committees for Union Affairs of Parliaments of the European Union) sono ulteriormente potenziate. / This research thesis deals with the role of national parliaments in Italy, United Kingdom and Germany. It analyses the way in which these Parliaments participate in the European Union and implement the Law of the European Union after the Treaty of Lisbon. The research focuses on a comparative analysis of parliamentary procedures, instruments, and practices in order to examine the respective roles of the European Institutions and the national parliaments within the European framework. The new legal framework laid down the Treaty of Lisbon encourages the creation of an integrated parliamentary system, based on the European Parliament and on the national parliaments which are assigned a more incisive role in the European decision-making process, in the belief that these innovations may contribute to guaranteeing a more effective level of democracy in the overall functioning of the Union. The national parliaments can contribute to making Europe more or less effective. They will be called on to play a more important role in the European law-making process, specifically in the pre-legislative dialogue with European institutions and particularly in the monitoring of the enforcement of the subsidiarity principle in European legislation proposals. The Treaty of Lisbon regulations introduce direct participation of national parliaments in the European law-making process, transforming them into the "guardians of subsidiarity". The Treaty of Lisbon and the related protocols recognise and encourage interparliamentary cooperation, entrusting national parliaments with the task of promoting and organising its achievement within the European Union. In this perspective, the competences of the Conference of Community and European Affairs Committees of Parliaments of the European Union (COSAC) are further enhanced. In this thesis, the reasons for overall inclusion of national parliaments in the European Union activities are analysed. The role of national parliaments in the EU according to the specific provisions of the EU treaties is also discussed and the largest part of the work is devoted to the ex ante subsidiarity principle control mechanism (the Early Warning System), which gives the right for the national parliaments to influence the EU legislative process.

國際研究中雖有針對國家級的銀行脆弱性作分析，卻並未定義或預測台灣系統性危機，本研究在這樣的背景下，決定建構台灣本土的銀行業預警系統，建立銀行危機的領先指標，希望不只順應國際潮流，更能發展適合台灣特殊性的模型。本研究利用貝氏網路模型的特殊性: (1)事後值(2)機率特性，以個體化資料著手，建構一總體性模型。故研究者能確切了解個別銀行財務狀況，對個別銀行發出預警。事後值的特性使研究者能同時考慮多項財務比率。另外，利用機率特性，可幫助研究者了解危機的程度，且能做總體的延伸運用。 本研究發展出兩種方法建構總體模型。第一種為百分比法，以危機銀行佔總銀行個數的比率為基礎；第二種為加權平均法，讓機率值高者有較大權數，機率小者有較小權數去建立一加權平均機率值。 將本研究的推論結果和「台灣金融服務業聯合總會委託計畫-台灣金融危機領先指標之研究」比較，顯示本模型的兩種方法皆與危機之發生有相同趨勢，而考慮危機訊號的設定後，方法二加權平均法顯然具備較佳的預測結果。此外相較總體面衝擊產生的危機，本模型在預測能力上，對來自銀行個體面造成的危機預測明顯較優異。 / International organizations defined and predicted country bank crises events without Taiwan, but they happened in Taiwan in the past twenty years. We construct the early warning system for banking crises in Taiwan and develop the specific model suited to our country. Using Bayesian Model’s specialities: (1) posterior value; (2) probability, we build a systematic model based on microeconomic data. So researcher can understand all financial conditions and predict the financial distresses of individual banks. The concept of posteriority lets researchers can consider a lot of financial ratio at the same time. The characteristic of probability makes researcher to extend the model to macroeconomic. We develop two methods to build systematic model. One is Percentage method which is based on the percentage of financial distress banks to all banks. The other one is weighted average method which used large weight in financial distress bank and small weight in financial sound banks. Comparing our results with the report that Taiwan Financial Services Roundtable issued in 2009, our methods have distress trends which link with crisis directly. But weighted average method has a better predict power than percentage method after considering the signals of distress we specify. Besides, our model has a stronger predictive power in crises from individual effect than crises from macroeconomic shocks.