Background : The heat load has an emergent role in the energy consumption of the heating system in buildings. The industry experts also have been constantly focusing on the heat load optimization techniques and in the recent years, numerous Machine Learning (ML) techniques have come into picture to resolve various tasks. Objectives : This study is mainly focused on to analyze the time-series hourly data and choose suitable Supervised Machine Learning approach among Multivariate Linear Regression (MLR), Support Vector Regression, and Multi-layer Perceptron (MLP) Regressor so as to predict heat demand for identifying the deviating behaviors and potentially faults. Methods : An experiment is performed and the method consists of imputing the missing values, extreme values and selection of six different feature sets. Cross validation on Multivariate Linear Regression, Support Vector Regression, and Multi-layer Perceptron Regressor was performed to find the best suitable algorithm. Finally the residuals of the best algorithm and the best feature set was used to find the fault using the calculation of studentized residuals. Because of the time-series based data in data set, regression based algorithms was the best suitable choice to work with such type of data that is continuous. The faults in the system were identified based on the studentized residuals that exceeds the threshold value of 3 are classified as fault. Results : Among the regression based algorithms, Multi-layer Perceptron Regressor resulted in Mean Absolute Error (MAE) of 1.77 and Mean Absolute Percentage Error (MAPE) 0.29% on the feature set 1. Multivariate Linear Regression shown Mean Absolute Error 1.83 and Mean Absolute Percentage Error 0.31% on feature set 1 that has relatively higher error for the metrics of Mean Absolute Error and Mean Absolute Percentage Error as comparing to Multi-layer Perceptron Regressor. Support Vector Regression (SVR) shown Mean Absolute Error 2.54 that is higher than that of both Multivariate Linear Regression and Multi-layer Perceptron Regressor, while theMean Absolute Percentage Error 0.24% that is similar to Multivariate Linear Regression and Multi-layer Perceptron Regressor on the feature set 1. So the best performing algorithm is Multi-layer Perceptron Regressor. The feature sets 4,5 and 6 which are super-sets of 1, 2 and 3 feature sets along with addition of outdoor temperature. These feature sets 4, 5 and 6 did not show much impact even after considering the outdoor temperature. From, the Table 5.1 the feature sets 1, 2 and 3 are comparitively better than feature sets 4, 5 and 6 for the metrics Mean Absolute Error and Mean Absolute Percentage Error.Finally on comparing the first three feature sets, the feature set 1 resulted in less error for all three algorithms as comparing to feature set 2 and feature set 3 that can be seen in Table 5.1. So the feature set 1 is the best feature set. Conclusions : Multi-layer Perceptron Regressor perfomed well on six different feature sets comparing with Multivariate Linear Regression and Support Vector Regression. The feature set 1 had shown Mean Absolute Error and Mean Absolute Percentage values relatively low than other feature sets. Therefore the feature set 1 was the best performing and the best suited algorithm was Multi-layer Perceptron Regressor. The Figure A.3 represents the flow of work done in the thesis.

Information systems and information technology are rapidly evolving, and the usage of it at the same pace. In different fields, predictive analysis is used daily. Within the area of e-retail, referring to online retailing, it is used for personalisation and as decision support. There’s a lot of research on how to increase the accuracy of the predictions and different methods for this, however, there’s a lack of research regarding the actions an organisation can take given different predictions. Hence, this master’s study researches what factors affording or constraining value are in relation to the usage of predictive tools given different organisational roles. This thesis is made by a case study with a qualitative approach, following the interpretivism paradigm. The data used in this research comes from document analysis followed by semi-structured interviews to gather additional information about what the document analysis or previous research has not covered. The empirical findings were analysed using thematic analysis and are then discussed in relation to the research questions and theoretical framework, together with what’s previously been stated in the literature. The research questions for this thesis are the following: RQ1: How do different organisational roles affect the actions taken on information from predictive analysis in e-retail? and RQ2: What are the key factors affording or constraining value generation in predictive analysis within e-retail? The empirical findings resulted in six themes, where three are relevant to each research question. The findings suggests that there are four major categories of roles that have similar affordances of predictive analysis, these are customer-, sales and financial operations-, management-, andlastly supply chain and inventory related. When several roles within an organisation use the same prediction tool, there are positive effects such as less biased decisions and improved communication through collaboration. Several factors, both constraining and affording value were found. The main constraining factors are related to technological knowledge and interpreted value as well as trustworthiness. The affording factors are instead the allowance of tying predictions to certain KPIs and the ability to be able to slice into the data to show what’s relevant for the individual. In addition to these factors, some desires for functionality were found. These were, among others, a confidence score of the predictions, prediction for certain goals, and predicted optimal send times for emails in the future. My suggestion for future research is to approach the same problem using another theoretical framework to further enhance a novel field, as well as involving participants with different backgrounds than was used in this thesis.

The art of unwinding voluminous data expects the expertise in analyzing meaningful decisions out of the acquired information. To encounter new age challenges, practitioners are trying hard to shatter the constraints and work edge-to-edge to achieve higher performance (Market, Financial and Operational performance). It is evident that organizations desire to exploit maximum of their injected resources, but often fail to reap their actual potential. Developing resource-based capabilities stands out to be the most concerned aspect for the firms in recent times, and the same is studied by the previous scholars. In the dearth of literature, it is challenging to find out evidence which marks up the effect of strategic resources in the development of dynamic organizational capability. This study is a two-fold attempt to examine the relationship between organizational capabilities, i.e. big data predictive analytics while achieving superior organizational performance; also, examining the effect of control variables on superior organizational of performance. We tested our research hypotheses using cross-sectional data of 209 responses collected using pre-tested single-informant questionnaire. The results underpin criticality human factor while developing analytical capabilities dynamic in nature in the process of achieving superior performance.

Avec la miniaturisation de plus en plus poussée des composants sur silicium, certains phénomènes, connus sous le nom des problèmes de la Compatibilité Électromagnétique peuvent surgir, ils sont les principales causes de la reconception des systèmes intégrés. Ce travail de thèse consiste à développer une méthodologie d'analyse prédictive de la compatibilité électromagnétique pour les systèmes électroniques, par anticipation design. Afin d'aboutir à ce modèle prédictif, différents champs d'investigation ont été mise en place afin d'appréhender l'ensemble du problème. Premièrement, nous avons développé une méthode de prédiction des parasites du boîtier électronique appelée « MCTL Matrix Method ». Cette méthode est issue du principe du prototypage virtuel du boîtier et elle est basée sur des lignes de transmission multiconducteurs. Deuxièmement, nous avons proposé une méthodologie d'analyse de l'immunité du système complet dès les premières phases de la conception. / Modern electronic systems require a high-level of integrations. As a result, some phenomena which are known as ElectroMagnetic Compatibility (EMC) issues are arising, and they are the major causes of system redesign. This main objective of this work is to develop a predictive methodology for systems immunity. To do so, different fields have been investigated: first, we have developed a predictive method for package parasites called “MCTL Matrix Method” which is based on a virtual package prototyping and Multiconductor transmission lines. Then we have proposed a new methodology for system immunity at the earliest design stages.

No description available.

自工業革命之後，人類與科技間關係的變化牽引著整個社會、經濟的發展，而其中泛用型科技(GPTs)又扮演著要角，科技持續以指數式速度發展，大數據的出現是有脈絡可循的，某個程度上來說(從資料及分析兩方面的演進觀之)，可以說是必然發生的。大數據分析，不是時尚名詞，而是一個影響著現在及未來的大趨勢，縱有許多反對的聲音與論述，但它確實已經是國家安全戰略的一環，也是企業生存戰賴以維生的命脈。 大數據與過去不同的是我們擁有更多資料的來源，資料可能來自外部(Open Data、第三方資料)，也可能是更精進的資料蒐集機制得來(如：設計誘因機制使顧客自願提供其資料或設計隨機試驗取得異於歷史資料的新資訊)，而在資料種類格式、資料取得與回饋反應的速度上，在新興的MapReduce技術、NoSQL資料庫及串流資料處理技術支撐下，均可有效即時或近即時地被完成。 大數據分析最重要的還是在於「預測分析」，而為了讓資料說話，我們要熟悉大數據的特性與缺點，而支持大數據的硬技術與軟技術發展上一日千里，更提升了大數據在各產業的應用可能，而投資大數據的企業營收比那些沒有投資大數據的企業可以高出12％以上，在多數產業紛紛投入這場軍備競賽取得初步成效之際，而傳統壽險產業在大數據及其他科技變革的因應上不如別的產業時，則應在壽險價值鏈上去觀察並利用大數據分析，突破現有商業模式，選擇最佳導入策略，尋覓理想的資料科學家擔任CDO，委任其組織分析團隊並擬定大數據成長策略，建立適切軟硬體的架構，並完成第一個先導計畫取得小規模成功，進而加強企業高層大數據分析的信心與投資意願，使得一的又一個專案得以遂行，最終形塑成資料導向的決策文化，成為可以因應未來的壽險公司，避免在這波科技變遷中成為被淘汰者。

Predictive Analysis is a process for extracting information from large amounts of data and using it to make qualified predictions about future results. While previously the lack of available data has been a challenge within the field, big questions today are instead how to use the results, and the way in which these are presented in order for the user to be able to take advantage of the information. The purpose of this thesis has been to create hypotheses for how predictive analysis can be used in practical decision-making contexts, whereby the decision- maker is under time pressure, especially with regard to how the result can be visualized. This has been done through a case study at the Uppsala Ambulance Monitoring Center. The method used for the study is called Contextual Design, which has helped create an understanding of the users and the system they work in. Using this understanding, a prototype has been created, which has been tested on the users to see how well they have been able to interpret the information that has been visualized. Predictive analysis has proved to be helpful primarily in less urgent cases and to help the decision maker to differentiate matters similar to each other. For visualization of the predictive results, it has been found that these is better shown as a comparison between the user's decision hypothesis and historical decision results rather than only as an absolute value. Furthermore, it has been found that a high degree of transparency in the information on which the results are based is preferable, but that it is important that clear explanations are given for the results shown.

A abelha Bombus terrestris é um eficiente polinizador, prestando importantes serviços ecossistêmicos na Europa e adjacências, onde é nativa. Suas colônias têm sido criadas em larga escala para polinização agrícola, as quais são comercializadas internacionalmente, inclusive em países fora de sua área de ocorrência nativa. Deliberada ou acidentalmente a espécie tem sido introduzida em ambientes alóctones, em muitos casos tornando-se invasora. Quando invasora, a espécie é um potencial vetor de doenças e um competidor com outras abelhas; vários impactos têm sido relatados em áreas invadidas ao redor do mundo. Na América do Sul, a espécie foi inicialmente introduzida em ambientes naturais no Chile, mas rapidamente a invasão se espalhou; atualmente é encontrada ocupando ambientes naturais na Argentina. A elevada capacidade invasiva da espécie e a alta velocidade de sua expansão, conforme tem sido relatada por pesquisadores, levantou a possibilidade de a espécie alcançar o Brasil por meio de corredores ambientais favoráveis que se conectam com áreas já invadidas, gerando preocupações sobre potenciais impactos aos sistemas naturais e agrícolas. Esta pesquisa empregou uma abordagem interdisciplinar, integrando uma variedade de métodos analíticos oriundos de diferentes áreas da ecologia e os mais avançados recursos de sistemas de informações geográficas para detectar globalmente as áreas susceptíveis à invasão por Bombus terrestris, considerando-se também as mudanças climáticas. Para o sul da América do Sul foram identificados os corredores de invasão que poderão permitir a espécie se espalhar e alcançar Brasil a partir de locais invadidos. Para o Brasil, foram identificados os municípios mais vulneráveis à entrada da espécie, e também aqueles que estão na rota de expansão da invasão Brasil adentro. Para os municípios brasileiros, por sua vez, foram verificadas as culturas agrícolas e as espécies de Bombus nativas que a invasora poderá interagir ao longo da rota de invasão potencial; com estas informações foram apontadas áreas prioritárias, subsidiando o planejamento de monitoramento e ações de controle do processo de invasão, mas também medidas preventivas e mitigadoras de impactos ambientais e econômicos após a invasão, caso venha a ocorrer. / The bee Bombus terrestris is an efficient pollinator, providing important ecosystem services in Europe and surrounding areas, where it is a native species. Their colonies have been reared in large-scale for agricultural pollination, which are internationally traded, including for countries outside its native range. The species has been deliberately or accidentally introduced into non-native environments, becoming invasive in many cases. When invasive, the species is a potential vector of diseases and competes with other bees for resources; many impacts have been reported in invaded countries around the world. In South America, the species was introduced in natural environments of Chile at first, but the invasion was quickly spread; nowadays it is found living in natural environments of Argentina. The high invasiveness of the species and the high speed of its invasive expansion, as has been reported by researchers, raised the possibility of the species reach Brazil through suitable environmental corridors that are connected with areas already invaded, raising the concerns about potential impacts to natural and agricultural systems. An interdisciplinary approach was used in this research, composing a variety of analytical methods from different areas of ecology and applying the most advanced resources of geographic information systems to detect areas susceptible to invasion by Bombus terrestris at global scale, considering also climate change. For the southern South America, the corridors of invasion that could allow the spreading of the species and that potentially reach Brazil were identified. For Brazil, the most susceptible municipalities at the entrance of the species have been identified, and also those that are on the route of expansion inside the country. For Brazilian municipalities, crops and native species of Bombus that the invasive species can interact with along the potential invasion route were verified; with this information, priority areas were identified, supporting the planning of monitoring and control actions of the invasion process, but also preventive and mitigating measures of environmental and economic impacts after the invasion, if it eventually occurs.

2011 was a seminal year in the history of the Middle East and North Africa (MENA). Popularly referred to as the Arab Spring, the region has experienced a wave of revolutions and instability. It can be classified in three broad categories within 2011: Uprisings that have resulted in the overthrow of standing regimes, uprisings that have failed to overthrow standing regimes, and states that have not experienced popular revolts. In the first category Libya, Egypt, Yemen, and Tunisia have all experienced uprisings resulting in the respective departure of Muamar Gaddafi, Hosni Mubarak, Ali Abdullah Saleh, and Zine Al Abidine Ben Ali. In contrast Syria and Bahrain have experienced uprisings that have not resulted into the toppling of their regimes thus far. Finally, countries such as Saudi Arabia and Iran have experienced none of the instability observed in 2011 within the same time period. In tracking the evolution of selectorates, I identified the rise of actors within the newly developing coalitions whose Islamist preferences are unaccounted for in the standard Selectorate Model. As later explained in detail, Selectorate Theory is driven by the public-private goods argument. The theory states that a leader’s political survival is based on the mix of private payoffs he can provide to his selectorate and public goods provided to the general population. The once secular despots are either gone or are on the way out as evident by the removal of Hosni Mubarak, Zine Abidine Ben-Ali, Saddam Hussein, Muamar Gaddafi, Ali Abdullah Saleh, and the currently embattled Bashar Al- Assad. They are being replaced or have already been removed by governments that are led by Islamic Parties. Therefore, newly elected or appointed leaders must take into account the role of religion in their calculus for political survival in a way that they did not before. This begs the question: what about the regimes in my case studies that have not been toppled such as Saudi Arabia, Iran, and Bahrain? Although these are highly autocratic governments, the leaders of such governments have a legitimacy that is derived from implicit approval of their Islamist allies. This strengthens the argument that religion must be accounted for beyond the standard Selectorate Model rationale for political survival in MENA. In such context I provide a revised Selectorate Model explanation that accounts for the role of religion. I conclude that the standard Selectorate Theory is insufficient for MENA because it is does not account for the role of religion. By testing the coalitional distribution and evolution of selectorates, I developed a revised Selectorate Model that includes the role of religion along with the standard private payoffs – public goods argument. The role of religion is expressed by the presence of religious stakeholders in the agent based model such as clerics, shura councils or Islamic parties present in all selectorates in MENA. I tracked the selectorates through a series of predictions made throughout the course of 2011 using the Senturion agent based model. It serves as a powerful alternative to standard historical analysis and wisdom. I provide an explanation of why certain regimes fell while others remained relatively stable and why some governments experiencing similar instability remain using agent based modeling (ABM) in application to Selectorate Theory.

A abelha Bombus terrestris é um eficiente polinizador, prestando importantes serviços ecossistêmicos na Europa e adjacências, onde é nativa. Suas colônias têm sido criadas em larga escala para polinização agrícola, as quais são comercializadas internacionalmente, inclusive em países fora de sua área de ocorrência nativa. Deliberada ou acidentalmente a espécie tem sido introduzida em ambientes alóctones, em muitos casos tornando-se invasora. Quando invasora, a espécie é um potencial vetor de doenças e um competidor com outras abelhas; vários impactos têm sido relatados em áreas invadidas ao redor do mundo. Na América do Sul, a espécie foi inicialmente introduzida em ambientes naturais no Chile, mas rapidamente a invasão se espalhou; atualmente é encontrada ocupando ambientes naturais na Argentina. A elevada capacidade invasiva da espécie e a alta velocidade de sua expansão, conforme tem sido relatada por pesquisadores, levantou a possibilidade de a espécie alcançar o Brasil por meio de corredores ambientais favoráveis que se conectam com áreas já invadidas, gerando preocupações sobre potenciais impactos aos sistemas naturais e agrícolas. Esta pesquisa empregou uma abordagem interdisciplinar, integrando uma variedade de métodos analíticos oriundos de diferentes áreas da ecologia e os mais avançados recursos de sistemas de informações geográficas para detectar globalmente as áreas susceptíveis à invasão por Bombus terrestris, considerando-se também as mudanças climáticas. Para o sul da América do Sul foram identificados os corredores de invasão que poderão permitir a espécie se espalhar e alcançar Brasil a partir de locais invadidos. Para o Brasil, foram identificados os municípios mais vulneráveis à entrada da espécie, e também aqueles que estão na rota de expansão da invasão Brasil adentro. Para os municípios brasileiros, por sua vez, foram verificadas as culturas agrícolas e as espécies de Bombus nativas que a invasora poderá interagir ao longo da rota de invasão potencial; com estas informações foram apontadas áreas prioritárias, subsidiando o planejamento de monitoramento e ações de controle do processo de invasão, mas também medidas preventivas e mitigadoras de impactos ambientais e econômicos após a invasão, caso venha a ocorrer. / The bee Bombus terrestris is an efficient pollinator, providing important ecosystem services in Europe and surrounding areas, where it is a native species. Their colonies have been reared in large-scale for agricultural pollination, which are internationally traded, including for countries outside its native range. The species has been deliberately or accidentally introduced into non-native environments, becoming invasive in many cases. When invasive, the species is a potential vector of diseases and competes with other bees for resources; many impacts have been reported in invaded countries around the world. In South America, the species was introduced in natural environments of Chile at first, but the invasion was quickly spread; nowadays it is found living in natural environments of Argentina. The high invasiveness of the species and the high speed of its invasive expansion, as has been reported by researchers, raised the possibility of the species reach Brazil through suitable environmental corridors that are connected with areas already invaded, raising the concerns about potential impacts to natural and agricultural systems. An interdisciplinary approach was used in this research, composing a variety of analytical methods from different areas of ecology and applying the most advanced resources of geographic information systems to detect areas susceptible to invasion by Bombus terrestris at global scale, considering also climate change. For the southern South America, the corridors of invasion that could allow the spreading of the species and that potentially reach Brazil were identified. For Brazil, the most susceptible municipalities at the entrance of the species have been identified, and also those that are on the route of expansion inside the country. For Brazilian municipalities, crops and native species of Bombus that the invasive species can interact with along the potential invasion route were verified; with this information, priority areas were identified, supporting the planning of monitoring and control actions of the invasion process, but also preventive and mitigating measures of environmental and economic impacts after the invasion, if it eventually occurs.