The failure analysis and resolution in cloud-computing environments are a a highly important issue, being their primary motivation the mitigation of the impact of such failures on applications hosted in these environments. Although there are advances in the case of immediate detection of failures, there is a lack of research in root cause analysis of failures in cloud computing. In this process, failures are tracked to analyze their causal factor. This practice allows cloud operators to act on a more effective process in preventing failures, resulting in the number of recurring failures reduction. Although this practice is commonly performed through human intervention, based on the expertise of professionals, the complexity of cloud-computing environments, coupled with the large volume of data generated from log records generated in these environments and the wide interdependence between system components, has turned manual analysis impractical. Therefore, scalable solutions are needed to automate the root cause analysis process in cloud computing environments, allowing the analysis of large data sets with satisfactory performance. Based on these requirements, this thesis presents a data-driven solution for root cause analysis in cloud-computing environments. The proposed solution includes the required functionalities for the collection, processing and analysis of data, as well as a method based on Bayesian Networks for the automatic identification of root causes. The validation of the proposal is accomplished through a proof of concept using OpenStack, a framework for cloud-computing infrastructure, and Hadoop, a framework for distributed processing of large data volumes. The tests presented satisfactory performance, and the developed model correctly classified the root causes with low rate of false positives. / A análise e reparação de falhas em ambientes de computação em nuvem é uma questão amplamente pesquisada, tendo como principal motivação minimizar o impacto que tais falhas podem causar nas aplicações hospedadas nesses ambientes. Embora exista um avanço na área de detecção imediata de falhas, ainda há percalços para realizar a análise de sua causa raiz. Nesse processo, as falhas são rastreadas a fim de analisar o seu fator causal ou seus fatores causais. Essa prática permite que operadores da nuvem possam atuar de modo mais efetivo na prevenção de falhas, reduzindo-se o número de falhas recorrentes. Embora essa prática seja comumente realizada por meio de intervenção humana, com base no expertise dos profissionais, a complexidade dos ambientes de computação em nuvem, somada ao grande volume de dados oriundos de registros de log gerados nesses ambientes e à ampla inter-dependência entre os componentes do sistema tem tornado a análise manual inviável. Por esse motivo, torna-se necessário soluções que permitam automatizar o processo de análise de causa raiz de uma falha ou conjunto de falhas em ambientes de computação em nuvem, e que sejam escaláveis, viabilizando a análise de grande volume de dados com desempenho satisfatório. Com base em tais necessidades, essa dissertação apresenta uma solução guiada por dados para análise de causa raiz em ambientes de computação em nuvem. A solução proposta contempla as funcionalidades necessárias para a aquisição, processamento e análise de dados no diagnóstico de falhas, bem como um método baseado em Redes Bayesianas para a identificação automática de causas raiz de falhas. A validação da proposta é realizada por meio de uma prova de conceito utilizando o OpenStack, um arcabouço para infraestrutura de computação em nuvem, e o Hadoop, um arcabouço para processamento distribuído de grande volume de dados. Os testes apresentaram desempenhos satisfatórios da arquitetura proposta, e o modelo desenvolvido classificou corretamente com baixo número de falsos positivos.

Företag måste hela tiden förbättras vilket ofta görs med ett systematiskt förbättringsarbete. Dessa bygger ofta på data vilket i företag med mycket manuella processer och låg användning av digitala system inte finns. Därför undersöker denna studie hur relevant data ska samlas in av sådana företag. Detta görs i form av en fallstudie på Elitfönster som är en ledande fönstertillverkare, som har en hög grad av omarbete vilket de har en ambition att reducera. Studien resulterar i en instruktion för hur en datainsamling ska genomföras för att generera relevant data till ett förbättringsarbete. Den undersöker även hur grundorsaker till slöserier hittas med hjälp av två olika metoder. Slutsatsen som författarna drog av arbetet blev att planeringen är viktig för att få en fungerande datainsamling som genererar ett bra resultat samt att fallföretaget bör utse ansvariga för de gemensamma resurser som skapar problem i produktionen. / Companies have to continuously improve which is often done through a systematic approach to improvements. This is often based on data which does not excist in companies with a high amount of manual labor and a low use of digital systems. Therefore this report studies how companies as the one previously mentioned should collect data. This is done as a case study at Elitfönster which is a leading window maker, who has a high degree of rework in their process which they aim to reduce. The result of the study is an instruction of how a data collection should be performed to generate relevant data to be able to improve. The study also investigates how root causes to waste are found through two different methods. The conclusion of this study is that the planning of a data collection is important to be able to get a good result. The company should also nominate a department that is responsible for the joint resources which causes problems in the production.

Identifying common root causes of systematic defects in a short time is crucial for yield improvement. Diagnosis driven yield analysis (DDYA) such as Root cause deconvolution (RCD) is a method to estimate root cause distribution by applying statistical analysis on volume diagnosis. By fixing identified common root causes, yield can be improved. With advanced technologies, smaller feature size and more complex fabrication processes for manufacturing VLSI semiconductor devices lead to more complicated failure mechanisms. Lack of domain knowledge of such failure mechanisms makes identifying the emerging root causes more and more difficult. These root causes include but are not limited to layout pattern (certain prone to fail layout shapes) and cell internal root causes. RCD has proved to have certain degree of success in previous work, however, these root cause are not included and pose a challenge for RCD. Furthermore, complex volume diagnosis brings difficulty in investigation on RCD. To overcome the above challenges to RCD, improvement based on better understanding of the method is desired. The first part of this dissertation proposes a card game model to create controllable diagnosis data which can be used to evaluate the effectiveness of DDYA techniques. Generally, each DDYA technique could have its own potential issues, which need to be evaluated for future improvement. However, due to limitation of real diagnosis data, it is difficult to, 1. Obtain diagnosis data with sufficient diversity and 2. Isolate certain issues and evaluate them separately. With card game model given correct statistical model parameters, impact of different diagnosis scenarios on RCD are evaluated. Overfitting problem from limited sample size is alleviated by the proposed cross validation method. In the second part of this dissertation, an enhanced RCD flow based on pre-extract layout patterns is proposed to identify layout pattern root causes. Prone to fail layout patterns are crucial factors for yield loss, but they normally have enormous number of types which impact the effectiveness of RCD. Controlled experiment shows effectiveness of enhanced RCD on both layout pattern root causes and interconnect root causes after extending to layout pattern root causes. Test case from silicon data also validates the proposed flow. The last part of this dissertation addresses RCD extension to cell internal root causes. Due to limitation of domain knowledge in both diagnosis process and defect behavior, parameters of RCD model are not perfectly accurate. As RCD moves to identify cell internal root causes, such limitation become an unescapable challenge for RCD. Due to inherent characteristics of cell internal root cause, RCD including cell internal root cause faces more difficulty due to less accurate model parameters. Rather than enhancing domain knowledge, supervised learning for more accurate parameters based on training data are proposed to improve accuracy of RCD. Both controlled experiments and real silicon data shows that with parameters learned from supervised learning, accuracy of RCD with cell internal root cause are greatly improved.

Aim: The aim of this study was to examine if student nurse participation in root cause analysis has the potential to reduce harm to patients from medication errors by increasing student nurse sensitivity to signal and responder bias. Background: Schools of nursing have traditionally relied on strategies that focus on individual characteristics and responsibility to prevent harm to patients. The modern patient safety movement encourages utilization of systems theory strategies like Root Cause Analysis (RCA). The Patient Risk Detection Theory (Despins, Scott-Cawiezell, & Rouder, 2010) supports the use of nurse training to reduce harm to patients. Method. Descriptive and inferential analyses of the demographic and major study variables were conducted. Validity and reliability assessments for the instruments were performed. The Safe Administration of Medications-Revised Scale (Bravo, 2014) was used to measure sensitivity to signal. The Safety Attitudes Questionnaire (SAQ; Sexton et al., 2006) was used to assess responder bias; this was the first use of this instrument with nursing students. Results: The sample consisted of 125 senior-level nursing students from three universities in the southeastern United States. The SAQ was found to be a valid and reliable test of safety attitudes in nursing students. Further support for the validity and reliability of the SAM-R was provided. A significant difference in safety climate between schools was observed. There were no differences detected between the variables. Conclusion: The results of this study provide support for the use of the SAQ and the SAM-R to further test the PRDT, and to explore methods to improve nursing student ability to administer medications safely.

Nobel Biocare är ett av de världsledande företagen som tillverkar tandimplantat. Företaget finns på många ställen runt om i världen. Examensarbetet utfördes på N1 avdelningen i Karlskoga.  Företaget har som mål att skapa smarta verkstäder och för att göra det krävs det att svagheter i produktionen identifieras för att sedan hitta lösningar på att de inte uppkommer igen.   Syftet med arbetet är att identifiera rotorsakerna till kassation i CNC processerna och därefter föreslå tre lösningar för att minska/eliminera detta problem. Företaget vill med andra ord veta vilka möjligheter de har idag samt hur de behöver gå till väga i deras strävan att om 2–3 år skapa smarta CNC-verkstäder.   För att undersöka rotorsakerna till kassation gjordes inledningsvis en rad olika analyser med hjälp av Excel där dokumenterat företagsdata användes. Operatörerna intervjuades och egna observationer noterades under besöken på Nobel Biocare.   Det som studien resulterade i var att företaget bör implementera tidtagningsur för varje operatör, byta verktyg efter bestämd tid och ha en genomgång av hur vacuumtrycket regleras. Rotorsakerna är fokuserade på metod och människa och de är att det finns en avsaknad av kvalitétsäkrad rutin för inställning av skärvätska, verktyg byts för sent, ineffektiv metod vid vacuumreglering, känsliga verktyg saknar sensorer och för många enheter produceras innan kontroll. / Nobel Biocare is one of the world's leading companies in the manufacture of dental implants. The company is in many places around the world. The degree project was carried out at the N1 department in Karlskoga.  The company's goal is to create smart workshops and to do so, it is necessary that weaknesses in the production are identified. To find solutions, it is necessary to identity the weaknesses so that they do not arise again.  The purpose of this work is to identify the root causes of scrapping in the CNC processes and then propose three solutions to reduce/eliminate these problems. The company wants to know what opportunities they have and how they need to improve to create smart CNC workshops in 2-3 years.  To investigate the root causes of scrapping, several different analyzes were initially performed using Excel, where documented company data was used. Operators were interviewed and their own observations were noted during the visits to Nobel Biocare.   The study resulted in that the company should implement a timer for each operator, change tools after a certain time and have a review of how the vacuum pressure is regulated. The root cases are focused on method and man, and they are that there is a lack of quality assured routine for setting cutting fluid, tools are changed too late, inefficient method in vacuum control, sensitive tools lack sensors and too many devices are produced before control.

TBO Haglinds AB är ett familjeföretag från Arboga som producerar balkonger och inglasningar samt monterar dessa hos flertal kunder över hela Sverige. All produktion sker på egen verkstad i Arboga och verksamheten har varit aktiv sedan 1970. Tillväxten av företaget har varit stor under de senare åren, dessutom blev företaget 2019 uppköpt av Balco Group AB. Med det ökande storleken på företaget finns stora möjligheter för förbättringsarbete och effektivisering av företagets produktion. Utöver det uppmärksammades att företaget har haft problem med att en vara har hamnat hos fel kund, vilket följaktligen medför onödiga kostnader för företaget långsiktigt. Målet med arbetet är att identifiera olika förbättringsmöjligheter för företagets produktion samt finna rotorsaker för problemet nämnt ovan. Informationen som företaget hanterar i hela kedjan är passerad skriftligt för det mesta och detta visar sig medföra en viss osäkerhet inom flödet och många av de metoder som används för att passera informationen går via ren erfarenhet. För att ge en större säkerhet och pålitlighet för att varor ska packas rätt och att rätt saker tillverkas från början så undersöktes möjligheten att kunna använda QR-kod (Quick-Read-kod) för att centralstyra flödet av material och information. Det konstaterades också att det är bra att ha en övergriplig syn på produktionen med hjälp av data för att enklare kunna göra analyser i syfte av förbättringar. / TBO Haglinds AB is a family company from the town of Arboga that produces balconies and balcony glazing, as well as assembly for customers all over Sweden. All the production happens at the company’s workshop in Arboga and TBO has made balconies and balcony glazing since 1970. The growth of the company has increased in the later years, in addition the company is since 2019 owned by Balco Group AB. In concideration of the growth of the company, the opportunities for improvement and effectivisation also has increased. Besides this it has been brought to attention that there is a issue when preparing a delivery that some items might end up at the wrong adress, which in the long run causes more cost for the company. The goal of this study is to identify the different possibilities of improvement for the production compartment and to find the root causes for the problem mentioned before. The informationen that the company processes in the entire chain is passed on hand-written and this is shown to bring uncertianty within the flow and many of the methods used to pass on the information is purely based on experience. To assure a larger certainty and trust for correct delivery preparation and correct production, the possibility of using QR-codes (Quick-Read-codes) to centralise and control the flow of material and information was brought into serious concideration. It was also brought to attention that keeping track of the production with help of data is a good thing to have, and it is to assist analysing the production by means of improvement.

Embedded devices almost involve every part of our lives, such as health condition monitoring, communicating with other people, traveling, financial transactions, etc. Within the embedded devices, our private information is utilized, collected and stored. Cryptography is the security mechanism within the embedded devices for protecting this secret information. However, cryptography algorithms can still be analyzed and attacked by malicious adversaries to steal secret data. There are different categories of attacks towards embedded devices, and the side-channel attack is one of the powerful attacks. Unlike analyzing the vulnerabilities within the cryptography algorithm itself in traditional attacks, the side-channel attack observes the physical effect signals while the cryptography algorithm runs on the device. These physical effects include the power consumption of the devices, timing, electromagnetic radiations, etc., and we call these physical effects that carry secret information side-channel leakage. By statistically analyzing these side-channel leakages, an attacker can reconstruct the secret information. The manifestation of side-channel leakage happens at the hardware level. Therefore, the designer has to ensure that the hardware design of the embedded system is secure against side-channel attacks. However, it is very arduous work. An embedded systems design including a large number of electronic components makes it very difficult to comprehensively capture every side-channel vulnerability, locate the root cause of the side-channel leakage, and efficiently fix the vulnerabilities. In this dissertation, we developed methodologies that can help designers detect and fix side-channel vulnerabilities within the embedded system design at low cost and early design stage. / Doctor of Philosophy / Side-channel leakage, which reveals the secret information from the physical effects of computing secret variables, has become a serious vulnerability in secure hardware and software implementations. In side-channel attacks, adversaries passively exploit variations such as power consumption, timing, and electromagnetic emission during the computation with secret variables to retrieve sensitive information. The side-channel attack poses a practical threat to embedded devices, an embedded device's cryptosystem without adequate protection against side-channel leakage can be easily broken by the side-channel attack. In this dissertation, we investigate methodologies to build up comprehensive side-channel resistant embedded systems. However, this is challenging because of the complexity of the embedded system. First, an embedded system integrates a large number of components. Even if the designer can make sure that each component is protected within the system, the integration of the components will possibly introduce new vulnerabilities. Second, the existing side-channel leakage evaluation of embedded system design happens post-silicon and utilizes the measurement on the prototype of the taped-out chip. This is too late for mitigating the vulnerability in the design. Third, due to the complexity of the embedded system, even though the side-channel leakage is detected, it is very hard to precisely locate the root cause within the design. Existing side-channel attack countermeasures are very costly in terms of design overhead. Without a method that can precisely identify the side-channel leakage source within the design, huge overhead will be introduced by blindly add the side-channel countermeasure to the whole design. To make the challenge even harder, the Power Distribution Network (PDN) where the hardware design locates is also vulnerable to side-channel attacks. It has been continuously demonstrated by researchers that attackers can place malicious circuits on a shared PDN with victim design and open the opportunities for the attackers to inject faults or monitoring power changes of the victim circuit. In this dissertation, we address the challenges mentioned above in designing a side-channel-resistant embedded system. We categorize our contributions into three major aspects—first, we investigating the effects of integration of security components and developing corresponding countermeasures. We analyze the vulnerability in a widely used countermeasure - masking, and identify that the random number transfer procedure is a weak link in the integration which can be bypassed by the attacker. We further propose a lightweight protection scheme to protect function calls from instruction skip fault attacks. Second, we developed a novel analysis methodology for pre-silicon side-channel leakage evaluation and root cause analysis. The methodology we developed enables the designer to detect the side-channel leakage at the early pre-silicon design stage, locate the leakage source in the design precisely to the individual gate and apply highly targeted countermeasure with low overhead. Third, we developed a multipurpose on-chip side-channel and fault monitoring extension - Programmable Ring Oscillator (PRO), to further guarantee the security of PDN. PRO can provide on-chip side-channel resistance, power monitoring, and fault detection capabilities to the secure design. We show that PRO as application-independent integrated primitives can provide side-channel and fault countermeasure to the design at a low cost.

Yes / This paper reports on a regionally based UK study uncovering what has worked well in learning from adverse incidents in hospitals. The purpose of this paper is to review the incident investigation methodology used in identifying strengths or weaknesses and explore the use of a database as a tool to embed learning. Documentary examination was conducted of all adverse incidents reported between 1 June 2011 and 30 June 2012 by three UK National Health Service hospitals. One root cause analysis report per adverse incident for each individual hospital was sent to an advisory group for a review. Using terms of reference supplied, the advisory group feedback was analysed using an inductive thematic approach. The emergent themes led to the generation of questions which informed seven in-depth semi-structured interviews. “Time” and “work pressures” were identified as barriers to using adverse incident investigations as tools for quality enhancement. Methodologically, a weakness in approach was that no criteria influenced the techniques which were used in investigating adverse incidents. Regarding the sharing of learning, the use of a database as a tool to embed learning across the region was not supported. Softer intelligence from adverse incident investigations could be usefully shared between hospitals through a regional forum. The use of a database as a tool to facilitate the sharing of learning from adverse incidents across the health economy is not supported.

Yes / The issues of food loss and waste (FLW) in the global supply chains have recently attracted attention. However, the causes of and strategies for mitigating FLW at different stages of the supply chains remain under researched. Our research aims to address these gaps in knowledge in a three-fold way: i) we identified the key causes (through root-cause analysis) of FLW in the supply chain of developed and less developed countries; ii) we systematically classified measures and policies that have been implemented to mitigate FLW; and iii) we developed an interdisciplinary conceptual framework for waste utilisation practices that can contribute towards the triple bottom-line in food systems. A root-cause analysis was performed and mitigation strategies identified by systematically analysing and synthesising the research published over the past 20 years (1998 to 2018) in the areas of FLW in the supply chain. We propose a conceptual model for the prevention of FLW utilising a systems approach through the concept of a circular economy. Since the agri-food sector is largely interdisciplinary, in our proposed model, we have also demonstrated a method of integrating contributions from multiple disciplines towards achieving total depollution (zero waste) in the supply chain. / Support provided by the British Academy/Leverhulme Small Research Grant, Reference No: SG160072, for the development of the study.

Poverty, Inequality & Terrorism Relationship in TurkeyUsing empirical evidence criminological studies have identified a relationship between poverty and crime and many studies have concluded that a high crime rate is associated with a higher poverty rate. Other studies indicate that inequalities are a better determinant of crime than absolute poverty. Social disorganization theory, anomie strain theory and Marxist theory have been used to explain the phenomenon. Guided by the aforementioned theories and previous literature on crime, this study looks at the terrorism issue and explores whether a relationship exists between poverty, inequality and terrorist incidents. The main hypothesis of this study indicates that higher poverty and higher inequalities are related to higher number of terrorist incidents. This study examines Turkey's terrorism problem in depth and identifies some factors that are related to the formation of terrorism. It is believed that this may assist help policy makers to develop new policies that can eliminate fertile ground where terrorism easily finds support. The researcher uses secondary data analysis; data for number of terrorist incidents are derived from the Turkish National Police's database, and other demographic and economic variable data are derived from Turkish Statistical Institute, and Government Planning Office. A multiple regression analysis technique is used to identify the effect of independent variables on the dependent variable, number of terrorist incidents. The results of the statistical analysis show that there is a statistically significant relationship between the percentage of population living below the poverty line, unequal distribution of some government resources, such as unequal distribution of education services, and unequal distribution of public investment. Findings also show that higher populated provinces may experience greater terrorist incidents. Additionally, the percentage of young in the population is also found to be related to the number of terrorist incidents.