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The majority of aircraft structures are designed to be damage-tolerant such that safe operation can continue in the presence of minor damage. It is necessary to schedule inspections so that minor damage can be found and repaired. It is generally not possible to perform structural inspections prior to every flight. The scheduling is traditionally accomplished through a deterministic set of methods referred to as Damage Tolerance Analysis (DTA). DTA has proven to produce safe aircraft but does not provide estimates of the probability of failure of future flights or the probability of repair of future inspections. Without these estimates maintenance costs cannot be accurately predicted. Also, estimation of failure probabilities is now a regulatory requirement for some aircraft. The set of methods concerned with the probabilistic formulation of this problem are collectively referred to as Probabilistic Damage Tolerance Analysis (PDTA). The goal of PDTA is to control the failure probability while holding maintenance costs to a reasonable level. This work focuses specifically on PDTA for fatigue cracking of metallic aircraft structures. The growth of a crack (or cracks) must be modeled using all available data and engineering knowledge. The length of a crack can be assessed only indirectly through evidence such as non-destructive inspection results, failures or lack of failures, and the observed severity of usage of the structure. The current set of industry PDTA tools are lacking in several ways: they may in some cases yield poor estimates of failure probabilities, they cannot realistically represent the variety of possible failure and maintenance scenarios, and they do not allow for model updates which incorporate observed evidence. A PDTA modeling methodology must be flexible enough to estimate accurately the failure and repair probabilities under a variety of maintenance scenarios, and be capable of incorporating observed evidence as it becomes available. This dissertation describes and develops new PDTA methodologies that directly address the deficiencies of the currently used tools. The new methods are implemented as a free, publicly licensed and open source R software package that can be downloaded from the Comprehensive R Archive Network. The tools consist of two main components. First, an explicit (and expensive) Monte Carlo approach is presented which simulates the life of an aircraft structural component flight-by-flight. This straightforward MC routine can be used to provide defensible estimates of the failure probabilities for future flights and repair probabilities for future inspections under a variety of failure and maintenance scenarios. This routine is intended to provide baseline estimates against which to compare the results of other, more efficient approaches. Second, an original approach is described which models the fatigue process and future scheduled inspections as a hidden Markov model. This model is solved using a particle-based approximation and the sequential importance sampling algorithm, which provides an efficient solution to the PDTA problem. Sequential importance sampling is an extension of importance sampling to a Markov process, allowing for efficient Bayesian updating of model parameters. This model updating capability, the benefit of which is demonstrated, is lacking in other PDTA approaches. The results of this approach are shown to agree with the results of the explicit Monte Carlo routine for a number of PDTA problems. Extensions to the typical PDTA problem, which cannot be solved using currently available tools, are presented and solved in this work. These extensions include incorporating observed evidence (such as non-destructive inspection results), more realistic treatment of possible future repairs, and the modeling of failure involving more than one crack (the so-called continuing damage problem). The described hidden Markov model / sequential importance sampling approach to PDTA has the potential to improve aerospace structural safety and reduce maintenance costs by providing a more accurate assessment of the risk of failure and the likelihood of repairs throughout the life of an aircraft. / Statistics

Background and objectives: Incorporating patient values and preferences as an essential input for decision-making has its potential merits in respecting the autonomy of patients, improving adherence and clinical outcomes. The Grading of Recommendations Assessment, Development and Evaluation (short GRADE) working group conceptualizes patient values and preferences as “the relative importance patient place on the main outcomes”. The objectives of this thesis include: 1) to provide an overview of a process for systematically incorporating values and preferences in guideline development; 2) to conduct a systematic review on outcome importance studies, using chronic obstructive pulmonary disease (COPD) as an example; 3) to provide guidance on how to assess certainty of evidence describing outcome importance using the GRADE criteria. Methods: We performed systematic reviews, asked clinical experts to provide feedback according to their clinical experience, and consulted patient representatives to obtain information about relative importance of outcomes in a new national guideline program. We conducted a systematic review to summarize the COPD related relative importance of outcome studies. We used a multi-pronged approach to develop the guidance for assessing certainty of evidence about relative importance of outcome and values and preferences. We applied the developed GRADE approach to relative importance of outcome systematic review examples and consulted the stakeholders in the GRADE working group for feedback. Results and conclusion: We provided an empirical strategy to find and incorporate values and preferences in guidelines by performing systematic reviews and eliciting information from guideline panel members and patient representatives. However, we identified the need for researches on how to assess the certainty of this evidence, and best summarize and present the findings. In our comprehensive systematic review project on COPD patient values and preferences we demonstrated the utility of rating evidence in systematic reviews of outcome importance. We describe the rationale for considering GRADE domains for the evidence about the importance of outcomes. We propose the assessment of the body of evidence starts at “high certainty”, and rate down for serious problems in GRADE domains including risk of bias, indirectness, inconsistency, imprecision and publication bias. Specific to risk of bias domain, we propose a preliminary consideration for risk of bias. The sources of indirectness for relative importance of outcome evidence include indirectness from PICO (population, intervention, comparison, and outcome) elements, and methodological indirectness. As meta-analyses are uncommon when summarizing the evidence about relative importance of outcome, inconsistency and imprecision assessments are challenging. Inconsistency arises from PICO and methodological elements that should be explored. The width of the confidence interval and sample size should inform judgments about imprecision. We also provide suggestions on how to detect publication bias based on empirical information. Finally, we also discuss the applicability of domains to rate up the certainty. We develop the GRADE approach for rating risk of bias, indirectness, inconsistency, imprecision and other domains when evaluating a body of evidence describing the relative importance of outcomes. Our examples should guide users and provide a basis for discussion and further development of the GRADE system. / Thesis / Doctor of Philosophy (PhD)

Background and Objectives: Understanding patient and public values and preferences is essential to healthcare and policy decision making. Discrete choice experiments (DCEs) are a common tool used to capture and quantify these preferences. Recent technological advances allow for a variety of approaches to create and analyze DCEs. However, there is no optimal DCE design, nor analysis method. Our objectives were to (i) survey DCE simulation studies to determine what design features affect statistical efficiency, and assess their reporting, (ii) further investigate these findings with a de novo simulation study, and (iii) explore the sensitivity of individuals’ preference of attributes to several methods of analysis. Methods: We conducted a systematic survey of simulation studies within the health literature, created a DCE simulation study of 3204 designs, and performed two empirical comparison studies. In one empirical comparison study, we determined addiction agency employees’ preferences on knowledge translation attributes using four models, and in the second, we determined elementary school children’s choice of bullying prevention programs using nine models. Results and Conclusions: In our evaluation of DCE designs, we identified six design features that impact the statistical efficiency of a DCE, several of which were further investigated in our simulation study. The reporting quality of these studies requires improvement to ensure that appropriate inferences can be made, and that they are reproducible. In our empirical comparison of statistical models to explore the sensitivity of individuals preferences of attributes, we found similar rankings in the relative importance measures of attributes’ mean part-worth utility estimates, which differed when using latent class models. Understanding the impact of design features on statistical efficiency are useful for designing optimal DCEs. Incorporating heterogeneity in the analysis of DCEs may be important to make appropriate inferences about individuals’ preferences of attributes within a population. / Thesis / Doctor of Philosophy (PhD) / This thesis focuses on the design and analysis of preference surveys, which are referred to as discrete choice experiments. These surveys are used to capture and quantify individuals’ preferences on various characteristics describing a product or service. They are applied in various health settings to better understand a population. For example, clinicians may want to further understand a patient population’s preferences in regards to multiple treatment alternatives. Currently, there is no optimal approach for designing or analyzing preference surveys. We investigated what factors help improve the design of a preference survey by exploring the literature and conducting our own simulation study. We also investigated how sensitive the results of a preference survey were based on the statistical model used. Overall, we found that (i) increasing the amount of information presented and reducing the number of variables to explore will maximize the statistical optimality of the survey; and (ii) analyzing the data with different statistical models will yield similar results in the ranking of individuals’ preferences of the variables explored.

Bakgrund: År 2020 var bröstcancer den cancerdiagnosen med flest nya fall. Globalt avled en fjärdedel av patienterna med bröstcancer till följd av diagnosen. Mastektomi är ett kirurgiskt ingrepp som kan utföras vid bröstcancer och innebär att delar av, eller hela bröstet opereras bort för att undvika metastasering. I litteraturöversikten användes Afaf Meleis transitionsteori som referensram och går att applicera när förändring hos individ eller dess miljö sker. Syfte: Syftet var att beskriva kvinnors upplevelse av välbefinnande i samband med mastektomi vid bröstcancer. Metod: Litteraturöversikt med kvalitativ ansats där 12 artiklar granskats för att sammanställa resultatet. Artiklarna har hämtats från databaserna Cinahl och Medline och har genomgått kvalitetsgranskning för kvalitativa artiklar. Fribergs fem steg (2022a) användes för att genomföra dataanalysen. Resultat: Att få diagnosen bröstcancer kom med många utmaningar för kvinnor och ställningstagande för mastektomi behövdes göras. Att genomgå mastektomi innebar utmaningar i kroppsuppfattning och kvinnorna ställdes inför samhällets stigmatisering. Ingreppet påverkade sexuella relationer och stöd från familj och vänner var viktigt. Slutsats: Resultatet ger en bild av hur kvinnor kan uppleva välbefinnande i samband med mastektomi. Utifrån resultatet framkommer stigmatisering för kvinnor som är mastektomerade. Därmed är det viktigt att fortsätta bedriva forskning inom ämnet för att uppnå välbefinnande. I litteraturöversikten belyses huvudfynden kroppsbild, sexualitet och stöd från omgivningen. / Background: In 2020 breast cancer was the cancer diagnosis with the most cases. Globally, a quarter of patients with breast cancer died as a result of the diagnosis. Mastectomy is a surgical procedure that can be performed in cases of breast cancer and means that parts of or the whole breast are surgically removed to avoid metastasis. Afaf Meleis transition theory was used as a frame of reference and can be applied when a change occurs in an individual or it’s environment. Aim: The aim was to describe women´s experience of well-being associated to mastectomy due to breast cancer. Method: Literature review with a qualitative approach where 12 articles were studied to compile the results. The articles have been retrieved from the databases Cinahl and Medline and have been reviewed according to qualitative articles. Friberg’s five steps (2022a) were used to carry out the data analysis. Results: Being diagnosed with breast cancer came with many challenges for the women and decision regarding mastectomy needed to be done. Undergoing a mastectomy meant challenges in body image and the women were faced with societal stigma. The surgery affected sexual relationships and support from family and friends was important.Conclusion: The result provide a picture of how women can experience well-being associated to mastectomy. Based on results, one can infer stigmatization for mastectomised women. It is important to continue research in the subject in order to achieve well-being. Main findings in this literature review was body image, sexuality and support from family and friends.

Ransomware is an ever-growing issue that has been affecting individuals and corporations since its inception, leading to losses of the order of billions each year. This research builds upon the existing body of research pertaining to ransomware detection for Windows-based platforms through behavioral analysis using sandboxing techniques and classification using machine learning (ML), considering the various predefined function calls, known as API (Application Programming Interface) calls, made by ransomware and benign samples as classifying features. The primary aim of this research is to study the effect of the frequency of API calls made by ransomware samples spanning across a large number of ransomware families exhibiting varied behavior, and benign samples on the classification accuracy of various ML algorithms. Conducting an experiment based on this, a quantitative analysis of the ML classification algorithms was performed, for the frequency of API calls based input and binary input based on the existence of an API call, resulting in the conclusion that considering the frequency of API calls marginally improves the ransomware recall rate. The secondary research question posed by this research aims to justify the ML classification of ransomware by conducting behavioral analysis of ransomware and goodware in the context of the API calls that had a major effect on the classification of ransomware. This research was able to provide meaningful insights into the runtime behavior of ransomware and goodware, and how such behavior including API calls and their frequencies were in line with the MLbased classification of ransomware. / Master of Science / Ransomware is an ever-growing issue that has been affecting individuals and corporations since its inception, leading to losses of the order of billions each year. It infects a user machine, encrypts user files or locks the user out of their machine, or both, demanding ransom in exchange for decrypting or unlocking user data. Analyzing ransomware either statically or behaviorally is a prerequisite for building detection and countering mechanisms. Behavioral analysis of ransomware is the basis for this research, wherein ransomware is analyzed by executing it on a safe sandboxed environment such as a virtual machine to avoid infecting a real-user machine, and its runtime characteristics are extracted for analysis. Among these characteristics, the various predefined function calls, known as API (Application Programming Interface) calls, made to the system by ransomware will serve as the basis for the classification of ransomware and benign software. After analyzing ransomware samples across various families, and benign samples in a sandboxed environment, and considering API calls as features, the curated dataset was fed to a set of ML algorithms that have the capability to extract useful information from the dataset to take classification decisions without human intervention. The research will consider the importance of the frequency of API calls on the classification accuracy and also state the most important APIs for classification along with their potential use in the context of ransomware and goodware to justify ML classification. Zero-Day detection, which refers to testing the accuracy of trained ML models on unknown ransomware samples and families was also performed.

The current study assessed perceived s of knowledge, involvement, and importance of transition planning and service delivery among secondary special education teachers in the Commonwealth of Virginia. Relationships were also explored between these levels and years of experience teaching students with special needs, category of students taught 1 highest degree earned, and contact hours training from in-service, coursework, and conferences in transition. A survey instrument was mailed to secondary special education teachers in the Commonwealth of Virginia. Ninety-two percent of the 236 survey recipients responded to the survey. Data from the survey included descriptive information regarding: years experience teaching students with disabilities, category taught, highest degree, and contact hours in conferences , courses, and in-services in years 1993-94, 1994-95, 1995-96. Data from the survey also included respondents' levels of knowledge, involvement, and importance of transition planning and service delivery. Survey data were analyzed to reveal differences among descriptive data and levels of knowledge, involvement, and importance. Significant findings from the study indicate that secondary special education teachers in Virginia perceive their knowledge of transition planning and service delivery in the low to medium range, their involvement in transition in the low to medium range, and the importance of transition planning and service delivery in the medium to high range. Significant findings from the study also included the positive relationship between knowledge of transition planning and service delivery and courses taken over the three year period of 1993-1996, conference contact hours over the same three year period, and inservice contact hours. Additionally significant was the positive relationship between involvement in transition planning and service delivery and inservice contact hours 1993-1996, conference contact hours over the same three year period, and courses taken. The level of importance of transition planning and service delivery was not affected by training options. Implications for LEAs in Virginia, implications for personnel preparation, and directions for future research are discussed. / Ed. D.

Previous research indicates that women prefer orgasms triggered by penile-vaginal intercourse (PVI) as compared to those triggered by direct manual stimulation of the clitoris. However, for reasons that are not well understood, most women are unable to reach PVI orgasms as often as they desire. In addition, it is unclear why many women prefer PVI orgasms to those triggered by direct clitoral stimulation. This study developed a more precise measure of PVI orgasm frequency and evaluated key predictors of this frequency, including duration of intercourse, physical and psychological health, and partner traits with implications for either mating quality or relationship quality. The present study also measured PVI orgasm importance and investigated why it is important for many women. The sample consisted of 835 adult women with experience in PVI. Mean PVI orgasm frequency was 50%, with 39.4% of women never or rarely having PVI orgasms, 37.1% sometimes having PVI orgasms, and 23.5% almost always or always having PVI orgasms. As a median response, women believed that PVI orgasm was “very important” and perceived importance was correlated with orgasm frequency (r = .31, p < .001), as were reasons for importance. Duration of intercourse showed a linear relationship with PVI orgasm frequency, but this finding was qualified for women at the low and high extremes of the orgasm frequency distribution. Body esteem, anxiety during intercourse, exercise, and general pain predicted PVI orgasm frequency. Sensitive male traits, although valued by women even more highly than alpha male traits, showed notably weaker relationships with PVI orgasm than did male alpha traits. This is consistent with evolutionary theories of orgasm, and it supports the view that the female orgasm may function to favor some males over others in terms of sire choice. Clinical and theoretical implications of the present findings are discussed.

Play plays a significant role in early childhood education. The purpose of this essay is to contribute to knowledge and understanding by exploring preschool teachers' perspectives on the importance of play in children's learning processes. The study investigates the significance of play and discusses similarities in the view of play among preschools in both Sweden and Zimbabwe. It also compares the different conditions that exist for preschools between these two countries. Six preschool teachers, three from Zimbabwe and three from Sweden, were interviewed. Despite challenges stemming from a flawed system, the teachers recognized the importance of play in preschools. The study's findings highlight the social and academic skills that play cultivates in children. Employing a qualitative interview method, the study draws upon the theoretical frameworks of William A., Friedrich Froebel, and Susan Isaacs to inform the analysis and interpretation of the data.