Implicit Assumption Research Articles

Evolution of ship damage stability assessment—Transitioning designers to direct numerical simulations

Theory and application of damage stability followed over the years two dissociated paths: static assessments and dynamic simulations. The first approach, being easy to apply and understand, has been preferred by ship designers and regulators; the second, more advanced and first-principle oriented, has been mainly reserved for research or high-level consultancy, especially for passenger ships. Nowadays, the availability of numerical flooding simulation tools across the scientific community and calculation power in the industry allows for a possible definitive transition of damage stability assessment towards direct numerical analyses. However, research should softly drive designers towards more advanced processes via a suitable didascalic calculation framework. The multi-level approach pursued in project FLARE is an example of such a transition from static to dynamic damage stability assessment. The present work initially carefully reviews the probabilistic concept of damage stability, critically comparing the prescriptive statistical methods with direct ones and providing insights and guidance on how researchers and designers can reconcile with the original implicit assumption of the probabilistic approach. Secondly, the development of the multi-level framework highlights incongruences concerning modelling of damages between static and dynamic assessments, disfavouring the comprehension of dynamic results to designers. Two detailed examples highlight the differences in dynamic simulation results between different damage breach modelling, leading to completely different flooding paths for the same damage case. Finally, the paper indicates how a compromise between academic approach and application could help designers to start their transition towards direct numerical damage stability analyses.

Classical Doppler shift explains the Michelson‐Morley null result

Here we review Michelson‐Morley's original analysis of their interferometer experiment and discuss its use of optical distance. We derive a formula for transverse Doppler shift from geometric considerations, apply this to the Michelson‐Morley interferometer, and present a phase analysis for the experiment. Furthermore, we present an equation for Doppler shift at a general angle and use this to derive the null phase shift result for round-trip interferometer paths at any arbitrary angle. We do not dispute the validity of the null result, nor the prediction of an arrival-time difference for the transverse and longitudinal arms of the interferometer; rather, we challenge the implicit assumption that an arrival time difference will necessarily result in an observable fringe shift.

Why Post-Truth Cannot Be Our Epistemological Compass

ABSTRACT This paper tackles some of the arguments Steve Fuller – arguably the best advocate of post-truth currently on the scene – put forward to show that, correctly understood, post-truth is the best conceptual tool to get a clear picture not only of what is happening in our societies today, but also of what has happened throughout the secular history of Western culture. The implicit assumption is that post-truth represents a reliable ‘epistemological compass’ – that is, a notion (or a set of notions) for proper orientation in both cultural and physical environments. The aim of the paper is to show that Fuller’s arguments do not work, because an epistemological compass can only be centered on a plausible notion of objectivity, and – it will be contended – this is exactly what Fuller lacks. Accordingly, it will be stressed how the upshot of his theses is the opposite of what he presumes it to be and, moreover, that his theses prove lethal to his own position.

Data Smoothing Structural Equation Modeling to Study Quality of Life and Model Selection

In this paper, we propose and present a nonparametric data smoothing method via the kernel smoothing functions to make structural equation modeling (SEM) robust to a specific type of model misspecification, that is an incorrect distributional assumption. Although most statistical techniques are based on an implicit assumption of normality, real data often exhibits nonnormal kurtosis (heavily peaked), skewness, or both. These characteristics, if ignored, can make model identification difficult and inference not reliable. It is important to note that these are characteristics present in most real multivariate high-dimensional datasets. There is much recent study devoted to this type of misspecification. Using a large scale Monte Carlo simulation study, we evaluate the efficacy of our proposed approach in improving the frequency with which a correctly specified model is selected by information complexity criteria when the normality is misspecified. We also show our results on a benchmark reference real dataset to study the quality of life. Our results indicate that the data smoothing kernel transformation (KDS-SEM) leads to a better fitting structural equation model (SEM) and model selection.

The test-retest reliability of the retrieval practice effect.

Retrieving information from memory, compared with different control conditions, improves subsequent retention of that information. Given the ubiquitous demonstration of this retrieval practice effect, researchers have asked whether such benefit is moderated by individual-difference variables. An implicit assumption in individual-difference research is that the retrieval practice effect at the participant level is reliable across participants. In this study, we tested this reliability assumption. In two sessions, 54 participants studied foreign-native word pairs, repeatedly restudied half of the word pairs, repeatedly retrieval-practised the other half, and, finally, took a final test for all pairs. Different word pairs were used in each session. We replicated the retrieval practice effect at the group level in Sessions 1 (d = 0.54, 95% confidence interval [CI] = [0.27, 0.80]) and 2 (d = 0.79 [0.53, 1.05]). In addition, we found that the retrieval practice effect at the participant level was reliable over a 1-week span both for absolute agreement, intraclass correlation coefficient (ICC) = .33 [.12, .51], and for consistency estimates, ICC = .35 [.14, .53]. The result bridges a gap in the literature of individual differences on the retrieval practice effect. We suggest that future studies identify whether, and under which experimental conditions, the retrieval practice effect at the participant level may show even greater reliability estimates than the ones reported here. The finding may also pave the way for studies assessing whether the magnitude of the retrieval practice effect is domain-general or paradigm-specific.

Acyclic behavior change diagrams: a tool to report and analyze interventions

ABSTRACT Background Behavior change interventions have a vital role in enhancing human health and well-being. Nevertheless, concerns have been raised about suboptimal reporting of behavior change interventions, making analyses, replications, and intervention re-use hard or impossible to conduct. Objective This paper introduces acyclic behavior change diagrams (ABCDs) to achieve more transparent development, evaluation, and reporting of behavior change interventions. ABCDs are a visual representation of the assumptions regarding causal-structural chains that underlie putative active ingredients of behavior change interventions. These causal-structural chains link the behavior change principles that are applied in an intervention to the (determinants of) behavior targeted in that intervention. Conclusions ABCDs are helpful in making implicit assumptions explicit and help communicate assumptions with team members and other stakeholders. Moreover, we believe they make evaluation easier, and their machine-readability allows for ABCDs to be imported directly into (systematic review) databases with negligible costs while disclosing complete and accurate data. Finally, the ABCD approach fits well with other initiatives to gain a deeper understanding and synthesis of the literature on active intervention elements.

Effects of reduced measurements independence on self-testing

Self-testing, which refers to device independent characterization of the state and the measurement, has been intensively studied in quantum information theory. One of the implicit assumptions in self-testing theorem is, measurement settings should be chosen randomly and independently by each party. The relaxation of this assumption potentially gives untrusted device providers the power to control the device with no quantum feature, even fake the quantum behavior by using the classical strategies within the device. In this paper, we analyze the effects of the reduced measurements independence on self-testing two-qubit entangled states based on the violation of Bell inequalities. By assuming adversary’s capabilities, we show to achieve the maximum CHSH violation in this case, four different state-measurements preparations should be provided. Without run information one could not really self-test the underlying system in each run anymore, but it must be equal with one of four entangled systems up to local unitaries.

What characteristics of performance information matter to citizens? Evidence from a survey experiment in South Korea

ABSTRACT This study examines the effects of multiple types of performance information on citizens’ performance evaluations of the energy voucher programme in South Korea, using an online survey experiment. We also investigate the moderating effect of political ideology in analysing citizens’ performance information use. Our results reveal that the impact of performance information is more positive and stronger when presented in the form of the output than the outcome of the energy voucher programme. Contrary to the implicit assumption in the literature, reporting output information rather than outcome information tends to increase citizens’ positive evaluation of programme performance. We also find that citizens make reference-dependent decisions in their judgement of public services. Our study highlights the heterogeneous effects of each reference point on citizens’ perceived performance, depending on the type of performance information.

Implicit resilience theories: a qualitative study of context-shapers at higher education institutions

ABSTRACT High dropout rates in STEM disciplines are certainly among the most crucial challenges that higher education is facing. While prior research has mainly applied an individual-centered approach that has helped identify factors explaining student dropout, an important yet under-examined issue is how those responsible for shaping the academic environment of students vary in their implicit assumptions of why some students persist while others drop out. In this study, we shift the focus toward the narratives regarding the reasons for student drop out. We report on an in-depth qualitative interview study comprising 59 deans of studies, administrators (i.e. quality managers and student advisors), and student representatives. This group of individuals contributes to the challenge of reducing student dropout by shaping the teaching and learning environment of students at German higher education institutions. Our grounded-theory model indicates that these context-shapers attend to different implicit theories on what constitutes resilient versus non-resilient students. The implicit resilience theories, in turn, result in dropout being legitimized in different ways, which determines the judgements made by context-shapers in terms of actions suitable to reduce student dropout.

The weak position of reception education for newly arrived migrant students in the educational field

Despite the group of Newly Arrived Migrant Students (NAMS) being very diverse, their educational outcomes are homogeneous and rather weak in many countries. In Flanders (Belgium) the educational trajectory of NAMS starts in a separate program that prepares them for their transition to regular secondary education. Rather than seeing the outcomes of reception education as the result of the behaviour of individual teachers and students, we argue that this problem should be addressed using a field approach that directs attention to the structural relationships between reception education and regular education. We rely on Bourdieu’s agency-structure bridging concept of social fields to reveal this relative position and the implicit assumptions or doxa that sustain it. Administrative data on teachers and students in secondary education, as well as policy texts, the transcripts of 37 interviews with teachers and the fieldnotes from the observations of 28 admission councils were analysed.

PP47 Modelling Non-small Cell Lung Cancer Treatment: Predicted and Observed Impact Of Immunotherapy In The Netherlands

IntroductionPatients treated with immunotherapy are divided into two subgroups: (i) long-term survivors (LTS) and (ii) moderate survivors. Nevertheless, clinical trials (RCTs) report only average treatment effects such as hazard rate (HRs). Health economic-models often only input average treatment effects, even though it has been shown that accounting for the LTS subgroup is crucial for accurate projection of long-term survival under immunotherapy. We investigated the incorporation of a statistical mixture cure model (MCM) in a health-economic model for lung cancer as a way to account for LTS while incorporating reported average RCT-based treatment effects.MethodsWe developed a microsimulation model describing disease progression under three treatment lines in advanced lung cancer using Dutch real-world data of chemotherapies treated patients. Here we focus on first-line treatment, for which we used gompertz distribution to simulate time-to-progression. To simulate the impact of immunotherapy, we adjusted base-model assuming MCM for first-line treatment, where the LTS subgroup was not at risk to progress, but instead die from background mortality. The subgroup of moderate survivors on the other hand are at risk to progress with adjusted progression-free HR (PF-HR). We simulated the model with size of LTS (prop_LTS) ranging from 14-34 percent (keynote-001 five-year overall survival [OS], 95% confidence interval) while fixing average RCT PF-HR at 0.5. Model predictions under the different prop_LTS were compared to real-world Dutch OS as well as the long-term RCT five-year OS.ResultsWith respect to observed short-term survival outcomes, model predictions were insensitive to assumptions regarding the size of the LTS subgroup. However, to match the five-year RCT OS rate reported (32%), the prop_LTS had to be equal to 34 percent. Under this latter setting for the prop_LTS, the progression HR in the subgroup of moderate survivors was calibrated to be 1.1.ConclusionsThe use of a mixture cure model improves long-term model-based projections with the implicit assumption that moderate survivors have little or no treatment benefit.

Default beliefs as a basis of social decision-making.

Default beliefs are implicit assumptions about the future existence of oneself, others, and the environment. They differ from individual and cultural beliefs in that they are held universally, implicitly, and with high confidence. What is the content of default beliefs? How are they represented in the brain? And what is their role in social decision-making?

Heterogeneity in normal aging: longitudinal trajectories of grey matter

AbstractBackgroundIn dementia research, the role of aging is usually de‐trended as a confounder, implying implicit assumptions on the nature of controls. In fact, normal aging brings about structural and functional damage, possibly reflecting brain resistance and resilience, unrelated to overt cognitive impairment (1). The aim of this exploratory study is to predict longitudinal trajectories of brain change in healthy elderly, based on their heterogeneous patterns at baseline, to uncover potentially lost information and link brain, cognitive and biological profiles. To our knowledge, it´s the first study investigating this in healthy aging.MethodCortical volumes and subcortical thickness were assessed through MRI from 307 healthy elderly from ADNI, JADNI and AIBL initiatives. We applied cross‐sectional clustering using unsupervised random forest (2), followed by multivariate mixture of generalized mixed effect model (3) to predict individual trajectories and group them based on similarities. The longitudinal clustering was replicated in the separate cohorts for validity check.Result4 different clusters were identified, which statistically differ for musculoskeletal comorbidities. Cluster 1 shows the healthiest brain and cognitive profile at the age of 60, but it deteriorates the most across time. Conversely, cluster 3 presents the worst grey and white matter profiles at 60, and the greatest cognitive decline over time. However, showing only minimal brain change, cluster 3 resembles brain resistance, whereas cluster 1 seems compatible with cognitive resilience.ConclusionThese different trajectories suggest the possibility of identifying resilient and resistant individuals already from healthy aging. Clinically, this promotes environmental protective factors and early diagnosis. Methodologically, this warns on the heterogeneity of healthy “controls” in experimental designs.

Criteria Comparative Learning for Real-Scene Image Super-Resolution

Real-scene image super-resolution aims to restore real-world low-resolution images into their high-quality versions. A typical RealSR framework usually includes the optimization of multiple criteria which are designed for different image properties, by making the implicit assumption that the ground-truth images can provide a good trade-off between different criteria. However, this assumption could be easily violated in practice due to the inherent contrastive relationship between different image properties. Contrastive learning (CL) provides a promising recipe to relieve this problem by learning discriminative features using the triplet contrastive losses. Though CL has achieved significant success in many computer vision tasks, it is non-trivial to introduce CL to RealSR due to the difficulty in defining valid positive image pairs in this case. Inspired by the observation that the contrastive relationship could also exist between the criteria, in this work, we propose a novel training paradigm for RealSR, named Criteria Comparative Learning (Cria-CL), by developing contrastive losses defined on criteria instead of image patches. In addition, a spatial projector is proposed to obtain a good view for Cria-CL in RealSR. Our experiments demonstrate that compared with the typical weighted regression strategy, our method achieves a significant improvement under similar parameter settings.

Religious accommodation, agonism, agnosticism in healthcare: A commentary on Joshua Hordern, 'Accommodating religion and belief in healthcare: Political threats, agonistic democracy and established religion'.

Religious accommodation, agonism, agnosticism in healthcare: A commentary on Joshua Hordern, 'Accommodating religion and belief in healthcare: Political threats, agonistic democracy and established religion'.

Physician Trust in the News Media and Attitudes toward COVID-19.

Previous research has established the importance of primary care physicians in communicating public health directives. The implicit assumption is that, because of their expertise, doctors provide accurate and up-to-date information to their patients independent of partisan affiliation or media trust. The authors conducted an online survey of 625 primary care physicians and used the results to test (1) whether physician trust in media outlets is consistent with their political partisanship, and (2) whether trust in media outlets influences (a) personal concern that someone in their family will get sick, (b) perceptions about the seriousness of the pandemic as portrayed in the media, and (c) trust in federal government agencies and scientists. Physicians are better positioned to critically evaluate health-related news, but they are subject to the same biases that influence public opinion. Physicians' partisan commitments influence media trust, and media trust influences concern that a family member will get sick, perceptions regarding the seriousness of the pandemic, and trust in federal government agencies and scientists. Physician trust in specific media outlets shapes their understanding of the pandemic, and-to the extent that they trust conservative media outlets-it may limit their effectiveness as health policy messengers.

Race conditions and data partitioning: risks posed by common errors to reproducible parallel simulations

When parallel algorithms for simulation were introduced in the 1970s, their development and use interested only experts in parallel computation. This circumstance changed as multi-core processors became commonplace, putting a parallel computer into the hands of every modeler. A natural outcome is growing interest in parallel simulation among persons not intimately familiar with parallel computing. At the same time, parallel simulation tools continue to be developed with the implicit assumption that the modeler is knowledgeable about parallel programming. The unintended consequence is a rapidly growing number of users of parallel simulation tools that are unlikely to recognize when the interaction of race conditions, partitioning strategies, and simultaneous action in their simulation models make results non-reproducible, thereby calling into question the validity of conclusions drawn from the simulation data. We illustrate the potential dangers of exposing parallel algorithms to users who are not experts in parallel computation with example models constructed using existing parallel simulation tools. By doing so, we hope to refocus tool developers on usability, even if this new focus incurs loss of some performance.

Multidimensional Model of High-Growth Companies: Do COVID-19 and the Ukraine–Russia Crisis Lead to Differences?

Economies and the companies operating within them are currently facing numerous challenges and threats that are caused by the COVID-19 outbreak and recovery and are prolonged by the Ukraine–Russia crisis. Both have drastically changed the way companies operate. High-growth companies (HGCs) can be an important group because of their characteristics and can provide guidelines for efficiently addressing these challenges and creating new opportunities. Our research contributes to this field, as the objective of the article is to find out whether the global challenges (i.e., the COVID-19 and Ukraine–Russia crisis) lead to a difference in the strength of the influence between the analyzed determinants—the growth factors of HGCs. For this purpose, a structural model equation (SEM) was applied and a multigroup analysis between the two data sets (before and during the global challenges) was performed on the pooled sample of n = 242 HGCs from the Republic of Slovenia. The results showed some statistically significant differences that can be explained by the time perspective and possibly by the influence of global challenges. Thus, this paper makes an important contribution to science, as a reassessment of the theories and implicit assumptions in current research is needed due to global challenges. It is also useful for policy makers who want to consider the impact of recent global challenges in their policy recommendations and for HGCs seeking sustainable high growth.

Disquiet concerning cesarean birth.

Cesarean birth has increased substantially in many parts of the world over recent decades and concerns have been raised about the propriety of this change in obstetric practice. Sometimes, a cesarean is necessary to preserve fetal and maternal health. But in balancing the risks of surgical intervention the implicit assumption has been that cesarean birth is an equivalent alternative to vaginal birth from the standpoint of the immediate and long-term health of the fetus and neonate. Increasingly, we realize this is not necessarily so. Delivery mode per se may influence short-term and abiding problems with homeostasis in offspring, quite independent of the indications forthe delivery and other potentially confounding factors. The probability of developing various disorders, including respiratory compromise, obesity, immune dysfunction, and neurobehavioral disorders has been shown in some studies to be higher among individuals born by cesarean. Moreover, many of these adverse effects are not confined to the neonatal period and may develop over many years. Although the associations between delivery mode and long-term health are persuasive, their pathogenesis and causality remain uncertain. Full exploration and a clear understanding of these relationships is of great importance to the health of offspring.

Are stunted child – overweight mother pairs a real defined entity or a statistical artifact?

In a methodological contribution, Dieffenbach & Stein (DS) (The Journal of Nutrition, 142(4), 771–773.) concluded that the double burden of malnutrition (DBM), represented by stunted child – overweight mother pairs (SCOM), is a statistical artifact, meaning that SCOM does not describe a unique phenomenon because the observed rates of SCOM across a number of countries were not strongly different from the product of observed rates of maternal overweight (OM) and child stunting (SC), which DS referred to as the expected rate of SCOM. However, a growing literature continues to use SCOM as an indicator of the DBM. This study shows that the analysis by DS is not sufficient to conclude that SCOM can be explained by the co-occurrence of OM and SC due to chance alone because the analysis by DS was conducted at the country level, but applied to SCOM, which is a household-level variable. Using Demographic and Health Surveys data from 202 country-year data sets, we do not confirm important implicit assumptions that are required for the claim by DS to be supported. We also outline that comparing the expected to the observed rate of SCOM is primarily informative when putting it in relation to factors that influence the supply and demand of food consumed by households. When considering these factors, we find further evidence that it is misleading to consider SCOM as a statistical artifact, as the difference between the observed and the expected rate of SCOM significantly differs by household wealth. Recognizing that SCOM is a distinct phenomenon is important for policymakers who develop double-duty strategies that address malnutrition, and for researchers who need useful indicators to study the determinants of malnutrition at the household level.