Virtual trial to evaluate the robustness of cementless femoral stems to patient and surgical variation

Primary stability is essential for the success of cementless femoral stems. In this study, patient specific finite element (FE) models were used to assess changes in primary stability due to variability in patient anatomy, bone properties and stem alignment for two commonly used cementless femoral stems, Corail® and Summit® (DePuy Synthes, Warsaw, USA). Computed-tomography images of the femur were obtained for 8 males and 8 females. An automated algorithm was used to determine the stem position and size which minimized the endo-cortical space, and then span the plausible surgical envelope of implant positions constrained by the endo-cortical boundary. A total of 1952 models were generated and ran, each with a unique alignment scenario. Peak hip contact and muscle forces for stair climbing were scaled to the donor’s body weight and applied to the model. The primary stability was assessed by comparing the implant micromotion and peri-prosthetic strains to thresholds (150 μm and 7000 µε, respectively) above which fibrous tissue differentiation and bone damage are expected to prevail. Despite the wide range of implant positions included, FE prediction were mostly below the thresholds (medians: Corail®: 20–74 µm and 1150–2884 µε, Summit®: 25–111 µm and 860–3010 µε), but sensitivity of micromotion and interfacial strains varied across femora, with the majority being sensitive (p < 0.0029) to average bone mineral density, cranio-caudal angle, post-implantation anteversion angle and lateral offset of the femur. The results confirm the relationship between implant position and primary stability was highly dependent on the patient and the stem design used.     

The Unabomber’s ethics

In this paper, I present and criticize Ted Kaczynski’s (“The Unabomber”) theory that industrialization has been terrible for humanity, and that we should use any means necessary, including violent means, to induce a return to pre‐industrial ways of living. Although Kaczynski’s manifesto, Industrial society and its future, has become widely known, his ideas have never before been subject to careful philosophical criticism. In this paper I show how Kaczynski’s arguments rely on a number of highly implausible philosophical premises. I further make the case that, although his theory as a whole should be rejected, Kaczynski raises a number of worries about technological development that ought to receive serious attention. Some of these worries have recently come to be shared by prominent defenders of human enhancement, including Nick Bostrom and Julian Savulescu. In the last section I indicate why I believe it is important that academic philosophers scrutinize ideas that motivate acts of violence.     

Perfluorooctanoic Acid (PFOA): Environmental Sources,Chemistry, Toxicology,and Potential Risks

Attention regarding perfluoroalkyl and polyfluoroalkyl substances (PFAS) has increased in recent years, due to recognition of widespread environmental presence, recognition of a chemical structure that confers resistance to degradation, and reported health concerns. Historical common exposure sources include food, drinking water, occupational circumstances, and products in commerce (e.g., carpeting, clothing, paper products). Early-2000’s data showed perfluorooctanoic acid (PFOA) was present in blood samples from nearly all the U.S. general population (>99%). Alterations in industrial manufacturing processes, increased regulatory scrutiny, and advanced water treatment options have reduced the reported human body burden of PFAS in the U.S., including for PFOA. Human serum concentrations of PFOA, which do not identify the source(s) of exposure, have exhibited a substantial decrease (~63%) between the 1999–2000 and the 2013–2014 NHANES monitoring by the Centers for Disease Control and Prevention (CDC). With respect to both noncancer and potential cancer effects that may be associated with reported serum levels of PFOA, conclusions regarding an absence of effects, or insufficient information to suggest adverse effects, prevent a consistent conclusion about the occurrence and magnitude of human health effects. In the last several years, a number of state and federal agencies have developed health advisories or guideline values for drinking water exposure that are in the sub-ppb range. In 2016, a USEPA Health Advisory of 0.07 ppb was released, which stands in contrast to 2016 Health-based Values from Health Canada ranging from 0.2 ppb (PFOA) to 30 ppb, and a few other U.S. states with values even less than that of USEPA (e.g., MN, NJ, VT). Further work is necessary to distinguish in transparent fashion between drinking water levels that intentionally are set on a conservative basis to protect human health vs exposure levels that may be associated with tangible adverse effects.     

Multivariate multiple test procedures based on nonparametric copula estimation

Multivariate multiple test procedures have received growing attention recently. This is due to the fact that data generated by modern applications typically are high‐dimensional, but possess pronounced dependencies due to the technical mechanisms involved in the experiments. Hence, it is possible and often necessary to exploit these dependencies in order to achieve reasonable power. In the present paper, we express dependency structures in the most general manner, namely, by means of copula functions. One class of nonparametric copula estimators is constituted by Bernstein copulae. We extend previous statistical results regarding bivariate Bernstein copulae to the multivariate case and study their impact on multiple tests. In particular, we utilize them to derive asymptotic confidence regions for the family‐wise error rate (FWER) of multiple test procedures that are empirically calibrated by making use of Bernstein copulae approximations of the dependency structure among the test statistics. This extends a similar approach by Stange et al. (2015) in the parametric case. A simulation study quantifies the gain in FWER level exhaustion and, consequently, power that can be achieved by exploiting the dependencies, in comparison with common threshold calibrations like the Bonferroni or ?idák corrections. Finally, we demonstrate an application of the proposed methodology to real‐life data from insurance.     

Regressive models for risk prediction of repeated multinomial outcomes: An illustration using Health and Retirement Study data

Life expectancy is increasing in many countries and this may lead to a higher frequency of adverse health outcomes. Therefore, there is a growing demand for predicting the risk of a sequence of events based on specified factors from repeated outcomes. We proposed regressive models and a framework to predict the joint probabilities of a sequence of events for multinomial outcomes from longitudinal studies. The Markov chain is used to link marginal and sequence of conditional probabilities to predict the joint probability. Marginal and sequence of conditional probabilities are estimated using marginal and regressive models. An application is shown using the Health and Retirement Study data. The bias of parameter estimates for all models from all bootstrap simulation is less than 1% in most of the cases. The estimated mean squared error is also very low. Results from the simulation study show negligible bias and the usefulness of the proposed model. The proposed model and framework would be useful to solve real-life problems from various fields and big data analysis.     

Impact of Human,workplace and indoor environmental risk factors on operator’s reliability in control rooms

Abstract

Adequate design of control rooms is very important for increasing the reliability of operators working in them. Therefore, it is necessary to choose the best solution for improving the compliance between operators and elements of control room while dealing with problems of illumination, thermal climate and noise. For this purpose we will analyze the impact of execution time of operator's information processing tasks, motor and logical operator's tasks, operator's functional state, workplace risk factors and indoor environment parameters on operator's reliability in control room. Methodological approaches for these five groups are formed and used for experimental research and to determine the level of their quantitative values. Research results are showing the following levels of reliability for specific indicators: reaction time ‐ very good, coefficient of task complexity ‐ optimal, operator's error caused by stress ‐ very good, workplace risk factor ‐ critical and indoor environment ‐ good. Because the functional appropriateness and efficiency in the operator's workplace were inadequate, the following recommendations and solutions were proposed: signaling and control elements should be placed on the central part of the control desk, because due to poor control desk construction, they cause uneven burden on various operator's body parts.     

Fuzzy evidence theory and Bayesian networks for process systems risk analysis

Abstract

Quantitative risk assessment (QRA) approaches systematically evaluate the likelihood, impacts, and risk of adverse events. QRA using fault tree analysis (FTA) is based on the assumptions that failure events have crisp probabilities and they are statistically independent. The crisp probabilities of the events are often absent, which leads to data uncertainty. However, the independence assumption leads to model uncertainty. Experts’ knowledge can be utilized to obtain unknown failure data; however, this process itself is subject to different issues such as imprecision, incompleteness, and lack of consensus. For this reason, to minimize the overall uncertainty in QRA, in addition to addressing the uncertainties in the knowledge, it is equally important to combine the opinions of multiple experts and update prior beliefs based on new evidence. In this article, a novel methodology is proposed for QRA by combining fuzzy set theory and evidence theory with Bayesian networks to describe the uncertainties, aggregate experts’ opinions, and update prior probabilities when new evidences become available. Additionally, sensitivity analysis is performed to identify the most critical events in the FTA. The effectiveness of the proposed approach has been demonstrated via application to a practical system.     

Ecological risk assessment of heavy metal contamination in mangrove habitats,using biochemical markers and pollution indices: A case study of Avicennia marina L. in the Rabigh lagoon,Red Sea

Contamination of mangrove ecosystems, including those of the Red Sea area, has caused serious concern globally. Spatial distribution of heavy metals and their bioaccumulation in one of the common mangrove plants of Saudi Arabia, Avicennia marina L., was evaluated in 8 stations at the Rabigh lagoon to assess the ecological risks due to heavy metal contamination. Among all the heavy metals, Fe concentration was recorded highest (8939.38 ± 312.63 mg/kg) at station S4. Contamination factor (CF) values for all heavy metals determined in this study were recorded in ascending order as Cu < Cr < Mn < Zn < Fe < Ni < Pb < Cd, with the pollution load index pattern recorded in descending order as S6 > S4 > S3 > S5 > S7 > S1 > S8 > S2. Bio-concentration factor (BCF) was <1 for all the heavy metals and there was a positive correlation between the antioxidants and lead (Pb), which can be a result of the ability of A. marina to exclude or detoxify this metal by its mechanism of exclusion or detoxification. A significant correlation existed between the heavy metals concentration in sediment and A. marina leaves at one combination or the other, except for Cu and Cd, which do not correlate with any other metal concentration. The information provided in the present study can be used in the monitoring and measurement of heavy metal pollution in marine ecosystems or other aquatic environments, to prevent several ecological risks to the mangrove ecosystem.