Contribution of viscous shear to friction in cold rolling of low-carbon steel

This work shows that for temperatures, pressures and shear rates that are common in cold rolling of low-carbon steel, viscous shear stress significantly contributes to the total friction force. Experiments were carried out to validate the theory on lubricant film formation and elasto-hydrodynamic lubrication, both with laboratory scale tribometers and a semi-industrial pilot mill facility. These experiments showed that at high shear rates, that are common in cold rolling, the lubricant does not behave as a Newtonian fluid anymore; moreover the viscosity at high pressure cannot accurately be described by a simple exponential law. With the correct relations implemented in a rolling model, both rolling force and forward slip are predicted with good accuracy for hydrodynamically lubricated cold rolling experiments.</p

Quantification of the In fluence of anisotropic plastic yielding on cold rolling force

Anisotropic mechanical properties in sheet material are of high importance, in particular for material to be used in stamping or deep drawing applications. The cold rolling process has a marked influence on the degree of anisotropy in the final product. However, the influence of material anisotropy on the cold rolling process itself has so far not been rigorously investigated. In this work, the degree of normal anisotropy is determined for two low-carbon steel grades after various degree of cold rolling reduction, both by X-ray diffraction as well as by tensile testing. The experimental work shows that material with high cold rolling reduction has non-negligible anisotropic properties; as a consequence the rolling force is seriously overestimated by cold rolling models with an isotropic yield criterion. It is therefore proposed to use the Hill48 yield criterion (instead of the von Mises criterion) in cold rolling models, this criterion takes anisotropic material behaviour into account. A comparison of cold rolling experiments with model predictions confirms that including the Hill48 yield criterion significantly improves the accuracy of a cold rolling model.</p

Developing a person-centred care environment aiming to enhance the autonomy of nursing home residents with physical impairments, a descriptive study

BackgroundEnhancing autonomy is important within the context of the care environment in nursing homes. A nursing home is a place for older adults with physical impairments, who need assistance, to live and where staff work who help them to exercise autonomy. Previous research shows that older adults and staff are influenced by the care environment to apply autonomy-enhancing activities. Therefore, organisational policies regarding the care environment seem promising for enhancing autonomy. The aim is to gain a deeper insight into the development and implementation of organisational policies aimed to enhance the autonomy of older adults with physical impairments.MethodsA qualitative descriptive design was chosen, using two methods. A document study was conducted on the policies, plans and proceedings in two care organisations. Moreover, interviews were conducted with 17 stakeholders involved in the policies, such as managers and members of the client council. The fragments of the 137 documents and 17 verbatim transcripts were coded and deductively categorised into the seven aspects (i.e., power-sharing, supportive organisational systems, appropriate skill mix, potential for innovation and risk-taking, the physical environment, effective staff relationships and shared decision-making systems) of the key domain care environment, as defined in the person-centred practice (PCP) framework developed by McCormack and McCance.ResultsThe aspect of power-sharing was used the most in the policies of the two participating organisations. The organisations expected much from the implementation of indirect interventions, such as access to the electronic care plan for residents and the development of staff towards self-managing teams. Less attention was paid to interventions in the physical environment, such as the interior of the building and privacy, and the collaboration processes between staff.ConclusionsThe PCP framework poses that all aspects of the key domain care environment are important to develop a person-centred practice. This is not yet the case in practice and the authors therefore recommend using all seven aspects of the care environment in a balanced combination with the other key domains of the PCP framework to achieve person-centred practice and as a result the enhancement of the autonomy of nursing home residents with physical impairments

A comparison of two dissimilarity functions for mixed-type predictor variables in the δ-machine

The δ-machine is a statistical learning tool for classification based on dissimilarities or distances between profiles of the observations to profiles of a representation set, which was proposed by Yuan et al. (J Claasif 36(3): 442–470, 2019). So far, the δ-machine was restricted to continuous predictor variables only. In this article, we extend the δ-machine to handle continuous, ordinal, nominal, and binary predictor variables. We utilized a tailored dissimilarity function for mixed type variables which was defined by Gower. This measure has properties of a Manhattan distance. We develop, in a similar vein, a Euclidean dissimilarity function for mixed type variables. In simulation studies we compare the performance of the two dissimilarity functions and we compare the predictive performance of the δ-machine to logistic regression models. We generated data according to two population distributions where the type of predictor variables, the distribution of categorical variables, and the number of predictor variables was varied. The performance of the δ-machine using the two dissimilarity functions and different types of representation set was investigated. The simulation studies showed that the adjusted Euclidean dissimilarity function performed better than the adjusted Gower dissimilarity function; that the δ-machine outperformed logistic regression; and that for constructing the representation set, K-medoids clustering achieved fewer active exemplars than the one using K-means clustering while maintaining the accuracy. We also applied the δ-machine to an empirical example, discussed its interpretation in detail, and compared the classification performance with five other classification methods. The results showed that the δ-machine has a good balance between accuracy and interpretability.NWOMultivariate analysis of psychological dat

Response to Dr Greenwood’s Comments on “Extending the Double-Hertz Model to Allow Modeling of an Adhesive Elliptical Contact”

© 2018, The Author(s). An adhesive elliptical contact is normally found in microscale applications that involve cylindrical solids, crossing at an angle between 0° and 90°. Currently, only one model is available to describe the elliptical contact’s surface interaction: the approximate Johnson–Kendall–Roberts (JKR) model which is limited to soft materials. In this paper, a new adhesive elliptical model is developed for a wide range of adhesive contacts by extending the double-Hertz theory, where adhesion is modeled by the difference between two Hertzian pressure distributions. Both Hertzian pressures are assumed to have an equivalent shape of contact areas, the only difference being in size. Assuming that the annular adhesive region is obtained by the area difference between the two Hertzian contact areas, the pull-off force curves can be calculated. In the limiting case of an adhesive circular contact, the results are very close to results from the existing models. However, for an adhesive elliptical contact in the JKR domain, lower pull-off forces are predicted when compared to the JKR values. Unlike the developed model, the shape of the JKR contact area varies throughout contact. Results show, particularly for conditions close to the JKR domain, that it is important to take into account that the adhesive region is the result of the two Hertzian contact areas having a non-equivalent shape

Cancer survivors who fully participate in the PROFILES registry have better health-related quality of life than those who drop out

Purpose Attrition and subsequent missing data pose a challenge in longitudinal research in oncology. This study examined factors associated with attrition in the PROFILES registry, and its impact on observed health-related quality of life (HRQOL) estimates. Methods Sociodemographic, clinical, and HRQOL data were collected annually from a cohort of 2625 colorectal cancer survivors between 2010 and 2015. Participant characteristics according to time of dropout were compared using analysis of variance and chi-square tests. Predictors of attrition were examined in logistic regression analysis. Multilevel linear mixed models were constructed to investigate associations between attrition and HRQOL over time. Results Participants who dropped out were more likely to be female (OR = 1.23, CI = 1.02–1.47), older (OR = 1.20, CI = 1.09–1.33), less educated (OR = 1.64, CI = 1.30–2.11), and to have depressive symptoms (OR = 1.84, CI = 1.39–2.44) than full responders, and less likely to have high socioeconomic status (OR = 0.74, CI = 0.61–0.94). Participants who dropped out earlier reported significantly worse HRQOL, functioning, and psychosocial symptoms, which declined at a steeper rate over time, than full responders. Conclusions Cancer survivors’ HRQOL may be overestimated in longitudinal research due to attrition of the most unwell participants. Implications for Cancer Survivors Cancer survivors with the poorest health are at risk of dropping out of PROFILES and possibly withdrawing from other activities. Optimizing participation in PROFILES—a potential mechanism for providing information and access to support—is an avenue for keeping this group engaged