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Turbine blades in gas turbine engines operate at elevated temperatures and in highly oxidising atmospheres that can be contaminated with fuel residues and sea water salts. These components, which are expensive to produce, are subjected to high stresses during operation but must be totally reliable during their design life. An economic way to maintain blade properties is to coat the base metal superalloy with a protective layer capable of resisting both high temperature oxidation and hot corrosion. Conventional aluminide coatings are widely used for this purpose but platinum aluminides offer improved corrosion resistance. A collaborative exercise involving Rolls‐Royce and Johnson Matthey has now resulted in the development of a platinum aluminide diffusion coating that offers some advantages over the commercial systems.

Mr. Fullman is well known to all Aslib members, as Information Officer at British Non‐Ferrous Metals Research Association. He has for years past given generously of his time to Aslib as a very active member of its Council, its Publication Committee, its Conference Committee, and, during the last year particularly, as Chairman of its Education Committee and of the sub‐committees, he has been largely responsible for drafting the syllabus for the Training of the Information Officer, thereby making a signal contribution to our Association.

This paper examines the effects on families of a broad range of intervention programs designed to assist family caregivers of persons with mental illness. In so doing, the paper critically discusses the intervention designs utilized, similarities and differences in intervention modalities, the characteristics of study subjects, and the effects of these intervention programs on a variety of caregiver outcomes. The paper addresses the degree to which particular interventions are more effective than others, and assesses the strengths, weaknesses, and limitations of these intervention studies. Implications for future practice, policy, and research are presented.

For a university to be a prime mover for sustainability transformation, all units of the university should contribute. However, organizational change in educational institutions is often studied by examining specific domains such as research or operation in isolation. This results in a less-than-complete picture of the potential for university-wide change. In contrast, this paper aims to examine the network of social relations that determine the diffusion and sustainability of change efforts across a university. The authors use McGill University (Canada) as a model system to study the network of actors concerned with sustainability to learn how this network influences the penetration of sustainability throughout the university.

To explore the existing social structure, the authors use an innovative approach to illuminate the influence of social structure on organizational change efforts. Using a mixed methods approach combining social network analysis with qualitative interview data, the authors examine the influence of the social structure on sustainability transformation at McGill University. The authors conducted 52 interviews between January and April 2019 with representatives of different sustainability groups at the university across six domains (research, education, administration, operations, connectivity and students).

The authors find that McGill University has a centralized system with a low density. The network is centralized around the Office of Sustainability. The limited cross-domain interaction appears to be a result of differences in motivation and priorities. This leads to a network that has many actors but only a limited number of connections between them. The quality of the relationships is often utilitarian, with only a few relationships aiming for support and mutual growth.

This study brings together social network analysis, sustainability transformation and higher education in a new way. It also illustrates the complexity of guiding a large organization, such as a university, toward a sustainability transformation. Furthermore, it reveals the importance of considering each part of the university as part of an interconnected network rather than as isolated components.

As the concentration of environmental samples was generally very low and existing analytical instruments had limited sensitivity, developing mini pre-treatment systems for effectively concentrating the components was very important and necessary. The purposed of this paper is to develop mini pretreatment system integrated with micro pre-concentrator and micro GC column.

In this work, a mini pre-treatment system integrated with a micro pre-concentrator and a micro gas chromatograph (GC) column was proposed. The micro pre-concentrator filled with single-walled carbon nanotubes as adsorbent materials was able to effectively concentrate the trace environmental sample, which dramatically improved the response of detectors. In addition, instead of conventional columns, micro GC columns were able to effectively separate gas mixtures, which are able to overcome low resolution and poor anti-interference ability of portable instruments.

The results demonstrated that the proposed pre-treatment system was able to concentrate the trace sample with a concentration factor of 15 and effectively separate the gas mixtures with a resolution over 1.5.

A mini pre-treatment system integrated with a micro pre-concentrator and a micro GC column was proposed.

This paper aims to introduce a three-dimensional smoothed particle hydrodynamics (SPH) framework for simulating supercooled large droplets (SLD) dynamics at aeronautical speeds.

To include the effects of the surrounding air, a multiphase model capable of handling high density-ratio problems is adopted. A diffusive term is incorporated to smooth the density field and avoid numerical instabilities. Additionally, a particle shifting technique is used to eliminate anisotropic particle distributions.

The framework is validated against low-speed droplet impingement experimental results and then applied to the droplet impingement at high speeds typical of SLD conditions. Preliminary parametric studies are conducted to investigate the post-impact splashing. It is observed that a thicker water film can decrease the crown diameter and a smaller impact angle can suppress upward and forward splashing.

A three-dimensional multiphase SPH framework for SLD dynamics at a wide range of impact speed is developed and validated. The effects of particle resolution, water film thickness and impact angle on the post-impact crown evolution are investigated.

There are many definitions of profound and multiple learning disabilities. Most definitions include having a high degree of learning disability in conjunction with at least one other severe impairment, such as visual, auditory or physical impairments (Male, 1996; Ware, 1996; Lacey, 1998). Bunning (1997) adds that people with such disabilities are very reliant on others for support, including support in taking part in communicative events. Establishing reliable and consistent methods of communication may be exceptionally difficult (Florian et al, 2000). However, it is important to consider the individuality and extreme diversity of this population (Detheridge, 1997; Hogg, 1998), which includes variability in communication strengths and needs (Granlund & Olsson, 1999; McLean et al, 1996). Communication is often given little attention when services are planning ways of supporting individuals to participate, develop independence and make choices (McGill et al, 2000). While the individual's communication strengths and needs should remain central within any discussion, the significant others and the environment will also have an important influence. This article explores some of the communication issues experienced by people with profound and multiple learning disabilities and highlights the importance of the communication partnership within interventions.

This paper aims to describe the physical and numerical modeling of a new computational fluid dynamics solver for hypersonic flows in thermo-chemical non-equilibrium. The code uses a blend of numerical techniques to ensure accuracy and robustness and to provide scalability for advanced hypersonic physics and complex three-dimensional (3D) flows.

The solver is based on an edge-based stabilized finite element method (FEM). The chemical and thermal non-equilibrium systems are loosely-coupled to provide flexibility and ease of implementation. Chemical non-equilibrium is modeled using a laminar finite-rate chemical kinetics model while a two-temperature model is used to account for thermodynamic non-equilibrium. The systems are solved implicitly in time to relax numerical stiffness. Investigations are performed on various canonical hypersonic geometries in two-dimensional and 3D.

The comparisons with numerical and experimental results demonstrate the suitability of the code for hypersonic non-equilibrium flows. Although convergence is shown to suffer to some extent from the loosely-coupled implementation, trading a fully-coupled system for a number of smaller ones improves computational time. Furthermore, the specialized numerical discretization offers a great deal of flexibility in the implementation of numerical flux functions and boundary conditions.

The FEM is often disregarded in hypersonics. This paper demonstrates that this method can be used successfully for these types of flows. The present findings will be built upon in a later paper to demonstrate the powerful numerical ability of this type of solver, particularly with respect to robustness on highly stretched unstructured anisotropic grids.

Manufacturing processes, such as laminations, may introduce uncertainties in the magnetic properties of materials used in electrical machines. This issue, together with magnetization errors, can cause serious deterioration in the performance of the machines. Hence, stochastic material models are required for the study of the influences of the material uncertainties. The purpose of this paper is to present a methodology to study the impact of magnetization pattern uncertainties in permanent magnet electric machines.

The impacts of material uncertainties on the performances of an interior permanent magnet (IPM) machine were analyzed using two different robustness metrics (worst-case analysis and statistical study). In addition, two different robust design formulations were applied to robust multi-objective machine design problems.

The computational analyses show that material uncertainties may result in deviations of the machine performances and cause nominal solutions to become non-robust.

In this paper, the authors present stochastic models for the quantification of uncertainties in both ferromagnetic and permanent magnet materials. A robust multi-objective evolutionary algorithm is demonstrated and successfully applied to the robust design optimization of an IPM machine considering manufacturing errors and operational condition changes.

Generalized structured component analysis (GSCA) and partial least squares path modeling (PLSPM) are two key component-based approaches to structural equation modeling that facilitate the analysis of theoretically established models in terms of both explanation and prediction. This study aims to offer a comparative evaluation of GSCA and PLSPM in a predictive modeling framework.

A simulation study compares the predictive performance of GSCA and PLSPM under various simulation conditions and different prediction types of correctly specified and misspecified models.

The results suggest that GSCA with reflective composite indicators (GSCA_R) is the most versatile approach. For observed prediction, which uses the component scores to generate prediction for the indicators, GSCA_R performs slightly better than PLSPM with mode A. For operative prediction, which considers all parameter estimates to generate predictions, both methods perform equally well. GSCA with formative composite indicators and PLSPM with mode B generally lag behind the other methods.

Future research may further assess the methods’ prediction precision, considering more experimental factors with a wider range of levels, including more extreme ones.

When prediction is the primary study aim, researchers should generally revert to GSCA_R, considering its performance for observed and operative prediction together.

This research is the first to compare the relative efficacy of GSCA and PLSPM in terms of predictive power.