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Includes recipes for wine, mead, and other liquors; illustrations of kitchen stoves using coal; illustrations of layout of dishes for multi-course meal. Sample recipes: To bake herrings, To make cream pancakes, To make a blanc-mange of isinglass, To make cowslip wine.

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Mode of access: Internet.

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BACKGROUND: Changes in health care and an ageing population have meant that more people are dying in the acute hospital setting. While palliative care principles have resulted in quality care for the dying, many patients die in an acute care, still receiving aggressive/resuscitative care. AIMS: The aims were to explore nurses' 'recognition of' and 'responsiveness to' dying patients and to understand the nurses' influence on end-of-life care. DESIGN: A qualitative approach was taken utilising non-participant observation to elicit rich data, followed by focus groups and individual semi-structured interviews for clarification. SETTING/PARTICIPANTS: This study was conducted in two acute medical wards in one health service, identified as having the highest rates of death, once palliative care and critical care areas were excluded. Twenty-five nurses consented to participate, and 20 episodes of observation were conducted. RESULTS: Nurses took a passive role in recognising dying, providing active care until a medical officer's declaration of dying. Ward design, nurse allocation and nurses' attitude to death impacts patient care. End-of-life care in a single room can have negative consequences for the dying. Nurses demonstrated varying degrees of discomfort, indicating that they were underprepared for this role. CONCLUSION: When patients are terminally ill, acknowledgement of dying is essential in providing appropriate care. It should not be assumed that all nurses are adequately prepared to provide dying care. Further work is necessary to investigate how the attitudes of nurses towards caring for dying patients in the acute hospital setting may impact care of the dying patient.

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Increasingly, studies are reported that examine how conceptual modeling is conducted in practice. Yet, typically the studies to date have examined in isolation how modeling grammars can be, or are, used to develop models of information systems or organizational processes, without considering that such modeling is typically done by means of a modeling tool that extends the modeling functionality offered by a grammar through complementary features. This paper extends the literature by examining how the use of seven different features of modeling tools affects usage beliefs users develop when using modeling grammars for process modeling. We show that five distinct tool features positively affect usefulness, ease of use and satisfaction beliefs of users. We offer a number of interpretations about the findings. We also describe how the results inform decisions of relevance to developers of modeling tools as well as managers in charge for making modeling-related investment decisions.

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Modern technology now has the ability to generate large datasets over space and time. Such data typically exhibit high autocorrelations over all dimensions. The field trial data motivating the methods of this paper were collected to examine the behaviour of traditional cropping and to determine a cropping system which could maximise water use for grain production while minimising leakage below the crop root zone. They consist of moisture measurements made at 15 depths across 3 rows and 18 columns, in the lattice framework of an agricultural field. Bayesian conditional autoregressive (CAR) models are used to account for local site correlations. Conditional autoregressive models have not been widely used in analyses of agricultural data. This paper serves to illustrate the usefulness of these models in this field, along with the ease of implementation in WinBUGS, a freely available software package. The innovation is the fitting of separate conditional autoregressive models for each depth layer, the ‘layered CAR model’, while simultaneously estimating depth profile functions for each site treatment. Modelling interest also lay in how best to model the treatment effect depth profiles, and in the choice of neighbourhood structure for the spatial autocorrelation model. The favoured model fitted the treatment effects as splines over depth, and treated depth, the basis for the regression model, as measured with error, while fitting CAR neighbourhood models by depth layer. It is hierarchical, with separate onditional autoregressive spatial variance components at each depth, and the fixed terms which involve an errors-in-measurement model treat depth errors as interval-censored measurement error. The Bayesian framework permits transparent specification and easy comparison of the various complex models compared.

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TiO2 nanofibers with different crystal phases have been discovered to be efficient catalysts for the transesterification of alcohols with dimethyl carbonate to produce corresponding methyl carbonates. Advantages of this catalytic system include excellent selectivity (>99%), general suitability to alcohols, reusability and ease of preparation and separation of fibrous catalysts. Activities of TiO2 catalysts were found to correlate with their crystal phases which results in different absorption abilities and activation energies on the catalyst surfaces. The kinetic isotope effect (KIE) investigation identified the rate-determining step, and the isotope labeling of oxygen-18 of benzyl alcohol clearly demonstrated the reaction pathway. Finally, the transesterification mechanism of alcohols with dimethyl carbonate catalyzed by TiO2 nanofibers was proposed, in which the alcohol released the proton to form benzyl alcoholic anion, and subsequently the anion attacks the carbonyl carbon of dimethyl carbonate to produce the target product of benzyl methyl carbonate.

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Time-domain models of marine structures based on frequency domain data are usually built upon the Cummins equation. This type of model is a vector integro-differential equation which involves convolution terms. These convolution terms are not convenient for analysis and design of motion control systems. In addition, these models are not efficient with respect to simulation time, and ease of implementation in standard simulation packages. For these reasons, different methods have been proposed in the literature as approximate alternative representations of the convolutions. Because the convolution is a linear operation, different approaches can be followed to obtain an approximately equivalent linear system in the form of either transfer function or state-space models. This process involves the use of system identification, and several options are available depending on how the identification problem is posed. This raises the question whether one method is better than the others. This paper therefore has three objectives. The first objective is to revisit some of the methods for replacing the convolutions, which have been reported in different areas of analysis of marine systems: hydrodynamics, wave energy conversion, and motion control systems. The second objective is to compare the different methods in terms of complexity and performance. For this purpose, a model for the response in the vertical plane of a modern containership is considered. The third objective is to describe the implementation of the resulting model in the standard simulation environment Matlab/Simulink.