933 resultados para Coffee - Drying process
Resumo:
This presentation will explore how BPM research can seamlessly combine the academic requirement of rigor with the aim to impact the practice of Business Process Management. After a brief introduction into the research agendas as they are perceived by different BPM communities, two research projects will be discussed that illustrate how empirically-informed quantitative and qualitative research, combined with design science, can lead to outcomes that BPM practitioners are willing to adopt. The first project studies the practice of process modeling using Information Systems theory, and demonstrates how a better understanding of this practice can inform the design of modeling notations and methods. The second project studies the adoption of process management within organizations, and leads to models of how organizations can incrementally transition to greater levels of BPM maturity. The presentation will conclude with recommendations for how the BPM research and practitioner communities can increasingly benefit from each other.
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In most art exhibitions, the creative part of the exhibition is assumed to be the artworks on display. But for the Capricornia Arts Mob’s first collective art exhibition in Rockhampton during NAIDOC Week in 2012, the process of developing the exhibition became the focus of creative action learning and action research. In working together to produce a multi-media exhibition, we learned about the collaborative processes and time required to develop a combined exhibition. We applied Indigenous ways of working – including yarning, cultural respect, cultural protocols, mentoring young people, providing a culturally safe working environment and sharing both time and food – to develop our first collective art exhibition. We developed a process that allowed us to ask deep questions, engage in a joint journey of learning, and develop our collective story. This paper explores the processes that the Capricornia Arts Mob used to develop the exhibition for NAIDOC 2012.
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In this editorial letter, we provide the readers of Information Systems Management with a background on process design before we discuss the content of the special issue proper. By introducing and describing a so-called process design compass we aim to clarify what developments in the field are taking place and how the papers in this special issue expand on our current knowledge in this domain.
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An investigation of the drying of spherical food particles was performed, using peas as the model material. In the development of a mathematical model for drying curves, moisture diffusion was modelled using Fick’s second law for mass transfer. The resulting partial differential equation was solved using a forward-time central-space finite difference approximation, with the assumption of variable effective diffusivity. In order to test the model, experimental data was collected for the drying of green peas in a fluidised bed at three drying temperatures. Through fitting three equation types for effective diffusivity to the data, it was found that a linear equation form, in which diffusivity increased with decreasing moisture content, was most appropriate. The final model accurately described the drying curves of the three experimental temperatures, with an R2 value greater than 98.6% for all temperatures.
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This study explored how the social context influences the stress-buffering effects of social support on employee adjustment. It was anticipated that the positive relationship between support from colleagues and employee adjustment would be more marked for those strongly identifying with their work team. Furthermore, as part of a three-way interactive effect, it was predicted that high identification would increase the efficacy of coworker support as a buffer of two role stressors (role overload and role ambiguity). One hundred and 55 employees recruited from first-year psychology courses enrolled at two Australian universities were surveyed. Hierarchical multiple regression analyses revealed that the negative main effect of role ambiguity on job satisfaction was significant for those employees with low levels of team identification, whereas high team identifiers were buffered from the deleterious effect of role ambiguity on job satisfaction. There also was a significant interaction between coworker support and team identification. The positive effect of coworker support on job satisfaction was significant for high team identifiers, whereas coworker support was not a source of satisfaction for those employees with low levels of team identification. A three-way interaction emerged among the focal variables in the prediction of psychological well-being, suggesting that the combined benefits of coworker support and team identification under conditions of high demand may be limited and are more likely to be observed when demands are low.
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Two varieties of grapes, white grape and red grape grown in the Campania region of Italy were selected for the study of drying characteristics, moisture diffusion, quality changes (colour) and shrinkage behaviour. Comparisons were made with treated and untreated grapes under constant drying condition of 50o C in a conventional drying system. This temperature was selected to represent farm drying conditions. Grapes were purchased from a local market from the same supplier to maintain the same size of grapes and same properties. An abrasive physical treatment was used as pretreatment. The drying curves were constructed and drying kinetics was calculated using several commonly available models. It was found that treated samples shows better drying characteristics than untreated samples. The objective of this study is to obtain drying kinetics which can be used to optimize the drying operations in grape drying.
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Plant tissue has a complex cellular structure which is an aggregate of individual cells bonded by middle lamella. During drying processes, plant tissue undergoes extreme deformations which are mainly driven by moisture removal and turgor loss. Numerical modelling of this problem becomes challenging when conventional grid-based modelling techniques such as Finite Element Methods (FEM) and Finite Difference Methods (FDM) have grid-based limitations. This work presents a meshfree approach to model and simulate the deformations of plant tissues during drying. This method demonstrates the fundamental capabilities of meshfree methods in handling extreme deformations of multiphase systems. A simplified 2D tissue model is developed by aggregating individual cells while accounting for the stiffness of the middle lamella. Each individual cell is simply treated as consisting of two main components: cell fluid and cell wall. The cell fluid is modelled using Smoothed Particle Hydrodynamics (SPH) and the cell wall is modelled using a Discrete Element Method (DEM). During drying, moisture removal is accounted for by reduction of cell fluid and wall mass, which causes local shrinkage of cells eventually leading to tissue scale shrinkage. The cellular deformations are quantified using several cellular geometrical parameters and a favourably good agreement is observed when compared to experiments on apple tissue. The model is also capable of visually replicating dry tissue structures. The proposed model can be used as a step in developing complex tissue models to simulate extreme deformations during drying.
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Passenger experience has become a major factor that influences the success of an airport. In this context, passenger flow simulation has been used in designing and managing airports. However, most passenger flow simulations failed to consider the group dynamics when developing passenger flow models. In this paper, an agent-based model is presented to simulate passenger behaviour at the airport check-in and evacuation process. The simulation results show that the passenger behaviour can have significant influences on the performance and utilisation of services in airport terminals. The model was created using AnyLogic software and its parameters were initialised using recent research data published in the literature.
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There is a wide variety of drivers for business process modelling initiatives, reaching from business evolution and process optimisation over compliance checking and process certification to process enactment. That, in turn, results in models that differ in content due to serving different purposes. In particular, processes are modelled on different abstraction levels and assume different perspectives. Vertical alignment of process models aims at handling these deviations. While the advantages of such an alignment for inter-model analysis and change propagation are out of question, a number of challenges has still to be addressed. In this paper, we discuss three main challenges for vertical alignment in detail. Against this background, the potential application of techniques from the field of process integration is critically assessed. Based thereon, we identify specific research questions that guide the design of a framework for model alignment.
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Purpose The purpose of this paper is to foster a common understanding of business process management (BPM) by proposing a set of ten principles that characterize BPM as a research domain and guide its successful use in organizational practice. Design/methodology/approach The identification and discussion of the principles reflects our viewpoint, which was informed by extant literature and focus groups, including 20 BPM experts from academia and practice. Findings We identify ten principles which represent a set of capabilities essential for mastering contemporary and future challenges in BPM. Their antonyms signify potential roadblocks and bad practices in BPM. We also identify a set of open research questions that can guide future BPM research. Research limitation/implication Our findings suggest several areas of research regarding each of the identified principles of good BPM. Also, the principles themselves should be systematically and empirically examined in future studies. Practical implications – Our findings allow practitioners to comprehensively scope their BPM initiatives and provide a general guidance for BPM implementation. Moreover, the principles may also serve to tackle contemporary issues in other management areas. Originality/value This is the first paper that distills principles of BPM in the sense of both good and bad practice recommendations. The value of the principles lies in providing normative advice to practitioners as well as in identifying open research areas for academia, thereby extending the reach and richness of BPM beyond its traditional frontiers.
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Small Businesses account for a significant portion of the Australian business sector. With Business Process Management (BPM) gaining prominence in recent decades as a means of improving business performance, it would seem to only be a matter of time before it gains momentum within the Small Business sector. One may even question why it has not already achieved more traction within the sector. This case study involves a BPM initiative to develop process infrastructure in an establishing Small Business. It explores whether mainstream BPM tools, techniques and technologies can be applied in a Small Business setting. The chapter provides a background to the case organisation, outlines the activities undertaken in the BPM initiative and distils key observations drawn from participation in the initiative and consultation with stakeholders. Based on the case study experiences, a number of implications are identified for further consideration by the BPM discipline as it continues to address the question of how it can become more widely adopted amongst Small Businesses.
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Fundamental understanding on microscopic physical changes of plant materials is vital to optimize product quality and processing techniques, particularly in food engineering. Although grid-based numerical modelling can assist in this regard, it becomes quite challenging to overcome the inherited complexities of these biological materials especially when such materials undergo critical processing conditions such as drying, where the cellular structure undergoes extreme deformations. In this context, a meshfree particle based model was developed which is fundamentally capable of handling extreme deformations of plant tissues during drying. The model is built by coupling a particle based meshfree technique: Smoothed Particle Hydrodynamics (SPH) and a Discrete Element Method (DEM). Plant cells were initiated as hexagons and aggregated to form a tissue which also accounts for the characteristics of the middle lamella. In each cell, SPH was used to model cell protoplasm and DEM was used to model the cell wall. Drying was incorporated by varying the moisture content, the turgor pressure, and cell wall contraction effects. Compared to the state of the art grid-based microscale plant tissue drying models, the proposed model can be used to simulate tissues under excessive moisture content reductions incorporating cell wall wrinkling. Also, compared to the state of the art SPH-DEM tissue models, the proposed model better replicates real tissues and the cell-cell interactions used ensure efficient computations. Model predictions showed good agreement both qualitatively and quantitatively with experimental findings on dried plant tissues. The proposed modelling approach is fundamentally flexible to study different cellular structures for their microscale morphological changes at dehydration.
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This paper addresses the problem of determining optimal designs for biological process models with intractable likelihoods, with the goal of parameter inference. The Bayesian approach is to choose a design that maximises the mean of a utility, and the utility is a function of the posterior distribution. Therefore, its estimation requires likelihood evaluations. However, many problems in experimental design involve models with intractable likelihoods, that is, likelihoods that are neither analytic nor can be computed in a reasonable amount of time. We propose a novel solution using indirect inference (II), a well established method in the literature, and the Markov chain Monte Carlo (MCMC) algorithm of Müller et al. (2004). Indirect inference employs an auxiliary model with a tractable likelihood in conjunction with the generative model, the assumed true model of interest, which has an intractable likelihood. Our approach is to estimate a map between the parameters of the generative and auxiliary models, using simulations from the generative model. An II posterior distribution is formed to expedite utility estimation. We also present a modification to the utility that allows the Müller algorithm to sample from a substantially sharpened utility surface, with little computational effort. Unlike competing methods, the II approach can handle complex design problems for models with intractable likelihoods on a continuous design space, with possible extension to many observations. The methodology is demonstrated using two stochastic models; a simple tractable death process used to validate the approach, and a motivating stochastic model for the population evolution of macroparasites.
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This paper introduces an integral approach to the study of plasma-surface interactions during the catalytic growth of selected nanostructures (NSs). This approach involves basic understanding of the plasma-specific effects in NS nucleation and growth, theoretical modelling, numerical simulations, plasma diagnostics, and surface microanalysis. Using an example of plasma-assisted growth of surface-supported single-walled carbon nanotubes, we discuss how the combination of these techniques may help improve the outcomes of the growth process. A specific focus here is on the effects of nanoscale plasma-surface interactions on the NS growth and how the available techniques may be used, both in situ and ex situ to optimize the growth process and structural parameters of NSs.
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Highly efficient solar cells (conversion efficiency 11.9%, fill factor 70%) based on the vertically aligned single-crystalline nanostructures are fabricated without any pre-fabricated p-n junctions in a very simple, single-step process of Si nanoarray formation by etching p-type Si(100) wafers in low-temperature environment-friendly plasmas of argon and hydrogen mixtures.