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The purpose of this paper is to fundamentally develop a mathematical model for predicting the particle size distribution (PSD) in fluidized beds because their hydrodynamics depend on the PSD and its evolution during operation. To predict the gradual PSD change in a fluidized bed by using the population balance method (PBM), the kinetic parameter for agglomerate formation should be known and this parameter, in this work, is determined by the results of computational fluid dynamic–discrete element method (CFD-DEM) simulation.

Momentum and energy conservation equations and soft-sphere DEM are used to simulate the agglomeration phenomenon at high temperature in a two-dimensional air-polyethylene fluidized bed in bubbling regime. The Navier–Stokes equations for motion of gas are solved by the SIMPLE algorithm. Newton’s second law of motion is applied to describe the motion of individual particles. Collision between particles is detected by the no-binary search algorithm.

A correlation is proposed for estimating the kinetic parameter for agglomerate formation based on collision frequency, collision efficiency and inlet gas temperature. Based on the corrected kinetic parameter, the PBM is able to predict the PSD evolution in the fluidized bed in a fairly good agreement with the results of the CFD-DEM.

The results of the agglomeration process cannot be compared quantitatively with experimental results. Because three-dimensional fluidized bed mostly contains millions of particles and simulating them takes a long computing time in DEM. As far as temperature is a dominant parameter in the agglomeration process, effects of inlet gas temperature are examined on the kinetic parameter. On the other hand, wider and deeper insights in which the effect of other parameters, such as velocity and so on will be studied, is one of the goals in the authors’ next works to compensate for the shortcomings in this work.

This study helps to understand the effect of the inlet gas temperature during the agglomeration process on the kinetic parameter and provides fundamental information in dealing with kinetic parameter to attain PSD in fluidized bed by the PBM.

Pharmaceuticals are essential for the management of many chronic conditions. As a result, it is important to examine how the administration of pharmaceutical benefits affects physicians and pharmacists providing chronic care services. In the 1990s, HMOs and PPOs began to more aggressively manage outpatient pharmaceutical benefits, leading to the growth of pharmaceutical benefit management companies (PBMs).

In this exploratory study, 10 primary care physicians and 12 pharmacists in the San Francisco area were interviewed in 1999, and 11 more pharmacists in 2004, on how they worked with PBMs and their controls on prescribing and dispensing. Responses indicated major problems for both health professionals in negotiating with the PBM as a third party payor, in coping with switches and multiple formularies, and in added work for the health care professional. Increased risk to chronically ill patients for poorer outcomes is an important related problem with PBMs.

The Medicare drug benefit law passed in 2003 will likely result in similar problems for many beneficiaries, including those with chronic care needs. The paper proposes some policy solutions to reduce PBM problems for physicians, pharmacists and the Medicare population.

The present research aimed to understand how to predict and promote plant-based meat (PBM) consumption.

In Study 1 (N = 550), the authors investigated the psychosocial antecedents of the intentions to add PBM and to replace animal meat with PBM. In Study 2 (N = 390), the authors tested the effectiveness of different environmental messages promoting PBM consumption. The authors compared the effects of an addition message condition (i.e. a message promoting the addition of PBM to one's diet), a replacement message condition (i.e. a message promoting the replacement of animal meat with PBM) and a control condition (i.e. no message). In both studies, the authors considered the moderation of past PBM consumption (PMB eaters vs PBM noneaters).

Study 1 showed that a positive attitude towards eating PBM and a high awareness of the environmental consequences of meat production were key antecedents of participants' intention to eat PBM. The role of the other psychosocial antecedents varied according to past PBM consumption. Study 2 showed that both addition and replacement messages increased non-PBM eaters' positive attitude towards eating PBM and in turn willingness to pay for PBM. Instead, only replacement messages increased PBM eaters' willingness to pay for PBM.

The present research developed a model integrating the key psychosocial predictors of people's intentions to eat PBM. Furthermore, it is the first research that compared the persuasiveness of different environment messages to promote PBM consumption.

The adverse effects on environmental sustainability, human health and animal welfare are often cited as the main reasons for reducing animal-based meat production and consumption. This study explored the food choice motives that determine consumer attitude toward plant-based meat (PBM) as a sustainable meat alternative. The theory of planned behavior (TPB) was applied to further determine whether an individual’s attitude toward PBM, subjective norms and perceived behavioral control influence their willingness to try novel meat substitutes (i.e. PBM). Finally, the moderating effect of meat attachment was also considered.

Online self-reported questionnaires were administered in Taiwan, and 294 valid questionnaires were collected. Structural equation modeling (SEM) and moderated regression were employed for analysis.

The results clarified the food choice motives that influenced consumer attitude toward PBM and revealed that attitude and subjective norms pertaining to trying PBM explained up to 35.03% of the variance in consumer willingness to try PBM. Notably, consumer meat attachment moderated the positive relationship between consumer attitude toward PBM and willingness to try PBM such that it became negative.

On the basis of the empirical findings regarding the food choice motives that influence consumer attitude and willingness to try PBM, this study provided practical implications for marketers seeking to increase consumer willingness to try PBM.

The main theoretical contribution of this research is that food choice motives should be considered in a TPB model to explain consumer willingness to try PBM. The moderating effect of consumer meat attachment should also be considered.

With expenditures totaling $227 billion in 2007, prescription drug purchases are a growing portion of the total medical expenditure, and as this industry continues to grow, prescription drugs will continue to be a critical part of the larger health care industry. This chapter presents a survey on the economics of the US pharmaceutical industry, with a focus on the role of R&D and marketing, the determinants (and complications) of prescription drug pricing, and various aspects of consumer behavior specific to this industry, such as prescription drug regulation, the patient's interaction with the physician, and insurance coverage. This chapter also provides background in areas not often considered in the economics literature, such as the role of pharmacy benefit managers in prescription drug prices and the differentiation between alternative measures of prescription drug prices.

The purpose of this paper is to identify and examine the major forces that are changing the way biopharmaceutical medications are purchased, distributed, and sold throughout the supply chain. This will become important as healthcare reform moves forward, and logistics will be transformed in this industry.

Multiple interviews with key informants at each level of the value chain were combined with manifest text analysis from practitioner articles to derive key insights into the primary change drivers influencing the future of the biopharmaceutical supply chain.

The research discovered radical shifts in the structure of the biopharmaceutical supply chain. Future research into biopharmaceutical supply chain practices will need to explore three primary issues: How will supply chain member compensation influence the power of parties within the network? How will the role of supply chain intermediaries change the landscape of medication delivery to the end customer? What impact will the role of regulatory constraints on product pedigree and proliferation have on this network? The relationship between these forces is mediated by operations strategy concerning inventory policy, supply chain visibility, and desired service levels.

The research was based on multiple interviews with a convenience sample, as well as text analysis from practitioner articles. These findings are an initial step to guide future more in‐depth research for this dynamic and contextually rich supply chain environment that impacts consumers in every country in the world.

The paper adds insights into the pharmaceutical supply chain, examining this from multiple perspectives.

Although student evaluation of teaching (SET) is widely used, there has been a significant disparity between its potential benefits with the actual impact on improving educational quality. This study aims to inquire into the factors contributing to this discrepancy and the underlying mechanisms hindering the effective utilisation of SET.

This qualitative case study used problem-based methodology to investigate the theories of action of administrators involved in SET. The study uses SET documents and individual interviews with 18 administrators from six Vietnamese higher education institutions.

While the administrators monitored SET data and addressed SET problems, few used it for quality improvement. The participants’ SET approaches were influenced by various constraints, especially the intricate interaction between neoliberal agendas with professional and cultural values.

Despite a lack of statistical generalisability, this study contributes to analytical generalisation by vividly portraying the complexity of the SET practice in a higher education context.

This study suggests a more inclusive approach to quality improvement that prioritises multi-stakeholder engagement.

The constraint analysis offers invaluable insights into the challenges and dynamics of SET practice, which can be used to enhance SET effectiveness and overall educational quality.

This paper addresses the need to examine the theories of action of administrators involved in SET, emphasising the significance of a comprehensive understanding of the stakeholders’ underlying reasoning process for successful evaluation implementation.

The purpose of this paper is to study the soot formation and evolution by using this newly developed Lagrangian particle tracking with weighted fraction Monte Carlo (LPT-WFMC) method.

The weighted soot particles are used in this MC framework and is tracked using Lagrangian approach. A detailed soot model based on the LPT-WFMC method is used to study the soot formation and evolution in ethylene laminar premixed flames.

The LPT-WFMC method is validated by both experimental and numerical results of the direct simulation Monte Carlo (DSMC) and Multi-Monte Carlo (MMC) methods. Compared with DSMC and MMC methods, the stochastic error analysis shows this new LPT-WFMC method could further extend the particle size distributions (PSDs) and improve the accuracy for predicting soot PSDs at larger particle size regime.

Compared with conventional weighted particle schemes, the weight distributions in LPT-WFMC method are adjustable by adopting different fraction functions. As a result, the number of numerical soot particles in each size interval could be also adjustable. The stochastic error of PSDs in larger particle size regime can also be minimized by increasing the number of numerical soot particles at larger size interval.

The multi-robot task allocation (MRTA) problem is a challenging issue in the robotics area with plentiful practical applications. Expanding the number of tasks and robots increases the size of the state space significantly and influences the performance of the MRTA. As this process requires high computational time, this paper aims to describe a technique that minimizes the size of the explored state space, by partitioning the tasks into clusters. In this paper, the authors address the problem of MRTA by putting forward a new automatic clustering algorithm of the robots' tasks based on a dynamic-distributed double-guided particle swarm optimization, namely, ACD³GPSO.

This approach is made out of two phases: phase I groups the tasks into clusters using the ACD³GPSO algorithm and phase II allocates the robots to the clusters. Four factors are introduced in ACD³GPSO for better results. First, ACD³GPSO uses the k-means algorithm as a means to improve the initial generation of particles. The second factor is the distribution using the multi-agent approach to reduce the run time. The third one is the diversification introduced by two local optimum detectors LODpBest and LODgBest. The last one is based on the concept of templates and guidance probability Pguid.

Computational experiments were carried out to prove the effectiveness of this approach. It is compared against two state-of-the-art solutions of the MRTA and against two evolutionary methods under five different numerical simulations. The simulation results confirm that the proposed method is highly competitive in terms of the clustering time, clustering cost and MRTA time.

The proposed algorithm is quite useful for real-world applications, especially the scenarios involving a high number of robots and tasks.

In this methodology, owing to the ACD³GPSO algorithm, task allocation's run time has diminished. Therefore, the proposed method can be considered as a vital alternative in the field of MRTA with growing numbers of both robots and tasks. In PSO, stagnation and local optima issues are avoided by adding assorted variety to the population, without losing its fast convergence.

With the continuous upgrading of the production mode of the manufacturing system, the characteristics of multi-variety, small batch and mixed fluidization are presented, and the production environment becomes more and more complex. To improve the efficiency of solving multi-objective flexible job shop scheduling problem (FJSP), an improved hybrid particle swarm optimization algorithm (IH-PSO) is proposed.

After reviewing literatures on FJSP, an IH-PSO algorithm for solving FJSP is developed. First, IH-PSO algorithm draws on the crossover and mutation operations of genetic algorithm (GA) algorithm and proposes a new method for updating particles, which makes the offspring particles inherit the superior characteristics of the parent particles. Second, based on the improved simulated annealing (SA) algorithm, the method of updating the individual best particles expands the search scope of the domain and solves the problem of being easily trapped in local optimum. Finally, analytic hierarchy process (AHP) is used in this paper to solve the optimal solution satisfying multi-objective optimization.

Through the benchmark experiment and the production example experiment, it is verified that the proposed algorithm has the advantages of high quality of solution and fast speed of convergence.

This method does not consider the unforeseen events that occur during the process of scheduling and cause the disruption of normal production scheduling activities, such as machine breakdown.

IH-PSO algorithm combines PSO algorithm with GA and SA algorithms. This algorithm retains the advantage of fast convergence speed of traditional PSO algorithm and has the characteristic of inheriting excellent genes. In addition, the improved SA algorithm is used to solve the problem of falling into local optimum.

This research provides an efficient scheduling method for solving the FJSP problem.

This research proposes an IH-PSO algorithm to solve the FJSP more efficiently and meet the needs of multi-objective optimization.