Optimization of microcavity OLED by varying the thickness of multi-layered mirror

We optimized the emission efficiency from a microcavity OLEDs consisting of widely used organic materials, N,N'-di(naphthalene-1-yl)-N,N'-diphenylbenzidine (NPB) as a hole transport layer and tris (8-hydroxyquinoline) (Alq(3)) as emitting and electron transporting layer. LiF/Al was considered as a cathode, while metallic Ag anode was used. TiO2 and Al2O3 layers were stacked on top of the cathode to alter the properties of the top mirror. The electroluminescence emission spectra, electric field distribution inside the device, carrier density, recombination rate and exciton density were calculated as a function of the position of the emission layer. The results show that for certain TiO2 and Al2O3 layer thicknesses, light output is enhanced as a result of the increase in both the reflectance and transmittance of the top mirror. Once the optimum structure has been determined, the microcavity OLED devices can be fabricated and characterized, and comparisons between experiments and theory can be made.

Organizations driving positive social change:a review and an integrative framework of change processes

Academic and practitioner interest in how market-based organizations can drive positive social change (PSC) is steadily growing. This paper helps to recast how organizations relate to society. It integrates research on projects stimulating PSC – the transformational processes to advance societal well-being – which is fragmented across different streams of research in management and related disciplines. Focusing on the mechanisms at play in how organizations and their projects affect change in targets outside of organizational boundaries, we 1) clarify the nature of PSC as a process, 2) develop an integrative framework that specifies two distinct PSC strategies, 3) take stock of and offer a categorization scheme for change mechanisms and enabling organizational practices, and 4) outline opportunities for future research. Our conceptual framework differentiates between surface- and deep-level PSC strategies understood as distinct combinations of change mechanisms and enabling organizational practices. These strategies differ in the nature and speed of transformation experienced by the targets of change projects and the resulting quality (pervasiveness and durability), timing, and reach of social impact. Our findings provide a solid base for integrating and advancing knowledge across the largely disparate streams of management research on Corporate Social Responsibility, Social Entrepreneurship, and Base of the Pyramid, and open up important new avenues for future research on organizing for PSC and on unpacking PSC processes.

EM optimization of latent-variable density models

There is currently considerable interest in developing general non-linear density models based on latent, or hidden, variables. Such models have the ability to discover the presence of a relatively small number of underlying `causes' which, acting in combination, give rise to the apparent complexity of the observed data set. Unfortunately, to train such models generally requires large computational effort. In this paper we introduce a novel latent variable algorithm which retains the general non-linear capabilities of previous models but which uses a training procedure based on the EM algorithm. We demonstrate the performance of the model on a toy problem and on data from flow diagnostics for a multi-phase oil pipeline.

Optimization of physical distribution problem in logistics management

Physical distribution plays an imporant role in contemporary logistics management. Both satisfaction level of of customer and competitiveness of company can be enhanced if the distribution problem is solved optimally. The multi-depot vehicle routing problem (MDVRP) belongs to a practical logistics distribution problem, which consists of three critical issues: customer assignment, customer routing, and vehicle sequencing. According to the literatures, the solution approaches for the MDVRP are not satisfactory because some unrealistic assumptions were made on the first sub-problem of the MDVRP, ot the customer assignment problem. To refine the approaches, the focus of this paper is confined to this problem only. This paper formulates the customer assignment problem as a minimax-type integer linear programming model with the objective of minimizing the cycle time of the depots where setup times are explicitly considered. Since the model is proven to be MP-complete, a genetic algorithm is developed for solving the problem. The efficiency and effectiveness of the genetic algorithm are illustrated by a numerical example.