Exploring geographical dispersion in Thailand-based food supply chain (FSC)

Purpose: The purpose of this paper is to explore the key influential factors and their implications on food supply chain (FSC) location decisions from a Thailand-based manufacturer's view. Design/methodology/approach: In total, 21 case studies were conducted with eight Thailand-based food manufacturers. In each case, key influential factors were observed along with their implications on upstream and downstream FSC location decisions. Data were collected through semi-structured interviews and documentations. Data reduction and data display in tables were used to help data analysis of the case studies. Findings: This exploratory research found that, in the food industry, FSC geographical dispersion pattern could be determined by four factors: perishability, value density, economic-political forces, and technological forces. Technological forces were found as an enabler for FSC geographical dispersion whereas the other three factors could be both barriers and enablers. The implications of these four influential factors drive FSC towards four key patterns of FSC geographical dispersion: local supply chain (SC), supply-proximity SC, market-proximity SC, and international SC. Additionally, the strategy of the firm was found to also be an influential factor in determining FSC geographical dispersion. Research limitations/implications: Despite conducting 21 cases, the findings in this research are based on a relatively small sample, given the large size of the industry. More case evidence from a broader range of food product market and supply items, particularly ones that have significantly different patterns of FSC geographical dispersions would have been insightful. The consideration of additional influential factors such as labour movement between developing countries, currency fluctuations and labour costs, would also enrich the framework as well as improve the quality and validity of the research findings. The different strategies employed by the case companies and their implications on FSC location decisions should also be further investigated along with cases outside Thailand, to provide a more comprehensive view of FSC geographical location decisions. Practical implications: This paper provides insights how FSC is geographically located in both supply-side and demand-side from a manufacturing firm's view. The findings can also provide SC managers and researchers a better understanding of their FSCs. Originality/value: This research bridges the existing gap in the literature, explaining the geographical dispersion of SC particularly in the food industry where the characteristics are very specific, by exploring the internationalization ability of Thailand-based FSC and generalizing the key influential factors - perishability (lead time), value density, economic-political forces, market opportunities, and technological advancements. Four key patterns of FSC internationalization emerged from the case studies. © Emerald Group Publishing Limited.

A new alternative for reduction of secondary flows in low pressure turbines

This paper describes a new flow mechanism for the reduction of secondary flows in Low Pressure Turbines using the benefit of contoured endwalls. The extensive application of contoured endwalls in recent years has provided a deeper understanding of the physical phenomenon that governs the reduction of secondary flows. Based on this understanding, the endwall geometry of a linear cascade of solid-thin profiles typical of Low Pressure Turbines has been redesigned. Experimental data are presented for the validation of this new solution. Based on these data, a reduction of 72% in the SKEH and 20% in the mixed-out endwall losses can be obtained. CFD simulations are also presented to illustrate the effect of the new endwall on the secondary flows. Furthermore, an explanation of the flow mechanism that governs the reduction of the SKEH and the losses is given. Copyright © 2006 by ASME.

Influence of film cooling hole angles and geometries on aerodynamic loss and net heat flux reduction

Turbine design engineers have to ensure that film cooling can provide sufficient protection to turbine blades from the hot mainstream gas, while keeping the losses low. Film cooling hole design parameters include inclination angle (α), compound angle (β ), hole inlet geometry and hole exit geometry. The influence of these parameters on aerodynamic loss and net heat flux reduction is investigated, with loss being the primary focus. Low-speed flat plate experiments have been conducted at momentum flux ratios of IR = 0.16, 0.64 and 1.44. The film cooling aerodynamic mixing loss, generated by the mixing of mainstream and coolant, can be quantified using a three-dimensional analytical model that has been previously reported by the authors. The model suggests that for the same flow conditions, the aerodynamic mixing loss is the same for holes with different α and β but with the same angle between the mainstream and coolant flow directions (angle κ). This relationship is assessed through experiments by testing two sets of cylindrical holes with different α and β : one set with κ = 35°, another set with κ = 60°. The data confirm the stated relationship between α, β, κ and the aerodynamic mixing loss. The results show that the designer should minimise κ to obtain the lowest loss, but maximise β to achieve the best heat transfer performance. A suggestion on improving the loss model is also given. Five different hole geometries (α =35.0°, β =0°) were also tested: cylindrical hole, trenched hole, fan-shaped hole, D-Fan and SD-Fan. The D-Fan and the SD-Fan have similar hole exits to the fan-shaped hole but their hole inlets are laterally expanded. The external mixing loss and the loss generated inside the hole are compared. It was found that the D-Fan and the SD-Fan have the lowest loss. This is attributed to their laterally expanded hole inlets, which lead to significant reduction in the loss generated inside the holes. As a result, the loss of these geometries is ≈ 50 % of the loss of the fan-shaped hole at IR = 0.64 and 1.44. Copyright © 2011 by ASME.

Transcription of multi-genre media archives using out-of-domain data

We describe our work on developing a speech recognition system for multi-genre media archives. The high diversity of the data makes this a challenging recognition task, which may benefit from systems trained on a combination of in-domain and out-of-domain data. Working with tandem HMMs, we present Multi-level Adaptive Networks (MLAN), a novel technique for incorporating information from out-of-domain posterior features using deep neural networks. We show that it provides a substantial reduction in WER over other systems, with relative WER reductions of 15% over a PLP baseline, 9% over in-domain tandem features and 8% over the best out-of-domain tandem features. © 2012 IEEE.

Kernel conditional quantile estimation via reduction revisited

Quantile regression refers to the process of estimating the quantiles of a conditional distribution and has many important applications within econometrics and data mining, among other domains. In this paper, we show how to estimate these conditional quantile functions within a Bayes risk minimization framework using a Gaussian process prior. The resulting non-parametric probabilistic model is easy to implement and allows non-crossing quantile functions to be enforced. Moreover, it can directly be used in combination with tools and extensions of standard Gaussian Processes such as principled hyperparameter estimation, sparsification, and quantile regression with input-dependent noise rates. No existing approach enjoys all of these desirable properties. Experiments on benchmark datasets show that our method is competitive with state-of-the-art approaches. © 2009 IEEE.

Geometric optimization methods for independent component analysis applied on gene expression data

DNA microarrays provide a huge amount of data and require therefore dimensionality reduction methods to extract meaningful biological information. Independent Component Analysis (ICA) was proposed by several authors as an interesting means. Unfortunately, experimental data are usually of poor quality- because of noise, outliers and lack of samples. Robustness to these hurdles will thus be a key feature for an ICA algorithm. This paper identifies a robust contrast function and proposes a new ICA algorithm. © 2007 IEEE.

Influence of film cooling hole angles and geometries on aerodynamic loss and net heat flux reduction

Turbine design engineers have to ensure that film cooling can provide sufficient protection to turbine blades from the hot mainstream gas, while keeping the losses low. Film cooling hole design parameters include inclination angle (a), compound angle (b), hole inlet geometry, and hole exit geometry. The influence of these parameters on aerodynamic loss and net heat flux reduction is investigated, with loss being the primary focus. Low-speed flat plate experiments have been conducted at momentum flux ratios of IR=0.16, 0.64, and 1.44. The film cooling aerodynamic mixing loss, generated by the mixing of mainstream and coolant, can be quantified using a three-dimensional analytical model that has been previously reported by the authors. The model suggests that for the same flow conditions, the aerodynamic mixing loss is the same for holes with different a and b but with the same angle between the mainstream and coolant flow directions (angle k). This relationship is assessed through experiments by testing two sets of cylindrical holes with different a and b: one set with k=35 deg, and another set with k=60 deg. The data confirm the stated relationship between α, β, k and the aerodynamic mixing loss. The results show that the designer should minimize k to obtain the lowest loss, but maximize b to achieve the best heat transfer performance. A suggestion on improving the loss model is also given. Five different hole geometries (α=35.0 deg, β=0 deg) were also tested: cylindrical hole, trenched hole, fan-shaped hole, D-Fan, and SD-Fan. The D-Fan and the SD-Fan have similar hole exits to the fan-shaped hole but their hole inlets are laterally expanded. The external mixing loss and the loss generated inside the hole are compared. It was found that the D-Fan and the SD-Fan have the lowest loss. This is attributed to their laterally expanded hole inlets, which lead to significant reduction in the loss generated inside the holes. As a result, the loss of these geometries is≈50% of the loss of the fan-shaped hole at IR=0.64 and 1.44. © 2013 by ASME.

Linear dimensionality reduction: Survey, insights, and generalizations

© 2015 John P. Cunningham and Zoubin Ghahramani. Linear dimensionality reduction methods are a cornerstone of analyzing high dimensional data, due to their simple geometric interpretations and typically attractive computational properties. These methods capture many data features of interest, such as covariance, dynamical structure, correlation between data sets, input-output relationships, and margin between data classes. Methods have been developed with a variety of names and motivations in many fields, and perhaps as a result the connections between all these methods have not been highlighted. Here we survey methods from this disparate literature as optimization programs over matrix manifolds. We discuss principal component analysis, factor analysis, linear multidimensional scaling, Fisher's linear discriminant analysis, canonical correlations analysis, maximum autocorrelation factors, slow feature analysis, sufficient dimensionality reduction, undercomplete independent component analysis, linear regression, distance metric learning, and more. This optimization framework gives insight to some rarely discussed shortcomings of well-known methods, such as the suboptimality of certain eigenvector solutions. Modern techniques for optimization over matrix manifolds enable a generic linear dimensionality reduction solver, which accepts as input data and an objective to be optimized, and returns, as output, an optimal low-dimensional projection of the data. This simple optimization framework further allows straightforward generalizations and novel variants of classical methods, which we demonstrate here by creating an orthogonal-projection canonical correlations analysis. More broadly, this survey and generic solver suggest that linear dimensionality reduction can move toward becoming a blackbox, objective-agnostic numerical technology.