Knowledge spillover in Indian automobile industry : the process and the coverage

In India, as the production of passenger cars increased, many local small and medium enterprises (SMEs) entered the parts and components manufacturing sector. The sources of knowledge for large enterprises and SMEs are different. Naturally, spillover effects among large enterprises and between large enterprises and SMEs are different. This paper focuses on knowledge spillover among large enterprises and from large enterprises to SMEs. Subcontractor can absorb relation-specific skills through repeated interaction with parent company. The results of field survey emphasizes that relation-specific skills are a determinant factor of spillover effects from assemblers and large auto component manufacturers to SMEs. Econometric analysis shows that spillover effects among medium and large automobile units and from medium and large automobile units to small units went beyond boundary of cluster.

Technology network for machine tools in Vietnam

When Vietnam joined the WTO, it accepted foreign direct investment and started to grow. Technically, it was then greatly influenced by the enterprises that entered the country through direct investment. This report shows that the technology network for machine tools is formed via direct investment and subcontracting.

Farming strategy of African smallholder farmers in transition from traditional to alternative agriculture : the case of the Nupe in central Nigeria

It is worthwhile to understand farming strategies of smallholder farmers in sub-Saharan Africa, especially those of farmers who are in transition from traditional to alternative agriculture in terms of adoption of innovative technologies. In a case study of inland valleys in central Nigeria, we investigated the farming strategy of Nupe farmers who have a long-term tradition of wet rice cultivation and indigenous methods of land preparation for soil, water and weed management. In this region, a new method of land preparation has recently been introduced along with a recommendation to use improved seeds and chemical fertilizers. Our findings reveal that Nupe farmers directly sow traditional seeds and apply a marginal amount of fertilizer to paddy plots prepared by labor-saving methods on drought-prone hydromorphic valley fringes and flood-susceptible valley bottoms, whereas they preferentially transplanted improved seedlings and applied a relatively large quantity of fertilizer to paddy fields prepared by a labor-intensive and mechanized method on a valley position where they can access to optimum water condition (less risky against the drought and flood).

Slow and steady wins the race : how the garment industry leads industrialization in low-income countries

This paper investigates how the garment industry escapes this vicious cycle and argues for the validity of labor-intensive industry as a starting point for full-fledged industrialization, even though it might at first seem to be a digression from the path to an innovation-led economy. By examining original firm-level data on garment-producing firms collected in 2002 and 2008 in Bangladesh, Cambodia, Kenya and Madagascar, the following conclusions are drawn: (1) low wages, though still sufficient for poverty reduction, are the main source of competitiveness in low-income countries; (2) after the successful initiation of industrialization causes wages to begin to rise, there is still a possibility for productivity enhancement; and (3) skill bias in technological progress is not yet a major factor, implying that the garment industry is still a labor-intensive industry. In sum, labor-intensive industry should not be discounted as a part of the development strategy of low-income countries.

Boundaries of firms and catching up by latecomers in global production networks : the case of a Mexican auto-parts manufacturer

For manufacturing firms in developing countries, there are high barriers to entry and to catching up with competitors in their global production networks (GPNs). This paper examines the case of a Mexican auto-parts manufacturer that succeeded in catching up in the automotive GPN. The author proposes that the door to GPNs is open thanks to frequent changes in the boundaries of firms, and also stresses the importance of the necessary conditions that generate opportunities, including institutional settings that facilitate market entry and catching up, and capability building by firms hopeful of entry.

Microtubule self-organization is gravity-dependent

Although weightlessness is known to affect living cells, the manner by which this occurs is unknown. Some reaction-diffusion processes have been theoretically predicted as being gravity-dependent. Microtubules, a major constituent of the cellular cytoskeleton, self-organize in vitro by way of reaction-diffusion processes. To investigate how self-organization depends on gravity, microtubules were assembled under low gravity conditions produced during space flight. Contrary to the samples formed on an in-flight 1 × g centrifuge, the samples prepared in microgravity showed almost no self-organization and were locally disordered.

A genetic estimation algorithm for parameters of stochastic ordinary differential equations

A generic method for the estimation of parameters for Stochastic Ordinary Differential Equations (SODEs) is introduced and developed. This algorithm, called the GePERs method, utilises a genetic optimisation algorithm to minimise a stochastic objective function based on the Kolmogorov-Smirnov statistic. Numerical simulations are utilised to form the KS statistic. Further, the examination of some of the factors that improve the precision of the estimates is conducted. This method is used to estimate parameters of diffusion equations and jump-diffusion equations. It is also applied to the problem of model selection for the Queensland electricity market. (C) 2003 Elsevier B.V. All rights reserved.

A scalarization technique for computing the power and exponential moments of gaussian random matrices

We consider the problems of computing the power and exponential moments EXs and EetX of square Gaussian random matrices X=A+BWC for positive integer s and real t, where W is a standard normal random vector and A, B, C are appropriately dimensioned constant matrices. We solve the problems by a matrix product scalarization technique and interpret the solutions in system-theoretic terms. The results of the paper are applicable to Bayesian prediction in multivariate autoregressive time series and mean-reverting diffusion processes.

Derivations of variational gaussian process approximation framework

Recently, within the VISDEM project (EPSRC funded EP/C005848/1), a novel variational approximation framework has been developed for inference in partially observed, continuous space-time, diffusion processes. In this technical report all the derivations of the variational framework, from the initial work, are provided in detail to help the reader better understand the framework and its assumptions.

Variational Markov Chain Monte Carlo for Bayesian smoothing of non-linear niffusions

In this paper we develop set of novel Markov chain Monte Carlo algorithms for Bayesian smoothing of partially observed non-linear diffusion processes. The sampling algorithms developed herein use a deterministic approximation to the posterior distribution over paths as the proposal distribution for a mixture of an independence and a random walk sampler. The approximating distribution is sampled by simulating an optimized time-dependent linear diffusion process derived from the recently developed variational Gaussian process approximation method. Flexible blocking strategies are introduced to further improve mixing, and thus the efficiency, of the sampling algorithms. The algorithms are tested on two diffusion processes: one with double-well potential drift and another with SINE drift. The new algorithm's accuracy and efficiency is compared with state-of-the-art hybrid Monte Carlo based path sampling. It is shown that in practical, finite sample, applications the algorithm is accurate except in the presence of large observation errors and low observation densities, which lead to a multi-modal structure in the posterior distribution over paths. More importantly, the variational approximation assisted sampling algorithm outperforms hybrid Monte Carlo in terms of computational efficiency, except when the diffusion process is densely observed with small errors in which case both algorithms are equally efficient.

A new variational radial basis function approximation for inference in multivariate diffusions

In this paper we present a radial basis function based extension to a recently proposed variational algorithm for approximate inference for diffusion processes. Inference, for state and in particular (hyper-) parameters, in diffusion processes is a challenging and crucial task. We show that the new radial basis function approximation based algorithm converges to the original algorithm and has beneficial characteristics when estimating (hyper-)parameters. We validate our new approach on a nonlinear double well potential dynamical system.

Wear and corrosion of mechanical seals

Mechanical seals are used extensively to seal machinery such as pumps, mixers and agitators in the oil, petrochemical and chemical industries. The performance of such machinery is critically dependent on these devices. Seal failures may result in the escape of dangerous chemicals, possibly causing injury or loss of life. Seal performance is limited by the choice of face materials available. These range from cast iron and stellited stainless steel to cemented and silicon carbides. The main factors that affect seal performance are the wear and corrosion of seal faces. This research investigated the feasibility of applying surface coating/treatments to seal materials, in order to provide improved seal performance. Various surface coating/treatment methods were considered; these included electroless nickel plating, ion plating, plasma nitriding, thermal spraying and high temperature diffusion processes. The best wear resistance, as evaluated by the Pin-on-Disc wear test method, was conferred by the sprayed tungsten carbide/nickel/tungsten-chromium carbide deposit, produced by the high energy plasma spraying (Jet-Kote) process. In general, no correlation was found between hardness and wear resistance or surface finish and friction. This is due primarily to the complexity of the wear and frictional oxidation, plastic deformation, ploughing, fracture and delamination. Corrosion resistance was evaluated by Tafel extrapolation, linear polarisation and anodic potentiodynamic polarisation techniques. The best corrosion performance was exhibited by an electroless nickel/titanium nitride duplex coating due to the passivity of the titanium nitride layer in the acidified salt solution. The surface coating/treatments were ranked using a systematic method, which also considered other properties such as adhesion, internal stress and resistance to thermal cracking. The sealing behaviour of surface coated/treated seals was investigated on an industrial seal testing rig. The best sealing performances were exhibited by the Jet-Kote and electroless nickel silicon carbide composite coated seals. The failure of the electroless nickel and electroless nickel/titanium nitride duplex coated seals was due to inadequate adhesion of the deposits to the substrate. Abrasion of the seal faces was the principal wear mechanism. For operation in an environment similar to the experimental system employed (acidified salt solution) the Jet-Kote deposit appears to be the best compromise.

Networking - a statistical physics perspective

Networking encompasses a variety of tasks related to the communication of information on networks; it has a substantial economic and societal impact on a broad range of areas including transportation systems, wired and wireless communications and a range of Internet applications. As transportation and communication networks become increasingly more complex, the ever increasing demand for congestion control, higher traffic capacity, quality of service, robustness and reduced energy consumption requires new tools and methods to meet these conflicting requirements. The new methodology should serve for gaining better understanding of the properties of networking systems at the macroscopic level, as well as for the development of new principled optimization and management algorithms at the microscopic level. Methods of statistical physics seem best placed to provide new approaches as they have been developed specifically to deal with nonlinear large-scale systems. This review aims at presenting an overview of tools and methods that have been developed within the statistical physics community and that can be readily applied to address the emerging problems in networking. These include diffusion processes, methods from disordered systems and polymer physics, probabilistic inference, which have direct relevance to network routing, file and frequency distribution, the exploration of network structures and vulnerability, and various other practical networking applications. © 2013 IOP Publishing Ltd.

Nonlinear Time-Fractional Differential Equations in Combustion Science

MSC 2010: 34A08 (main), 34G20, 80A25

Variational Markov chain Monte Carlo for Bayesian smoothing of non-linear diffusions

In this paper we develop set of novel Markov Chain Monte Carlo algorithms for Bayesian smoothing of partially observed non-linear diffusion processes. The sampling algorithms developed herein use a deterministic approximation to the posterior distribution over paths as the proposal distribution for a mixture of an independence and a random walk sampler. The approximating distribution is sampled by simulating an optimized time-dependent linear diffusion process derived from the recently developed variational Gaussian process approximation method. The novel diffusion bridge proposal derived from the variational approximation allows the use of a flexible blocking strategy that further improves mixing, and thus the efficiency, of the sampling algorithms. The algorithms are tested on two diffusion processes: one with double-well potential drift and another with SINE drift. The new algorithm's accuracy and efficiency is compared with state-of-the-art hybrid Monte Carlo based path sampling. It is shown that in practical, finite sample applications the algorithm is accurate except in the presence of large observation errors and low to a multi-modal structure in the posterior distribution over paths. More importantly, the variational approximation assisted sampling algorithm outperforms hybrid Monte Carlo in terms of computational efficiency, except when the diffusion process is densely observed with small errors in which case both algorithms are equally efficient. © 2011 Springer-Verlag.