New product development in an emerging economy: analysing the role of supplier involvement practices by using Bayesian Markov chain Monte Carlo technique

The research question is whether the positive relationship found between supplier involvement practices and new product development performances in developed economies also holds in emerging economies. The role of supplier involvement practices in new product development performance is yet to be substantially investigated in the emerging economies (other than China). This premise was examined by distributing a survey instrument (Jayaram’s (2008) published survey instrument that has been utilised in developed economies) to Malaysian manufacturing companies. To gauge the relationship between the supplier involvement practices and new product development (NPD) project performance of 146 companies, structural equation modelling was adopted. Our findings prove that supplier involvement practices have a significant positive impact on NPD project performance in an emerging economy with respect to quality objectives, design objectives, cost objectives, and “time-to-market” objectives. Further analysis using the Bayesian Markov Chain Monte Carlo algorithm, yielding a more credible and feasible differentiation, confirmed these results (even in the case of an emerging economy) and indicated that these practices have a 28% impact on variance of NPD project performance. This considerable effect implies that supplier involvement is a must have, although further research is needed to identify the contingencies for its practices

A multi-objective genetic algorithm for the design of pressure swing adsorption

Pressure Swing Adsorption (PSA) is a cyclic separation process, more advantageous over other separation options for middle scale processes. Automated tools for the design of PSA processes would be beneficial for the development of the technology, but their development is a difficult task due to the complexity of the simulation of PSA cycles and the computational effort needed to detect the performance at cyclic steady state. We present a preliminary investigation of the performance of a custom multi-objective genetic algorithm (MOGA) for the optimisation of a fast cycle PSA operation, the separation of air for N2 production. The simulation requires a detailed diffusion model, which involves coupled nonlinear partial differential and algebraic equations (PDAEs). The efficiency of MOGA to handle this complex problem has been assessed by comparison with direct search methods. An analysis of the effect of MOGA parameters on the performance is also presented

Globally Optimal Energy-Efficient Power Control and Receiver Design in Wireless Networks

The characterization of the global maximum of energy efficiency (EE) problems in wireless networks is a challenging problem due to the non-convex nature of investigated problems in interference channels. The aim of this work is to develop a new and general framework to achieve globally optimal solutions. First, the hidden monotonic structure of the most common EE maximization problems is exploited jointly with fractional programming theory to obtain globally optimal solutions with exponential complexity in the number of network links. To overcome this issue, we also propose a framework to compute suboptimal power control strategies characterized by affordable complexity. This is achieved by merging fractional programming and sequential optimization. The proposed monotonic framework is used to shed light on the ultimate performance of wireless networks in terms of EE and also to benchmark the performance of the lower-complexity framework based on sequential programming. Numerical evidence is provided to show that the sequential fractional programming framework achieves global optimality in several practical communication scenarios.Comment: Accepted for publication in the IEEE Transactions on Signal Processin

Generalized Kernel-based Visual Tracking

In this work we generalize the plain MS trackers and attempt to overcome standard mean shift trackers' two limitations. It is well known that modeling and maintaining a representation of a target object is an important component of a successful visual tracker. However, little work has been done on building a robust template model for kernel-based MS tracking. In contrast to building a template from a single frame, we train a robust object representation model from a large amount of data. Tracking is viewed as a binary classification problem, and a discriminative classification rule is learned to distinguish between the object and background. We adopt a support vector machine (SVM) for training. The tracker is then implemented by maximizing the classification score. An iterative optimization scheme very similar to MS is derived for this purpose.Comment: 12 page

Divergence or Convergence in Research and Development and Innovation Between ‘East’ and ‘West’?

Book description: Research suggests that innovation and technological change are crucial for the economic recovery of the former centrally planned countries in Central and Eastern Europe. This book analyses the development of innovation systems and technology policy in this region from various perspectives, demonstrating not only its importance but also its complexity

The countryside in urbanized Flanders: towards a flexible definition for a dynamic policy

The countryside, the rural area, the open space, … many definitions are used for rural Flanders. Everyone makes its own interpretation of the countryside, considering it as a place for living, working or recreating. The countryside is more than just a geographical area: it is an aggregate of physical, social, economic and cultural functions, strongly interrelated with each other. According to international and European definitions of rural areas there would be almost no rural area in Flanders. These international definitions are all developed to be used for analysis and policy within their specific context. They are not really applicable to Flanders because of the historical specificity of its spatial structure. Flanders is characterized by a giant urbanization pressure on its countryside while internationally rural depopulation is a point of interest. To date, for every single rural policy initiative – like the implementation of the European Rural Development Policy – Flanders used a specifically adapted definition, based on existing data or previously made delineations. To overcome this oversupply of definitions and delineations, the Flemish government funded a research project to obtain a clear and flexible definition of the Flemish countryside and a dynamic method to support Flemish rural policy aims. First, an analysis of the currently used definitions of the countryside in Flanders was made. It is clear that, depending on the perspective or the policy context, another definition of the countryside comes into view. The comparative study showed that, according to the used criteria, the area percentage of Flanders that is rural, varies between 9 and 93 per cent. Second, dynamic sets of criteria were developed, facilitating a flexible definition of the countryside, according to the policy aims concerned. This research part was focused on 6 policy themes, like ‘construction, maintenance and management of local (transport) infrastructures’ and ‘provision of (minimum) services (education, culture, health care, …)’. For each theme a dynamic set of criteria or indicators was constructed. These indicators make it possible to show where a policy theme manifests itself and/or where policy interventions are possible or needed. In this way every set of criteria makes up a new definition of rural Flanders. This method is dynamic; new data or insights can easily be incorporated and new criteria sets can be developed if other policy aims come into view. The developed method can contribute to a more region-oriented and theme-specific rural policy and funding mechanism

Deep Item-based Collaborative Filtering for Top-N Recommendation

Item-based Collaborative Filtering(short for ICF) has been widely adopted in recommender systems in industry, owing to its strength in user interest modeling and ease in online personalization. By constructing a user's profile with the items that the user has consumed, ICF recommends items that are similar to the user's profile. With the prevalence of machine learning in recent years, significant processes have been made for ICF by learning item similarity (or representation) from data. Nevertheless, we argue that most existing works have only considered linear and shallow relationship between items, which are insufficient to capture the complicated decision-making process of users. In this work, we propose a more expressive ICF solution by accounting for the nonlinear and higher-order relationship among items. Going beyond modeling only the second-order interaction (e.g. similarity) between two items, we additionally consider the interaction among all interacted item pairs by using nonlinear neural networks. Through this way, we can effectively model the higher-order relationship among items, capturing more complicated effects in user decision-making. For example, it can differentiate which historical itemsets in a user's profile are more important in affecting the user to make a purchase decision on an item. We treat this solution as a deep variant of ICF, thus term it as DeepICF. To justify our proposal, we perform empirical studies on two public datasets from MovieLens and Pinterest. Extensive experiments verify the highly positive effect of higher-order item interaction modeling with nonlinear neural networks. Moreover, we demonstrate that by more fine-grained second-order interaction modeling with attention network, the performance of our DeepICF method can be further improved.Comment: 25 pages, submitted to TOI