End-to-end versus hop-by-hop soft state refresh for multi-hop signalling systems

To ensure state synchronization of signalling operations, many signaling protocol designs choose to establish “soft” state that expires if it is not refreshed. The approaches of refreshing state in multi-hop signaling system can be classified as either end-to-end (E2E) or hop-by-hop (HbH). Although both state refresh approaches have been widely used in practical signaling protocols, the design tradeoffs between state synchronization and signaling cost have not yet been fully investigated. In this paper, we investigate this issue from the perspectives of state refresh and state removal. We propose simple but effective Markov chain models for both approaches and obtain closed-form solutions which depict the state refresh performance in terms of state consistency and refresh message rate, as well as the state removal performance in terms of state removal delay. Simulations verify the analytical models. It is observed that the HbH approach yields much better state synchronization at the cost of higher signaling cost than the E2E approach. While the state refresh performance can be improved by increasing the values of state refresh and timeout timers, the state removal delay increases largely for both E2E and HbH approaches. The analysis here shed lights on the design of signaling protocols and the configuration of the timers to adapt to changing network conditions.

Minimum description length, regularisation and multi-modal data

Relationships between clustering, description length, and regularisation are pointed out, motivating the introduction of a cost function with a description length interpretation and the unusual and useful property of having its minimum approximated by the densest mode of a distribution. A simple inverse kinematics example is used to demonstrate that this property can be used to select and learn one branch of a multi-valued mapping. This property is also used to develop a method for setting regularisation parameters according to the scale on which structure is exhibited in the training data. The regularisation technique is demonstrated on two real data sets, a classification problem and a regression problem.

Globally optimal on-line learning rules for multi-layer neural networks

We present a method for determining the globally optimal on-line learning rule for a soft committee machine under a statistical mechanics framework. This rule maximizes the total reduction in generalization error over the whole learning process. A simple example demonstrates that the locally optimal rule, which maximizes the rate of decrease in generalization error, may perform poorly in comparison.

A multi-agent system using iterative bidding mechanism to enhance manufacturing agility

The global market has become increasingly dynamic, unpredictable and customer-driven. This has led to rising rates of new product introduction and turbulent demand patterns across product mixes. As a result, manufacturing enterprises were facing mounting challenges to be agile and responsive to cope with market changes, so as to achieve the competitiveness of producing and delivering products to the market timely and cost-effectively. This paper introduces a currency-based iterative agent bidding mechanism to effectively and cost-efficiently integrate the activities associated with production planning and control, so as to achieve an optimised process plan and schedule. The aim is to enhance the agility of manufacturing systems to accommodate dynamic changes in the market and production. The iterative bidding mechanism is executed based on currency-like metrics; each operation to be performed is assigned with a virtual currency value and agents bid for the operation if they make a virtual profit based on this value. These currency values are optimised iteratively and so does the bidding process based on new sets of values. This is aimed at obtaining better and better production plans, leading to near-optimality. A genetic algorithm is proposed to optimise the currency values at each iteration. In this paper, the implementation of the mechanism and the test case simulation results are also discussed. © 2012 Elsevier Ltd. All rights reserved.

Management role in employee participation:a comparative study of multi-national enterprises in India and the U.K.

This research is an Anglo-Indian comparative case study. It studies managerial action in the participation arena of two British multi-nationals i.e. Cadbury Limited and ICI plc. The research was carried out in matched pairs of factories of the above named companies, located in the Midlands of the UK and in Bombay in India. The data for this research was collected through semi-structured interviews with managers and non-management actors, study of company documents and non-participant observation of some participation forums. The research conceptualises the idea of a `participation arena' consisting of the structures, processes, purposes and dynamics of participation. This arena is visualised as broadly reflecting the organisation structure and can be divided into corporate, unit and shopfloor level. Managerial action in this arena is examined in terms of interaction between three sets of factors i.e. company business objectives, strategies and policies; managerial values of power and control; and the responses of unions. Similarities and differences between management action in the home and host plants of the two multi-national companies are also examined. The major findings of the research are as follows. There is significant difference between the participation arena of the parent and the subsidiary company. The latter is marked by absence of higher level participation forums and lack of opportunity for employees to discuss, let alone influence, key decisions. This results from parent company control over key activities of the subsidiary. The similarities in management action in the participation arenas of the two companies in both countries can be attributed to the operation of the three sets of factors mentioned above. Nevertheless, the particular circumstances of each company are a greater influence on managerial action than the national context. Finally, future areas of research in this field are explored.

Improved production of industrially relevant recombinant proteins: application of multi-factorial design of experiments and scalable process modelling

The work described in this thesis focuses on the use of a design-of-experiments approach in a multi-well mini-bioreactor to enable the rapid establishments of high yielding production phase conditions in yeast, which is an increasingly popular host system in both academic and industrial laboratories. Using green fluorescent protein secreted from the yeast, Pichia pastoris, a scalable predictive model of protein yield per cell was derived from 13 sets of conditions each with three factors (temperature, pH and dissolved oxygen) at 3 levels and was directly transferable to a 7 L bioreactor. This was in clear contrast to the situation in shake flasks, where the process parameters cannot be tightly controlled. By further optimisating both the accumulation of cell density in batch and improving the fed-batch induction regime, additional yield improvement was found to be additive to the per cell yield of the model. A separate study also demonstrated that improving biomass improved product yield in a second yeast species, Saccharomyces cerevisiae. Investigations of cell wall hydrophobicity in high cell density P. pastoris cultures indicated that cell wall hydrophobin (protein) compositional changes with growth phase becoming more hydrophobic in log growth than in lag or stationary phases. This is possibly due to an increased occurrence of proteins associated with cell division. Finally, the modelling approach was validated in mammalian cells, showing its flexibility and robustness. In summary, the strategy presented in this thesis has the benefit of reducing process development time in recombinant protein production, directly from bench to bioreactor.

Simulating the value of collaboration in multi-actor conservation planning

The loss of habitat and biodiversity worldwide has led to considerable resources being spent for conservation purposes on actions such as the acquisition and management of land, the rehabilitation of degraded habitats, and the purchase of easements from private landowners. Prioritising these actions is challenging due to the complexity of the problem and because there can be multiple actors undertaking conservation actions, often with divergent or partially overlapping objectives. We use a modelling framework to explore this issue with a study involving two agents sequentially purchasing land for conservation. We apply our model to simulated data using distributions taken from real data to simulate the cost of patches and the rarity and co-occurence of species. In our model each agent attempted to implement a conservation network that met its target for the minimum cost using the conservation planning software Marxan. We examine three scenarios where the conservation targets of the agents differ. The first scenario (called NGO-NGO) models the situation where two NGOs are both are targeting different sets of threatened species. The second and third scenarios (called NGO-Gov and Gov-NGO, respectively) represent a case where a government agency attempts to implement a complementary conservation network representing all species, while an NGO is focused on achieving additional protection for the most endangered species. For each of these scenarios we examined three types of interactions between agents: i) acting in isolation where the agents are attempting to achieve their targets solely though their own actions ii) sharing information where each agent is aware of the species representation achieved within the other agent’s conservation network and, iii) pooling resources where agents combine their resources and undertake conservation actions as a single entity. The latter two interactions represent different types of collaborations and in each scenario we determine the cost savings from sharing information or pooling resources. In each case we examined the utility of these interactions from the viewpoint of the combined conservation network resulting from both agents' actions, as well as from each agent’s individual perspective. The costs for each agent to achieve their objectives varied depending on the order in which the agents acted, the type of interaction between agents, and the specific goals of each agent. There were significant cost savings from increased collaboration via sharing information in the NGO-NGO scenario were the agent’s representation goals were mutually exclusive (in terms of specie targeted). In the NGO-Gov and Gov-NGO scenarios, collaboration generated much smaller savings. If the two agents collaborate by pooling resources there are multiple ways the total cost could be shared between both agents. For each scenario we investigate the costs and benefits for all possible cost sharing proportions. We find that there are a range of cost sharing proportions where both agents can benefit in the NGO-NGO scenarios while the NGO-Gov and Gov-NGO scenarios again showed little benefit. Although the model presented here has a range of simplifying assumptions, it demonstrates that the value of collaboration can vary significantly in different situations. In most cases, collaborating would have associated costs and these costs need to be weighed against the potential benefits from collaboration. The model demonstrates a method for determining the range of collaboration costs that would result in collaboration providing an efficient use of scarce conservation resources.

Multi-criteria logistics distribution network design using SAS/OR

This paper explores the use of the optimization procedures in SAS/OR software with application to the contemporary logistics distribution network design using an integrated multiple criteria decision making approach. Unlike the traditional optimization techniques, the proposed approach, combining analytic hierarchy process (AHP) and goal programming (GP), considers both quantitative and qualitative factors. In the integrated approach, AHP is used to determine the relative importance weightings or priorities of alternative warehouses with respect to both deliverer oriented and customer oriented criteria. Then, a GP model incorporating the constraints of system, resource, and AHP priority is formulated to select the best set of warehouses without exceeding the limited available resources. To facilitate the use of integrated multiple criteria decision making approach by SAS users, an ORMCDM code was implemented in the SAS programming language. The SAS macro developed in this paper selects the chosen variables from a SAS data file and constructs sets of linear programming models based on the selected GP model. An example is given to illustrate how one could use the code to design the logistics distribution network.

Simulating the value of collaboration in multi-actor conservation planning

The loss of habitat and biodiversity worldwide has led to considerable resources being spent on conservation interventions. Prioritising these actions is challenging due to the complexity of the problem and because there can be multiple actors undertaking conservation actions, often with divergent or partially overlapping objectives. We explore this issue with a simulation study involving two agents sequentially purchasing land for the conservation of multiple species using three scenarios comprising either divergent or partially overlapping objectives between the agents. The first scenario investigates the situation where both agents are targeting different sets of threatened species. The second and third scenarios represent a case where a government agency attempts to implement a complementary conservation network representing 200 species, while a non-government organisation is focused on achieving additional protection for the ten rarest species. Simulated input data was generated using distributions taken from real data to model the cost of parcels, and the rarity and co-occurrence of species. We investigated three types of collaborative interactions between agents: acting in isolation, sharing information and pooling resources with the third option resulting in the agents combining their resources and effectively acting as a single entity. In each scenario we determine the cost savings when an agent moves from acting in isolation to either sharing information or pooling resources with the other agent. The model demonstrates how the value of collaboration can vary significantly in different situations. In most cases, collaborating would have associated costs and these costs need to be weighed against the potential benefits from collaboration. Our model demonstrates a method for determining the range of costs that would result in collaboration providing an efficient use of scarce conservation resources.

End-to-end versus hop-by-hop state refresh in soft state signaling protocols

The concept of soft state (i.e., the state that will expire unless been refreshed) has been widely used in the design of network signaling protocols. The approaches of refreshing state in multi-hop networks can be classified to end-to-end (E2E) and hop-by-hop (HbH) refreshes. In this article we propose an effective Markov chain based analytical model for both E2E and HbH refresh approaches. Simulations verify the analytical models, which can be used to study the impacts of link characteristics on the performance (e.g., state synchronization and message overhead), as a guide on configuration and optimization of soft state signaling protocols. © 2009 IEEE.