Environmental and Economic Optimisation of Buildings in Portugal and Hungary

Life cycle assessment (LCA) is a scientific method for evaluating the environmental impact of products. Standards provide a general framework for conducting an LCA study and calculation rules specifically for buildings. The challenge is to design energy-efficient buildings that have a low environmental impact, reasonable costs, and high thermal comfort as these are usually conflicting aspects. Efficient mathematical optimisation algorithms can be applied to such engineering problems. In this paper, a framework for automated optimisation is described, and it is applied to a multi-story residential building case study in two locations, Portugal and Hungary. The objectives are to minimise the life cycle environmental impacts and costs. The results indicate that optimum solutions are found at a higher cost but lower global warming potential for Portugal than for Hungary. Optimum solutions have walls with a thermal transmittance in the intervals of 0.29–0.39 and 0.06–0.19 W/m2K for Portugal and Hungary, respectively. Multi-objective optimisation algorithms can be successfully applied to find solutions with low environmental impact and an eco-efficient thermal envelope.

A linear programming-based strategy to save pipette tips in automated DNA assembly

Laboratory automation and mathematical optimisation are key to improving the efficiency of synthetic biology research. While there are algorithms optimising the construct designs and synthesis strategies for DNA assembly, the optimisation of how DNA assembly reaction mixes are prepared remains largely unexplored. Here, we focus on reducing the pipette tip consumption of a liquid-handling robot as it delivers DNA parts across a multi-well plate where several constructs are being assembled in parallel. We propose a linear programming formulation of this problem based on the capacitated vehicle routing problem, along with an algorithm which applies a linear programming solver to our formulation, hence providing a strategy to prepare a given set of DNA assembly mixes using fewer pipette tips. The algorithm performed well in randomly generated and real-life scenarios concerning several modular DNA assembly standards, proving capable of reducing the pipette tip consumption by up to 61% in large-scale cases. Combining automatic process optimisation and robotic liquid-handling, our strategy promises to greatly improve the efficiency of DNA assembly, either used alone or in combination with other algorithmic methods.

Resources Package Modelling Supporting Border Surveillance Operations

The purpose of this work is to propose a military planning tool capable of providing logistical bases and patrol packages to most effectively support border surveillance. Presently, military patrols are employed along geographical borders to combat transnational crimes; acts such as drug trafficking, smuggling of goods and illegal natural resources exploitation. The patrols make temporary stops within specific time windows at specific places characterised by a high incidence of crime (hotspots). These hotspots have different criticalities within given time windows. To optimise the results, the proposed model allows additional stops in more critical hotspots. It achieves this using a mathematical optimisation model. Considering that there are not adequate logistical-military capacities (logistical bases and patrols) at all needed locations, developing a border surveillance plan that optimises resource use is imperative. The model was run using black hole-based optimisation and a real patrol mission’s database to ensure timely solutions. The solutions were then evaluated in terms of quality (number of bases and patrols, coverage efforts, and travel time) and computational processing time. Next, they were compared with solutions using the traditional method, thereby demonstrating the model’s robustness in providing timely surveillance schemes that ensure high coverage with minimum resources.

Duality Theorem for a Minimax Vector Optimization Problem

A duality theorem is stated and proved for a minimax vector optimization problem where the vectors are elements of the set of products of compact Polish spaces. A special case of this theorem is derived to show that two metrics on the space of probability distributions on countable products of Polish spaces are identical. The appendix includes a proof that, under the appropriate conditions, the function studied in the optimisation problem is indeed a metric. The optimisation problem is comparable to multi-commodity optimal transport where there is dependence between commodities. This paper builds on the work of R.S. MacKay who introduced the metrics in the context of complexity science in [4] and [5]. The metrics have the advantage of measuring distance uniformly over the whole network while other metrics on probability distributions fail to do so (e.g total variation, Kullback–Leibler divergence, see [5]). This opens up the potential of mathematical optimisation in the setting of complexity science.

Bug Handling in Service Sector Software

The technology enabled service industry is emerging as the most dynamic sectors in world's economy. Various service sector industries such as financial services, banking solutions, telecommunication, investment management, etc. completely rely on using large scale software for their smooth operations. Any malwares or bugs in these software is an issue of big concern and can have serious financial consequences. This chapter addresses the problem of bug handling in service sector software. Predictive analysis is a helpful technique for keeping software systems error free. Existing research in bug handling focus on various predictive analysis techniques such as data mining, machine learning, information retrieval, optimisation, etc. for bug resolving. This chapter provides a detailed analysis of bug handling in large service sector software. The main emphasis of this chapter is to discuss research involved in applying predictive analysis for bug handling. The chapter also presents some possible future research directions in bug resolving using mathematical optimisation techniques.

Recent advances and future perspectives for automated parameterisation, Bayesian inference and machine learning in voltammetry

Advanced data analysis tools such as mathematical optimisation, Bayesian inference and machine learning have the capability to revolutionise the field of quantitative voltammetry.