Can(Plan)+: Extending the Operational Semantics of the BDI Architecture to deal with Uncertain Information

The BDI architecture, where agents are modelled based on their beliefs, desires and intentions, provides a practical approach to develop large scale systems. However, it is not well suited to model complex Supervisory Control And Data Acquisition (SCADA) systems pervaded by uncertainty. In this paper we address this issue by extending the operational semantics of Can(Plan) into Can(Plan)+. We start by modelling the beliefs of an agent as a set of epistemic states where each state, possibly using a different representation, models part of the agent's beliefs. These epistemic states are stratified to make them commensurable and to reason about the uncertain beliefs of the agent. The syntax and semantics of a BDI agent are extended accordingly and we identify fragments with computationally efficient semantics. Finally, we examine how primitive actions are affected by uncertainty and we define an appropriate form of lookahead planning.

A new self-learning TLBO algorithm for RBF neural modelling of batteries in electric vehicles

One of the main purposes of building a battery model is for monitoring and control during battery charging/discharging as well as for estimating key factors of batteries such as the state of charge for electric vehicles. However, the model based on the electrochemical reactions within the batteries is highly complex and difficult to compute using conventional approaches. Radial basis function (RBF) neural networks have been widely used to model complex systems for estimation and control purpose, while the optimization of both the linear and non-linear parameters in the RBF model remains a key issue. A recently proposed meta-heuristic algorithm named Teaching-Learning-Based Optimization (TLBO) is free of presetting algorithm parameters and performs well in non-linear optimization. In this paper, a novel self-learning TLBO based RBF model is proposed for modelling electric vehicle batteries using RBF neural networks. The modelling approach has been applied to two battery testing data sets and compared with some other RBF based battery models, the training and validation results confirm the efficacy of the proposed method.

Multi-agents modelling of EV purchase willingness based on questionnaires

Traditional experimental economics methods often consume enormous resources of qualified human participants, and the inconsistence of a participant’s decisions among repeated trials prevents investigation from sensitivity analyses. The problem can be solved if computer agents are capable of generating similar behaviors as the given participants in experiments. An experimental economics based analysis method is presented to extract deep information from questionnaire data and emulate any number of participants. Taking the customers’ willingness to purchase electric vehicles (EVs) as an example, multi-layer correlation information is extracted from a limited number of questionnaires. Multi-agents mimicking the inquired potential customers are modelled through matching the probabilistic distributions of their willingness embedded in the questionnaires. The authenticity of both the model and the algorithm is validated by comparing the agent-based Monte Carlo simulation results with the questionnaire-based deduction results. With the aid of agent models, the effects of minority agents with specific preferences on the results are also discussed.

Characterisation and modelling of the thermorheological properties of pharmaceutical polymers and their blends using capillary rheometry: Implications for hot melt processing of dosage forms.

Given the growing interest in thermal processing methods, this study describes the use of an advanced rheological technique, capillary rheometry, to accurately determine the thermorheological properties of two pharmaceutical polymers, Eudragit E100 (E100) and hydroxypropylcellulose JF (HPC) and their blends, both in the presence and absence of a model therapeutic agent (quinine, as the base and hydrochloride salt). Furthermore, the glass transition temperatures (Tg) of the cooled extrudates produced using capillary rheometry were characterised using Dynamic Mechanical Thermal Analysis (DMTA) thereby enabling correlations to be drawn between the information derived from capillary rheometry and the glass transition properties of the extrudates. The shear viscosities of E100 and HPC (and their blends) decreased as functions of increasing temperature and shear rates, with the shear viscosity of E100 being significantly greater than that of HPC at all temperatures and shear rates. All platforms were readily processed at shear rates relevant to extrusion (approximately 200–300 s−1) and injection moulding (approximately 900 s−1). Quinine base was observed to lower the shear viscosities of E100 and E100/HPC blends during processing and the Tg of extrudates, indicative of plasticisation at processing temperatures and when cooled (i.e. in the solid state). Quinine hydrochloride (20% w/w) increased the shear viscosities of E100 and HPC and their blends during processing and did not affect the Tg of the parent polymer. However, the shear viscosities of these systems were not prohibitive to processing at shear rates relevant to extrusion and injection moulding. As the ratio of E100:HPC increased within the polymer blends the effects of quinine base on the lowering of both shear viscosity and Tg of the polymer blends increased, reflecting the greater solubility of quinine within E100. In conclusion, this study has highlighted the importance of capillary rheometry in identifying processing conditions, polymer miscibility and plasticisation phenomena.

Information Gathering and Processing with Fluorescent Molecules

Molecular information gathering and processing – a young field of applied chemistry - is undergoing good growth. The progress is occurring both in terms of conceptual development and in terms of the strengthening of older concepts with new examples. This review critically surveys these two broad avenues. We consider some cases where molecules emulate one of the building blocks of electronic logic gates. We then examine molecular emulation of various Boolean logic gates carrying one, two or three inputs. Some single-input gates are popular information gathering devices. Special systems, such as ‘lab-on-a-molecule’ and molecular keypad locks, also receive attention. A situation deviating from the Boolean blueprint is also discussed. Some pointers are offered for maintaining the upward curve of the field.

Demand-side characterisation of the smart city for energy modelling.

This paper presents a new methodology for characterising the energy performance of buildings suitable for city-scale, top-down energy modelling. Building properties that have the greatest impact on simulated energy performance were identified via a review of sensitivity analysis studies. The methodology greatly simplifies the description of a building to decrease labour and simulation processing overheads. The methodology will be used in the EU FP7 INDICATE project which aims to create a master-planning tool that uses dynamic simulation to facilitate the design of sustainable, energy efficient smart cities.

Improving the robustness of the iterative solver in state-space modelling of guitar distortion circuitry

Iterative solvers are required for the discrete-time simulation of nonlinear behaviour in analogue distortion circuits. Unfortunately,these methods are often computationally too expensive for realtime simulation. Two methods are presented which attempt to reduce the expense of iterative solvers. This is achieved by applying information that is derived from the specific form of the non linearity.The approach is first explained through the modelling of an asymmetrical diode clipper, and further exemplified by application to the Dallas Rangemaster Treble Booster guitar pedal, which provides an initial perspective of the performance on systems with multiple nonlinearities.

Modelling Duqu 2.0 Malware using Attack Trees with Sequential Conjunction

In this paper we identify requirements for choosing a threat modelling formalisation for modelling sophisticated malware such as Duqu 2.0. We discuss the gaps in current formalisations and propose the use of Attack Trees with Sequential Conjunction when it comes to analysing complex attacks. The paper models Duqu 2.0 based on the latest information sourced from formal and informal sources. This paper provides a well structured model which can be used for future analysis of Duqu 2.0 and related attacks.

Modelling the fresh properties of self-compacting concrete using support vector machine approach

The main objective of the study presented in this paper was to investigate the feasibility using support vector machines (SVM) for the prediction of the fresh properties of self-compacting concrete. The radial basis function (RBF) and polynomial kernels were used to predict these properties as a function of the content of mix components. The fresh properties were assessed with the slump flow, T50, T60, V-funnel time, Orimet time, and blocking ratio (L-box). The retention of these tests was also measured at 30 and 60 min after adding the first water. The water dosage varied from 188 to 208 L/m3, the dosage of superplasticiser (SP) from 3.8 to 5.8 kg/m3, and the volume of coarse aggregates from 220 to 360 L/m3. In total, twenty mixes were used to measure the fresh state properties with different mixture compositions. RBF kernel was more accurate compared to polynomial kernel based support vector machines with a root mean square error (RMSE) of 26.9 (correlation coefficient of R2 = 0.974) for slump flow prediction, a RMSE of 0.55 (R2 = 0.910) for T50 (s) prediction, a RMSE of 1.71 (R2 = 0.812) for T60 (s) prediction, a RMSE of 0.1517 (R2 = 0.990) for V-funnel time prediction, a RMSE of 3.99 (R2 = 0.976) for Orimet time prediction, and a RMSE of 0.042 (R2 = 0.988) for L-box ratio prediction, respectively. A sensitivity analysis was performed to evaluate the effects of the dosage of cement and limestone powder, the water content, the volumes of coarse aggregate and sand, the dosage of SP and the testing time on the predicted test responses. The analysis indicates that the proposed SVM RBF model can gain a high precision, which provides an alternative method for predicting the fresh properties of SCC.

The inventorying of building stone performance-in-use to assess susceptibility to decay

The Natural Stone Database for Northern Ireland was constructed to address the paucity of information available to stone conservation practitioners within the region. Almost 2000 listed buildings and monuments were surveyed over three years to produce an interactive GIS database. This contained information on stone sources, together with details of stone condition and decay processes. This paper uses elements of this GIS to investigate stone decay patterns across Northern Ireland. The results demonstrate that as the level of stone decay increases, so does the proportion of buildings with sandstone as the primary stone type. It appears that a
higher open porosity level combined with Northern Ireland’s wetter climate and maritime location leads to rapid wetting and drying cycles within sandstones. This is coupled with the ingress and crystallisation of marine and other salts within stone pores leading to considerably
higher rates of decay than for any other stone type.

An investigation of IAQ, thermal comfort and Sick Building Syndrome symptoms in UK energy efficient homes

Purpose
– Concern of the deterioration of indoor environmental quality as a result of energy efficient building design strategies is growing. Apprehensions of the effect of airtight, super insulated envelopes, the reduction of infiltration, and the reliance on mechanical systems to provide adequate ventilation (air supply) is promoting emerging new research in this field. The purpose of this paper is to present the results of an indoor air quality (IAQ) and thermal comfort investigation in UK energy efficient homes, through a case study investigation.

Design/methodology/approach
– The case study dwellings consisted of a row of six new-build homes which utilize mechanical ventilation with heat recovery (MVHR) systems, are built to an average airtightness of 2m3/m2/hr at 50 Pascal’s, and constructed without a central heating system. Physical IAQ measurements and occupant interviews were conducted during the summer and winter months over a 24-hour period, to gain information on occupant activities, perception of the interior environment, building-related health and building use.

Findings
– The results suggest inadequate IAQ and perceived thermal comfort, insufficient use of purge ventilation, presence of fungal growth, significant variances in heating patterns, occurrence of sick building syndrome symptoms and issues with the MVHR system.

Practical implications
– The findings will provide relevant data on the applicability of airtight, mechanically ventilated homes in a UK climate, with particular reference to IAQ.

Originality/value
– IAQ data of this nature is essentially lacking, particularly in the UK context. The findings will aid the development of effective sustainable design strategies that are appropriate to localized climatic conditions and sensitive to the health of building occupants.