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With the rapid growth in clean distributed energy resources involving micro-generation and flexible loads, users can actively manage their own energy and have the capability to enter in a market of energy services as prosumers while reducing their carbon footprint. The coordination between these distributed energy resources is essential in order to ensure fair trading and equality in resource sharing among a community of prosumers. Peer-to-Peer (P2P) networks can provide the underlying mechanisms for supporting such coordination and offer incentives to prosumers to participate in the energy market. In particular, the federation of energy clusters with P2P networks has the potential to unlock access to energy resources and lead to the development of new energy services in a fast-growing sharing energy economy. In this paper, we present the formation and federation of smart energy clusters using P2P networks with a view to decentralise energy markets and enable access and use of clean energy resources. We implement a P2P framework to support the federation of energy clusters and study the interaction of consumers and producers in a market of energy resources and services. We demonstrate how energy exchanges and energy costs in a federation are influenced by the energy demand, the size of energy clusters and energy types. We conduct our modelling and analysis based on a real fish industry case study in Milford Haven, South Wales, as part of the EU H2020 INTERREG piSCES project.

The digitalization of the construction industry has been supported recently with various building information modelling (BIM) methods applied to lifecycle stages. This has enabled the transition towards an improved management of information required in the development of different construction project tasks. In a digital construction context, traceability of models and collaboration across disciplines is required especially when dealing with sensitive data and critical infrastructures. Blockchain technology has the potential to advance the automation of processes in the construction industry by offering a higher level of data traceability, incentivising participation, and ensuring transparency with projects. This paper presents how BIM and Blockchain can be utilised to facilitate the establishment and implementation of Digital Twins for construction projects. A highway construction case study is used for demonstrating the implementation of smart contracts for managing tasks and processes across different disciplines involved in a construction project.

Sick Building Syndrome (SBS) is a condition where an occupant experiences a range of symptoms whilst inside a particular building and where a degree of relief is given on departure from the environment in question. This paper explored the prevalence of SBS among 19 PhD students within a selected University building using a digital twinning approach. A research-based questionnaire was distributed to the occupants to obtain a range of personal and building-related information. This included an architectural evaluation, assessment of adaptive measures and an SBS symptom analysis. Energyplus was used to simulate indoor temperature (°C) and relative humidity (%) levels within selected building zones. Simulation results showed that indoor temperatures within the summer and spring months were above recommended values, with correlations seen in low scores throughout architectural evaluation and temperature-reducing activities within the adaptive measures. Exceedingly low relative humidity levels were also seen in up to 45.9% of days throughout the year. Results showed that only 42% of occupants developed 1 or more symptoms. The highest prevalence included back-aches and fatigue, with no counts for digestive-related symptoms such as stomach-ache and abdominal pain. It was also found that females developed more symptoms than males, with significant correlations found for strained and irritated eyes ( $\mathrm{P}=0.002$ ), dry skin ( $\mathrm{P}=0.049$ ) and a sore throat ( $\mathrm{P}=0.049$ ). Recommended mitigation methods for the selected building include the application of a suitable BVAC system that can monitor and control ventilation rates and indoor temperatures. It is also advised to humidify the building to alleviate SBS symptoms. Correlating to questionnaire results, additional methods should include replacement of unsuitable building furnishings and the keeping of high-quality maintenance. It is recommended that further investigation into additional environmental parameters can provide a more in-depth examination on SBS and its prevalence within the building.

Several attempts have been made in the past to advance fire safety standards for residential buildings. However, the UK government has only partially succeeded in delivering detailed legislation that has been both successfully implemented and enforced across all types of dwellings. To further understand the government's approach, the author carried out detailed research into the common causes and triggers of fire that took place using a case study approach involving terrace housing, whilst also assessing the fitness for purpose of legislation with respect to the UK and EU regulatory landscape. In addition, independent research addressed all key elements of terraced dwellings, including information on fire alarm systems and detection devices, fire-resistant materials within external walls, compartmentalisation and combustibility of facades in roofing, quality of egress routes, and mitigation measures in place. Compiling data from multiple reputable sources such as the HSA, BRE and Home Office and comparing it with current legislation from Approved Document B: Fire Safety (vol. 2010-2019), indicated the government's failure to identify measures that could be adopted in order to retrofit existing homes and improve their resilience to the risk of fire. This severe deficiency in the lack of applicable legislation and safety methods in place for terrace housing confirms the need to implement contemporary approaches and advanced techniques for fire safety. This paper aims at exploring the resilience of the UK domestic housing to the risk of fire and provides simulation analysis from a real building case study identifying relevant fire propagation factors.

Cyber-Physical Systems (CPS) are helping to make smart cities a reality, thanks to technological advancements in the construction industry. Through use of Information Communication Technologies (ICT), CPS integrates information and the physical world. Integrating Building Information Models (BIM) with IoT and sensor using CPS is becoming a more common goal in the industry. The adoption of digital twins in the future will open up new opportunities for CPS using tracking, simulation, and optimization strategies. Researchers, on the other hand, often overlook the cybersecurity implications. Assimilation of BIM data and cybersecurity principles has been difficult so far, and as a result, cybersecurity has been neglected. Specifically, this research will address research question; What are the key cybersecurity needed practices of built environment stakeholders? To answer these questions, this paper will survey the research of adoption of the cyber-physical system and digital twins regarding the industry. and determine obstacles to the adoption of cybersecurity for digital twins/CPS in the built environment. The findings of this study validated the value of using digital twins in industry. As a result, this paper assesses the use of digital twins in CPSs in order to determine the practices for bolstering cybersecurity and integrating digital twin and city standards in future urban cities.

Building Information Modelling (BIM) paves the way to more effective multidisciplinary collaborations, with a total lifecycle and supply chain integration perspective. BIM is also helping the sustainability agenda because the digitalisation of product and process information provides a unique opportunity to optimise energy efficiency (EE) related decisions across the entire lifecycle and supply chain. This study aims to broaden the BIM training agenda to support Saudi Arabia's building EE agenda. It requires a broad awareness and expertise in BIM practice across different asset types and different roles in the industry. This study will endeavour to enhance the skills, qualifications and capabilities of construction practitioners (from high professionals to blue-collar workers). This will increase the market penetration and adoption of key technological development in BIM, given the timeliness of the need for training in combined green and functional performance engineering. Our interviews focus on understanding and utilising BIM as a toolset to optimise energy in the construction industry across the lifecycle of a building. As such, the authors focus on demonstrating the benefits of BIM in maximising EE and sustainable outcomes.

The recent ongoing digital transformation of the energy landscape provides new opportunities to decarbonise energy-intensive industries, including the fish processing sector. The paper explores the potential of deploying multi-vector smart micro-grid solutions in fishery ports, sourced from dispatchable renewable generation, including solar energy. This is demonstrated in the Milford Haven Port in South Wales, United Kingdom. The proposed system is modelled using control scenarios developed based on data and energy models of the port. The control scenario makes energy use decisions based on the availability of dispatchable renewable sources and the price of energy from the local energy market. Also, we consider local energy storage by utilising the local electric fishing boat fleet as an alternative energy storage system. The results demonstrate optimised energy use through multi-vector smart micro-grid model by providing more than 70 percent reduction of energy use from grid.

This paper presents a cloud-based building energy management system, underpinned by semantic middleware, that integrates an enhanced sensor network with advanced analytics, accessible through an intuitive Web-based user interface. The proposed solution is described in terms of its three key layers: 1) user interface; 2) intelligence; and 3) interoperability. The system’s intelligence is derived from simulation-based optimized rules, historical sensor data mining, and a fuzzy reasoner. The solution enables interoperability through a semantic knowledge base, which also contributes intelligence through reasoning and inference abilities, and which are enhanced through intelligent rules. Finally, building energy performance monitoring is delivered alongside optimized rule suggestions and a negotiation process in a 3-D Web-based interface using WebGL. The solution has been validated in a real pilot building to illustrate the strength of the approach, where it has shown over 25% energy savings. The relevance of this paper in the field is discussed, and it is argued that the proposed solution is mature enough for testing across further buildings.

Fish processing industries involve the usage of energy-intensive equipment, such as refrigerators, air conditioners and ice making machines leading to high energy costs and, indirectly, to an increase of the carbon emissions. As most fish industries sites are old, there is a strong need to make them more sustainable and achieve economic competitiveness in the energy market. Micro-grids have been utilised as efficient solutions in energy-intensive industries greatly balancing energy consumption and production at different scales. Smart micro-grids can also reduce carbon emissions by using renewable energy resources and applying smart energy management techniques.In this paper, we propose a smart micro-grid system for fish-processing industries with a validation use-case at Milford Haven Port in South Wales, UK. The system has been modelled using EnergyPlus and Matlab with the infinite grid, renewable energy resource, battery and charge/discharge controllers utilized for optimising energy consumption and production and for reducing carbon emissions. The preliminary results show that local power demand can meet the local power generation with the implementation of smart energy management techniques to support decision making for fish-processing industries.

There has been a recent decline in the use of vernacular building traditions, which have been replaced by modern methods of construction. However, many academics and researchers have emphasized the significance of vernacular architecture, due to such technologies being the result of centuries of experiments focussed on adaptation to local climatic conditions. One of the best-known vernacular architecture elements is the Rawshan (Mashrabiyyah), which is not only aesthetically appealing and improves the indoor environment, but also fulfils a vital social/religious role, i.e. privacy. A Rawshan increases daylight penetration while minimizing energy consumption, thus positively contributing to the wellbeing of occupants. However, in order to optimise comfort, there is a need to reconcile the amount of daylight entering the living space with heat gain during summer and heat loss during winter. This research employed a genetic algorithm to optimise the design of a Rawshan for a residential building, with the aim of enhancing the occupants’ comfort while minimising energy consumption. Grasshopper was used for modelling the residential building and Galapagos for programming and configuring a genetic algorithm. The results reveal that this proposed genetic algorithm-based Rawshan design enhanced comfort, while minimising energy consumption and reducing the negative impact of solar gain on energy consumption.

As devices existing at the edge of the network improve in their processing and data storage capacity, there is increasing potential to host and enact services on such devices. A workflow that was traditionally enacted on a data centre can be fragmented across both edge and data centre hosted resources. The following aspects are investigated in this work: (i) mechanisms for dividing a workflow across edge and cloud/data centre resources; (ii) service hosting environments that can be shared across edge and data centre resources; (iii) performance metrics that can influence service placement and selection. An "edge orchestrator" is a resource manager that makes such decisions on the behalf of a user application, and which may be centralised or distributed. An industry scenarios is used to illustrate decision points that influence such choices within an edge orchestrator. The overall objective considered is the completion of the workflow within some deadline constraint by the edge orchestrator.

Estimating energy costs for an industrial process can be computationally intensive and time consuming, especially as it can involve data collection from different (distributed) monitoring sensors. Industrial processes have an implicit complexity involving the use of multiple appliances (devices/ sub-systems) attached to operation schedules, electrical capacity and optimisation setpoints which need to be determined for achieving operational cost objectives. Addressing the complexity associated with an industrial workflow (i.e. range and type of tasks) leads to increased requirements on the computing infrastructure. Such requirements can include achieving execution performance targets per processing unit within a particular size of infrastructure i.e. processing & data storage nodes to complete a computational analysis task within a specific deadline. The use of ensemblebased edge processing is identifed to meet these Quality of Service targets, whereby edge nodes can be used to distribute the computational load across a distributed infrastructure. Rather than relying on a single edge node, we propose the combined use of an ensemble of such nodes to overcome processing, data privacy/ security and reliability constraints. We propose an ensemble-based network processing model to facilitate distributed execution of energy simulations tasks within an industrial process. A scenario based on energy profiling within a fisheries plant is used to illustrate the use of an edge ensemble. The suggested approach is however general in scope and can be used in other similar application domains.

Modern, decentralised, multi-energy vector districts have great potential to reduce energy consumption and emissions. However, due to the complex nature of these systems, they require intelligent management to maximise their benefit. Therefore, this paper models the energy generation of a district heating plant for the purpose of hourly, operational optimisation. Crucially, non-linear, part-load efficiency curves, and minimum load percentages are included in the energy generation modelling as well as thermal energy storage. Due to the non-linearities, a genetic algorithm, optimisation approach was utilised. The optimisation framework was applied to a case study district with three distinct thermal energy generation sources, a gas CHP, gas boilers, and biomass boilers. The optimisation controlled the load percentage of each technology as well as varying thermal storage capacity to minimise the cost of meeting the heat demand. The study found that compared to the current, rule-based approach, the optimisation achieved a significant cost saving of 12.7% without any thermal storage. As the thermal storage capacity was increased the potential cost saving was also shown to increase proportionally to 22.6% with 1000 kWh of storage.

This paper introduces a novel, adaptive, heating set point scheduler that aims to minimise the heating energy consumption in a building while maintaining thermal comfort levels. The presented control strategy couples two computational intelligence techniques, an Artificial Neural Network, ANN and a Genetic Algorithm, GA. The ANN surrogate model was trained using data from multiple Energy Plus building simulations with varied heating set points. This allows quick, computationally cheap, calculation of a fitness function, opposed to time consuming Energy Plus simulations. The ANN's inputs include weather and occupancy predictions as well as taking account of the buildings thermal inertia to predict energy consumption, predicted percentage dissatisfied, PPD, and indoor temperature. A multi-objective GA uses the ANN to calculate the energy sum over 24 hours and the average occupied PPD as its two objectives. The optimisation strategy was applied on a week in January over which it reduced energy consumption by 4.93% and improved PPD by 0.76%. The great advantage of this controller is that it could be simply adapted to take account of the changing energy environment. This could include an addition of renewable resources or demand response controls as part of a district heating network.

The 2008 Wenchuan earthquake is one of the largest and deadliest events of the last century, both in terms of victims and seismically-triggered geo-environmental hazards. The combined effects resulting from the seismic activity of 2008 led to major damages in several domains of the local society, including the built environment and the social component, highlighting the extensive gap related to resilience measures aimed at both prevention and recovery in case of a disruption. Thus, it is imperative for the development of a multi-objective and comprehensive formulation for resilience combining holistically all the diverse facets of a local community. To this regard, this paper presents a wide-spectrum analysis and preliminary findings, being structured in working threads specifically addressed in the context of an ongoing project (REACH). The focus is on the built environment damage analysis following the field work which took place in December 2016 in the Wenchuan territory, involving 3D laser scanning activity of 7 high-risk areas and the related audit and preliminary structural analysis of the damage. As a result, a set of external and internal factors have been identified as possible key causes of the surveyed damages. Of these, external factors mainly relate to environmental and hazardous properties, whereas internal indicators involve structural features as well as building regulations.

Handling real world, acquired point cloud data within a sector such as architecture, engineering, and construction (AEC) is currently a difficult task. A highly desirable, future goal is to fully automate the scan-to-BIM process which at this time has a high dependency on manual work effort. Improvements within this workflow will speed up the production of detailed 3d building models and reduce associated costs. By increasing the level of automation in the scan-to-BIM process it becomes possible to speculate the expansion of the typical use case from a single structure, targeted acquisition towards urban area data collection and modelling. The scale and characteristic differences of an urban area point cloud dataset and that of a single structure create opportunities to validate the applicability of novel analytical approaches to process automation. A decrease in process complexity could be achieved by reducing both the depth of prerequisite knowledge and the level of intervention expected from an operator by a modelling platform. This would also provide an alternative perspective and an opportunity to model operator tasks at a higher, more abstract level. There lacks a completeness of modern documentation within preexisting civil structures. Building information modelling of the as-built condition can reduce overheads associated with key areas such as collaboration, maintenance, and future modifications. However, it is often made more difficult by a lack of accurate documentation due to the age of the building. A common trend that can be observed in countries such as the United Kingdom is that much of future building stock is already standing. Therefore, it is apparent the need to record the detailed, as-built condition of structures essentially from scratch will not resolve itself in the near future. This paper will overview a case study of an urban area modelling conducted in Ebbw Vale, Wales and introduces an abstract scan-to-BIM process automation methodology. This will be supported by a review of a selection of applied research literature. This paper is part of the early development stages of a point cloud processing platform, pcl_toolkit. The command line software aims to simplify approaches commonly associated with various point cloud processing tasks and provide a foundation for rapid development in the near future.

This study presents the anonymization of consumer data in a district-level smart grid using the k-anonymity approach. The data utilized in this study covers the demographic information and associated energy consumption of consumers. The anonymization process is implemented at the prosumer level, considering their importance in sharing flexibility and distributed generation at the low voltage grid, and the fact that they need to interact with each other and the grid while keeping their data private. The proposed approach is tested under three anonymization scenarios: prosecutor, journalist, and marketer. The smart grid data are investigated mostly under the prosecutor scenario with three risk levels: lowest, medium and highest. The results of the k-anonymity approach are compared to k-map and k-map + k-anonymity. No difference has been found between the three investigated approaches for the selected data set. Since, the aim of the k-anonymity is to not transform the information about any individual record among those k-1 individuals, the recorded type and the number of attributes play a key role in the anonymization process. One of the risks is the using continuous attributes in the anonymization process which may cause the information lose in the anonymization process such as near real-time energy consumptions. Hence we have focused on to anonymization of the consumers' demographic information, rather than their energy consumption.

The application of information and communications technology to support urban operational decision makers has received vast interest from industry and academia. This has helped to mature several fields of research within the smart city domain, such as the internet of things, cybernetics, and informatics. However, these fields of research remain siloed, which leads to a clear gap in the literature. The paper recognizes the mentioned gap manifesting in a new smart urban area in Wales, UK, and presents a platform which intends to demonstrate the benefits of exploiting the synergies between these fields of research. Following consultation with various stakeholders at the pilot site, the platform utilizes advanced sensing, analytics, interoperability, and visualization components to provide valuable human-machine interactions to facility managers in the district. Delivering this high value knowledge in a timely, engaging, and accessible manner through advanced decision support interfaces. The paper presents the platform's software architecture, before discussing the decision support interface, intelligent web services, and interoperability components in more detail. The solution's key contributions beyond existing internet of things platforms are the use of a 3D game engine, machine learning and optimization web services, and the integration across the knowledge value chain. This knowledge integration is achieved through semantic modelling of the buildings, urban environment, socio-technical systems, and smart devices in the district.

District heating (DH) is a promising energy solution to alleviate environmental negative impacts caused by fossil fuels. Improving the performance of DH systems is one of the major challenges to promote larger scale adoption. This paper presents a dynamic simulation of a DH distribution network located in Ebbw Vale, south Wales. A numerical simulation model is developed in Simscape/Simulink to analyze heat losses in the distribution network at different periods of the week. Results show that heat losses in the network vary between 1–2% during weekday daytime, while they increase to 8–12% at night. Supply and return temperatures of each building are presented and simulation results are in line with measured data. In addition, node flow rates and node temperatures are analyzed. This model can be used to provide reference for selecting the best pipe configuration, including size and insulation materials to minimize heat losses.

Potable water distribution networks (WDNs) and wastewater collection networks (WWCNs) are the two fundamental constituents of the complex urban water infrastructure. Such water networks require adapted design interventions as part of retrofitting, extension, and maintenance activities. Consequently, proper optimization methodologies are required to reduce the associated capital cost while also meeting the demands of acquiring clean water and releasing wastewater by consumers. In this paper, a systematic review of the optimization of both WDNs and WWCNs, from the preliminary stages of development through to the state-of-the-art, is jointly presented. First, both WDNs and WWCNs are conceptually and functionally described along with illustrative benchmarks. The optimization of water networks across both clean and waste domains is then systematically reviewed and organized, covering all levels of complexity from the formulation of cost functions and constraints, through to traditional and advanced optimization methodologies. The rationales behind employing these methodologies as well as their advantages and disadvantages are investigated. This paper then critically discusses current trends and identifies directions for future research by comparing the existing optimization paradigms within WDNs and WWCNs and proposing common research directions for optimizing water networks. Optimization of urban water networks is a multidisciplinary domain, within which this paper is anticipated to be of great benefit to researchers and practitioners.

The use of cloud computing for AEC (Architecture/ Engineering/ Construction) projects has been intensively explored in recent research. Companies have adopted cloud computing for storing the massive amount of data that the AEC industry generates. In most cases these cloud systems are private, isolated and limited in terms of the actual computing operations. With the emergence of "multi-Clouds" or Cloud-of-Clouds environments it has become possible to link such systems and create a distributed environment. The key benefits suggested for such an aggregation is: (i) reduced reliance on a single cloud infrastructure, (ii) improved fault tolerance (whereby failure in one Cloud system does not render the entire infrastructure inoperable), (iii) improved security -- similar argument to fault tolerance, i.e. a breach in one Cloud system does not impact the entire infrastructure, (iv) the ability to utilise capability (and data) that may only be available in one Cloud system and not be easily transferable to another. Such infrastructure becomes efficacious when multiple individuals and organisations work collaboratively. In this paper we present a coordination model for distributed clouds where the interacting parties are represented by AEC industry organisations. We present the underlying architecture that has been implemented as part of the "Clouds-for-Coordination"(C4C) project and provide a real use case where different organisations work jointly on an AEC project. We validate our system by measuring an "aggregated time-to-complete" metric over a number of different scenarios.

Many applications require data to be captured and processed in real time, migrating all the data to a central site prior to analysis, a procedure that can create significant overhead. Examples include the variety of sensor network-based applications, where sensors interface with real world artifacts and must respond to physical phenomenon that cannot be predicted apriori. The amount of data likely to be generated by a sensor and processing requirements in such applications can not be pre-determined (as they are often dependent on the rate of change of the physical phenomenon being measured and potential occurrence of "trigger events" which are non-deterministic). We propose the use of a multilayer Cloud infrastructure that distributes processing over both sensing nodes, multiple intermediate/gateways nodes and the more complex centralised data centre. Such layers need to work in coordination to ensure more reliable and efficient use of computing and network resources, preventing the need to move data to a central location when this is not necessary and creating data processing paths from data capture to analysis. We outline the basis for a decision function that evaluates: (i) where processing should be carried out, (ii) what processing should be undertaken centrally vs at an edge node, (iii) how processing can be distributed across multiple data centre locations to achieve QoS and cost targets. We present a prototype that has been implemented using the CometCloud system, deployed across three sites in the UK and the US and validate using an application which calculates energy flow in a Sports facility in Italy.

With increasing availability of instrumented infrastructures in built environments, it is necessary to understand how such data will be stored, processed and analysed in a timely manner. Many "smart cities" applications, for instance, identify how data from building sensors can be combined together to support applications such as emergency response, energy management, etc. Enabling sensor data to be transmitted to a Cloud environment for processing provides a number of benefits, such as scalability and elastic provisioning of computational resources - as the total data size may not be known apriori. In this application-based case study, we describe the integration of an in-building sensor network (both for sensing and actuation) with a distributed Cloud environment. Energy optimisation in buildings represents a class of problems that requires significant computational resources and generally is a time consuming process. We describe the use of Cloud computing for efficiently running and deploying EnergyPlus simulations with sensor data in order to fulfil a number of energy related objectives for buildings. We describe and evaluate the establishment of such a sensor based application using a CometCloud implementation with data collection from a real building pilot. Although our focus is on a single application, the general architecture and analysis carried out can be generalised to other similar scenarios.

This paper presents the ICT components and tools that will be developed to optimize the design and perform realtime energy management of low carbon districts in the context of the EC-funded RESILIENT project. This ICT framework will provide all needed ICT support to the various stakeholders, including energy experts, business experts, pilot managers, endusers, and will be validated in the context of three real pilot demonstrators.

Executive staff of public research and development (R&D) organisations worldwide are continuously faced with the challenge of exploring several innovation trajectories to attain their vision. The decision making in this paper is reliant on the Analytic Hierarchy Process (AHP) conducted in a case study drawn from Thailand, an example of a developing country where public R&D dominates the research landscape in science and technology. The AHP study provides a set of innovation factors as the criteria to meet the goal of selecting the highest impact plan on innovation factors. The findings reveal that the plan focusing on commercial orientation has higher impact than the plan focusing on societal orientation. However, given that the selected case study is a taxpayer-funded public R&D organisation, the societal expectations have to be factored into its innovation plan. A sensitivity analysis was therefore employed, which indicated potential for a mixed orientation considering both the commercial and societal orientation plans.

Information System Development Methodologies (ISDM) have attracted a lot of interest and research from industry and academia. However, the majority of studies of ISDM adoption have been widely undertaken to highlight the experience of developed countries, whereas, very little has been done to address ISDM adoption in developing countries. Studies concerned with the status of ISDM usage in many developing countries including the factors that influence and motivate their use, current trends, difficulties, and barriers to adoption are lacking. This paper examines these identified gaps in a developing country, namely Oman. The initial findings reveal that there is limited knowledge and understanding of the concept of ISDM amongst Omani Information Systems (IS) personnel. This is reflected in the quality of the software products being developed and released. However, the analyzed data also reveals a trend whereby a majority of Omani organizations are gradually moving towards increased ISDM adoption and deployment.

The increasing availability of Internet services has stimulated the development of peer-to-peer markets. These electronic markets have the potential for improving the efficiency of trading by reducing search and transaction costs. They also allow buyers to choose the best possible service for every transaction and interact with different sellers over time. In such markets, the establishment of trust is mandatory in order to ensure reliable market exchanges. We identify how trust can be established within a trading community, making use of pre-established Service Level Agreements between participants. The effectiveness of the approach in selecting trusted participants is evaluated via simulation.

The paper provides a comparative study exploring factors affecting information security awareness of end users, including information system decision makers, in the context of three higher education environments: the US, the UK, and the UAE. An interpretive case-study approach is employed for the UK and the UAE using multiple data gathering methods, while for the US research relies on secondary data drawn from a large- scale survey. The concept of design theorizing is used to interpret and discuss the results. The research reveals a number of similarities between the three studied environments while identifying in the case of the UAE a number of key socio-cultural factors, including conscientiousness, cultural assumptions and beliefs, and social conditions affecting university end users behavior and attitude towards work, in general, and information security awareness, in particular. A model is proposed to initiate and promote IS security awareness in the studied environments.

To improve the performance of building facility management tools and optimize operational costs, a semantic model is being developed that is synchronized in almost real time with a range of sensors throughout the building. The semantic aspect of the model will be comprised of novel ontological perspectives with identified scopes and restricted domain theories that will simplify the modeling representations to deliver easier knowledge maintenance and continued integrity of the overall system. Specialized ontologies are linked to shared higher level abstract ontologies (to facilitate interoperation), and target a wide range of domains including those to capture physical processes and product models.

The paper identifies socio-technical requirements to promote the adoption of alliance modes of operation by SMEs in the construction sector. A multiple case study approach is used to research and clarify a number issues, including: (a) the concept of an SME alliance and its key features, (b) business relationships management in an SME alliance and the role of a broker, and (c) the role of information and communication technologies in supporting an alliance. An initial specification of a technological solution using a service-oriented architecture is given. Finally, the paper discusses innovation management in an alliance regime of peers, and provides recommendations for future research in the form of open questions to the research community.

This paper presents a new conceptual indexing technique intended to overcome the major problems resulting from the use of Term Frequency (TF) based approaches. To resolve the semantic problems related to TF approaches, the proposed technique disambiguates the words contained in a document and creates a list of super ordinates based on an external knowledge source. In order to reduce the dimension of the document vector, the final set of index values is created by extracting a set of common concepts, shared by multiple related words, from the list of hypernyms. Subsequently, a weight is assigned to each concept index by considering its position in the knowledge source's hierarchical tree (i.e. distance from the substituted words) and its number of occurrences. By applying the proposed technique, we were able to disambiguate words within different contexts, extrapolate concepts from documents, assigning appropriate normalised weights, and significantly reduce the vector dimension.

The research presented in this paper focuses on the pre-processing stage of the clustering process, proposing a novel indexing technique which goes beyond the syntax of terms; trying to capture their unambiguous meaning from their context and to derive a set of concepts to be used to represent the documents. This approach overcomes some of the major drawbacks deriving from the use of bag of words and term frequency based indexing techniques. The proposed approach is evaluated by using unsupervised performance measures and by comparing the clustering results achieved against the ones obtained when using a traditional indexing method. The experimental results show that better clustering results are achieved through the use of the proposed indexing approach, which also led to a substantial reduction of the index term dimension.

This paper provides a review of knowledge management (KM) literature by adapting and extending McElroy's [1] KM generations model. An interpretive stance is adopted so as to provide a holistic understanding and interpretation of organizational KM research and related Knowledge Management Systems (KMS) and models. It is argued that to be effective organizations need not only to negotiate their migration from a knowledge sharing (first generation) to a knowledge creation (second generation) culture, but also to create sustained organizational and societal values. The latter forms the third generation KM and represents key challenges faced by modern organizations.

The objective of this empirical study is to review current knowledge management (KM) practices in Thailand, and in particular to explore the knowledge sharing maturity and capability of Thai organizations and their readiness to embrace a knowledge creation culture. The survey focuses on a number of organizations drawn from an established taxonomy of organizations in Thailand using the stratified random sampling technique. The results indicate that (a) there is an established knowledge sharing culture supported by a corporate KM strategy, amongst high-tech organizations, as well as a preparedness for change to migrate to a knowledge creation culture; (b) a number of international organizations exhibit ad-hoc knowledge sharing practices as well as a growing awareness about the value of knowledge sharing and what can be termed as intangible assets; (c) a clear lack of any knowledge-oriented practice within ministry and department of public sectors. This empirical study provides a foundation to further the research and develop a framework for Thai organizations that promotes adoption of knowledge sharing practices as well as the migration from a knowledge sharing to a knowledge creation model.

Information modelling for the construction industry can address the fragmentation, multitude of professions and companies that often require collaboration and data exchange. Construction projects involve various professions, including design teams, contractors, facility managers, product manufacturers and suppliers, user associations, clients and investors, and local/regional/national/international authorities. The increasing complexity of buildings is reflected in the continuous introduction of new procurement paths and methods, construction technologies, materials and construction methods to meet various economic, environmental and societal challenges. To address this level of complexity Building Information Modelling (BIM) can create synergies and support collaboration not only between traditional disciplines and roles (architecture, structure, mechanical and electrical), but also support many new professions and skills in areas such as energy, environment, waste and connected objects / Internet of Things.In this paper, we explore the dynamic nature of BIM with associated skills and roles and demonstrate how engagement and training can be informed by social media analysis to identify roles, skills and training needs. We conduct a data mining process by analysing the Twitter data of various companies and institutions involved in the BIM construction sector to discover new skills and roles for energy efficiency.