Phases Of Life Cycle Research Articles

CIMLA: A Modular and Modifiable Data Preparation, Organization, and Fusion Infrastructure to Partially Support the Development of Context-aware MMLA Solutions

Multimodal Learning Analytics (MMLA) solutions aim to provide a more holistic picture of a learning situation by processing multimodal educational data. Considering contextual information of a learning situation is known to help in providing more relevant outputs to educational stakeholders. However, most of the MMLA solutions are still in prototyping phase and dealing with different dimensions of an authentic MMLA situation that involve multiple cross-disciplinary stakeholders like teachers, researchers, and developers. One of the reasons behind still being in prototyping phase of the development lifecycle is related to the challenges that software developers face at different levels in developing context-aware MMLA solutions. In this paper, we identify the requirements and propose a data infrastructure called CIMLA. It includes different data processing components following a standard data processing pipeline and considers contextual information following a data structure. It has been evaluated in three authentic MMLA scenarios involving different cross-disciplinary stakeholders following the Software Architecture Analysis Method. Its fitness was analyzed in each of the three scenarios and developers were interviewed to assess whether it meets functional and non-functional requirements. Results showed that CIMLA supports modularity in developing context-aware MMLA solutions and each of its modules can be reused with required modifications in the development of other solutions. In the future, the current involvement of a developer in customizing the configuration file to consider contextual information can be investigated.

Adapting the CROPGRO model to simulate growth and yield of guar, Cyamopsis tetragonoloba L, an industrial legume crop

Guar (Cyamopsis tetragonoloba L.) is a relatively unexplored seed legume crop with a high interest for production of galactomannan (guar gum), which is highly valued for industrial purposes. Until now no crop simulation models exist for guar. This paper describes the adaptation of the CROPGRO module of the Cropping System Model (CSM) to simulate guar growth and yield based on literature knowledge as well as optimization against growth analysis and yield data collected in multiple field experiments conducted in Texas and New Mexico. CSM-CROPGRO is a mechanistic model that simulates daily crop growth and development dependent on daily weather, soil properties, crop management, and genetic parameters. Model adaptation started with CROPGRO-Soybean as the template and involved setting tissue compositions, and especially the cardinal temperature dependencies of processes including the rate of leaf appearance, rate of progress to reproductive events, photosynthesis, pod addition, and seed growth rate, all parameterized in the species file. Adapted cultivar parameters include life cycle phase durations, duration to end of leaf area expansion, pod adding duration, leaf photosynthesis, seed size, seed protein, seed lipid, and individual seed filling duration. Compared to soybean, guar appears more sensitive to cold temperature because the cardinal base temperatures for photosynthesis, rate of leaf appearance, leaf area expansion, and seed growth had to be increased by 2–4 ˚C. The model adaptations highlight the drought tolerance of guar, with slower leaf and root senescence and greater tolerance of N-fixation under drought. The model simulated well the time-series growth of two cultivars under irrigated and rainfed conditions at Chillicothe, TX and gave acceptable simulations of end-of-season biomass, pod, and seed yield of guar in response to different weather, irrigation, soils, and seasons across three sites. This is the first creation of a crop model for guar and will be released in DSSAT.

Microhabitat sharing for basking between squamate species in Poland

Aggregations (e.g. group basking) by snakes are usually limited to specific life cycle phases (e.g. mating) or are a consequence of drastic environmental changes (e.g. habitat destruction), high prey densities or highly limited resources within an environment (e.g. basking sites, wintering dens). Here, we report intra- and interspecific observations of four reptile species (primarily Natrix natrix and Vipera berus) sharing basking sites at the confluence of the rivers Dunajec and Poprad near the town of Stary Sącz in southern Poland. From a total of 84 records in the field between 2020–2022, there were 11 interactions from 24 July 2020 to 1 May 2022. Previous studies have indicated direct competition or interference in many species, which we did not observe. There is a noticeable lack of such observations of microhabitat sharing for basking between squamate species in scientific literature. Hence, the accumulation of such observations has the potential to reveal new insights into the behaviour and ecology of N. natrix and V. berus.

Digital Twins along the product lifecycle: A systematic literature review of applications in manufacturing

Background: The evolution of product expectations in the era of mass customization implies an improvement and a better control of individualized creation and production processes throughout the product lifecycle. The application of the digital twin (DT) seems to be a favoured solution in this context, but its study during the lifecycle of a product has only been partially evoked in the literature. Methods: The purpose of this state-of-the-art article is to identify current trends of applications of the digital twin concept in the literature under two main dimensions: its nature regarding the interaction structure between digital and physical objects (defined as: Pre-Digital Twin, digital model, digital shadow, and digital twin), but also its applications, along the product lifecycle (Design, Production, Operation, Disposal). To achieve this analysis a systematic literature review was carried out. The publications selection was based on the presence in this of a case of application of a digital twin with a focus in the Manufacturing sector. Within this review, 188 scientific papers were compiled and analyzed. Results: Results showed that although the term digital twin is widely used, the deployment of DT technologies in manufacturing is still at an early stage as most of the digital twin applications were in fact prototypes focused on the real-time observability of the physical system, either for optimization or predictive maintenance. Moreover, regarding the product lifecycle, most of the applications have been focused on the production and operational phases whereas those at the design and disposal phases are still limited. Conclusions: This paper presents an original approach to the study of digital twins, not focusing on a single application area or lifecycle phase but which aims to establish future perspectives on the use of digital twins along the lifecycle.

Revisiting the concept of waste and its causes in construction from analytical and conceptual perspectives

The phenomenon of waste in the construction industry has received considerable attention from the project professionals since the emergence of lean construction. In the research community, however, identifying causes of waste together with waste detection and elimination in the context of construction have been addressed in a limited manner. Moreover, there are very few studies, if any, which have tried to revisit the concept of waste ontologically and to look into the causes of waste through the lens of their relation to other elements in construction projects such as delivery elements and lifecycle phases. Therefore, this study aims to fill this knowledge gap through developing a novel conceptualization of waste and exploring causes of waste in construction projects to reveal their connection to various project attributes. To do so, a systematic literature study was conducted through which relevant studies were located and analysed to achieve the purposes of this study. The findings propose a novel conceptualization of waste and value based on their overlooked components. Moreover, the results present a model which reveals the connection between the identified causes of waste and project attributes (life cycle phases, relevant party, project delivery element, and waste categories in general). The obtained results can be insightful for project practitioners and the research community to reach in-depth understanding of the waste concept.

Prognostic interictal electroencephalographic biomarkers and models to assess antiseizure medication efficacy for clinical practice: A scoping review.

Antiseizure medication (ASM) is the primary treatment for epilepsy. In clinical practice, methods to assess ASM efficacy (predict seizure freedom or seizure reduction), during any phase of the drug treatment lifecycle, are limited. This scoping review identifies and appraises prognostic electroencephalographic (EEG) biomarkers and prognostic models that use EEG features, which are associated with seizure outcomes following ASM initiation, dose adjustment, or withdrawal. We also aim to summarize the population and context in which these biomarkers and models were identified and described, to understand how they could be used in clinical practice. Between January 2021 and October 2022, four databases, references, and citations were systematically searched for ASM studies investigating changes to interictal EEG or prognostic models using EEG features and seizure outcomes. Study bias was appraised using modified Quality in Prognosis Studies criteria. Results were synthesized into a qualitative review. Of 875 studies identified, 93 were included. Biomarkers identified were classed as qualitative (visually identified by wave morphology) or quantitative. Qualitative biomarkers include identifying hypsarrhythmia, centrotemporal spikes, interictal epileptiform discharges (IED), classifying the EEG as normal/abnormal/epileptiform, and photoparoxysmal response. Quantitative biomarkers were statistics applied to IED, high-frequency activity, frequency band power, current source density estimates, pairwise statistical interdependence between EEG channels, and measures of complexity. Prognostic models using EEG features were Cox proportional hazards models and machine learning models. There is promise that some quantitative EEG biomarkers could be used to assess ASM efficacy, but further research is required. There is insufficient evidence to conclude any specific biomarker can be used for a particular population or context to prognosticate ASM efficacy. We identified a potential battery of prognostic EEG biomarkers, which could be combined with prognostic models to assess ASM efficacy. However, many confounders need to be addressed for translation into clinical practice.

Corruption Propensity and Mitigation at Different Phases of Infrastructure Development

Corruption is a social evil that resonates far and deep in societies, eroding trust in government, weakening the rule of law, impairing economic development, and exacerbating poverty, social tension, and inequality. Each phase of infrastructure development life cycle has peculiar vulnerabilities to corrupt practices. Pre-emptive identification of avenues for corrupt actions at each phase can serve as a starting point for new initiatives or for renewed reinforcement of existing corruption mitigation programs. The existing literature on corruption does not address the issue from an infrastructure life cycle perspective. The objective of this paper is to identify opportunities at each phase of the infrastructure life cycle where stakeholders and actors can help undertake specific actions to mitigate corruption. The paper’s methodology characterizes corruption and mitigation measures in a framework based on the phases of infrastructure development life cycle and then connects these measures to the relevant actors and stakeholders.

Extended reality (XR) technologies in the construction safety: systematic review and analysis

PurposeExtended reality (XR) is an emerging technology, with its popularity rising in different industry sectors, where its application has been recently considered in construction safety. This study aims to investigate the applications of XR technologies in the safety of construction through projects lifecycle perspective.Design/methodology/approachScientometric analysis was conducted to discover trends, keywords, contribution of countries and publication outlets in the literature. The content analysis was applied to categorize previous studies into three groups concerning the phase of lifecycle in which they used XR.FindingsResults of the content analysis showed that the application of XR in the construction safety is mostly covered in two areas, namely, safety training and risk management. It was found that virtual reality was the most used XR tool with most of its application dedicated to safety training in the design phase. The amount of research on the application of augmented reality and mixed reality in safety training, and risk management in all phases of lifecycle is still insignificant. Finally, this study proposed three main areas for using the XR technologies regarding the safety issues in future research, namely, control of safety regulations and safety coordination in construction phase, and safety reports in the operation phase.Originality/valueThis paper inspected the utilization of all types of XR for safety in each phase of construction lifecycle and proposed future directions for research by addressing the safety challenges in each phase.

Relevance of Environmental Factors in the Steel Life Cycle for a Transition toward Circular Sustainable Production and Consumption Systems: A Joint Bibliometric and Bibliographic Analysis

The design of steel structures has evolved thanks to the increased ability to model the behavior of more complex structures. However, further constraints arise from the need for a transition toward more sustainable production and consumption systems. In particular, the assessment of the economic benefits and efficiency of existing production systems should be integrated with assessment of environmental, economic and social sustainability. In the case of steel, and limited to the environmental dimension, the literature covers various study areas, ranging from the analysis of resource flows to the assessment of steel’s environmental impacts. However, an integrated view of existing peer-reviewed studies is currently missing. The purpose of this work is to overcome this shortcoming with a review that considers and integrates research on the steel life cycle from various perspectives: analysis of material flows; quantification of emissions; environmental monitoring and indicators; and circular economy aspects, including reuse and recycling. This study is based on a deep bibliometric and bibliographical analysis of the above-cited aspects, including the key topics, authors and journals, to single out some potential research directions that have previously been neglected. The results of the analyses indicate that, even though discussed in the literature, the redesign of products is still lacking adequate consideration. The same gap was also evidenced when it came to studies on the management of waste materials and recommissioning. There is also still a lack of knowledge on the possible meaningful indicators of environmental sustainability in the case of steel. Moreover, while digital technologies that enable sustainability are being intensely developed and widely implemented, the design, testing and application of sensors for the environmental monitoring of steel production is under-studied and the interaction of environmental factors with steel structures is poorly addressed. Finally, this work evidenced poor attention with respect to water and soil pollution generated in different phases of the steel life cycle. All these aspects should be considered in future research, which would also have a beneficial effect in the implementation of informed policies for a transition toward a circular and sustainable steel life cycle.

СОВРЕМЕННЫЕ ТЕХНОЛОГИИ, ИСПОЛЬЗУЕМЫЕ НА СТАДИИ ПРОИЗВОДСТВА, ПРИМЕНИТЕЛЬНО К РАЗЛИЧНЫМ ФАЗАМ ЖИЗНЕННОГО ЦИКЛА ИЗДЕЛИЙ И ДЕТАЛЕЙ МАШИН

The study objective is to identify modern production technologies used at almost all phases of the life cycle. To achieve this objective, there have been analyzed and reviewed papers and foreign studies published mainly within 2016-2020 in journals covered by SCOPUS and WoS indexing services, as well as studies of Russian scientists over the past ten years, which are indexed in the above-mentioned international services, as well as in the national RSCI system. The references analyzed in this paper prove that additive technologies are inferior to combined (additive-subtractive, additive-subtractive-hardened, etc.) technologies from the point of view of obtaining high-quality products at the present stage of production development. Conclusions: it is proposed to include the stage of capital repairs and updating of the general model at the stage of modeling the life cycle of machine-building products, which can be the right direction for choosing the most effective technology regardless of the life cycle phase; the choice of technology for the production stage of a particular product or part should be justified in terms of improving the basic operational properties and increase of the residual resource.

Security Best Practices: A Critical Analysis Using IoT as a Case Study

Academic research has highlighted the failure of many Internet of Things (IoT) product manufacturers to follow accepted practices, while IoT security best practices have recently attracted considerable attention worldwide from industry and governments. Given current examples of security advice, confusion is evident from guidelines that conflate desired outcomes with security practices to achieve those outcomes. We explore a surprising lack of clarity, and void in the literature, on what (generically) best practice means, independent of identifying specific individual practices or highlighting failure to follow best practices. We consider categories of security advice, and analyze how they apply over the lifecycle of IoT devices. For concreteness in discussion, we use iterative inductive coding to code and systematically analyze a set of 1,013 IoT security best practices, recommendations, and guidelines collated from industrial, government, and academic sources. Among our findings, of all analyzed items, 68% fail to meet our definition of an (actionable) practice, and 73% of all actionable advice relates to the software development lifecycle phase, highlighting the critical position of manufacturers and developers. We hope that our work provides a basis for the community to better understand best practices, identify and reach consensus on specific practices, and find ways to motivate relevant stakeholders to follow them.

Challenges to the Implementation of Building Information Modeling (BIM) for Sustainable Construction Projects

Successful concepts are adopted throughout the phases of the building lifecycle to provide maximum comfort and benefits to occupiers without compromising the function of such a project. Although there is limited information on building information modeling (BIM) execution in developing countries, BIM drivers have received significant attention from different researchers, but with a limited investigation into the influence of BIM barriers on such building projects. Our goal with this research is to identify and remove any challenges that may stand in the way of using BIM in developing country construction projects. To this end, a comprehensive literature search uncovered impediments to BIM implementation. To assess the relative importance of the numerous challenges to BIM mentioned in the literature, a survey questionnaire was distributed to a sample of specialists in the construction industry. Exploratory factor analysis (EFA) was used to classify these challenges, and partial least square structural equation modeling (PLS-SEM) was created to bring attention to the most pressing ones in the context of BIM adoption. The results of this research will inform policymakers in underdeveloped nations interested in adopting BIM on the pitfalls they should avoid.

A Transformed Approach for Benchmarking the Performance of ‘Sustainable’ Infrastructure

Environmental sustainability priorities for infrastructure development have traditionally focused on aspects including minimising negative impacts in areas such as water and air quality, erosion control, biodiversity and waste management, both in compliance and voluntary frameworks. Associated project performance priorities have focused on avoiding damage beyond ‘pre-project baselines’. In contrast, ‘best practice’ regenerative performance requires infrastructure project outcomes that not only avoid damage but contribute positively to social and ecological systems. For such best practice to become mainstream, industry frameworks, standards and rating schemes must evolve. However, there is limited knowledge regarding ‘how’ regenerative performance could be encouraged as a business-as-usual infrastructure expectation. This paper therefore explores the potential for a benchmarking methodology called Ecological Performance Standards (EPS) as a transformed approach to facilitate the mainstreaming of regenerative performance expectations. Three research workshops (Phoenix, AZ, USA; Sydney and Brisbane, Australia) were undertaken to investigate the potential for this methodology in infrastructure applications. Mapping was undertaken to align the EPS process steps with associated infrastructure lifecycle phases. Research findings include the synthesis of key opportunities for capturing EPS within infrastructure sustainability rating schemes to leverage current efforts and pivot towards regenerative performance. The authors present a comprehensive matrix mapping 18 ecosystem services against the Infrastructure Sustainability (IS) Rating Scheme credits and categories, summarising where ecosystem services are addressed within the current scheme. The authors conclude the presence of significant opportunities for a new ‘business-as-usual’ for infrastructure through the integration of regenerative performance benchmarking.

Research on carbon emission standards of automobile industry in BRI participating countries

As emerging markets, the Belt and Road Initiative (BRI) participating countries have great development room for automobile industries. Yet the greater climate change pressure may hinder the development of the upstream and downstream industries chain of domestic automobile industries. Therefore, a sound carbon emission standard system should be established to guide carbon emission accounting, emissions management and so on. This study selects Singapore, Indonesia, Poland, South Africa, Pakistan as the typical research objects, and thereby analyzes the development status of carbon emissions standards in BRI participating countries’ automobile industries from the perspective of standard type, standard name, vehicle model range, target value, accounting boundaries, etc. In this paper, standard types are categorized based on management objects, such as fuel standard and carbon emissions standard. The accounting boundary, namely, the scope included in management, is divided into driving phase, life cycle phase and so on. In the meanwhile, based on comparative analysis method, China, United States (US) and the European Union (EU) are selected as the control group to study the existing problems of the carbon emission standards in BRI participating countries. The results show that the automobile industry of the BRI participating countries is still facing several problems to be solved, such as the lack of low-carbon technology, technical standard system of carbon emission, and the targeted carbon reduction policy framework, etc. Based on the above analysis results, policy recommendations have been proposed based on two categories of BRI participating countries to give scientific and reasonable advice for the construction of carbon emission standards for automobile industry.

Fingerprinting Network Sessions for the Discovery of Cyber Threats

A
 rtificial intelligence (AI) assisted cyber-attacks, within the network cybersecurity domain, have evolved to be more successful at every phase of the cyber threat lifecycle. This involves, amongst other tasks, reconnaissance, weaponisation, delivery, exploitation, installation, command & control, and actions. The result has been AI-enhanced attacks, such as DeepLocker, self-learning malware and MalGan, which are highly targeted and undetectable, and automatically exploit vulnerabilities in existing cyber defence systems
 . Countermeasures would require significant improvements in the efficacy of existing cyber defence systems to enable the discovery and detection of AI-enhanced attacks in networks in general. The challenge is that rule-and-anomaly-based intrusion detection approaches would need to be evolved into a dynamic self-learning approach before being able to discover “undetectable” network threats. The problem is that, when considering current state-of-the-art network cybersecurity countermeasures, this has not yet been achieved. One of the key challenges in achieving this is the inability to extract meaningful information from network packets. The novel solution proposed in this paper is to fingerprint network sessions. Each fingerprint is represented by a two-dimensional matrix that can be visualised, comprising a unique session key, the protocol discourse and the transmitted data. This is achieved by extracting information, summarising network session key events, encoding the received data, and merging it with existing fingerprints. The unique key and transmitted data are encoded using a Hilbert curve, while the protocol discourse is encoded into a tornado diagram. The resulting visualised network session fingerprints reveal hidden patterns that are ideal for subsequent pattern recognition, reinforcement learning (RL) or support vector machines (SVM) training to discover AI-enhanced cyber threats as they evolve.

Smart Multi-Agent Framework for Automated Audio Testing

With the widespread use of embedded software in consumer electronics, automotive industry, medical devices, and industrial environments, embedded software testing is gaining significance as an indispensable part of development and deployment of embedded products. With more than 20 years of research, development, and testing of various consumer technologies and products based on digital signal processors (DSPs) and advanced reduced instruction set computers (ARMs), we obtained insight into typical embedded development process and testing, and the pros and cons of various testing approaches and environments. In this paper, we propose the Smart Multi-Agent Framework based on IoT and Jenkins agents, customised for audio technologies in the Home Audio domain. We evaluated our solution on several complex immersive audio technologies implemented on a multicore DSP. Our uniform, customised, fully automated approach proved to be time efficient, error resilient, easy to replicate and use across all development, certification, and deployment phases of the product life cycle.

The road to zero emission shipbuilding Industry: A systematic and transdisciplinary approach to modern multi-energy shipyards

The International Maritime Organisation focuses on decarbonising the operational phase of a ship's life cycle. However, shipbuilding contributes to a significant amount of greenhouse gas emissions and air pollutants and has negative impacts on society. Holistic and transdisciplinary studies of the shipbuilding energy sector are lacking and a holistic approach is needed to discuss the potential of measures and tools to improve the shipbuilding industry with zero emissions. This study is an interdisciplinary approach to provide trends, recommendations and policies for decarbonisation of the shipping industry from a life cycle perspective. Taking into account a holistic and transdisciplinary approach, the energy sector in shipbuilding is categorised into an energy supply system, an energy economic system and an energy ecosystem, and the main disciplines for improving energy efficiency and promoting “zero emissions” for shipyards are identified, measures and tools within each discipline are proposed, and their mitigation potential and key issues for improving energy efficiency and reducing air emissions from shipyard activities are discussed. The case study highlights the economic, environmental and sustainability benefits of implementing the proposed modern energy system in an Italian shipyard. Although there is no silver bullet to eliminate air emissions in the shipbuilding industry due to the complexity, the different reduction potentials, the costs and the relationship and interaction between measures and tools, the implementation of the energy management framework can accelerate the transition to a zero-emission shipbuilding industry.

Agglomeration vs amenities? Unraveling the latent engine of growth in metropolitan Greece

Economic downturns, social change, and migrations shape population expansion and shrinkage, making city life cycles particularly complex over time and intrinsically diversified over space. Identifying local drivers of population change plays a major role when addressing metropolitan cycles of growth and decline and provides insights to any policy and planning strategy aimed at promoting together local development, economic competitiveness, and socio-environmental sustainability at large. Timing of metropolitan cycles is, however, heterogeneous and reflects the individual development path of any city. Assuming economic downturns and the associated social processes at the base of spatially heterogeneous patterns of population growth and decline in Mediterranean Europe, we adopted a spatial econometric approach investigating short-term and long-term demographic dynamics (1960–2010) in metropolitan Athens (Greece), with the aim at identifying contextual drivers of population change. Spatial regressions evaluated the role of economic and non-economic dimensions of metropolitan growth, quantifying the impact of agglomeration, scale, accessibility, and amenities at different phases of the city life cycle. Settlement models grounded on scale and agglomeration processes—with growing population in high- and medium-density municipalities—were observed under economic expansion. Recession consolidated a settlement model with population growth in socially dynamic and accessible (low density) districts with natural/cultural amenities, reflecting the inherent decline of agglomeration economies. Based on such dynamics, the polarized hierarchy of central and peripheral locations resulting from radio-centric population expansion was replaced with a settlement model grounded on population increase in “intermediate-density,” attractive locations.

Using Slutsky equation to find the hotel growth model in the destination life cycle: an empirical study in Myrtle Beach

PurposeThe purpose of this paper is to examine the hotel growth model including hotel brand, culture and life cycle phases of the Myrtle Beach, South Carolina, the fastest growing tourism destination in the United States.Design/methodology/approachCulture reflecting consuming behaviour of low-context innovators and high-context imitators is measured by the price elasticity of demand (PED). Hotel brand reflecting guests’ hotel class is measured by the income elasticity of demand. Autoregressive distributed lag has been conducted on the Smith Travel Research data in 33 years (1989–2022) to determine the relationship among hotel brand, culture and life cycles.FindingsSkilled labour is the key to make hotels grow. Therefore, increase room rates when hotels possess skilled professionals and decrease room rates when hotels have no skilled professionals. During the rejuvenation in Myrtle Beach (1999–2003), hoteliers increased room rates for innovators due to skilled professionals to increase revenue. Otherwise, a decrease in room rates due to lack of skilled professionals would lead to increase revenue.Research limitations/implications(1) Although Myrtle Beach is one of the fastest growing tourism destinations in the US, it has a relatively small geographic area relative to the country. (2) Data cover over one tourist life cycle, so the time span is relatively short. Hoteliers can forecast the number of guests in different culture by changing room rates.Practical implicationsTo optimize revenue, hoteliers can select skilled labour in professional design hotel brands which could make an increase in demand for leisure transient guests no matter what room rates increase after COVID-19 pandemic.Social implicationsThe study has considered the applied ethical processes regarding revenue management that would maximize both revenue and customer satisfaction when it set up an increase in room rates to compensate for professional hotel room design or it decreases room rates for low-income imitators in exploration and development.Originality/valueThis research highlights that (1) skilled design in the luxury hotel brand is the key for the hotel growth and (2) there is a steady state of the growth model in the destination life cycle.

Model-based cloud service deployment optimisation method for minimisation of application service operational cost

Many currently existing cloud cost optimisation solutions are aimed at cloud infrastructure providers, and they often deal only with specific types of application services. Unlike infrastructure providers, the providers of cloud applications are often left without a suitable cost optimisation solution, especially concerning the wide range of different application types. This paper presents an approach that aims to provide an optimisation solution for the providers of applications hosted in the cloud environments, applicable at the early phase of a cloud application lifecycle and for a wide range of application services. The focus of this research is the development of the method for identifying optimised service deployment option in available cloud environments based on the model of the service and its context, intending to minimise the operational cost of the cloud service while fulfilling the requirements defined by the service level agreement. A cloud application context metamodel is proposed that includes parameters related to both the application service and the cloud infrastructure relevant for the cost and quality of service. By using the proposed optimisation method, knowledge is gained about the effects of the cloud application context parameters on the service cost and quality of service, which is then used to determine the optimal service deployment option. The service models are validated using cloud applications deployed in laboratory conditions, and the optimisation method is validated using the simulations based on the proposed cloud application context metamodel. The experimental results based on two cloud application services demonstrate the ability of the proposed approach to provide relevant information about the impact of cloud application context parameters on service cost and quality of service and use this information for reducing service operational cost while preserving the acceptable service quality level. The results indicate the applicability and relevance of the proposed approach for cloud applications in the early service lifecycle phase since application providers can gain valuable insights regarding service deployment decision without acquiring extensive datasets for the analysis.