Journal articles on the topic 'System of Systems, Systems Engineering, District Energy Management, decision-making framework'

In this paper, a methodology for optimal decision making for electrical systems is addressed. This methodology seeks to identify and to prioritize the replacement and maintenance of a power asset fleet optimizing the return of investment. It fulfills this objective by considering the risk index, the replacement and maintenance costs, and the company revenue. The risk index is estimated and predicted for each asset using both its condition records and by evaluating the consequence of its failure. The condition is quantified as the probability of failure of the asset, and the consequence is determined by the impact of the asset failure on the whole system. Failure probability is estimated using the health index as scoring of asset condition. The consequence is evaluated considering a failure impact on the objectives of reliability (energy not supplied -ENS), environment, legality, and finance using Monte Carlo simulations for an assumed period of planning. Finally, the methodology was implemented in an open-source library called PywerAPM for assessing optimal decisions, where the proposed mathematical optimization problem is solved. As a benchmark, the power transformer fleet of the New England IEEE 39 Bus System was used. Condition records were provided by a local utility to compute the health index of each transformer. Subsequently, a Monte Carlo contingency simulation was performed to estimate the energy not supplied for a period of analysis of 10 years. As a result, the fleet is ranked according to risk index, and the optimal replacement and maintenance are estimated for the entire fleet.

Currently, studies on the energy efficiency of manufacturing systems usually lack synthetic and systematic techniques. In this paper, a holistic framework is demonstrated in order to achieve more sustainable manufacturing, which covers machine-level, system-level and life-cycle-level energy efficiency techniques. Based on these, the mechanism of how energy consumption is affected by machining processes and system operation is analyzed to achieve a comprehensive decision on energy efficiency optimization. Four main topics are included in this paper: (1) Hierarchical sustainability goals and metrics for energy-efficient manufacturing; (2) Machine-level machining processes optimization for energy efficiency enhancement; (3) System-level innovations for efficient consumption management; (4) Life-cycle level energy flow modeling and energy recycling strategy. An automotive engine manufacturing system is taken as an example to build a concrete understanding of the application of the framework. Moreover, this holistic framework establishes the theoretical basis for promoting the energy efficiency of automotive engine manufacturing systems. Furthermore, the proposed techniques can provide decision-making support for achieving sustainable manufacturing in a wider scope of mechanical manufacturing.

Social, technological, and environmental systems have become increasingly interconnected. Integrated problems arising between embedded water, energy, and food systems, require political and strategic cooperation between the actors involved at multiple governance levels. A holistic design approach is needed to guide the inherent decision-making processes. In this article, we developed a normative decision-making framework based on System of Systems Engineering (SoSE) and demonstrated how it can help to foster the cross-sectoral design of solutions to these interlinked water, energy, and food issues. The actors involved in our case study demonstrated a strong interest in collaborating across sectors and participating in the transition to cross-sectoral and sustainable resource management practices. However, experts from science and practice face a high degree of uncertainty when they design solutions to cope with the existing regional problems. As almost all regions of the world are highly integrated in national and global markets, future research might consider conducting larger research projects that also link the design approaches to inter-regional, national, and international levels. Our methodological approach illustrates how such a project could be structured on a regional level and identifies the processes that are important to consider.

Progressing digitalization and networking of systems and organizations representing Critical Infrastructures opens promising new potentials and opportunities, which on the downside, are accompanied by rising complexity and increasingly opaque interdependencies. The consequently increasing lack of knowledge leads to uncertainties affecting risk assessment and decision-making in case of adverse events. This trend motivated recent discussions and developments in risk science, emphasizing the need to handle such uncertainties. Complementarily, research in the resilience domain focuses on system capabilities to handle surprising hazardous situations. Several frameworks presented in the literature aim at combining both perspectives but either lack the focus on operational management, have a rather theoretical approach, or are designed for specific applications. Based on this observation, we propose an approach that integrates resilience management into the actual operation of Critical Infrastructure Systems and Organizations by providing an operational process that coordinates the fundamental resilience capabilities of responding, monitoring, anticipation, and learning. Furthermore, we tackle the challenge of uncertainties resulting from a lack of knowledge by aligning the concepts of digital twin and resilience management. The proposed framework is extensively discussed, and required processes are presented in detail. Eventually, its applicability and potential are reviewed by means of a complex hazardous situation at a Bavarian district heating power plant.

The purpose of this work is the development of mathematical tools for formalizing decisionmaking problems in open expert real-time control systems. The goal was achieved by defining and formally describing all the elements of a formal system. The most significant result was the proposed approach to formalization. With its help, within the framework of a single formalism, the dynamic properties of the subject area and the logical-analytical activity of the power system dispatcher, presented in different classes of formal logics, were described. The significance of the results obtained lies in the possibility of a rigorous description of various aspects of knowledge within the framework of a single formal apparatus with further pragmatic interpretation in the management process. The proposed approach was distinguished by using the axioms of aletic and deontic logics and the development of axioms that reflect the specifics of the problems being solved. The introduced system of basic concepts and relations makes it possible to classify many decision-making problems for the power systems management. The goals were described within the framework of a single formalism form the basis of the apparatus for formalizing the decision-making problems of the class under consideration. The formalization apparatus provides a description of the dynamic properties of the system within each aspect of knowledge of the content paradigm. The direction of further research is the construction of an appropriate formal theory based on the proposed formal system.

Sustainable development is a priority for the future of our society. Sustainable development is of particular importance to the Architecture, Engineering, and Construction (AEC) industry, both for new buildings and for the renovation of existing buildings. Great potential for sustainable development lies in the renovation of existing office buildings. This paper introduces a new framework for identifying the best set of renovation strategies for existing office buildings. The framework applies selected green building rating system criteria and cost-effective sustainable renovation solutions based on cost-benefit analysis (CBA), and thus provides a novelty in decision-making support for the sustainable renovation of office buildings at an early-stage. The framework covers all necessary steps and activities including data collection, determination of the required level of renovation, selection of the green building rating system, identification of impact categories and criteria, and final evaluation and decision-making using CBA. The framework can be used in conjunction with different systems and according to different regional characteristics. The applicability of the addressing procedure is shown through a case study of a comprehensive renovation of an office building in the city of Maribor.

In the context of carbon peak and carbon neutrality, digital green innovation development is becoming more and more important for enterprises. In order to effectively improve green competitiveness and increase profits, photovoltaic building materials enterprises must choose digital green innovation projects for investment. The purpose of this study is to build a reasonable investment project selection framework system and propose appropriate methods for photovoltaic building materials enterprises to help them correctly choose digital green innovation investment projects. This study firstly combines relevant theories and digital green innovation characteristics of target investment projects to build a framework system for photovoltaic building materials enterprises to select investment projects. Secondly, this study innovatively proposes a dynamic intuitionistic fuzzy multi-attribute group decision-making method considering the interaction between attributes. Finally, this study takes Yingli Group as the research object and conducts an empirical study on it to verify the scientific nature and reliability of the framework system and method selection. The results show that the framework system includes four aspects: external support system, commercialization expectation, project operation ability and project operation resources. Yingli Group should choose project A3 for cooperation. The framework system and method proposed in this study are feasible and can help Yingli Group correctly choose digital green innovation investment projects. At the same time, this study also brings positive enlightenment to other photovoltaic building materials enterprises in the world when choosing digital green innovation investment projects.

The information system with function of data acquisition and processing could be an essential foundation of decision making and planning for government on energy resource management in China. However, some existing barriers have impeded the usage of data, such as inefficiency mode of data reporting and transmission, sluggishness of data collection and summarization, deficiency of data standardization and serious information asymmetry. In this paper, a framework planning of an info management platform in provincial level is proposed for energy resource condition analysis and prediction. The various types of data from industries of electrical power, coal, petroleum and others such as bio-gas is acquired and analyzed based on certain data processing methods. And the related systems of statistic index, early warning index, monitoring index and unit GDP energy consumption statistic index and evaluation are established. The proposed platform can support info service to government or related department, help keep the balance and stabilization of energy supply.

In recent years, interest in home energy management systems (HEMS) has grown significantly, as well as the development of Voice Assistants that substantially increase home comfort. This paper presents a novel merging of HEMS with the Assistant paradigm. The combination of both concepts has allowed the creation of a high-performance and easy-to-manage expert system (ES). It has been developed in a framework that includes, on the one hand, the efficient energy management functionality boosted with an Internet of Things (IoT) platform, where artificial intelligence (AI) and big data treatment are blended, and on the other hand, an assistant that interacts both with the user and with the HEMS itself. The creation of this ES has made it possible to optimize consumption levels, improve security, efficiency, comfort, and user experience, as well as home security (presence simulation or security against intruders), automate processes, optimize resources, and provide relevant information to the user facilitating decision making, all based on a multi-objective optimization (MOP) problem model. This paper presents both the scheme and the results obtained, the synergies generated, and the conclusions that can be drawn after 24 months of operation.

The development of wireless sensors and wearable devices has led health care services to the new paramount. The extensive use of sensors, nodes, and devices in health care services generate an enormous amount of health data which is generally unstructured and heterogeneous. Many generous methods and frameworks have been developed for efficient data exchange frameworks, security protocols for data security and privacy. However, very less emphasis has been devoted to structuring and interpreting health data by fuzzy logic systems. The wireless sensors and device performances are affected by the remaining battery/energy, which induces uncertainties, noise, and errors. The classification, noise removal, and accurate interoperation of health data are critical for taking accurate diagnosis and decision making. Fuzzy logic system and algorithms were found to be effective and energy efficient in handling the challenges of raw medical data uncertainties and data management. The integration of fuzzy logic is based on artificial intelligence, neural network, and optimization techniques. The present work entails the review of various works which integrate fuzzy logic systems and algorithms for enhancing the performance of healthcare-related apps and framework in terms of accuracy, precision, training, and testing data capabilities. Future research should concentrate on expanding the adaptability of the reasoning component by incorporating other features into the present cloud architecture and experimenting with various machine learning methodologies.

Commercial fishing is a critical economic sector for Norway, yet deficiency of scientific information, regulatory instruments, inadequate implementation, and lack of management infrastructure are among the significant causes of mismanagement of fishing gear (FG) resources. Mismanagement of FGs results in leakage of plastics through abandoned, lost, or discarded fishing gears (ALDFG), which is the most threatening litter fraction for marine wildlife. In EU-EEA states, the management of ALDFG is prioritized through a dedicated circular economy (CE) action plan. Historically, systems engineering (SE) methods are successfully applied for resource management studies. This study adopts and applies the SPADE method to evaluate sustainable management for the system of FG resources in Norway. SPADE comprises five problem-solving activities covering stakeholders, problem formulation, analysis, decision-making, and continuous evaluation. Each activity is accomplished by data collected through stakeholder interviews and literature analysis to establish an initial structure of problems and associated management strategies across FG’s life cycle phases. The application of SPADE spanned across four years (2017–2020) and resulted in scientific outcomes aimed at the common goal of improving the system of FG resources in Norway within the framework of sustainable development goals and CE. SPADE’s practice to integrate stakeholders at each step and provision for continual systems evaluation proved effective in building a holistic understanding of the complex system.

This paper proposes a multiphysics simulation structure for predicting Li-ion batteries’ useful life by consolidating battery cell electrochemical and thermal-aging models into the electrical domain of PV-battery standalone systems. This model can consider the effect of operating conditions at the system level, such as charge/discharge patterns and energy management strategies, to evaluate battery capacity fade at the cell level. The proposed model is validated using experimental observations with a RRMSE of 1.1%. Results show that the operating conditions of the battery bank affect its lifetime significantly. A wide range of 2.7 to 12.5 years of battery lifetime is predicted by applying the model to different case studies. In addition, the model predicts that managing the maximum cell state of charge level can enhance the battery bank lifetime by 60%. The developed model is a generic multiscale decision-making framework to investigate the effect of operating conditions on battery service life.

Planning for the intensive use of renewable energy sources (RESs) has attracted wide attention to limit global warming and meet future load growth. Existing studies have shown that installing projects such as transmission lines, energy storage systems (ESSs), fault current limiters, and FACTs facilitate the integration of RESs into power systems. Different generation and transmission network expansion planning models have been developed in the literature; however, a planning model that manages multiple types of projects while maximizing the hosting capacity (HC) is not widely presented. In this paper, a novel planning framework is proposed to enhance and control the HC level of RESs by comparing various kinds of renewables, ESSs, fault current limiters, and FACTs to choose the right one, economically and technically. The proposed problem is formulated as a challenging mixed-integer non-linear optimization problem. To solve it, a solution methodology based on a developed decision-making approach and an improved meta-heuristic algorithm is developed. The decision-making approach aims to keep the number of decision variables as fixed as possible, regardless of the number of projects planned. While an improved war strategy optimizer that relies on the Runge-Kutta learning strategy is applied to strengthen the global search ability. The proposed decision-making approach depends primarily on grouping candidate projects that directly impact the same system state into four separate planning schemes. The first scheme relies on the impedance of devices installed in any path to optimally identify the location and size of the new circuits and the series-type FACTs. The second scheme is based on optimally determining the suitable types of ESSs. On the other hand, the third scheme optimizes the reactive power dispatched from the ESSs and shunt-type FACTs simultaneously. The fourth scheme is concerned with regulating the power dispatched from different types of RESs. All of the simulations, which were carried out on the Garver network and the 118-bus system, demonstrated the ability of the investigated model to select the appropriate projects precisely. Further, the results proved the robustness and effectiveness of the proposed method in obtaining high-quality solutions in fewer runs compared to the conventional method.

In the last years, software engineering researchers have defined sustainability as a quality requirement of software, but not enough effort has been devoted to develop new methods/techniques to support the analysis and assessment of software sustainability. In this study, we present the Sustainability Assessment Framework (SAF) that consists of two instruments: the software sustainability–quality model, and the architectural decision map. Then, we use participatory and technical action research in close collaboration with the software industry to validate the SAF regarding its applicability in specific cases. The unit of analysis of our study is a family of software products (Geographic Information System- and Mobile-based Workforce Management Systems) that aim to address sustainability goals (e.g., efficient collection of dead animals to mitigate social and environmental sustainability risks). The results show that the sustainability–quality model integrated with the architectural decision maps can be used to identify sustainability–quality requirements as design concerns because most of its quality attributes (QAs) have been either addressed in the software project or acknowledged as relevant (i.e., creating awareness on the relevance of the multidimensional sustainability nature of certain QAs). Moreover, the action–research method has been helpful to enrich the sustainability–quality model, by identifying missing QAs (e.g., regulation compliance, data privacy). Finally, the architectural decision maps have been found as useful to guide software architects/designers in their decision-making process.

Abstract: The efficiency and robustness increased after using embedded technology for monitoring and control applications. Due to traffic load, intensity can get stuck or they are delayed in reaching their destination. The system describes an intelligent and complex application that helps to clear traffic and to improve security while having fast response of the emergency services. By making use of embedded and wireless communication we can develop an application to clear the traffic while coming in the path. There is a system called as “Intelligent traffic light controller”. Where traffic light intelligently decides based on the total traffic on all adjacent roads. This paper presents the current framework of decision support systems for traffic management based on short and medium term predictions and includes some reflections on their likely evolution, based on current scientific research and the evolution of the availability of new types of data and their associated methodologies. When unexpected and unplanned events such as natural disasters, traffic accidents or even terrorist attacks happen, it is necessary to prepare a quick evacuation process and the provisioning of the quickest access routes for emergency services units.

The scientific-practical basis of the quality of passenger and clientele service at the airport were studied. This was prompted by the need to determine the role of aviation safety (AS) and its impact on the quality and competitiveness of airport services. It was proved that within the framework of the system, quality monitoring is performed in the areas of the service of aviation safety (SAS). As a result of research, a quantitative assessment of the level of aviation safety of the airport, which is the basis for making a relevant management decision in the analysis subsystems, was determined. Management , in this case, implies the system of measures to improve the activities of the SAS. A procedure for assessing the quality of airport services, which reveals the essence of aviation safety in the system of airport quality management , was devised. The sequence of assessment of the system of aviation safety, reflecting the cost approach, the application of which involves determining the reserves for ensuring aviation safety in the continuous implementation of the quality management system, was proposed. The share of costs of aviation safety (AS) was determined on the example of three airports by economic elements (the model of criteria for choosing a subject) to determine the cost of ensuring an adequate level of aviation safety. It was proposed to introduce a comprehensive indicator of the quality of aviation safety for its functional components, which further makes up the integrated indicator of the quality of provided services. As a result of calculations, the amount of payment for aviation safety using adjustment coefficients to counteract the threat of a possible act of unlawful interference (AUI) will increase. This enables an aircraft company to form timely a reserve to prevent or eliminate the consequences of the AUI by including the aviation safety fee in the total cost of air transport services.

Purpose Urban typogenetics investigates the use of machine intelligence for the evaluation of performance measures as a decision support system (DSS) with a focus on urban aesthetics evaluation. This framework allows designers to address performance measures, urban measures and aesthetic criteria in an adaptive, interactive generative design approach. The purpose of this paper is to provide an understanding of the structure and the nature of the framework and the application of human-in-the-loop design systems to urban design. Design/methodology/approach Significant literature reviewed lead to the identification of an application potential in the decision-making process. This potential is situated around the use of AI for the evaluation of subjective performance criteria in a DSS. Recognising that the key decisions about urban aesthetics are based on the individual evaluation of the designer, an HITL approach for computational design software to support creative decisions is presented in this paper. Findings Urban typogenetics for interactive generative urban design allows the exploration of complex design spaces by using a human-in-the-loop design system in the context of urban aesthetics. Hybrid aesthetic evaluation allows the designer to analyse morphological features and urban aesthetics during exploratory search and reveal hidden aspects of the urban context by visualisation of the results of the aesthetic evaluation. Integrating performance measures and urban aesthetics in urban typogenetics addresses major criteria of urban design at the beginning of the creative process. Originality/value The use of a broad interactive approach to typogenetic design in an application to urban scenarios is a novel conceptual approach to the design of urban configurations. The suggested adaptive mechanism would allow the user of a typogenetic tool to subjectively evaluate solutions by sight and reason about aesthetic, social and cultural implication of the reviewed design solutions.

Rapid urbanization rates impact significantly on the nature of Land Cover patterns of the environment, which has been evident in the depletion of vegetal reserves and in general modifying the human climatic systems (Henderson, et al., 2017; Kumar, Masago, Mishra, & Fukushi, 2018; Luo and Lau, 2017). This study explores remote sensing classification technique and other auxiliary data to determine LULCC for a period of 50 years (1967-2016). The LULCC types identified were quantitatively evaluated using the change detection approach from results of maximum likelihood classification algorithm in GIS. Accuracy assessment results were evaluated and found to be between 56 to 98 percent of the LULC classification. The change detection analysis revealed change in the LULC types in Minna from 1976 to 2016. Built-up area increases from 74.82ha in 1976 to 116.58ha in 2016. Farmlands increased from 2.23 ha to 46.45ha and bared surface increases from 120.00ha to 161.31ha between 1976 to 2016 resulting to decline in vegetation, water body, and wetlands. The Decade of rapid urbanization was found to coincide with the period of increased Public Private Partnership Agreement (PPPA). Increase in farmlands was due to the adoption of urban agriculture which has influence on food security and the environmental sustainability. The observed increase in built up areas, farmlands and bare surfaces has substantially led to reduction in vegetation and water bodies. The oscillatory nature of water bodies LULCC which was not particularly consistent with the rates of urbanization also suggests that beyond the urbanization process, other factors may influence the LULCC of water bodies in urban settlements. Keywords: Minna, Niger State, Remote Sensing, Land Surface Characteristics References Akinrinmade, A., Ibrahim, K., & Abdurrahman, A. (2012). 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The purpose of this work is the development of mathematical tools for formalizing decisionmaking problems in open expert real-time control systems. The goal was achieved by defining and formally describing all the elements of a formal system. The most significant result was the proposed approach to formalization. With its help, within the framework of a single formalism, the dynamic properties of the subject area and the logical-analytical activity of the power system dispatcher, presented in different classes of formal logics, were described. The significance of the results obtained lies in the possibility of a rigorous description of various aspects of knowledge within the framework of a single formal apparatus with further pragmatic interpretation in the management process. The proposed approach was distinguished by using the axioms of aletic and deontic logics and the development of axioms that reflect the specifics of the problems being solved. The introduced system of basic concepts and relations makes it possible to classify many decision-making problems for the power systems management. The goals were described within the framework of a single formalism form the basis of the apparatus for formalizing the decision-making problems of the class under consideration. The formalization apparatus provides a description of the dynamic properties of the system within each aspect of knowledge of the content paradigm. The direction of further research is the construction of an appropriate formal theory based on the proposed formal system.

Prognostics and Health Management (PHM) systems are usually only considered and set up in the late stage of design or even during the system’s lifetime, after the major design decision have been made. However, considering the PHM system’s impact on the system failure probabilities can benefit the system design early on and subsequently reduce costs. The identification of failure paths in the early phases of engineering design can guide the designer toward a safer, more reliable and cost-efficient design. Several functional failure modeling methods have been developed recently. One of their advantages is to allow for risk assessment in the early stages of the design. Risk and reliability functional failure analysis methods currently developed do not explicitly model the PHM equipment used to identify and prevent potential system failures. This paper proposes a framework to optimize prognostic systems selection and positioning during the early stages of a complex system design. A Bayesian network, incorporating the PHM systems, is used to analyze the functional model and failure propagation. The algorithm developed within the proposed framework returns the optimized placement of PHM hardware in the complex system, allowing the designer to evaluate the need for system improvement. A design tool was developed to automatically apply the proposed method. A generic pressurized water nuclear reactor primary coolant loop system is used to present a case study illustrating the proposed framework. The results obtained for this particular case study demonstrate the promise of the method introduced in this paper. The case study notably exhibits how the proposed framework can be used to support engineering design teams in making better informed decisions early in the design phase.

Abstract This research focuses on the predictive energy management of connected human-driven hybrid electric vehicles (HEVs) to improve their fuel efficiency while robustly satisfying system constraints. We propose a hierarchical control framework that effectively exploits long-term and short-term decision-making benefits by integrating real-time traffic data into the energy management strategy. A pseudo-spectral optimal controller (PSOC) with discounted cost is utilized at the high level to find an approximate optimal solution for the entire driving cycle. At the low-level, a long short-term memory neural network (NN) is developed for higher quality velocity predictions over the low-level's short time horizons. Tube-based model predictive controller is then used at the low level to ensure constraints satisfaction in the presence of velocity prediction errors. Simulation results over real-world traffic data show an improvement in fuel economy for the proposed controller that is real-time applicable and robust to the driving cycle's uncertainty.

Abstract The incorporation of renewable energy into the future world energy matrix challenges its efficient use because renewable energy is not always available due to its dependence on natural factors such as wind and sunlight. This work develops a new resource management system to evaluate the renewable energy resources stored in salt caves using power-to-gas (P2G) and compressed air energy storage (CAES) technologies in the initial phase of a project (sub-commercial phase). To fulfill this objective, bibliographical research, document analysis, and consultations with specialists were used as the methodological basis. Two systems were identified to be used as a reference for the proposed methodology: Petroleum Resources Management System (PRMS) and CO2 Storage Resources Management System (SRMS). A classification framework is proposed for energy storage and an application of the framework is presented for a case study in Portugal. Similar to these reference systems, a sub-commercial project momentum was established, and three stages called total potential resource (R3), total probable resource (R2), and total proved resource (R1) were defined. The results support corporate and governmental decision-making on project continuity for both the market and governments, thus demonstrating their importance in new global energy reality. It is recommended to define the sub-commercial stage as well as the mapping of R2 in the Brazilian territory as was done recently in Europe.

Purpose Nowadays, cloud platforms are used in many fields, including e-commerce, web applications, data storage, healthcare, gaming, mobile social networks, etc. However, security and privacy are still two significant concerns in this area. The target of this paper is to present a system for trust management in industrial cloud computing using the multi-criteria decision making (MCDM) approach. MCDM techniques have been developed to accommodate a wide range of applications. As a result, hundreds of approaches have been generated with even minor variations on current approaches spawning new study fields. Design/methodology/approach Cloud computing provides a fully scalable, accessible and flexible computing platform for various applications. Due to the multiple applications that cloud computing has found in numerous life features, users and providers have considered providing security in cloud communications. Due to its distributive nature, dynamic space and lack of transparency in performing cloud computing, it faces many challenges in providing security. For security improvement, trust management can play a very influential role. This paper proposes a generic analytical methodology that uses a series of assessment criteria to evaluate current trust management testing prototypes in industrial cloud computing and related fields. The authors utilize a MCDM approach in the present article. Due to the multi-dimensionality of the sustainability objective and the complexities of socio-economic and biophysical processes, MCDM approaches have become progressively common in decision-making for sustainable energy. Findings The results of comparing and evaluating the performance of this model show its ability to manage trust and the ability to adapt to changes in the behavior of service providers quickly. Using a simulation, all results are confirmed. The results of simulations and evaluation of the present paper indicate that the proposed model provides a more accurate evaluation of the credibility of cloud service providers than other models. Practical implications The number of cloud services and customers is vast and extremely competitive in cloud environments, where novel cloud services and customers can join at any time, while others can withdraw whenever they want. Because of cloud services' highly dynamic and dispersed design, trust management mechanisms must be highly flexible to obtain feedback and update trust outcomes as quickly as possible. The model presented in this article tries to improve users' trust in the cloud industry. Originality/value Using a method (MCDM) to find the best trust management solution based on user experience in industrial cloud computing is the novelty of this paper.

AbstractAppropriate sanitation is crucial to alleviate pressures on environmental and human health hazards. Conventional (sewered) sanitation systems are often not viable in rapidly developing urban areas, where over 70% of the world population is expected to live in 2050. Freshwater is polluted and valuable resources such as nutrients and organics are lost. At present, many alternative sanitation technologies and systems are being developed with the aim to alleviate these pressures through (1) independency from sewers, water, and energy, therefore better adapted to the needs of fast and uncontrolled developing urban areas; and (2) contribute to a circular economy through the recovery of nutrients, energy, and water for reuse. Unfortunately, these innovations hardly find their way into practice because there exists a lack of data and knowledge to systematically consider them in strategic planning processes. To this end, we have developed SANitaTIon system Alternative GeneratOr (SANTIAGO)—a software that provides a comprehensive list of potential technologies and system configurations and quantifies their local appropriateness as well as their resource recovery and loss potentials. The aim is to provide a manageable but diverse set of decision options together with information needed to rank the alternatives and to select the preferred one in a structured decision making process. To make this software useful for practice, an easily accessible interactive user interface is required that (1) facilitates data collection and input; and (2) the exploration and presentation of results. As a first step in creating this user interface, we develop a framework that summarizes (1) the requirements that arise from practical applications of SANTIAGO, and (2) a comprehensive user understanding on the basis of 21 interviews with international practitioners caught in five personas: capacity developers, engineering experts, planners, researchers, teachers and trainers. This framework aids the development of any academic software into a tool useful for practice and policy makers. Here specifically, it enables contribution to sustainable development goals 6 (clean water and sanitation), and 11 (sustainable cities and communities).

Abstract Background Energy-efficient university campuses will play a vital role in the development of future sustainable cities, and will be important for achieving the Chinese carbon-neutrality goals. It is, therefore, necessary to develop new decision-making tools for evaluating the sustainability of campus buildings. Since university campuses typically comprise a broad variety of building types, standardized evaluation methods and tools, such as Green BIM, are needed. Green BIM (Building Information Modeling) emphasizes the importance and role of BIM technology in the design and construction of green buildings, providing a standardized framework for the decision-making process, and methods for improving the green performance of buildings. Methods This study develops a method based on the Green BIM framework, using BIM architecture to analyse building performance, and the Assessment Standard for Green Building (GB/T 50378-2019) standard to establish benchmark values for evaluation, and project objectives. The method is evaluated on three examples of the most representative university buildings in northern China. The goal is to understand common denominators and differences between different types of campus buildings, in terms of green building indicators, that are important to consider in the early design stages of campus building complexes. Results In this study, a library is used as a case study to demonstrate the tools for evaluating green performance. The study optimizes green performance from five aspects: surrounding environment, function layout, envelope performance and system transformation, and management measures improvement. The results show that this optimization scheme can achieve reductions of the annual loads of about 47.4%, in line with the national energy efficiency standards for public buildings. In particular, the heating load was reduced by 59.1%, and the cooling load reduced by 21.5%. Conclusion A comprehensive approach, combining the aspects of planning, building design, system design, energy management, and energy conservation planning, is required to improve the green performance of university buildings to meet the goals. In the future, it will be further necessary to perform data mining of energy consumption patterns, and continue energy retrofitting of existing buildings and energy systems, to achieve the goal of green and low-carbon campuses.