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The purpose of this paper is to propose a new system frame named ACPI for research and development (R&D) management of complex equipments according to the ideas of artificial societies, computational experiments, parallel execution and interactive optimization.

An artificial system which can effectively model, simulate and recur the main features and behaviors of a real R&D system of complex equipment is established at first. The structure and function of the system and its subsystems, and the relation of factors in the system are analyzed. Then one can perform computation experiment, modeling and simulation in the artificial system to obtain the optimal solutions. Finally, practice these solutions in a real system and at the same time perform the solutions in artificial system, forecasting and warning the possible new situations and problems in a realistic process, and provide controlling scheme.

The typical characteristics and solutions of the R&D system of complex equipment are analyzed. The sketch scheme of ACPI, the system frame of ACPI for R&D management of complex equipments are proposed, and the key technologies used in implementing ACPI of R&D system of complex equipment are studied.

The outcome of this paper can be used in computation experiments, management and optimization of R&D systems of complex equipment.

The sketch scheme of ACPI, the system frame of ACPI for R&D management of complex equipments are proposed first. The ACPI system can supply a high‐performance, open and interactive platform for the analog simulation and computation experiments of the R&D process management of complex equipment.

The purpose of this paper is to collect information about barriers and enablers experienced by international experts when transferring medical equipment to countries affected by humanitarian emergencies and to discuss the suitability of the principles of “openness”, “interconnections” and “non-linearity” of systems to understand the nature of the barriers and enablers as described by the international experts.

In this study, six semi-structured interviews were conducted with experts from humanitarian organizations. The interviews were based on a simplified model of the transfer of medical equipment adapted from supply chain literature. The model ensured that all the process steps undertaken by humanitarian organizations were considered. Afterwards, the interviews were transcribed and structurally analysed to derive barriers and enablers. Finally, the results were described in light of three theoretical principles of systems thinking.

In total, 14 types of barriers and 12 types of enablers were uncovered that illustrate the complexity of transferring medical equipment in humanitarian emergencies. The paper concludes with a proposal for future research to investigate if, and how, an approach guided by systems thinking could help to create a designated space for the formulation of original, synergetic solutions that address the identified barriers.

This study is the first to explore the specific logistic challenges implicit in the transfer of medical equipment in humanitarian emergencies with a lifecycle perspective. Furthermore, the concept of systems thinking is rather novel in the field of transfer of medical technology.

The purpose of this paper is to explore new ways and lay a solid foundation to solve the problem of reliability growth analysis of major aerospace equipment with various uncertainty data through propose new concepts of general uncertainty data (GUD) and general uncertainty variable (GUV) and build the operation system of GUVs.

The characteristics of reliability growth data of major aerospace equipment and the limitations of current reliability growth models have been analyzed at first. The most commonly used uncertainty system analysis methods of probability statistics, fuzzy mathematics, grey system theory and rough set theory have been introduced. The concepts of GUD and GUV for reliability growth data analysis of major aerospace equipment are proposed. The simplified form of GUV based on the “kernel” and the degree of uncertainty of GUV is defined. Then an operation system of GUVs is built.

(1) The concept of GUD; (2) the concept of GUV; (3) The novel operation rules of GUVs with simplified form.

The method exposed in this paper can be used to integrate complex reliability growth data of major aerospace equipment. The reliability growth models based on GUV can be built for reliability growth evaluation and forecasting of major aerospace equipment in practice. The reliability evaluation example of a solid rocket motor shows that the concept and idea proposed in this paper are feasible. The research of this paper opens up a new way for the analysis of complex uncertainty data of reliability growth of major aerospace equipment. Moreover, the operation of GUVs could be extended to the case of algebraic equation, differential equation and matrix which including GUVs.

The new concepts of GUD and GUV are given for the first time. The novel operation rules of GUVs with simplified form were constructed.

The paper attempts to establish a two‐dimension frame to analyze dynamic supplier risks of large‐scale and complex equipment and study the probability distribution of the occurring risks.

Starting from the dynamic and correlated supplier risks of large‐scale and complex equipment, a two‐dimension frame to analyze these risks is established. A maximum entropy model is also built to research the probability distribution of the risks; then, K‐T conditions are proved to solve the model. A real case is also studied in the last part of the paper.

The results are convincing: in order to analyze the supplier risk dynamically of large‐scale and complex equipment development project, a two‐dimension analysis frame is established and a maximum entropy model is worked out. The case study shows that they are valuable.

The two‐dimension frame gives a new viewpoint for risk management, but also the maximum entropy model supplies a valuable method for risk management.

The paper succeeds in creating a dynamic analysis frame to study risks and building a new method to research the disciplines of the dynamic risks.

The chapter considers the significance of the oil and gas industry for the Russian economy. The authors analyze the current state of the oil and gas industry, their specific weight in the structure of Russian GDP, and tax revenues from this industry to the Russian budget that was estimated. We give scenario analysis that considers the problems that the Russian economy may face because of the sanctions, the price fluctuations at the commodity market, and the crisis phenomena in the world economy. The chapter points out that localization of technology production and development of technologies for offshore oil and gas production in the Arctic zone may become an incentive to further ensure import substitution for Russia. At present, the experience of Arctic defense enterprises in the production of equipment for oil and gas production and processing is becoming increasingly popular. The chapter elaborates the most significant examples of the creation of new industries in the Arctic zone, the prospects of seismic exploration on the Arctic shelf, and that localization of production capacities and service bases will allow obtaining a multiplicative incentive for a qualitatively new industrial and infrastructure development of the northern territories. Also, we provide an assessment of the development of liquefied natural gas (LNG) industry, which makes economically attractive use of natural gas on a regional level as LNG opens the way to fuel high-power needs and to long-distance transport.

Development and implementation of new process equipment within the process industries frequently necessitate strong collaboration between process firms and their equipment suppliers in joint process development projects. However, collaboration in this setting entails significant challenges over the lifecycle of these projects. Accordingly, the purpose of this article is to explore the problems and opportunities faced by equipment suppliers during collaboration with process firms, throughout the various lifecycle stages of process development projects.

The article synthesizes results from 22 interviews in a multiple case study of eight equipment suppliers in the process industries and a comprehensive review of relevant literature to identify critical problems of opportunities during the lifecycle. In total, data were gathered from firms in six different countries.

A deficient pre‐study may create problems due to miscommunication during development when close interaction is required. Purchasing discussions can be done simultaneously to development when a supplier has been selected, although uncertainty is a problem. It is important to get end‐user feedback and commitment during development and later stages. During assembly and installation and start‐up, a variety of actors are working simultaneously which requires coordination and planning from an early stage. Close interaction and education with end‐user is critical for the technology transfer in the start‐up stage. Sharing of experiences enhances operational performance during production.

This study contributes by employing information rich case study data to describe the problem and opportunities faced over the full lifecycle in joint process development projects in the process industry. In particular, key issues over the lifecycle have been identified and described (e.g. purchasing, start‐up, education). Moreover, the adoption of a lifecycle perspective has indicated how activities, issues, and managerial challenges in specific stages are interconnected and affect the joint work in the following stages.

The findings of this article serves as guidelines to managers in equipment supplier firms and their customers by highlighting the problems and opportunities for improvement that occur during the interconnected stages of process development projects.

By focusing on the collaborative activities in different stages, this study higlights the critical problems and opportunities in the lifecycle of process equipment. In addition this article outlines how joint process development activities can facilitate enhanced operational performance, by means of collaborative design, installation and operation of new process equipment – i.e. “open operation”.

One of the fashionable talking points of the moment is the electronic microprocessor. It has suddenly become news. The government is to spend an initial £50 million of taxpayers' money on establishing a Microelectronics Unit; domestic appliances are now advertised as ‘containing MPUs — an electronic marvel with many thousands of transistors in a minute space’. Already, in the United States, a huge complex of companies who design and manufacture MPUs and similar devices has assembled in what is known as Silicon Valley (actually the Santa Clara Valley) in the same way that cars gravitated to Detroit and music to Memphis. Machines are in operation, and many more are being designed, which can simulate the thinking process and make decisions on the basis of data fed to them or which they can call for. Machines that think can be seen as a potential threat to some workers and fears are already being expressed about the effect of the MPU revolution on the work situation and on unemployment in particular. There can be no doubt that most jobs as we know them today will be changed, some of them out of all recognition, by this latest innovation — the MPU Revolution.

Online health monitoring of large complex equipment has become a trend in the field of equipment diagnostics and prognostics due to the rapid development of sensing and computing technologies. The purpose of this paper is to construct a more accurate and stable grey model based on similar information fusion to predict the real-time remaining useful life (RUL) of aircraft engines.

First, a referential database is created by applying multiple linear regressions on historical samples. Then similarity matching is conducted between the monitored engine and historical samples. After that, an information fusion grey model is applied to predict the future degradation trajectory of the monitored engine considering the latest trend of monitored sensory data and long-term trends of several similar referential samples, and the real-time RUL is obtained correspondingly.

The results of comparative analysis reveal that the proposed model, which is called similarity-based information fusion grey model (SIFGM), could provide better RUL prediction from the early degradation stage. Furthermore, SIFGM is still able to predict system failures relatively accurately when only partial information of the referential samples is available, making the method a viable choice when the historical whole life cycle data are scarce.

The prediction of SIFGM method is based on a single monotonically changing health indicator (HI) synthesized from monitoring sensory signals, which is assumed to be highly relevant to the degradation processes of the engine.

The SIFGM can be used to predict the degradation trajectories and RULs of those online condition monitoring systems with similar irreversible degradation behaviors before failure occurs, such as aircraft engines and centrifugal pumps.

This paper introduces the similarity information into traditional GM(1,1) model to make it more suitable for long-term RUL prediction and also provide a solution of similarity-based RUL prediction with limited historical whole life cycle data.

CONTROLLERS and Air Traffic Control (ATC) systems are now fully prepared for the introduction into service of the first commercial supersonic transport aircraft, and avionics is playing a major part in providing the equipment for this leap forward in performance. For transatlantic operations, an organised fixed two‐track system is envisaged which will be required for at least the initial two years of service (from 1976) when traffic density in this area will be low. The lateral separation of these tracks will be 60 nautical miles (fig 1) and as supersonic traffic builds up a third track could be provided on the south side of the system.