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The aim of this study is to demonstrate that hybrid redundancy systems are superior to the conventional N‐modular redundancy (NMR) systems.

The hybrid redundancy system is a synthesis of the NMR system and the standby redundancy. Each module of the NMR has access to k cold spares (k<N) and a repair facility. A semi‐Markov model for the hybrid redundancy system is developed and transient analysis is performed.

Some dependability measures such as availability, reliability, mean time to failure and steady‐state availability are obtained.

This paper presents the transient analysis of the hybrid redundancy systems. The results obtained will be useful to reliability engineers and reliability practitioners.

This paper aims to analyse performance measurement ambiguities in hybrid universities. In accounting research, performance measurement of universities has been discussed in detail, and there is some research on impacts of hybridity in institutional systems. However, there is a particular need in accounting research for more sophisticated theorizations of the ambiguities associated with measuring performance in hybrid organizations. Moreover, there is a dearth of accounting-related interdisciplinary studies conceptualizing the hybridity of universities with important implications for measuring and reporting performance. This paper fills this research gap by providing more elaborate basis for conceptualizing performance measurement ambiguities through the lenses of hybrid universities.

The authors critically scrutinize the promise of performance measurement in hybrid universities and explore why it may result in new policy problems. Questions asked are as follows: How can we better understand those institutional mechanisms through which the promise of performance measurement may ultimately result in new forms of ambiguities and unexpected outcomes, and what are the specific characteristics of hybridity that make those mechanisms possible in universities?

The authors propose a conceptual model for studying universities in hybrid performance settings. The model provides a new inter-disciplinary approach for conceptualizing performance measurement ambiguities in universities when they are influenced by hybridity, hybrid arrangements and hybrid governance.

This paper provides more elaborate basis for understanding hybridity of universities, not only through reforms for combining business, government and collegial professional logics (e.g. corporatization, marketization) or through new hybrid mixes of professions but also as a more comprehensive, inter-disciplinary understanding of institutional structures, logics and practices at modern universities.

This study aims to show the inconsistency of the approach to the development of artificial intelligence as an independent tool (just one more tool that humans have developed); to describe the logic and concept of intelligence development regardless of its substrate: a human or a machine and to prove that the co-evolutionary hybridization of the machine and human intelligence will make it possible to reach a solution for the problems inaccessible to humanity so far (global climate monitoring and control, pandemics, etc.).

The global trend for artificial intelligence development (has been) was set during the Dartmouth seminar in 1956. The main goal was to define characteristics and research directions for artificial intelligence comparable to or even outperforming human intelligence. It should be able to acquire and create new knowledge in a highly uncertain dynamic environment (the real-world environment is an example) and apply that knowledge to solving practical problems. Nowadays artificial intelligence overperforms human abilities (playing games, speech recognition, search, art generation, extracting patterns from data etc.), but all these examples show that developers have come to a dead end. Narrow artificial intelligence has no connection to real human intelligence and even cannot be successfully used in many cases due to lack of transparency, explainability, computational ineffectiveness and many other limits. A strong artificial intelligence development model can be discussed unrelated to the substrate development of intelligence and its general properties that are inherent in this development. Only then it is to be clarified which part of cognitive functions can be transferred to an artificial medium. The process of development of intelligence (as mutual development (co-development) of human and artificial intelligence) should correspond to the property of increasing cognitive interoperability. The degree of cognitive interoperability is arranged in the same way as the method of measuring the strength of intelligence. It is stronger if knowledge can be transferred between different domains on a higher level of abstraction (Chollet, 2018).

The key factors behind the development of hybrid intelligence are interoperability – the ability to create a common ontology in the context of the problem being solved, plan and carry out joint activities; co-evolution – ensuring the growth of aggregate intellectual ability without the loss of subjectness by each of the substrates (human, machine). The rate of co-evolution depends on the rate of knowledge interchange and the manufacturability of this process.

Resistance to the idea of developing co-evolutionary hybrid intelligence can be expected from agents and developers who have bet on and invested in data-driven artificial intelligence and machine learning.

Revision of the approach to intellectualization through the development of hybrid intelligence methods will help bridge the gap between the developers of specific solutions and those who apply them. Co-evolution of machine intelligence and human intelligence will ensure seamless integration of smart new solutions into the global division of labor and social institutions.

The novelty of the research is connected with a new look at the principles of the development of machine and human intelligence in the co-evolution style. Also new is the statement that the development of intelligence should take place within the framework of integration of the following four domains: global challenges and tasks, concepts (general hybrid intelligence), technologies and products (specific applications that satisfy the needs of the market).

Hybrid simulation, which is a general technique for obtaining the seismic response of an entire structure, is an improvement of the traditional seismic test technique. In order to improve the analysis accuracy of the numerical substructure in hybrid simulation, the purpose of this paper is to propose an innovative hybrid simulation technique. The technique combines the multi-scale finite element (MFE) analysis method and hybrid simulation method with the objective of achieving the balance between the accuracy and efficiency for the numerical substructure simulation.

To achieve this goal, a hybrid simulation system is established based on the MTS servo control system to develop a hybrid analysis model using an MFE model. Moreover, in order to verify the efficiency of the technique, the hybrid simulation of a three-storey benchmark structure is conducted. In this simulation, a ductile column—represented by a half-scale scale specimen—is selected as the experimental element, meanwhile the rest of the frame is modelled as microscopic and macroscopic elements in the Abaqus software simultaneously. Finally, to demonstrate the stability and accuracy of the proposed technique, the seismic response of the target structure obtained via hybrid simulation using the MFE model is compared with that of the numerical simulation.

First, the use of the hybrid simulation with the MFE model yields results similar to those obtained by the fine finite element (FE) model using solid elements without adding excessive computing burden, thus advancing the application of the hybrid simulation in large complex structures. Moreover, the proposed hybrid simulation is found to be more versatile in structural seismic analysis than other techniques. Second, the hybrid simulation system developed in this paper can perform hybrid simulation with the MFE model as well as handle the integration and coupling of the experimental elements with the numerical substructure, which consists of the macro- and micro-level elements. Third, conducting the hybrid simulation by applying earthquake motion to simulate seismic structural behaviour is feasible by using Abaqus to model the numerical substructure and harmonise the boundary connections between three different scale elements.

In terms of the implementation of the hybrid simulation with the MFE model, this work is helpful to advance the hybrid simulation method in the structural experiment field. Nevertheless, there is still a need to refine and enhance the current technique, especially when the hybrid simulation is used in real complex engineering structures, having numerous micro-level elements. A large number of these elements may render the relevant hybrid simulations unattainable because the time consumed in the numeral calculations can become excessive, making the testing of the loading system almost difficult to run smoothly.

The MFE model is implemented in hybrid simulation, enabling to overcome the problems related to the testing accuracy caused by the numerical substructure simplifications using only macro-level elements.

This paper is the first to recognise the advantage of the MFE analysis method in hybrid simulation and propose an innovative hybrid simulation technique, combining the MFE analysis method with hybrid simulation method to strike a delicate balance between the accuracy and efficiency of the numerical substructure simulation in hybrid simulation. With the help of the coordinated analysis of FEs at different scales, not only the accuracy and reliability of the overall seismic analysis of the structure is improved, but the computational cost can be restrained to ensure the efficiency of hybrid simulation.

To introduce a Petri nets model that describes a networked manufacturing system and its dynamics.

A hybrid Petri nets model is constructed, the continuous part of which is to describe the dynamics of the production process within a manufacturing system and the discrete part of which is to describe the dynamics of the ordering and delivering process between every two manufacturing systems. In addition, the mathematical formulation of the dynamics of networked manufacturing systems is proposed to describe its behaviors in detail. Based on the model, the control system architecture of networked manufacturing systems is constructed to make and execute the production plan, solve the conflicts among manufacturing systems and realize the reconfiguration of the network.

There are two key aspects in the dynamics of this hybrid system: first, in the continuous part of this hybrid system, the production process, the variables are discrete. Accordingly, the change of the systems states is not always continuous. Second, in the discrete part of this hybrid system the control variables are actually the safety and objective inventory levels and the minimum quantity of cumulative orders to trigger deliveries which determine the time and quantities of ordering or delivery. However, the relations between them are non‐linear.

Based on the model, the control system architecture of networked manufacturing systems can be constructed to make and execute the production plan, solve the conflicts among manufacturing systems and realize the reconfiguration of the network.

The purpose of this paper is to discuss how tacit and explicit knowledge determine grey knowledge and how these are stimulated through interactions within networks, forming the grey hybrid intelligent systems (HISs). The feedback processes and mechanisms between internal and external knowledge determine the apparition of grey knowledge into an intelligent system (IS). The extension of ISs is determined by the ubiquity of the internet but, in our framework, the grey knowledge flows assure the viability and effectiveness of these systems.

Some characteristics of the Hybrid Intelligent Knowledge Systems are put forward along with a series of models of hybrid computational intelligence architectures. More, relevant examples from the literature related to the hybrid systems architectures are presented, underlying their main advantages and disadvantages.

Due to the lack of a common framework it remains often difficult to compare the various HISs conceptually and evaluate their performance comparatively. Different applications in different areas are needed for establishing the best combinations between models that are designed using grey, fuzzy, neural network, genetic, evolutionist and other methods. But all these systems are knowledge dependent, the main flow that is used in all parts of every kind of system being the knowledge. Grey knowledge is an important part of the real systems and the study of its proprieties using the methods and techniques of grey system theory remains an important direction of the researches.

The paper discusses the differences among the three types of knowledge and how they and the grey systems theory can be used in different hybrid architectures.

This paper aims to propose a comprehensive methodology for setting up rural electrifications for indigenous villages with minimum budgets and the lowest possible cost of electricity (COE). The electricity accessibility of rural area in Malaysia is not fully covered and the cost of extending the grid to these areas can be high as RM 2.7m per km. Lack of vigorous policies and economic attraction of the rural areas are also the main barriers to rural electrification. Electricity is an essential element of economic activities and the lack of electricity exacerbates poverty and contributes to its perpetuation. Therefore, a hybrid standalone power system can be an alternative solution for the rural electrification. A hybrid standalone power system is studied to investigate the potential of the implementation and the budget required.

A site survey has been carried out in a village in Peninsular Malaysia, namely, Kampung Ulu Lawin Selatan. A standalone hybrid system is modeled in HOMER Pro software and the data collected from the selected site are used to obtain the system configuration with the lowest COE. The load following and cycle charging energy dispatch methods are compared to identify the optimal system configuration that yields the lowest COE. The diesel generator-only system is chosen as a benchmark for comparisons.

The results show that the hybrid system constituted from the diesel generator, photovoltaic (PV), micro-hydro and energy storage using the load following energy dispatch method yields the lowest COE of RM 0.519 per kWh. The COE of the hybrid system is 378 per cent lower than that of the diesel generator-only system. The lead-acid energy storage system (ESS) is able to reduce 40 per cent of COE as compared to the system without ESS.

The results indicate that the COE of the diesel-micro hydro-PV-ESS system with load following dispatch strategy is RM 0.519 per kWh, and this value is 35 per cent higher than the average electricity price in Malaysia. However, it is important to note that the costs of extending the grid to the rural area are not taken into account. If this cost is considered into the electricity price, then the standalone hybrid power system proposed by this study is still a competitive alternative for rural electrification.

An intelligent hybrid system, called GloStra (developed by the author), for developing global marketing strategy and associated Internet marketing strategy is reported in this paper. The hybrid system is built to integrate the strengths of expert systems, fuzzy logic, artificial neural networks and decision support technology; and to link the development of global marketing strategy with the formulation of associated Internet marketing strategy. In the paper, the system architecture, the functional modules of the hybrid system and other associated technical issues are addressed. The directions for further research in this field are also highlighted.

This paper aims to present a hybrid actuator controller to obtain fast and stiff position response without any overshoot by blending input signals of a DC servomotor and a particle brake.

The hybrid actuator controller has a module to estimate instantaneous changes in inertia and a blending algorithm that adjusts input signals to the motor and the brake so that together, as a hybrid actuator, they can achieve a fast, stiff position response without overshoot. The control logic implemented in the controller is derived from the kinematics of the system. For the blending algorithm, two separate cases are explored in which the user has the option to either utilize the full‐braking capacity or specify a safe deceleration limit for the system.

The blending algorithm enables the system to operate nearly twice as fast as the motor‐only case without any overshoot or oscillations. The controller can reject inertial load changes and significant external disturbances.

Such hybrid actuators along with the developed controller can be used in robotics and automation to increase the system accuracy and operational speed resulting in higher production rates. In addition, much stiffer haptic force feedback interfaces for virtual reality applications can be designed with smaller actuators. The blending algorithm provides considerable improvements and uses a physics‐based simple and easy‐to‐implement structure.

The purpose of this study is to examine state-of-the-art in hybrid-electric propulsion system modeling and suggest new methodologies for sizing such advanced concepts. Many entities are involved in the modelling and design of hybrid electric aircraft; however, the highly multidisciplinary nature of the problem means that most tools focus heavily on one discipline and over simplify others to keep the analysis reasonable in scope. Correctly sizing a hybrid-electric system requires knowledge of aircraft and engine performance along with a working knowledge of electrical and energy storage systems. The difficulty is compounded by the multi-timescale dynamic nature of the problem. Furthermore, the choice of energy management in a hybrid electric system presents multiple degrees of freedom, which means the aircraft sizing problem now becomes not just a root-finding exercise, but also a constrained optimization problem.

The hybrid electric vehicle sizing problem can be sub-divided into three areas: modelling methods/fidelity, energy management and optimization technique. The literature is reviewed to find desirable characteristics and features of each area. Subsequently, a new process for sizing a new hybrid electric aircraft is proposed by synthesizing techniques from model predictive control and detailed conceptual design modelling. Elements from model predictive control and concurrent optimization are combined to formulate a new structure for the optimization of the sizing and energy management of future aircraft.

While the example optimization formulation provided is specific to a hybrid electric concept, the proposed structure is general enough to be adapted to any vehicle concept which contains multiple degrees of control freedom that can be optimized continuously throughout a mission.

The proposed technique is novel in its application of model predictive control to the conceptual design phase.