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The purpose of this paper is to propose a new method for computing validities in the multimodel approach.

The multimodel approach offers an interesting alternative and a powerful tool to bypass the difficulties to model, control and diagnose a nonlinear and complex system. Its idea is defined as the apprehension of a nonlinear behaviour of a system by a set of local models characterizing the system operation in different operating zones. In spite of the success of its application in different fields, many problems related to the synthesis of multimodel approach remain open. These include, in particular, the method of obtaining the contribution degrees, also called validities, of the base-models for the deduction of the multimodel output.

The presented method may lead to superior results in comparison with the residue approach commonly used in the calculation of validities. Numerical simulation results and an experimental validation on a semi-batch reactor clearly illustrated the effectiveness of the proposed method and proved its impact on the improvement of the performances of the multimodel approach. Moreover, the multimodel approach using the new validities’ computation method can lead to perfect modelling of the process.

The proposed method discussed in the paper has the potential to make the multimodel approach more efficient in the modelling of complex real systems.

A significant contribution of the paper is the formulation of a new constrained optimization problem that can be solved by using a powerful mathematical tool such as the active set method, allowing to estimate the validity indexes in the multimodel approach. The obtained optimal solution can lead to perfect modelling of nonlinear and complex real systems.

The purpose of this paper is to present a new concept based on a neural network validity approach in the area of multimodel for complex systems.

The multimodel approach was recently developed in order to solve the modeling problems and the control of complex systems. The strategy of this approach coincides with the usual approach of the engineer which consists in subdividing a complex problem to a set of simple, manageable sub‐problems that can be solved separately. However, this approach still faces some problems in design, especially in determining models and in finding the appropriate method of calculating validities.

A novel approach based on neural network validity shows very remarkable performances in multimodel for complex systems.

The validity of each model is based on the convergence of each neural network. For a fast convergence the proposed approach can be online to give a good performance in multimodel representation for system with rapid dynamics.

The proposed concept discussed in the paper has the potential to be applied to complex systems.

The suggested approach is implemented and reviewed with a complex dynamic and fast process compared to the residue approach commonly used in the calculation of validities. The results prove to be satisfactory and show a good accuracy.

Believes that an urgent need for in‐depth understanding of the relationship of staff development to education quality exists in current educational reforms, policy making, and teacher education. Based on the existing knowledge of education quality, quality management and effective schools, aims to propose a framework to show how staff development can be designed and managed to contribute to the assurance and enhancement of school education quality from the perspective of seven multimodels of school education quality. Different models emphasize different aspects of school education quality and propose different strategies to enhance it. For ensuring long‐term school education quality in a changing educational environment, staff development can be organized and managed according to the major concerns of multimodels. Proposes some practical considerations for designing and implementing school‐based staff development.

The process of creating complex models often requires different modelling methods and tools to be integrated. This paper provides a concise description of an object‐oriented environment for creating composite models. The proposed approach is based on using simulation abstractions as basic model building blocks. The basic environment is built up of a Prolog interpreter, SIMLIB and object‐oriented Petri nets.

Various approaches and algorithms have been proposed since the 1950s to solve the assembly line (AL) balancing problem. These methods have established an AL configuration from the beginning. However, a prebalanced AL may have to be rebalanced in real life for many reasons, such as changes in the cycle time, production demand, product features or task operation times. This problem has increasingly attracted the interest of scientists in recent years. This study aims to offer a detailed review of the assembly line rebalancing problems (ALRBPs) to provide a better insight into the theoretical and practical applications of ALRBPs.

A structured database search was conducted, and 41 ALRBP papers published between 2005 and 2022 were classified based on the problem structure, objective functions, problem constraints, reasons for rebalancing, solution approaches and type of data used for solution evaluation. Finally, future research directions were identified and recommended.

Single model, straight lines with deterministic task times were the most studied type of the ALRBPs. Eighteen percent of the studies solved worker assignment problems together with ALRBP. Product demand and cycle time changes were the leading causes of the rebalancing need. Furthermore, seven future research opportunities were suggested.

Although there are many review studies on AL balancing problems, to the best of the authors’ knowledge, there have been no attempts to review the studies on ALRBPs.

The purpose of this paper is to describe a two‐level hierarchical control strategy for electrical energy transfers in multisource renewable energy systems. The aim of the control design is to perform the energy transfers, according to the sources power variations and the load characteristics.

The controller determines the operating mode of the multisource renewable energy system and the power ratio provided by each source to satisfy the load demand. The study is based on an accurate model of the DC/DC converters coupled on the DC bus. The performance of the controller is compared with the usual method based on the measurements of the system variables with sensors (solar radiation, shaft speed, voltages, and currents).

The proposed method does not need extra sensors to measure the available power for each source.

The method is developed for an hybrid system with two sources (photovoltaic and lead‐acid battery bank) and specific zero voltage switch full‐bridge isolated buck DC/DC power converters but can easily extended to more sources and other classes of DC/DC converters.

The method is assessed through computer simulations using a simple comprehensive model. An experimental device is also developed by the GREAH Research Group of University Le Havre (France). The GREAH also participates to a technologic centre with similar topology on the site of Fecamp (France).

The proposed autonomous control schema is suitable to control hybrid systems with several energy sources in remote areas.

The main contributions of this work are first to introduce a two stages controller and second to use the duty cycle value of the power converters as decision criteria to switch off/on the sources.

Using fuzzy measures and fuzzy integrals, the paper presents a mathematical model of learning which is able to learn through fuzzy information. The characteristics of the model are studied theoretically and in numerical examples, where the model is compared with an ordinary Bayesian learning model. The problem of seeking an extremum of multimodel objective function is given as an example.

Data protection requirements heavily increased due to the rising awareness of data security, legal requirements and technological developments. Today, NoSQL databases are increasingly used in security-critical domains. Current survey works on databases and data security only consider authorization and access control in a very general way and do not regard most of today’s sophisticated requirements. Accordingly, the purpose of this paper is to discuss authorization and access control for relational and NoSQL database models in detail with respect to requirements and current state of the art.

This paper follows a systematic literature review approach to study authorization and access control for different database models. Starting with a research on survey works on authorization and access control in databases, the study continues with the identification and definition of advanced authorization and access control requirements, which are generally applicable to any database model. This paper then discusses and compares current database models based on these requirements.

As no survey works consider requirements for authorization and access control in different database models so far, the authors define their requirements. Furthermore, the authors discuss the current state of the art for the relational, key-value, column-oriented, document-based and graph database models in comparison to the defined requirements.

This paper focuses on authorization and access control for various database models, not concrete products. This paper identifies today’s sophisticated – yet general – requirements from the literature and compares them with research results and access control features of current products for the relational and NoSQL database models.

The focus of preventive maintenance (PM) programmes in industry is shifting from a pure statistical basis to online condition monitoring. Examines the shortcomings of statistical‐based PM which are contributing to this shift, and the potential benefits of and current research issues within condition‐based PM. Notes that statistics and quality control techniques will continue to play a critical role in this evolution.

The purpose of this paper is to introduce robust optimization approaches to balance mixed model assembly lines with uncertain task times and daily model mix changes.

Scenario planning approach is used to represent the input data uncertainty in the decision model. Two kinds of robust criteria are provided: one is min‐max related; and the other is α‐worst scenario based. Corresponding optimization models are formulated, respectively. A genetic algorithm‐based robust optimization framework is designed. Comprehensive computational experiments are done to study the effect of these robust approaches.

With min‐max related robust criteria, the solutions can provide an optimal worst‐case hedge against uncertainties without a significant sacrifice in the long‐run performance; α‐worst scenario‐based criteria can generate flexible robust solutions: through rationally tuning the value of α, the decision maker can obtain a balance between robustness and conservatism of an assembly line task elements assignment.

This paper is an attempt to robust mixed model assembly line balancing. Some more efficient and effective robust approaches – including robust criteria and optimization algorithms – may be designed in the future.

In an assembly line with significant uncertainty, the robust approaches proposed in this paper can hedge against the risk of poor system performance in bad scenarios.

Using robust optimization approaches to balance mixed model assembly line.