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The focus of industrial cluster innovation lies in the cooperation between enterprises and universities/scientific research institutes to make a theoretical breakthrough in the system and mechanism of industrial cluster network. Under the theoretical framework of cluster network, industrial structure can be optimized and upgraded, and enterprise benefit can be improved. Facing the increasing proliferation and multi-structured enterprise data, how to obtain potential and high-quality innovation features will determine the ability of industrial cluster network innovation, as well as the paper aims to discuss these issues.

Based on complex network theory and machine learning method, this paper constructs the structure of “three-layer coupling network” (TLCN), predicts the innovation features of industrial clusters and focuses on the theoretical basis of industrial cluster network innovation model. This paper comprehensively uses intelligent information processing technologies such as network parameters and neural network to predict and analyze the industrial cluster data.

From the analysis of the experimental results, the authors obtain five innovative features (policy strength, cooperation, research and development investment, centrality and geographical position) that help to improve the ability of industrial clusters, and give corresponding optimization strategy suggestions according to the result analysis.

Building a TLCN structure of industrial clusters. Exploring the innovation features of industrial clusters. Establishing the analysis paradigm of machine learning method to predict the innovation features of industrial clusters.

This study proposes a predictive neural network model reference decoupling control method for the coupling problem between the leg joints of hydraulic quadruped robots, and verifies its decoupling effect..

The machine–hydraulic cross-linking coupling is studied as the coupling behavior of the hydraulically driven quadruped robot, and the mechanical dynamics coupling force of the robot system is controlled as the disturbance force of the hydraulic system through the Jacobian matrix transformation. According to the principle of multivariable decoupling, a prediction-based neural network model reference decoupling control method is proposed; each module of the control algorithm is designed one by one, and the stability of the system is analyzed by the Lyapunov stability theorem.

The simulation and experimental research on the robot joint decoupling control method is carried out, and the prediction-based neural network model reference decoupling control method is compared with the decoupling control method without any decoupling control method. The results show that taking the coupling effect experiment between the hip joint and knee joint as an example, after using the predictive neural network model reference decoupling control method, the phase lag of the hip joint response line was reduced from 20.3° to 14.8°, the amplitude attenuation was reduced from 1.82% to 0.21%, the maximum error of the knee joint coupling line was reduced from 0.67 mm to 0.16 mm and the coupling effect between the hip joint and knee joint was reduced from 1.9% to 0.48%, achieving good decoupling.

The prediction-based neural network model reference decoupling control method proposed in this paper can use the neural network model to predict the next output of the system according to the input and output. Finally, the weights of the neural network are corrected online according to the predicted output and the given reference output, so that the optimization index of the neural network decoupling controller is extremely small, and the purpose of decoupling control is achieved.

The technology used by an organization is significantly influenced by the organization’s preferred competitive capabilities. The Internet of things (IoT) is an important technology, which is implemented by most prominent business organizations. The purpose of this paper is to investigate the relationship between an organization’s strategies and the IoT architectures implemented by the organization.

This study has been carried out on primary data collected with the help of a structured questionnaire. The data have been analyzed by statistical techniques like cluster analysis and discriminant analysis through SPSS.

The empirical investigation of data revealed that there is a relationship between organizational strategy and IoT architectures. The three-layered architecture of the IoT is most suitable for caretakers; the three-, four- or five- layered architectures are suitable for marketeers; whereas innovators find it more suitable to use five- or more-layered architecture of the IoT. This paper draws the conclusion based on maximum likelihood rather than using statistical analyses like ANOVA. The idea behind using the maximum likelihood estimate is that there are many subjective parameters in deciding the architectures of the IoT. These subjective parameters are difficult to quantify, so it is not possible to apply ANOVA on these parameters.

This study considers three organizational strategies; the relationship between other organizational strategies and IoT architecture will be studied in future.

This study offers multiple opportunities to practitioners and consulting firms of the IoT to adopt a suitable IoT architecture according to the organizational strategy. This study equips IoT development engineers to select suitable technology for data capturing, data transmission, and data management and access for an IoT architecture.

Although a lot of work has already been done on the architecture of IoT for different industries and businesses, to the best of our knowledge, this is the first study that relates organizational strategies to IoT architectures. This study applies to all the major industry types.

In this paper, an assembly sequence planning system, based on binary vector representations, is developed. The neural network approach has been employed for analyzing optimum assembly sequence for assembly systems.

The input to the assembly system is the assembly's connection graph that represents parts and relations between these parts. The output to the system is the optimum assembly sequence. In the constitution of assembly's connection graph, a different approach employing contact matrices and Boolean operators has been used. Moreover, the neural network approach is used in the determination of optimum assembly sequence. The inputs to the networks are the collection of assembly sequence data. This data is used to train the network using the back propagation (BP) algorithm.

The proposed neural network model outperforms the available assembly sequence‐planning model in predicting the optimum assembly sequence for mechanical parts. Due to the parallel structure and fast learning of neural network, this kind of algorithm will be utilized to model another types of assembly systems.

In the proposed neural approach, the back propagation algorithm is used. Various training algorithms can be employed.

The simulation results suggest that the neural predictor would be used as a predictor for possible practical applications on modeling assembly sequence planning system.

This paper discusses a new modelling scheme known as artificial neural networks. The neural network approach has been employed for analyzing feasible assembly sequences and optimum assembly sequence for assembly systems.

This paper deals with the organizing of interactive product development. Developing products in interaction between firms may provide benefits in terms of specialization, increased innovation, and possibilities to perform development activities in parallel. However, the differentiation of product development among a number of firms also implies that various dependencies need to be dealt with across firm boundaries. How dependencies may be dealt with across firms is related to how product development is organized. The purpose of the paper is to explore dependencies and how interactive product development may be organized with regard to these dependencies.

The analytical framework is based on the industrial network approach, and deals with the development of products in terms of adaptation and combination of heterogeneous resources. There are dependencies between resources, that is, they are embedded, implying that no resource can be developed in isolation. The characteristics of and dependencies related to four main categories of resources (products, production facilities, business units and business relationships) provide a basis for analyzing the organizing of interactive product development.

Three in-depth case studies are used to explore the organizing of interactive product development with regard to dependencies. The first two cases are based on the development of the electrical system and the seats for Volvo’s large car platform (P2), performed in interaction with Delphi and Lear respectively. The third case is based on the interaction between Scania and Dayco/DFC Tech for the development of various pipes and hoses for a new truck model.

The analysis is focused on what different dependencies the firms considered and dealt with, and how product development was organized with regard to these dependencies. It is concluded that there is a complex and dynamic pattern of dependencies that reaches far beyond the developed product as well as beyond individual business units. To deal with these dependencies, development may be organized in teams where several business units are represented. This enables interaction between different business units’ resource collections, which is important for resource adaptation as well as for innovation. The delimiting and relating functions of the team boundary are elaborated upon and it is argued that also teams may be regarded as actors. It is also concluded that a modular product structure may entail a modular organization with regard to the teams, though, interaction between business units and teams is needed. A strong connection between the technical structure and the organizational structure is identified and it is concluded that policies regarding the technical structure (e.g. concerning “carry-over”) cannot be separated from the management of the organizational structure (e.g. the supplier structure). The organizing of product development is in itself a complex and dynamic task that needs to be subject to interaction between business units.

The objective of this paper is to provide a brief review of recent developments in the area of applications of ANN, Fuzzy Logic, and Wavelet Transform in fault diagnosis. The purpose of this work is to provide an approach for maintenance engineers for online fault diagnosis through the development of a machine condition‐monitoring system.

A detailed review of previous work carried out by several researchers and maintenance engineers in the area of machine‐fault signature‐analysis is performed. A hybrid expert system is developed using ANN, Fuzzy Logic and Wavelet Transform. A Knowledge Base (KB) is created with the help of fuzzy membership function. The triangular membership function is used for the generation of the knowledge base. The fuzzy‐BP approach is used successfully by using LR‐type fuzzy numbers of wavelet‐packet decomposition features.

The development of a hybrid system, with the use of LR‐type fuzzy numbers, ANN, Wavelets decomposition, and fuzzy logic is found. Results show that this approach can successfully diagnose the bearing condition and that accuracy is good compared with conventionally EBPNN‐based fault diagnosis.

The work presents a laboratory investigation carried out through an experimental set‐up for the study of mechanical faults, mainly related to the rolling element bearings.

The main contribution of the work has been the development of an expert system, which identifies the fault accurately online. The approaches can now be extended to the development of a fault diagnostics system for other mechanical faults such as gear fault, coupling fault, misalignment, looseness, and unbalance, etc.

There has been enormous interest over the past decade in the use of artificial neural networks (ANNs) for data processing applications. By and large, this interest has been well‐founded. ANNs, however, offer no panacea to the data analyst. They are as prone to misuse as any other method and the details of their functioning are often clouded in mystique. This has made a firm understanding of their functioning difficult. Presents a brief introduction to the most widely applied class of ANN, the feed‐forward network. Gives an overview of its functioning for both classification and regression problems.

Traditional machining centre selection methods may not guarantee a cost effective solution. Properly trained back‐propagation artificial neural network (BPANN) tend to select reasonable machining centres when presented with machining parameters that they have never seen before. The aim of this paper is to demonstrate the applicability of artificial neural networks (ANNs) to machine centre selection problems.

A three‐layer feedforward back‐propagation supervised training approach is selected to address the machining centre selection problem and demonstrated its potential through an example. This is intended to help readers understand implications on manufacturing system design and future research.

Very limited studies attempted the machining centre selection problem. Feedforward ANN approach has been applied to a wide variety of manufacturing problems. Neural networks have training capability to solve problems that are difficult for conventional computers or human beings. The developed BPANN model has potential to solve the machine centre selection problem with notable consistency and reasonable accuracy.

The BPANN model is an innovative approach fundamentally based on artificial intelligence, which is not directly visible to the user, but is able to solve through a simpler and supervised feedforward back‐propagation training process. The model consists of an input layer, a hidden layer and an output layer. The 18 neurons fixed in the input layer are same as the set of machining centre parameters which are taken directly from the machine tool manufacturer's catalogues. Evidently the proposed three‐layer ANN model has the capability of solving the machine centre selection problem with three hidden neurons for threshold level of 0.9, noise level of 0.05 and tolerance of 0.01.

The work size, weight, travel range, spindle speed range, horse power, feed, accuracy, tool magazine and price are used as machining centre selection parameters. Machining centres' information in the form of 24 patterns along with the desired machining centres' were used to train and test the network.

The discussion about public sector performance is still present today, despite the profound research that has already tried to address this subject. Furthermore, theory links negative effects on organizational performance with increased levels of organizational complexity. However, literature thus far did not succeed to put forward a successful theory that explains why and how public organizations became increasingly complex. To answer this question, we argue that increased organizational complexity can be explained by viewing public organizations as the hybrid result of different institutional logics, which are shaped by various management views. However, former research mainly concentrated on the separate study of management views such as traditional public management (TPM), NPM, and post-NPM. Although appealing, research that approaches hybridity from this perspective is fairly limited.

We conducted a literature review in which we studied 80 articles about traditional public management, NPM, and post-NPM.

We found that these management views essentially differ on the base of three fault lines, depending on the level of the organizational culture. These fault lines, according to the management view, together result in nine dimensions. By combing dimensions of the different management views, we argue that a public organization becomes hybrid. Furthermore, in line with findings of contingency theory, we explain the level of hybridity might depend on the level of tight coupling for a given organization. Finally, we developed propositions that explain hybridity as the result of isomorphic forces, organizational change, and organizational resistance to change and that link hybridization with processes of selective coupling.

The value of this chapter lies in its real-life applicability.

This study aims to examine information (stock return, volatility and extreme risk) spillovers and interconnectedness within dual-banking systems.

Using multilayer information spillover networks, this paper conduct a deep analysis of contagion dynamics among 24 Islamic and 46 conventional banks from 2006 to 2022.

The findings show the network’s rapid response to financial shocks. Through cross-sector analysis, this paper identify information spillovers between and within Islamic and conventional banking systems. Furthermore, this research illustrates distinct roles played by Islamic and conventional banks within the multilayer network structure, contingent upon the nature of the financial shock.

Understanding the differential roles of Islamic and conventional banks in information transmission can aid policymakers and financial institutions in devising more effective risk management strategies, thereby enhancing financial stability within dual-banking systems.

This study contributes to the literature by emphasizing the necessity of examining contagion mechanisms beyond traditional single-layer network structures, shedding light on the shadow dynamics of information transmission in dual-banking systems.