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This paper aims to propose numerical-based and experiment-based identification processes, accounting for uncertainties to identify structural parameters, in a wave propagation framework.

A variant of the inhomogeneous wave correlation (IWC) method is proposed. It consists on identifying the propagation parameters, such as the wavenumber and the wave attenuation, from the frequency response functions. The latters can be computed numerically or experimentally. The identification process is thus called numerical-based or experiment-based, respectively. The proposed variant of the IWC method is then combined with the Latin hypercube sampling method for uncertainty propagation. Stochastic processes are consequently proposed allowing more realistic identification.

The proposed variant of the IWC method permits to identify accurately the propagation parameters of isotropic and composite beams, whatever the type of the identification process in which it is included: numerical-based or experiment-based. Its efficiency is proved with respect to an analytical model and the Mc Daniel method, considered as reference. The application of the stochastic identification processes shows good agreement between simulation and experiment-based results and that all identified parameters are affected by uncertainties, except damping.

The proposed variant of the IWC method is an accurate alternative for structural identification on wide frequency ranges. Numerical-based identification process can reduce experiments’ cost without significant loss of accuracy. Statistical investigations of the randomness of identified parameters illustrate the robustness of identification against uncertainties.

This paper studies the impact of different chief executive officer (CEO) succession strategies on consumer evaluation of family firms. CEO succession is critical for family firms as improper succession planning has been shown to be the primary reason for high mortality rates of such firms. Furthermore, the choice of CEO (internal vs external) by family firms can send different signals to stakeholders and thereby impact their appraisal of such firms.

In this paper, the authors use an experiment-based approach to test how the type of CEO succession (internal vs external) influences the consumer's evaluation of family firms.

The authors find that appointing an internal CEO leads to higher perception of firm capability, trust towards the firm and more favorable consumer attitudes. All these factors, in turn, lead to higher purchase intentions. External CEOs in family firms do not seem to have any de facto advantage regarding perceptions of higher capability.

Thus, the authors contribute to the literature of family firms by showing how family firm's strategic decisions during succession can affect consumer behavior.

The purpose of this study is to investigate the prediction and optimization of multiple performance characteristics in the face milling process of tool steel ASSAB XW-42.

The face milling parameters (cutting speed, feed rate and axial depth of cut) and flow rate (FR) of cryogenic cooling were optimized with consideration of multiple performance characteristics, i.e. surface roughness (SR), cutting force (F_c) and metal removal rate (MRR). FR of cryogenic cooling has two levels, whereas the three face milling parameters each have three levels. Using Taguchi method, an L₁₈ mixed-orthogonal array was selected as the design of experiments. The rough estimation of the optimum face milling parameters was determined by using grey fuzzy analysis. The global optimum face milling parameters were searched by applying the backpropagation neural network-based genetic algorithm (BPNN-GA) method.

The optimum SR, cutting force (F_c) and MRR could be obtained by setting FR, cutting speed, feed rate and axial depth of cut at 0.5 l/min, 280 m/min, 90 mm/min and 0.2 mm, respectively. The experimental confirmation results showed that BPNN-based GA optimization method could accurately predict and significantly improve all of the multiple performance characteristics.

To the best of the authors’ knowledge, there were no publications available regarding multi-response optimization using the combination of grey fuzzy analysis and BPNN-based GA methods during cryogenically face milling process.

In the quest for continuous quality improvement for both products and processes, design of experiments based on Shainin’s Variables Search is still playing a pivotal role among engineers and quality control practitioners. Briefly describes the Variables Search Designs formulated and promoted by Dorian Shainin for identifying the most critical variables which influence the process performance. In order to illustrate the potential of this powerful method, a simple paper helicopter experiment was carried out by the author. The results of the study have shown that Shainin’s Variables Search Design is a powerful tool for identifying the key process variables and therefore should be utilised by the engineering fraternity in manufacturing organisations as a problem‐solving tool.

Experimental design (ED) is a powerful technique which involves the process of planning and designing an experiment so that appropriate data can be collected and then analysed by statistical methods, resulting in objective and valid conclusions. It is an alternative to the traditional inefficient and unreliable one‐factor‐at‐a‐time approach to experimentation, where an experimenter generally varies one factor or process parameter at a time keeping all other factors at a constant level. This paper presents a step‐by‐step approach to the optimisation of a production process (of retaining a metal ring in a plastic body by a hot forming method) through the utilisation of Taguchi methods of experimental design. The experiment enabled the behaviour of the system to be understood by the engineering team in a short period of time and resulted in significantly improved performance (with the opportunity to design further experiments for possible greater improvements).

The present study is intended to develop an effective approach to the real-time modeling of general dynamic nonlinear systems based on the multidimensional Taylor network (MTN).

The authors present a detailed explanation for modeling the general discrete nonlinear dynamic system by the MTN. The weight coefficients of the network can be obtained by sampling data learning. Specifically, the least square (LS) method is adopted herein due to its desirable real-time performance and robustness.

Compared with the existing mainstream nonlinear time series analysis methods, the least square method-based multidimensional Taylor network (LSMTN) features its more desirable prediction accuracy and real-time performance. Model metric results confirm the satisfaction of modeling and identification for the generalized nonlinear system. In addition, the MTN is of simpler structure and lower computational complexity than neural networks.

Once models of general nonlinear dynamical systems are formulated based on MTNs and their weight coefficients are identified using the data from the systems of ecosystems, society, organizations, businesses or human behavior, the forecasting, optimizing and controlling of the systems can be further studied by means of the MTN analytical models.

MTNs can be used as controllers, identifiers, filters, predictors, compensators and equation solvers (solving nonlinear differential equations or approximating nonlinear functions) of the systems of ecosystems, society, organizations, businesses or human behavior.

The operating efficiency and benefits of social systems can be prominently enhanced, and their operating costs can be significantly reduced.

Nonlinear systems are typically impacted by a variety of factors, which makes it a challenge to build correct mathematical models for various tasks. As a result, existing modeling approaches necessitate a large number of limitations as preconditions, severely limiting their applicability. The proposed MTN methodology is believed to contribute much to the data-based modeling and identification of the general nonlinear dynamical system with no need for its prior knowledge.

Customization, a marketing strategy through providing personalized products, might be a new solution to motivate consumer feedbacks in electronic commerce (e-commerce) websites. Taking the dual-value of customization (emotional involvement and uniqueness expression) as the theoretical basis, this study aims to investigate the impact of customization on consumer's word-of-mouth (WOM) behaviors and contents by motivating: (1) more, (2) faster, (3) positive at first and then negative, (4) longer and (5) more helpful WOMs.

A field study was conducted with multi-sourced data: customer order data from a Chinese retailer and WOM data from Amazon.com. The two datasets were matched to filter out 463 online reviews among 6,892 customers who placed customized orders. Heckman's two-stage model, logistic regression, Ordinary least squares regression, Tobit regression, analysis of covariance and Lind–Mehlum U Test were used in the data analysis.

This study has found that (1) customization level motivates WOM behaviors including WOM posting and WOM speed, (2) an inverted U-shaped relationship exists between customization level and consumer rating and (3) customization level has a significantly positive impact on WOM helpfulness but not on WOM length.

This study advances theoretical development in the area of WOM motivators by proposing a new product-centric approach, customization, to stimulate voluntary WOMs. Empirical field research that analyzes consumer's real responses to customization is in scarcity. The dual-value of customized products is proposed as the underlying mechanism to explain the impact of customization level on consumer's WOM behaviors/contents. An interesting inverted U-shaped relationship is found between customization level and customer rating. This research provides nuanced practical guidance for websites, companies and consumers.

The study explores the role of dialogic public communication and information quality (IQ) in evaluating the operational performance of donation-based crowdfunding (DBC) tasks. These tasks are primarily used to support disaster relief operations. The authors also test the influence of cognitive trust and swift trust as moderating variables in explaining the relationship between both IQ and dialogic communication with operational performance.

The authors used a primary survey to test the hypotheses. A total of 203 responses were collected from multiple crowdfunding platforms. The authors used archival data from task creators on donation-based crowdfunding platforms, and a structured questionnaire is also used to collect responses. Data are analyzed using Warp PLS 6.0. Warp PLS 6.0 works on the principle of partial least square (PLS) structured equation modeling (SEM) and has been used widely to test path analytical models.

The authors found out that the operational performance is explained significantly by the quality of information and its association with dialogic public communication. The results support the arguments offered by dialogic public communication theory and trust transfer theory in assessing the operational success of DBC. The study also confirms that cognitive trust positively moderates the relationship between IQ and organizational public dialogic communication and operational performance. It is also revealed that the duration of the DBC task has no significant control over dialogic public communication.

The study lays practical foundations for task creators on DBC platforms and website designers as it sets the importance of both IQ and dialogic communication channels. The communication made by the task creator and/or the DBC platforms with the donors and potential donors in the form of timely and appropriate information forms the key to the success of any DBC task. The study also helps task creators choose a suitable platform to improve performance.

The authors propose a unique framework by integrating two theoretical perspectives: dialogic public relation theory and trust transfer theory in understanding the operational performance of donation-based crowdfunding tasks. The authors address DBC tasks catering to disaster relief operations by collecting responses from task creators on DBC platforms. The study uniquely positions itself in the area of information and communication.

The purpose of this paper is to develop a new inverse controller for servo‐system position tracking control based on neural network (NN) and model reference adaptive control (MRAC).

First, the model of general servo‐systems is analyzed. Then, a MRAC based on neural network control (NNC) is proposed with mathematical prove of stability. In addition, several simulation cases and experiments are listed to verify the usability of the control scheme.

This scheme consists of an MRAC, an online NN controller and a robust controller in velocity‐loop. For reducing influence which arose from modeling error, unknown model dynamics, parameter variation, and load changes, the NN controller is introduced to counteract the various influence mentioned above dynamically. MRAC, NNC, and robust controller adjust system to track the approximate velocity‐loop reference model. In this way, the position‐loop is not sensitive to the disturbance on velocity‐loop, and the whole velocity‐loop can be treated as a simple linear model when designing the other parts of the system. In addition, a novel inverse control method based on linear velocity signal filter is introduced to this scheme. In this case, the MRAC, NNC, and robust controller perform as an adaptive inverse controller, which keeps the velocity signal tracking the position loop controller output.

The paper presents a new inverse controller with NNC and MRAC which is practical and flexible.

This study describes how the copper deposition rate and the degree of copper coverage in the holes on printed circuit boards were investigated. The composition of the chemical copper solution was varied, and consequently the deposition parameters were changed. By using Taguchi methods, the level of significance of the process parameters was determined.