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A chaotic system is a nonlinear deterministic system that displays complex, noisy‐like and unpredictable behavior. The interest in chaotic systems lies mostly upon their complex, unpredictable behavior, and extreme sensitivity to initial conditions as well as parameter variations. Based on wavelet neural network's (WNN) online approximation ability, the purpose of this paper is to propose an adaptive Gaussian wavelet neural control (AGWNC) system to control a chaotic system.

The proposed AGWNC system is composed of a wavelet neural controller and a compensation tangent controller. The wavelet neural controller utilizes a Gaussian WNN to mimic an ideal controller, and the compensation tangent controller is designed to compensate the approximation error between the ideal and the wavelet neural controllers. The controller parameters of the proposed AGWNC can online tune in the Lyapunov sense, thus the uniformly ultimately bounded stability of closed‐loop system can be guaranteed.

The proposed AGWNC system is applied to a chaotic system. Simulation results are used to demonstrate the effectiveness and performance of the proposed AGWNC scheme. Simulation results show that not only the favorable control performance can be achieved but also the control efforts without any chattering phenomena. Moreover, all controller parameters can be online tuning by the derived adaptive laws based on the Lyapunov function.

The proposed AGWNC approach is interesting for the design of an intelligent control scheme. The main contributions of this paper are: the overall closed‐loop control system is globally stable in uniform ultimate boundedness; the tracking error can be asymptotically attenuated to a desired small level around zero by appropriate chosen parameters and learning rates; and the AGWNC system can achieve favorable tracking performance.

The DC‐DC converters which convert one level of electrical voltage to the desired level are widely used in many electrical peripherals. During the past two decade, many different control laws have been developed. The proportional‐integral (PI) control and sliding‐mode control have been carried out for the DC‐DC converters since they are simple to implement and easy to design. However, its performance using PI control and sliding‐mode control is obviously quite limited. The purpose of this paper is to a self‐tuning nonlinear function control (STNFC) propose for the DC‐DC converters. The adaptation laws of the proposed STNFC system are derived in the sense of Lyapunov function, thus not only the controller parameters can be online tuned itself, but also the system's stability can be guaranteed.

In general, the accurate mathematical models of the DC‐DC converters are difficult to derive. This paper proposes a model‐free STNFC design method. Since the proposed STNFC uses a simple fuzzy system with three fuzzy rules base to implement the control law, the computational loading of the fuzzy inference mechanism is slight. So the proposed STNFC system is suitable for the real‐time practical applications. The controller parameters of the proposed STNFC system can online tune in the Lyapunov sense, thus the stability of closed‐loop system can be guaranteed.

The proposed STNFC system is applied to a DC‐DC converter based on a field‐programmable gate array chip. The experimental results are provided to demonstrate the proposed STNFC system can cope with the input voltage and load resistance variations to ensure the stability while providing fast transient response.

The proposed STNFC approach is interesting for the design of an intelligent control scheme. The main contributions of this paper are: the successful development of STNFC system without heavy computational loading. The parameter‐learning algorithm is design based on the Lyapunov stability theorem to guarantee the system stability; the successful applications of the STNFC system to control the forward DC‐DC converter. And, the proposed STNFC methodology can be easily extended to other DC‐DC converters.

An investigation has been performed on the improved solder wettability of oxidized aluminum (Al) with lead-free solder (96.5Sn-3.5Ag) using Ar-H₂ plasmas. The lead-free solder wettability was raised from 62.2 per cent wetting for Al oxidized in air at 250 C for 4 h to 98.4 per cent wetting of oxidized Al modified by Ar-H₂ plasmas at a certain H₂ flow rate. This study aims to gain insight on the surface characteristics of Al affecting the solder wettability with a liquid lead-free solder.

Ar-H₂ plasmas at certain H₂ flow rates are intended to reduce Al oxides on the surfaces of oxidized Al substrates both by physical bombardments via Ar plasmas and chemical reductions with H₂ plasmas, while Al substrates are exposed in Ar-H₂ plasmas to improve the solder wettability with a liquid lead-free solder.

Surface characteristics of oxidized Al substrates have been identified to play key roles for enhanced lead-free solder wettability using Ar-H₂ plasmas. A decrease in polar surface free energy and an increase in dispersive surface free energy on the surfaces of oxidized Al substrates are exploited to advance the lead-free solder wettability. Decreased composition ratios of O to Al, detected by X-ray photoelectron spectroscopy (XPS) for oxidized Al substrates, are crucial for improved lead-free solder wettability.

XPS is typically used to analyze the surface compositions of Al oxides. To provide a rapid and non-expansive method to identify the surfaces of Al substrates prior to soldering to assure lead-free solder wettability, this study proposes a measurable skill, a so-called sessile drop test method, to investigate surface free energies such as total, polar and dispersive surface free energy on the surfaces of Al substrates, to illuminate how the lead-free solder wettability of oxidized Al is improved by Ar-H₂ plasmas.

The purpose of this study is use a bibliometric analysis to explore the relational nature of knowledge creation in WFM in operations. Companies live under constant pressure to find the best ways to plan their workforce, and the workforce emangement (WFM) is one of the biggest challenges faced by managers. Relevant research on WFM in operations has been published in a several range of journals that vary in their scope and readership, and thus the academic contribution to the topic remains largely fragmented.

To address this gap, this review aims to map research on WFM in operations to understand where it comes from and where it is going and, therefore, provides opportunities for future work. This study combined two bibliometric approaches with manual document coding to examine the literature corpus of WFM in operations to draw a holistic picture of its different aspects.

Content and thematic analysis of the seminal studies resulted in the extraction of three key research themes: workforce cross-training, planning workforce mixed methods and individual workforce characteristics. The findings of this study further highlight the gaps in the WFM in operations literature and raise some research questions that warrant further academic investigation in the future.

Likewise, this study has important implications for practitioners who are likely to benefit from a holistic understanding of the different aspects of WFM in operations.