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The purpose of this paper is to design and construct an amplitude shift keying (ASK) modulator, which, using the digital binary modulating signal, controls a floating memristor emulator (MR) internally without the need for additional control circuits to achieve the ASK modulated wave.

A binary digital unipolar signal to be modulated is converted by a pre-processor circuit into a suitable bipolar modulating direct current (DC) signal for the control of the MR state, using current conveyors the carrier signal’s amplitude is varied with the change in the memristance of the floating MR. A high pass filter is then used to remove the DC control signal (modulating signal) leaving only the modulated carrier signal.

The results from the experiment and simulation are in agreement showed that the MR can be switched between two states and that a change in the carrier signals amplitude can be achieved by using an MR. Thus, showing that the circuit behavior is in line with the proposed theory and validating the said theory.

In this paper, the binary signal to be modulated is modified into a suitable control signal for the MR, thus the MR relies on the internal operation of the modulator circuit for the control of its memristance. An ASK modulation can then be achieved using a floating memristor without the need for additional circuits or signals to control its memristance.

Reports a study of 1,304 Chinese brand names of ten types of products in China. These brand names are content analyzed following a linguistic approach which the authors developed from their earlier studies. The ten types of brand names are presented in three broad categories representing the three different developing stages of the consumer product industry in China: brands of traditional products (illustrated by matches and spirits), brands of traditional products with current development (illustrated by bicycles, shoes, and toothpastes), and brands of new and modern products (illustrated by cosmetics, soft drinks, washing machines, refrigerators and TV sets). The conclusion drawn from the analysis is that one of the variables in determining how linguistic principles are being applied to Chinese brand naming is the respective stages of development of such products in the context of the Chinese market economy.

The purpose of this paper is to find out the characteristics and evolutionary trends of China’s inter-regional innovation correlation network, the status and roles of each province in China’s inter-regional innovation correlation network and the influencing factors of China’s inter-regional innovation correlation effect.

Based on the patent data of 30 provinces (autonomous regions and municipalities) in China from 1991 to 2017, social network analysis was used to find out the characteristics and evolutionary trends of China’s inter-regional innovation correlation network and the status and roles of each province in China’s inter-regional innovation correlation network. Furthermore, the QAP method was used to find out the influencing factors of China’s inter-regional innovation correlation effect.

China’s inter-regional innovation correlation is becoming increasingly close and inter-regional innovation correlation network is becoming increasingly stable. Jiangsu, Zhejiang, Beijing, Shanghai, Guangdong and other eastern coastal provinces are at the core in the inter-regional innovation correlation network, while the western regions are marginal actors. China’s regional innovation development territory can be divided into four blocks, namely, “bidirectional spillover block,” “net spillover block,” “main beneficial block” and “net beneficial block,” and gradient transfer mechanism is obvious between the blocks. The geographical adjacency and similarity in regional industrial structure, urbanization level and government attention degree have significant positive effect on China’s inter-regional innovation correlation effects.

This paper only uses patent application as a measure of regional innovation level to analyze inter-regional innovation correlation effect. Meanwhile, this paper carries out an empirical study only from the provincial level and not from the city level.

This paper provides the practical basis for further promoting the coordinated development of regional innovation and promoting the construction of regional innovation systems with different characteristics.

This paper contributes to understand the status and role of each province in inter-regional innovation correlation network. Meanwhile, this paper also helps to understand the influence of the proximity and external environmental factors on inter-regional innovation correlation effect.

The purpose of this paper is to improve the corrosion resistance of stainless-steel bipolar plate by magnetron sputtering.

TiC/amorphous carbon composite film was deposited by magnetron sputter at four different temperature of 25°C, 200°C, 300°C and 400°C. The morphology, composition and structure of the film were characterized by scanning electron microscopy, atomic force microscopy and X-ray photoelectron spectroscopy. And its corrosion behavior was analyzed through electrochemical impedance spectroscopy, potentiodynamic and potentiostatic polarization tests.

A compact TiC/amorphous carbon film was prepared by magnetron sputtering on 316L stainless steel, and the particles of the film were refined with the increase in sputtering temperature. High temperature promoted the formation of TiC and C–C sp² hybrid carbon, but excessively high temperature caused the oxidation of Ti and a significant decrease in sp² hybrid carbon. The corrosion resistance of the film increased with the temperature, and the corrosion current density polarization at 0.86 V and 1.8 V for TiC/a–C film prepared at 400 °C is only 1.2% and 43.2% of stainless steel, respectively.

The corrosion resistance of amorphous carbon films was improved by the doping of Ti carbide, and the appropriate sputtering temperature was obtained.

The primary aim of this study is to detail the use of soft computing techniques in business and management research. Its objectives are as follows: to conduct a comprehensive scientometric analysis of publications in the field of soft computing, to explore the evolution of keywords, to identify key research themes and latent topics and to map the intellectual structure of soft computing in the business literature.

This research offers a comprehensive overview of the field by synthesising 43 years (1980–2022) of soft computing research from the Scopus database. It employs descriptive analysis, topic modelling (TM) and scientometric analysis.

This study's co-citation analysis identifies three primary categories of research in the field: the components, the techniques and the benefits of soft computing. Additionally, this study identifies 16 key study themes in the soft computing literature using TM, including decision-making under uncertainty, multi-criteria decision-making (MCDM), the application of deep learning in object detection and fault diagnosis, circular economy and sustainable development and a few others.

This analysis offers a valuable understanding of soft computing for researchers and industry experts and highlights potential areas for future research.

This study uses scientific mapping and performance indicators to analyse a large corpus of 4,512 articles in the field of soft computing. It makes significant contributions to the intellectual and conceptual framework of soft computing research by providing a comprehensive overview of the literature on soft computing literature covering a period of four decades and identifying significant trends and topics to direct future research.

This study aims to explore the effect of urban green space (UGS) on residents' subjective well-being (SWB) among different social groups.

Using national SWB and UGS data obtained from the China Urban Construction Statistical Yearbook and the Chinese General Social Survey, a multiple regression model was developed to estimate the effect of UGS on residents' SWB. Grouping regression for samples from distinct socioeconomic groups was performed to further discuss group-wise differences in SWB.

The green coverage rate of built-up areas and the number of parks accessed by every 10,000 individuals are significantly positively correlated with residents' SWB, whereas the green space area per capita and greening investment ratio are significantly negatively correlated with residents' SWB; the effect of UGS on residents' SWB varies among individuals with respect to gender, age, and income, with the most significant difference observed among groups with different incomes.

The empirical results of this study are expected to support the planning and construction of UGS by providing a reference for optimizing their service capabilities and highlighting their positive role in improving residents' SWB.

The purpose of this paper is to consider the corrosion properties of laser nitrided Ti‐6Al‐4V alloys that have been reported previously by several researchers.

Different kinds of surface nitriding methods of titanium alloys, such as plasma nitriding, ion nitriding, gas and laser nitriding, are introduced. Microstructure changes, such as phase formation and the influence of laser processing parameters in laser nitriding layers of Ti‐6Al‐4V alloys, were investigated using scanning electron microscope, transmission electron microscope, X‐ray photo‐electron spectroscopy, and X‐ray diffraction. Based on investigations presented in the literature, the effect of laser nitriding on the corrosion behavior of Ti‐6Al‐4V alloy was reviewed.

By regulating the laser processing parameter, the microstructure of the nitrided layer can be controlled to optimize corrosion properties. This layer improves corrosion behavior in most environments, due to the formation of a continuous TiNxOy passive film, which can retard the ingress of corrosive ions into the substrate and can maintain a constant value of a current density. Therefore, the laser gas nitrided specimens have a relatively noble corrosion potential and a very small corrosion current, as compared to untreated specimens.

This paper comprises a critical review, and its collection of references is useful. It summarizes current knowledge in laser surface treatment research.

This paper aims to propose an improved version of portfolio optimization model through the prediction of the future behavior of stock returns using a combined wavelet-based long short-term memory (LSTM).

First, data are gathered and divided into two parts, namely, “past data” and “real data.” In the second stage, the wavelet transform is proposed to decompose the stock closing price time series into a set of coefficients. The derived coefficients are taken as an input to the LSTM model to predict the stock closing price time series and the “future data” is created. In the third stage, the mean-variance portfolio optimization problem (MVPOP) has iteratively been run using the “past,” “future” and “real” data sets. The epsilon-constraint method is adapted to generate the Pareto front for all three runes of MVPOP.

The real daily stock closing price time series of six stocks from the FTSE 100 between January 1, 2000, and December 30, 2020, is used to check the applicability and efficacy of the proposed approach. The comparisons of “future,” “past” and “real” Pareto fronts showed that the “future” Pareto front is closer to the “real” Pareto front. This demonstrates the efficacy and applicability of proposed approach.

Most of the classic Markowitz-based portfolio optimization models used past information to estimate the associated parameters of the stocks. This study revealed that the prediction of the future behavior of stock returns using a combined wavelet-based LSTM improved the performance of the portfolio.

Given the regional heterogeneity of economic development, electricity consumption in various regions exhibits a discrepant growth pattern. The purpose of this study is to construct a grey system forecasting model with intelligent parameters for predicting provincial electricity consumption in China.

First, parameter optimization and structural expansion are simultaneously integrated into a unified grey system prediction framework, enhancing its adaptive capabilities. Second, by setting the minimum simulation percentage error as the optimization goal, the authors apply the particle swarm optimization (PSO) algorithm to search for the optimal grey generation order and background value coefficient. Third, to assess the performance across diverse power consumption systems, the authors use two electricity consumption cases and select eight other benchmark models to analyze the simulation and prediction errors. Further, the authors conduct simulations and trend predictions using data from all 31 provinces in China, analyzing and predicting the development trends in electricity consumption for each province from 2021 to 2026.

The study identifies significant heterogeneity in the development trends of electricity consumption systems among diverse provinces in China. The grey prediction model, optimized with multiple intelligent parameters, demonstrates superior adaptability and dynamic adjustment capabilities compared to traditional fixed-parameter models. Outperforming benchmark models across various evaluation indicators such as root mean square error (RMSE), average percentage error and Theil’s index, the new model establishes its robustness in predicting electricity system behavior.

Acknowledging the limitations of traditional grey prediction models in capturing diverse growth patterns under fixed-generation orders, single structures and unadjustable background values, this study proposes a fractional grey intelligent prediction model with multiple parameter optimization. By incorporating multiple parameter optimizations and structure expansion, it substantiates the model’s superiority in forecasting provincial electricity consumption.