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Previous research has predominantly concentrated on examining risk spillovers through single-layer networks, neglecting the multi-related and multilayer network characteristics of the economic system. This study constructs multilayer connectedness networks, including return, volatility and extreme risk layers, to systematically analyze the risk spillovers across Chinese industries at the system and industry levels.

Previous studies have constructed multilayer networks using Diebold and Yilmaz’s (2012) approach or the time-varying parameter vector autoregressive (TVP-VAR) connectedness model. In this study, we employ the TVP-VAR-extended joint connectedness approach, which improves these methods and captures risk spillovers more accurately.

At the system level, the risk spillover across industries exhibits distinct network structures and dynamic evolution behaviors across different layers. During extreme events, the intensity, scope and speed of risk spillovers increase markedly across all layers, with volatility and extreme risk layers demonstrating greater sensitivity to crises. At the industry level, industrial and optional consumption typically serve as risk transmitters, while medicine and health, as well as financial real estate, tend to be risk receivers across three layers. Moreover, industrial, optional consumption and materials exhibit significant systemic importance.

To the best of our knowledge, this is the first study to apply multilayer networks with return, volatility and extreme risk layers to systematically examine risk spillovers between Chinese industries.

The aim of this study is to report the effect of different content of calcium oxide on the process of electromelting magnesia.

The process of molten magnesia was analyzed by finite element simulation and proved by scanning electron microscope.

The results show that with the increase of CaO content, the maximum temperature appreciation increases from 3,616°C To 3,729°C, showing an approximate nonlinear evolution. Low thermal conductivity and low specific heat of CaO result in higher temperature. With the increase of CaO content and temperature, the maximum flow velocity of MgO slag increases from 0.043 to 1.34 mm/s. Under different initial CaO contents, the distribution trend of CaO volume fraction is basically the same, and the CaO volume fraction is evenly distributed between 50 and 225 mm in the furnace.

The influence of different contents of impurity calcium oxide on the process of electromelting magnesia was analyzed and a theoretical system was established.

National parks are trademarks for unspoilt landscape and authentic nature experience, a quality which is one of the most important competition factors in tourism and which does comply with today's most popular trends in tourism. But up to now the surrounding tourism regions do not use the attraction value of German national parks for their destination marketing. In 2002, the UN‐Year of Ecotourism and the national tourism campain for Germany's natural heritage, made the tourism sector aware of national parks. The situation analysis carried out in German national park regions revealed, that national parks in traditional destinations only play a secondary role, while they are paramount idea for marketing in underdeveloped regions with still modest tourist infrastructure. On the other hand there are medium or even higher developed destinations where national parks are playing a quite dominant role in tourism. The different types of national park regions require different strategies of destination management. As one strategy a corporate identity and policy for creating and establishing a touristic brand “German National Parks” is proposed.

The purpose of this paper is to empirically examine the roles of self-esteem (SE), negative affect (NA), and consumer susceptibility to normative influence in the enactment of impulse buying behavior.

A theoretical model is developed through an extensive review of literature. Survey research is conducted to collect the data from respondents. Structural equation modeling is performed to test the model and the hypotheses.

The outcome of the study reveals that the act of impulsive buying is preceded by buying impulse (BI). BI is positively influenced by consumer susceptibility to normative influence and impulsive buying tendency (IBT). SE influences the generation of BI partially mediated by IBT.

The study is limited in its generalizability in terms of its geographic location, culture, and the context of product categories.

The findings of the study have practical implications in developing marketing communications, merchandising, and personal selling strategies.

In view of the contradictory empirical evidences in extant literature regarding the role NA the present study re-examines whether NA influences impulse buying. The study, conducted in the field setting also ascertains the external validity of the findings not tested in the prior research. Furthermore, in light of psychology literature, the relationship between SE and IBT was hypothesized and empirically established in the present study.

This study aims to address the challenge of the real-time state of charge (SOC) estimation for lithium-ion batteries in robotic systems, which is critical for monitoring remaining battery power, planning task execution, conserving energy and extending battery lifespan.

The authors introduced an optimal observer based on adaptive dynamic programming for online SOC estimation, leveraging a second-order resistor–capacitor model for the battery. The model parameters were determined by fitting an exponential function to the voltage response from pulse current discharges, and the observer's effectiveness was verified through extensive experimentation.

The proposed optimal observer demonstrated significant improvements in SOC estimation accuracy, robustness and real-time performance, outperforming traditional methods by minimizing estimation errors and eliminating the need for iterative steps in the adaptive critic and actor updates.

This study contributes a novel approach to SOC estimation using an optimal observer that optimizes the observer design by minimizing estimation errors. This method enhances the robustness of SOC estimation against observation errors and uncertainties in battery behavior, representing a significant advancement in battery management technology for robotic applications.

In this study, we empirically investigate the effect of military expenditure on economic growth in the five South Asian countries of Bangladesh, India, Pakistan, Nepal, and Sri Lanka over the period of 1990–2006. By applying a Solow Growth Model, empirical evidences derived from panel estimation methods indicate that defense has a negative effect on economic growth in the region.

Using social network theory (SNT), this study empirically examines the impact of digital supply chain announcements disclosing corporate social responsibility (CSR) information on stock market value.

Based on 172 digital supply chain announcements disclosing CSR information from Chinese A-share listed companies, this study uses event study method to test the hypotheses.

First, digital supply chain announcements disclosing CSR information generate positive and significant market reactions, which is timely. Second, strategic CSR and value-based CSR disclosed in digital supply chain announcements have a more positive impact on stock market, however there is no significant difference when the CSR orientation is either towards internal or external stakeholders. Third, in terms of digital supply chain network characteristics, announcements reflecting higher relationship embeddedness and higher digital breadth and depth lead to more positive increases of stock value.

First, the authors consider the value of CSR information in digital supply chain announcements, using an event study approach to fill the gap in the related area. This study is the first examination of the joint impact of digital supply chain and CSR on market reactions. Second, compared to the previous studies on the single dimension of digital supply chain technology application, the authors innovatively consider supply chain network relationship and network structure based on social network theory and integrate several factors that may affect the market reaction. This study improves the understanding of the mechanism between digital supply chain announcements disclosing CSR information and stock market, and informs future research.

The purpose of this paper is to propose a new brand association density metric and evaluate its performance in terms of correlations with recall, consideration, brand equity and market share and to compare different data collection methodologies to identify brand associations.

The authors present results from two studies covering three product categories. The authors use an open free association question and associations to a set of pre-defined brand attributes. The responses to the open free format question are text-mined prior to further analysis.

The authors find that the brand association density metric performs better than a metric that only uses the number of distinct associations. The authors also find that these metrics work best when derived from open free association data.

First, in addition to focusing on trying to build specific brand associations in consumers’ minds, it may be equally important, if not more important, to manage the number and inter-connectedness of the brand’s associations. Second, firms should complement their existing survey approaches with open-ended free association questions.

The brand association density concept presented is believed to be new. The empirical comparison between the use of free association to pre-defined attributes is also new.

The purpose of this paper is to develop an approach for estimating the remaining useful life (RUL) of metropolitan train wheels considering measurement error.

The paper proposes a wear model of a metropolitan train wheel based on a discrete state space model; the model considers the wheel’s stochastic degradation and measurement error simultaneously. The paper estimates the RUL on the basis of the estimated degradation state. Finally, it presents a case study to verify the proposed approach. The results indicate that the proposed method is superior to methods that do not consider measurement error and can improve the accuracy of the estimated RUL.

RUL estimation is a key issue in condition-based maintenance and prognostics and health management. With the rapid development of advanced sensor technologies and data acquisition facilities for the maintenance of metropolitan train wheels, condition monitoring (CM) is becoming more accurate and more affordable, creating the possibility of estimating the RUL of wheels using CM data. However, the measurements of the wheels, especially the wayside measurements, are not yet precise enough. On the other hand, few existing studies of the RUL estimation of train wheels consider measurement error.

The approach described in this paper will make the RUL estimation of metropolitan train wheels easier and more precise.

Hundreds of million yuan are wasted every year due to over re-profiling of rail wheels in China. The ability to precisely estimate RUL will reduce the number of re-profiling activities and achieve significant economic benefits. More generally, the paper could enrich the body of knowledge of RUL estimation for a slowly degrading system considering measurement error.

Trajectory tracking error of robotic manipulator has limited its applications in trajectory tracking control systems. This paper aims to improve the trajectory tracking accuracy of robotic manipulator, so a linear-extended-state-observer (LESO)-based prescribed performance controller is proposed.

A prescribed performance function with the convergence rate, maximum overshoot and steady-state error is derived for the output error transformation, whose stability can guarantee trajectory tracking accuracy of the original robotic system. A LESO is designed to estimate and eliminate the total disturbance, which neither requires a detailed system model nor a heavy computation load. The stability of the system is proved via the Lyapunov theory.

Comparative experimental results show that the proposed controller can achieve better trajectory tracking accuracy than proportional-integral-differential control and linear active disturbance rejection control.

In the LESO-based prescribed performance control (PPC), the LESO was incorporated into the PPC design, it solved the problem of stabilizing the complex transformed system and avoided the costly offline identification of dynamic model and estimated and eliminated the total disturbance in real-time with light computational burden. LESO-based PPC further improved control accuracy on the basis of linear-active-disturbance-rejection-control. The new proposed method can reduce the trajectory tracking error of the robotic manipulators effectively on the basis of simplicity and stability.