Search results

This study aims to better understand the morphological characteristics of single particle and the health risk characteristics of heavy metals in PM_2.5 in different functional areas of Handan City.

High resolution transmission electron microscopy was used to observe the aerosol samples collected from different functional areas of Handan City. The morphology and size distribution of the particles collected on hazy and clear days were compared. The health risk evaluation model was applied to evaluate the hazardous effects of particles on human health in different functional areas on hazy days.

The results show that the particulate matter in different functional areas is dominated by spherical particles in different weather conditions. In particular, the proportion of spherical particles exceeds 70% on the haze day, and the percentage of soot aggregates increases significantly on the clear day. The percentage of each type of particle in the teaching and living areas varied less under different weather conditions. Except for the industrial area, the size distribution of each type of particle in haze samples is larger than that on the clear day. Spherical particles contribute more to the small particle size segment. Soot aggregate and other shaped particles contribute more to the large size segment. The mass concentrations of hazardous elements (HEs) in PM_2.5 in different functional areas on consecutive haze pollution days were illustrated as industrial area > traffic area > living area > teaching area. Compared with the other functional areas, the teaching area had the lowest noncarcinogenic risk of HEs. The lifetime carcinogenic risk values of Cr and As elements in each functional area have exceeded residents’ threshold levels and are at high risk of carcinogenicity. Among the four functional areas, the industrial area has the highest carcinogenic and noncarcinogenic risks. But the effects of HEs on human health in the other functional areas should also be taken seriously and continuously controlled.

The significance of the study is to further understand the morphological characteristics of single particles and the health risks of heavy metals in different functional areas of Handan City. the authors hope to provide a reference for other coal-burning industrial cities to develop plans to improve air quality and human respiratory health.

This paper aims to study the issues of adaptive fuzzy control for a category of switched under-actuated systems with input nonlinearities and external disturbances.

A control scheme based on sliding mode surface with a hierarchical structure is introduced to enhance the responsiveness and robustness of the studied systems. An equivalent control and switching control rules are co-designed in a hierarchical sliding mode control (HSMC) framework to ensure that the system state reaches a given sliding surface and remains sliding on the surface, finally stabilizing at the equilibrium point. Besides, the input nonlinearities consist of non-symmetric saturation and dead-zone, which are estimated by an unknown bounded function and a known affine function.

Based on fuzzy logic systems and the hierarchical sliding mode control method, an adaptive fuzzy control method for uncertain switched under-actuated systems is put forward.

The “cause and effect” problems often existing in conventional backstepping designs can be prevented. Furthermore, the presented adaptive laws can eliminate the influence of external disturbances and approximation errors. Besides, in contrast to arbitrary switching strategies, the authors consider a switching rule with average dwell time, which resolves control problems that cannot be resolved with arbitrary switching signals and reduces conservatism.

Smoke and dust emissions from industrial furnaces can do great harm to the environment and human health. This paper aims to analyze the morphology, diameter and elements of the submicron particles from the furnace flues and the nearby ambient air by using two typical industrial furnaces, the sintering furnace and the electric furnace.

Two typical industrial furnaces, the sintering furnace and the electric furnace, were chosen in this study, to analyze the morphology, diameter and elements of the submicron particles from the furnace flues and the near-by ambient air.

The results show that the particles from the two furnaces are mainly in the small sizes of 0.3-0.6 μm. Particles from sintering plant flue are mainly spherical and rich in K and Cl, whereas those from the electric plant flue are mainly particles rich in metal elements, such as Zn and Fe, and have different morphology.

The particles in the atmosphere nearby the two furnaces contain aged particles from the flue, lots of spherical particles, rectangle particles and various aggregations. The elements of those particles are complex.

This study aims to focus on the submicron particles (with diameter of 0.2-1.0 μm) of the ambient air from a coal-fired power plant. A systematic examination of their morphology, particle size and chemical element will be analyzed, so as to provide more scientific information and theoretical basis for the formation and control method of inhalable particles, as well as data support for environmental impact and ecological effects assessments.

In this paper, the morphology, size distribution and elemental characteristics of submicron particles from ambient air of a coal-fired power plant are studied by single particle analysis.

The results show that atmospheric particles in coal-fired power plant are mainly spherical particles, and most of them are soot aggregates adhered or coated with other particles with few rectangle particles. The particles collected in the afternoon and evening are mainly of spherical particles, and small-sized particles collected in the morning are mainly spherical ones, while the overall concentration is larger than that of the spherical particles in the size range above 0.5 μm. The results indicated that the larger-sized spherical particles have a lower concentration.

Coal-fired power plants are still the main supply of electricity in China, but the inhalable particles, especially sub-micron particles (0.1-1.0 μm) cannot be effectively captured by the dust removal device from the coal-fired power plant. Thus, a large amount of inhalable particles is emitted into the atmosphere, becoming the major air pollutants in China.

As a kind of NP-hard combinatorial optimization problem, pipe routing design (PRD) is applied widely in modern industries. In the offshore oil and gas industry, a semi-submersible production platform is an important equipment for oil exploitation and production. PRD is one of the most key parts of the design of semi-submersible platform. This study aims to present an improved ant colony algorithm (IACO) to address PRD for the oil and gas treatment system when designing a semi-submersible production platform.

First, to simplify PRD problem, a novel mathematical model is built according to real constraints and rules. Then, IACO, which combines modified heuristic function, mutation mechanism and dynamical parameter mechanism, is introduced.

Based on a set of specific instances, experiments are carried out, and the experimental results show that the performance of IACO is better than that of two variants of ACO, especially in terms of the convergence speed and swarm diversity. Finally, IACO is used to solve PRD for the oil and gas treatment system of semi-submersible production platform. The simulation results, which include nine pipe paths, demonstrate the practicality and high-efficiency of IACO.

The main contribution of this study is the development of method for solving PRD of a semi-submersible production platform based on the novel mathematical model and the proposed IACO.

Innovativeness is crucial for industrial cluster firms to gain sustained competitive advantage. This study aims to investigate the effects of inter-firm coopetition on firm innovativeness within a cluster and examines the moderating role of institutional support.

This research adopts an empirical survey method using multi-source data from 181 industrial cluster firms. Regression is used to test the hypotheses of this study.

The results show that cooperation and constructive conflict promote firm innovativeness, while destructive conflict is detrimental to firm innovativeness. Moreover, the study also finds that cooperation interacts with both types of conflict to affect firm innovativeness, where cooperation and constructive conflict interact negatively on firm innovativeness, while cooperation and destructive conflict interact positively on firm innovativeness. In addition, institutional support weakens the effects of cooperation and destructive conflict on innovativeness, respectively, but has no significant moderating effect on the relationship between constructive conflict and innovativeness.

These findings enrich the current research on coopetition. The interaction effects of cooperation and both types of conflict on innovativeness deepen the concept of coopetition and responds to the call to further explore the interaction effects within coopetition. The moderating role of institutional support fills a gap in the empirical research on the role of institutional factors affecting coopetition on innovation and also provides valuable suggestions for firm managers and governments in industrial clusters.

External knowledge should not be limited in one zone or level. Researchers have paid more attention to the perspective of multilevel cluster networks. However, little research has empirically studied the various dimensions of external knowledge. The purpose of this paper is to study different levels of external knowledge, their relation with trade and non-trade interdependence and their relation with different kinds of innovations, namely, exploitation and exploration.

Both quantitative and qualitative research methods were adopted in this study. In terms of the quantitative research method, data were collected from 168 companies in the Shenzhen Hi-Tech Industrial Park of China using convenience sampling. As for the qualitative research method, a total of 35 interviews were carried out in this study.

The quantitative results indicate that different levels of external knowledge in the Shenzhen Hi-Tech Park have different effects. First of all, the results indicate that cross-national connections have a positive influence on trade interdependence, which helps firms to produce exploration. Second, cross-regional connections have a positive influence on both trade and non-trade interdependence, which further help firms to create innovative exploitation and exploration. Third, inter-regional connections have a positive influence on non-trade interdependence, which helps firms to increase innovative exploitation. The qualitative result makes a plausible explanation for the quantitative results. The interview results indicate that as the telecommunications industry has so much to do with China’s national security, there are several initiatives of market protection strategies and political interventions, which help firms to form different levels of knowledge flow in Shenzhen.

There are several limitations of this study which primarily relate to the case study method. The results can be contextually generalized to the domestic-oriented cluster in developing countries.

This study has several managerial implications. First, this research ensures that it is important to consider the multilevel nature of external knowledge before starting with the decision-making process of a firm in a cluster. Second, all levels of administrators and managers in a company should investigate what kinds of involvement and innovation are needed and most highly valued for organizational development. Third, the research framework of this study can be applied to understand which level of external knowledge influences organizational performance.

This study is an initial attempt to provide an examination of external knowledge, organizational involvement and innovation performance of an industrial cluster via a mixed method.

In past decades, artificial neural network (ANN) models have revolutionised various stock market operations due to their superior ability to deal with nonlinear data and garnered considerable attention from researchers worldwide. The present study aims to synthesize the research field concerning ANN applications in the stock market to a) systematically map the research trends, key contributors, scientific collaborations, and knowledge structure, and b) uncover the challenges and future research areas in the field.

To provide a comprehensive appraisal of the extant literature, the study adopted the mixed approach of quantitative (bibliometric analysis) and qualitative (intensive review of influential articles) assessment to analyse 1,483 articles published in the Scopus and Web of Science indexed journals during 1992–2022. The bibliographic data was processed and analysed using VOSviewer and R software.

The results revealed the proliferation of articles since 2018, with China as the dominant country, Wang J as the most prolific author, “Expert Systems with Applications” as the leading journal, “computer science” as the dominant subject area, and “stock price forecasting” as the predominantly explored research theme in the field. Furthermore, “portfolio optimization”, “sentiment analysis”, “algorithmic trading”, and “crisis prediction” are found as recently emerged research areas.

To the best of the authors’ knowledge, the current study is a novel attempt that holistically assesses the existing literature on ANN applications throughout the entire domain of stock market. The main contribution of the current study lies in discussing the challenges along with the viable methodological solutions and providing application area-wise knowledge gaps for future studies.

This paper aims to investigate an adaptive prescribed performance control problem for switched pure-feedback non-linear systems with input quantization.

By using the semi-bounded continuous condition of non-affine functions, the controllability of the system can be guaranteed. Then, a constraint variable method is introduced to ensure that the tracking error satisfies the prescribed performance requirements. Meanwhile, to avoid the design difficulties caused by the input quantization, a non-linear decomposition method is adopted. Finally, the feasibility of the proposed control scheme is verified by a numerical simulation example.

Based on neural networks and prescribed performance control method, an adaptive neural control strategy for switched pure-feedback non-linear systems is proposed.

The complex deduction and non-differentiable problems of traditional prescribed performance control methods can be solved by using the proposed error transformation approach. Besides, to obtain more general results, the restrictive differentiability assumption on non-affine functions is removed.

This study aims to understand employee career retention in the hospitality industry. Building on the socially embedded model of thriving, signaling theory and social cognitive career theory (SCCT), the study examines a structural model of psychological contract fulfillment (PCF), perceived organizational support (POS), thriving, career satisfaction and career turnover intention.

The proposed research model was tested using structural equation modeling with responses from 300 US hospitality employees. Data were collected using an online survey available through a panel service at Qualtrics.

Empirical results supported all proposed hypotheses and confirmed the critical mediation role of thriving for workplace contextual factors and employee career retention. As predicted, both PCF and POS positively influenced employee thriving, which positively influenced career satisfaction and negatively influenced career turnover intention.

Findings of this study yielded several recommendations for hospitality managers, including using the concept of thriving as an indicator and a remedy for employee career development and retention.

Given the increased need for qualified talent and reduced career satisfaction, this study sheds light on the further understanding of sustainable employment in the hospitality industry. Using signaling theory and SCCT as an overarching framework, this study extends the socially embedded model of thriving as well as SCCT and supports combining psychological contract theory and organizational support theory to better understand hospitality career retention.