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This study aims to investigate the rarefaction effects on flow and thermal performances of an equivalent sand-grain roughness model for aerodynamic thrust bearing.

In this study, a model of gas lubrication thrust bearing was established by modifying the wall roughness and considering rarefaction effect. The flow and lubrication characteristics of gas film were discussed based on the equivalent sand roughness model and rarefaction effect.

The boundary slip and the surface roughness effect lead to a decrease in gas film pressure and temperature, with a maximum decrease of 39.2% and 8.4%, respectively. The vortex effect present in the gas film is closely linked to the gas film’s pressure. Slip flow decreases the vortex effect, and an increase in roughness results in the development of slip flow. The increase of roughness leads to a decrease for the static and thermal characteristics.

This work uses the rarefaction effect and the equivalent sand roughness model to investigate the lubrication characteristics of gas thrust bearing. The results help to guide the selection of the surface roughness of rotor and bearing, so as to fully control the rarefaction effect and make use of it.

This study aims to investigate the influence and impact mechanism of capital tax incentives on firm innovation.

This study employs the difference-in-differences (DID) method, in conjunction with the exogenous impact of accelerated depreciation (AD) pilot policy. This study selects Chinese listed companies from 2010 to 2017 as the research sample.

Firstly, AD exerts a substantial positive effect on the quantity and quality of the innovation output of firms, and the positive impact results primarily from heightened investment in fixed assets, particularly, machinery and equipment. Secondly, the influence of the policy is pronounced in non-state-owned enterprises, mature enterprises, less capital-intensive enterprises and non-high-tech industries, which all exhibit strong innovation incentives. Lastly, the tax incentive policy significantly stimulates firm innovation in the short term, but its long-term impact on innovation incentives lacks statistical significance.

This study highlights the significance of capital tax incentives in facilitating the innovation process in firms.

The purpose of this study is to prepare a new adsorbent activated carbon immobilized on polystyrene (ACPS) for uranium (VI) and thorium (IV) removal from an aqueous solution. Activated carbon (AC) was derived from biochar material by chemical activation to increase the active sites on its surface and enhance the adsorption capacity. Activated carbon (AC) was immobilized on polystyrene (PS) to improve the physical properties and facilitate separation from the working solution. A feasibility study for the adsorption of uranium (VI) and thorium (IV) on the new adsorbent (ACPS) has been achieved. Adsorption kinetics, isotherms, and thermodynamics models of the adsorption process were used to describe the reaction mechanism.

Activated carbon was synthesized from biochar charcoal by 2 M H₂SO₄. Activated carbon was immobilized on the pretreatment polystyrene by hydrothermal process forming new adsorbent (ACPS). Characterization studies were carried out by scanning electron microscope, energy-dispersive X-ray spectrometer, infrared spectroscopy and X-ray diffraction techniques. Different factors affect the adsorption process as pH, contact time, solid/liquid ratio, initial concentration and temperature. The adsorption mechanism was explained according to kinetic, isothermal and thermodynamic studies. Also, the regeneration of spent ACPS was studied.

The experimental results showed that pH and equilibrium time of the best adsorption were 6.0 and 60 min for U(VI), 4.0 and 90 min for Th(IV), (pH_PZC = 3.4). The experimental results fit well with pseudo-second order, Freundlich and Dubinin–Radushkevich models proving the chemisorption and heterogenous adsorption reaction. Adsorption thermodynamics demonstrated that the adsorption process is exothermic and has random nature of the solid/liquid interface. In addition, the regeneration of spent ACPS research showed that the adsorbent has good chemical stability. According to the comparative study, ACPS shows higher adsorption capacities of U(VI) and Th(IV) than other previous bio-adsorbents.

This study was conducted to improve the chemical and physical properties of bio-charcoal purchased from the local market to activated carbon by hydrothermal method. Activated carbon was immobilized on polystyrene forming new adsorbent ACPS for eliminating U(VI) and Th(IV) from aqueous solutions.

This study aims to examine the impact of employee treatment on stock price crash risk in emerging markets. The study further sheds light on the economic channels and boundary conditions between employee treatment and crash risk.

This study employs a large-scale archival dataset of Chinese A-share listed firms covering 2010 to 2021. To establish causality, the study leverages multi-way fixed effects, Oster’s test, change regression and instrumental variable methods to alleviate endogeneity concerns.

The results reveal that employee-friendly treatment leads to a lower crash risk. Moreover, improving internal control quality and enhancing firm reputation appear to be the two plausible economic channels through which employee treatment mitigates crash risk. Cross-sectionally, the documented impact is more evident for human-capital-intensive firms, firms with weaker external monitoring and those operating in fiercely competitive industries.

This study is among the first to show that employee treatment has a favorable consequence for shareholder benefit through reducing crash risk. The study thus adds to the ongoing debate regarding the relationship between employee treatment and shareholder wealth. The study also extends the nascent literature on the role of rank-and-file employees in shaping corporate information landscapes.

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.

Public–Private Partnerships (PPPs) are crucial to the procurement of global infrastructure projects. Moreover, a price mode based on a cluster of core concessionary items is key to the delivery of value-for-money and successful project outcomes. However, existing research has yet to fully identify PPP concessionary items, nor yet described the range of practical price modes. This study provides taxonomy of core concessionary items impacting PPP projects, systematically classifies price modes, and assesses the applicability and risk impacts of those price modes on PPP projects.

This study adopts a comparative case study method in analyzing core concessionary items and alternative price modes. China is taken as the context, as it is one of the world’s largest PPP markets. In ensuring research validity and reliability, diverse data sources are utilized, with a graphic content analysis tool developed to capture the structure of price modes.

Eight PPP price modes are identified. These are: (1) UP (Unit Price) mode, (2) ALS (Annual Lump Sum) mode, (3) IRR (Internal Rate of Return) mode, (4) RP (Return for Investing Capital (RIC) - Profit Rate of O&M (PROM)) mode, (5) RFP (RIC - Financing Interest Rate (FR) - PROM) mode, (6) RFPL (RIC - FR - PROM - Lower Limit of User Charge (LLoUC)) mode, (7) RFL (RIC - FR - Lump Sum/Fixed Unit Price O&M Contract (LSOM/FUP)) mode, and (8) RFLL (RIC - FR - LSOM/FUP - LLoUC) mode. Other main findings are as follows: (1) Five risk allocation configurations can be achieved via these price modes. Yet while different price modes enable the allocation of specific risks, these do not always align with contracting parties’ original intentions. (2) IRR and RP modes may be less applicable in general because of their vulnerability in allocating critical risks and capacity for spurring opportunistic behavior.

By depicting the paths by which concessionary items in price modes affect cash flow, a systematic analysis of price modes was conducted exposing structural characteristics, along with risk allocation choice implications. The study is unique in: (1) Providing a systematic classification of PPP price modes used in PPP projects, (2) Presenting a comprehensive identification and streamlining of concessionary items in PPP practice, and (3) Analyzing the risk effects of different price modes. Together, these outcomes offer a hitherto unavailable perspective on PPP project risk management. The value of the study lies in the following: (1) Existing studies employ diverse concessionary items, but their applicability varies. This study offers an overarching framework facilitating decision-making in selecting appropriate PPP price modes and in determining concessionary items. (2) This study adds to the understanding of PPP price modes in significant ways that will aid local governments and potential sponsors in crafting and administrating more workable contract designs.

This paper attempts to study the relationship between the level of urban innovation and subjective well-being (SWB). The following questions are discussed: (1) Does improving the level of innovation in cities affect the SWB of residents? (2) If urban innovation levels affect residents' SWB, can improving urban innovation level improve residents' SWB? (2) If urban innovation level improves residents' SWB, what is the possible mechanism? (3) Is there any difference in the effect of urban innovation level on the SWB of different groups?

This paper chooses the ordinary least squares (OLS) regression method for baseline regression with robust standard errors, and the regression results of Oprobit will be presented in detail in the robustness test section. The model is constructed by matching the micro data and the macro data. Meanwhile, the instrumental variable (IV) method is also used to deal with the possible endogeneity problem.

This paper finds that urban innovation has significantly improved the SWB of residents. This finding still holds after considering the endogeneity issue and conducting a series of robustness tests. Moreover, the level of innovation positively influences residents' SWB mainly through the paths of increasing income levels, improving environmental quality and promoting convenience in life. What's more, urban innovation has a more significant effect on the SWB of low-income groups and those with low education levels. Finally, based on the findings, this paper also proposes some policy recommendations to further improve the SWB of residents.

Given the limitations of the data, the above study deals with China. In the future, international data can be further used to further explore the impact of the innovation level of international cities on the SWB of residents and then the research conclusions can be applied to the international community to provide theoretical basis and empirical support for policy makers of various countries, which will help improve the SWB of residents of various countries and enrich the theoretical and empirical research on the impact of innovation on the SWB of residents.

Well-being is a goal constantly pursued by human beings. This paper shows that urban innovation has significantly improved the SWB of residents. The findings provide guides to improve the SWB of residents and provide a certain theoretical basis and empirical reference for policy makers.

The possible innovations or contributions of this paper include: (1) based on the China Household Income Survey (CHIP) and the “Report on Innovation of Chinese Cities and Industries”, this paper studies the impact of urban innovation levels on residents' SWB, supplementing the gaps in existing literature; (2) through group analysis, the heterogeneity of the impact of urban innovation level on groups with different incomes and education levels was discussed and (3) the mechanism of urban innovation level on residents' SWB was identified. These studies and discussions provide a basis for more effective improvement of residents' SWB and provide a valuable reference for promoting people's pursuit of a better life.

In prior literature, online endorsement system allowing the users to “like” or “dislike” shared information is found very useful in information filtering and trust elicitation in most social networks. This paper shows that such systems could fail in the context of investment communities due to several psychological biases.

This study develops a series of regression analyses to model the “like”/“dislike” voting process and whether or not such endorsement distinguishes between valuable information and noise. Trading simulations are also used to validate the practical implications of the findings.

The main findings of this research are twofold: (1) in the context of investment communities, online endorsement system fails to signify value-relevant information and (2) bullish information and “wisdom over the past event” information receive more “likes” and fewer “dislikes” on average, but they underperform in stock market price discovery.

This study demonstrates that biased endorsement may lead to the failure of the online endorsement system as information gatekeeper in investment communities. Two underlying mechanisms are proposed and tested. This study opens up new research opportunities to investigate the causes of biased endorsement in online environment and motivates the development of alternative information filtering systems.

Artificial intelligence (AI) is a significant game changer in human resource development (HRD). The launch of ChatGPT has accelerated its progress and amplified its impact on organizations and employees. This study aims to review and examine literature on AI in HRD, using a bibliometric approach.

This study is a bibliometric review. Scopus was used to identify studies in the field. In total, 236 papers published in the past 10 years were examined using the VOSviewer program.

The obtained results showed that most cited documents and authors are mainly from computer sciences, emphasizing machine learning over human learning. While it was expected that HRD authors and studies would have a more substantial presence, the lesser prominence suggests several interesting avenues for explorations.

This study provides insights and recommendations for researchers, managers, HRD practitioners and policymakers. Prioritizing the development of both humans and machines becomes crucial, as an exclusive focus on machines may pose a risk to the sustainability of employees' skills and long-term career prospects.

There is a dearth of bibliometric studies examining AI in HRD. Hence, this study proposes a relatively unexplored approach to examine this topic. It provides a visual and structured overview of this topic. Also, it highlights areas of research concentration and areas that are overlooked. Shedding light on the presence of more research originating from computer sciences and focusing on machine learning over human learning represent an important contribution of this study, which may foster interdisciplinary collaboration with experts from diverse fields, broadening the scope of research on technologies and learning in workplaces.

The purpose of this article is to develop relationships between many major issues relevant to the agriculture supply chain.

With the purpose of gaining an all-encompassing understanding of the agriculture supply chain, this work uses 233 filtered research articles and three bibliometric analysis tools, namely VOSviewer, term frequency-inverse document frequency (TF-IDF) and Person correlation. The collected research publications were also catalogued using Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA).

Using analytic techniques, a total of 12 keywords were obtained. The study found that agri-products are in dire need of digitisation via Internet of things (IoT) and blockchain due to the usage of economic variables and comprehensive management of total food waste throughout transportation, anchoring quality and the predominant variable.

The study was limited to the Scopus and Web of Science (WoS) indexing in order to assess the viability of the linked idea and problem.

This study aims to generate vital knowledge in the field of horticulture-focused agriculture supply chain based on previous justification and relationship formation.