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In the current urban and increasingly digital/information era, the service of property management towards residents is of key importance to the well-being of society, which is especially well reflected in its role during the COVID-19 pandemic in China. However, professional knowledge management (KM) has yet to be applied to the daily operation of the property management companies in this sector. The authors posit that introduction of KM concepts to property management at this time would significantly help facilitate the transition of traditional property management towards a higher and more effective value-added service model. From the perspective of KM, this study aims to offer both a theoretical and practical analysis of such a novel business model for companies in the property management industry.

Given the current status quo of property management, the authors integrate theories from both KM and information-digital management to qualitatively analyse the challenges and difficulties companies are facing and importantly offer practical suggestions for the centralization, digital-information platform establishment and service innovation.

Following the analysis of KM and digital-information modeling, the authors identify three main challenges facing property management services offered in the current digital society including market fragmentation, lack of digital platforms and the absence of a unitary service model. Therefore, the authors propose strategic solutions to resolve these aforementioned problems. Specifically, the authors suggest centralizing property management service, the establishment of a KM-based digital platform and the upgrading of the service model, towards offering a new impetus for the development of companies in property management.

The authors offer essential guidelines derived from knowledge and information management and explore their implications both from a theoretical, as well as pragmatic/practical perspective that overall would support property management companies’ crucial transition from the traditional service model to a more digital-based foundation, and hence, provide a higher value-added service for the residents.

The current study is one of the first attempts to analyse property management services from the perspective of KM with digital transformation enabling its transition to an information-based internet of things infrastructure. The study not only offers practical guidance to the business of property management but importantly, also contributes to the theoretical underpinnings of KM especially as related to secure serviceability, well-being, security and efficiency of the residential environment by integrating people, place, process and technology.

This article aims to elaborate on the current relevant policies, research literature and technical applications of administrative intelligent decision-making in major countries in the world, to summarize the current focus areas of international competition in administrative intelligent decision-making and to provide references and insights for other countries.

The knowledge graph tool CiteSpace was used to conduct a quantitative analysis of the field of administrative intelligent decision-making, using the two dimensions of research literature and technology patents to analyze the basic status and evolution process of artificial intelligence applied to administrative decision-making.

Over the past 30 years, theoretical research on administrative intelligent decision-making has primarily centered on countries like the United States of America, China and Japan. The main themes include leveraging artificial intelligence (AI) technology to enable digital government transformation, enhance administrative efficiency and promote public value through service-oriented initiatives. Countries like South Korea, China and the United States of America have demonstrated significant advantages in technology development and patent applications, focusing on e-government systems, intelligent government auditing, addressing policy mismatches between supply and demand and integrating online and offline administrative services. The international competitive landscape in this field is shaped by three key factors: foundational theories, technological advancements and practical scenario applications.

Despite the rapid advancements in AI technology and the acceleration of enterprise digital transformation, discussions on the intelligent transformation of administrative decision-making leveraging next-generation information technology remain limited. This study summarizes the development trajectory of administrative intelligent decision-making in major countries worldwide, analyzes the international competitive landscape of its theoretical and application research and highlights key focus areas. These findings offer valuable insights for advancing the construction of administrative intelligent decision-making systems in other nations.

Large capacity current carrier printed circuit board (PCB) imposes strict control requirements on the hole wall roughness. The key factors are chip removal, drilling temperature and tool wear. This paper aims to find out a cryogenic drilling process to control the chip removal, chip morphology, tool wear and finally reduce the hole wall roughness.

The chip removal process, chip morphology, tool wear and hole wall roughness of glass fiber epoxy resin copper clad laminate (FR-4) drilling were observed and analyzed. The influence of cold air on the chip removal process, chip morphology, tool wear and hole wall roughness was also investigated. An optimization process of cold air auxiliary drilling was proposed to control the hole wall roughness of FR-4.

The results showed that the discharge time of copper foil chips with obvious characteristics can be used as the evaluation criterion for the smoothness of chip removal. The cold air can promote chip removal and reduce tool wear. In addition, the chip removal and cooling performance will be the best when using −4.7 °C cold air with the injection angle consisted with the angle of helical flute of the drill. The hole wall roughness of FR-4 could be controlled by drilling with −4.7°C cold air.

This paper was the first study of the effect of three kinds of cold air on PCB drilling. This provided a reference for the possibility that the cryogenic drilling methods apply to PCB drilling. A new cold air auxiliary drilling process was developed for large capacity current carrier FR-4 manufacturing.

This study aims to evaluate the performance of LLMs with various prompt engineering strategies in the context of health fact-checking.

Inspired by Dual Process Theory, we introduce two kinds of prompts: Conclusion-first (System 1) and Explanation-first (System 2), and their respective retrieval-augmented variations. We evaluate the performance of these prompts across accuracy, argument elements, common errors and cost-effectiveness. Our study, conducted on two public health fact-checking datasets, categorized 10,212 claims as knowledge, anecdotes and news. To further analyze the reasoning process of LLM, we delve into the argument elements of health fact-checking generated by different prompts, revealing their tendencies in using evidence and contextual qualifiers. We conducted content analysis to identify and compare the common errors across various prompts.

Results indicate that the Conclusion-first prompt performs well in knowledge (89.70%,66.09%), anecdote (79.49%,79.99%) and news (85.61%,85.95%) claims even without retrieval augmentation, proving to be cost-effective. In contrast, the Explanation-first prompt often classifies claims as unknown. However, it significantly boosts accuracy for news claims (87.53%,88.60%) and anecdote claims (87.28%,90.62%) with retrieval augmentation. The Explanation-first prompt is more focused on context specificity and user intent understanding during health fact-checking, showing high potential with retrieval augmentation. Additionally, retrieval-augmented LLMs concentrate more on evidence and context, highlighting the importance of the relevance and safety of retrieved content.

This study offers insights into how a balanced integration could enhance the overall performance of LLMs in critical applications, paving the way for future research on optimizing LLMs for complex cognitive tasks.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-02-2024-0111

Stock prices are subject to the influence of news and social media, and a discernible co-movement pattern exists among multiple stocks. Using a knowledge graph to represent news semantics and establish connections between stocks is deemed essential and viable.

This study presents a knowledge-driven framework for predicting stock prices. The framework integrates relevant stocks with the semantic and emotional characteristics of textual data. The authors construct a stock knowledge graph (SKG) to extract pertinent stock information and use a knowledge graph representation model to capture both the relevant stock features and the semantic features of news articles. Additionally, the authors consider the emotional characteristics of news and investor comments, drawing insights from behavioral finance theory. The authors examined the effectiveness of these features using the combined deep learning model CNN+LSTM+Attention.

Experimental results demonstrate that the knowledge-driven combined feature model exhibits significantly improved predictive accuracy compared to single-feature models.

The study highlights the value of the SKG in uncovering potential correlations among stocks. Moreover, the knowledge-driven multi-feature fusion stock forecasting model enhances the prediction of stock trends for well-known enterprises, providing valuable guidance for investor decision-making.

This paper considers an e-tailer planning to distribute a product under one direct sales channel and multiple asymmetric agency platforms. Based on the multinomial logit (MNL) choice model, this study optimizes the pricing strategy and channel selection strategy to maximize the e-tailer’s profit.

A two-stage channel selection and pricing problem is formulated, where the profit-maximizing e-tailer first optimally selects a specified number of agency platforms from a set of alternatives to distribute the product and then determines the optimal prices in those channels.

An optimal pricing strategy is proposed to maximize the e-tailer’s total profit on multiple asymmetric channels. The results show that the e-tailer can obtain a higher profit by selling products on more asymmetric agency platforms. Moreover, an effective channel selection algorithm is provided to help the e-tailer optimally select the M agency platforms from N alternatives.

This study enriches the relevant research on multichannel selection and pricing by proposing an optimal pricing strategy and an effective channel selection algorithm. Evaluation results based on real-world industrial data show that the proposed optimal multichannel pricing strategy in this paper can significantly improve the profit of a real-world e-tailer compared to the e-tailer’s actual profit.

The purpose of this paper is to describe some tribological experiments which were executed to find the influence of braking pressure on tribological performance of non‐asbestos brake shoe used in mine hoister during its emergency braking.

The WSM‐3 non‐asbestos brake shoe, which has been widely used in mine hoister, was selected as experimental material. Some tribological experiments of the brake shoe sliding on 16Mn steel were investigated on the X‐DM friction tester by simulating of emergency braking conditions of mine hoister. Three kinds of tribological indexes: friction coefficient, stability coefficient of friction coefficient, and wear rate were considered to score the tribological performance and the morphology of worn surfaces were observed through the S‐3000N scanning electron microscopy (SEM) to explore the tribological mechanisms.

It was found first, that the instant friction coefficient is not constant during emergency braking. After a short climbing period, it rises gradually to steady value. Second, with the increasing of braking pressure, the mean friction coefficient rises first then falls, while its stability coefficient falls gradually. The wear rate rises continuously with the braking pressure increasing. Also, the rising velocity of wear rate at high pressure is higher than it is at low pressure. Third, the instant surface temperature rises first then falls during braking and the mean surface temperature rises continuously with the braking pressure increasing.

It is found that the increasing of braking pressure within a certain range is helpful for achieving a high friction coefficient and a steady wear rate. But too high pressure will cause contrarily the falling of frictional performance and serious of wear performance. So it is not reliable to rise the braking pressure without limited during emergency braking.

Outlines the history of accounting in China and reviews the literature published in English on the full range of Chinese accounting issues. Summarizes the contents of three books, refers to sections in other books and analyses journal articles by period, journal, research topic and research method. Argues that this accounting research has historical, academic and practical value,believes it will continue to improve and calls for greater use of more rigid research methodologies in this area.