Search results

We examine the role of internet speed in sophistication among individual investors.

This paper utilizes archival data and regression-based analyses in examining the research question.

We first show that higher individual investor internet speed (IIIS) is associated with geographic measures of education, job type and income of users accessing EDGAR filings. We then show that higher IIIS is positively related to the market reaction to newly released 10-K, 10-Q and 8-K filings and that the relation is stronger for companies and filings where greater sophistication is needed. Higher IIIS is also associated with a lower price drift after the release of a 10-K, 10-Q and 8-K filing on EDGAR.

Overall, our findings suggest that greater internet access speeds can aid in the sophistication and price discovery process for individual investors.

This study aims to minimize the warpage issue in memory-computing integrated chiplets with 2.5D packaging on a large-scale wafer subjected to multistress through synergistic optimization of key structure paraeters.

In this study, memory-computing integrated chiplet-based 2.5D packaging was designed and the warpage optimization under electro-thermal-vibration coupling was conducted with finite element analysis simulation. Compared studies were also conducted with the imposing condition of single electrical and thermal stress.

The research results indicated that electrical and thermal stress had a significant impact on the packaging warpage while that of vibration on warpage was minimal. For structure parameters, the chiplet thickness had a significant effect on the warpage of the model, while the influence of chiplet size was relatively small. When the chiplet thickness decreased to 100 µm, the warpage was reduced by 8.96%. Under thermal stress loading, the impact of packaging density on the overall warpage of the chiplet-based 2.5D packaging model is relatively small. However, under the loading of electrical stress or electro-thermal coupling, the packaging density has a severe impact on warpage, with the maximum approaching 1.3011 µm for just one chiplet. Compared with vibration alone, electro-thermal-vibration coupling slightly increased the warpage, which is primarily evident in the exacerbation of warpage in stacked chiplets and edge locations. Lower packaging density with 150 µm chiplet thickness contributed to a minimal warpage in stacked chiplet, the edge locations of which exhibited a relatively severe warpage.

The research provides a theoretical basis for warpage optimization of memory-computing integrated chiplets with 2.5D packaging subjected to multistress coupling.

When designing the layout of chips for large-sized high-reliability silicon substrates, this method can be used to control the most severely warped areas at the edges, thus making it easier to optimize warpage.

With the approaching of physical limits dictated by Moore’s Law, advanced packaging technologies, primarily centered around 2.5D packaging and three-dimensional packaging, were attached importance to. It can integrated multifunctional chips, which have become increasingly complex, leading to greater difficulty in design and implementation, and an obvious increase in overall manufacturing costs. In this context, chiplets offered a viable approach for future chip designs. With the high demand for high-performance computing, memory-computing integrated chiplets were designed. The warpage of 2.5D packaging by altering structural parameters under multiphysical field coupling conditions was studied to provide a theoretical basis for warpage optimization of memory-computing integrated chiplets with 2.5D packaging.

The rapid development of information technology, epitomized by AIGC and the metaverse, presents unprecedented challenges to techno-ethics, exposing society to significant risks and uncertainties. A systematic investigation and discussion of the construction of techno-ethical order become crucial under the paradigm of risk society theory. The selection of conditions and pathways for constructing a techno-ethical order under the risk society theory paradigm becomes an unavoidable and vital issue.

Drawing on risk society theory, this study employs scientometrics and qualitative comparative analysis (QCA) to empirically analyze the key factors and conditional pathways in the construction of techno-ethical order. Initially, a quantitative analysis is conducted on 1,490 thematic literature retrieved from CNKI and WoS to identify the hot topics and core concepts in techno-ethical research. Subsequently, QCA configuration analysis is applied to calculate eight evaluation indicators and their weights from the perspectives of government, society and individuals. Finally, the study explores the mechanisms of the impact of these indicators’ weights on the construction of techno-ethical order.

The analysis of factor weights and pathways indicates that the selection of pathways for constructing techno-ethical order is influenced both by the inherent development patterns of technology and societal systems and cultural values. Literature metrics analysis reveals an overall trend of sustained growth in techno-ethical research, indicating an unprecedented prosperity in this field. Alongside technological advancements, keywords related to “artificial intelligence” play a crucial role in current techno-ethical research. Configuration analysis demonstrates that conditional variables from the dimensions of government, society and individuals form a configuration pathway, influencing and synergistically impacting the overall level of techno-ethical order construction. Attention should be given to the mutual constraints and synergistic effects of factors related to technological development, societal systems and cultural values.

This study, grounded in the risk society theory paradigm, quantitatively explores the key factors and pathways of techno-ethical order construction in academic texts, expanding new perspectives, providing novel insights, establishing innovative methodologies and extending new boundaries in the field. Further enrichment of the dataset and in-depth discussions are warranted for continued advancement.

Salt rock from salt lakes can serve as a cost-effective material for subgrade filling, as demonstrated in projects like the Qarhan Salt Lake section of the Qinghai-Tibet Railway and the Qarhan Salt Lake section of the G215 Highway. This state-of-the-art paper aims to summarize the engineering properties of salt rock filling and present the advances of its utilization.

This paper collects and analyzes laboratory and field data of salt rock filling from previous studies to present a comprehensive analysis of the engineering properties and utilization of salt rock fillings.

Salt rock primarily contains minerals such as halite and glauberite, which contribute to its unique phase-changing behavior under varying environmental conditions, impacting its mechanical properties. Salt rock filling shrinks when in contact with vapor or unsaturated brine and expands under cooling or evaporation. Its use is particularly recommended for arid regions, with specific restrictions depending on the structure type. This paper discusses suggested countermeasures to mitigate these issues, as well as key quality acceptance indices for salt rock filling compaction. Moisture content after air-drying is recommended as a crucial parameter for construction quality control.

This review aims to support future research and engineering practices in salt rock subgrade applications.

The COVID-19 pandemic and the corresponding control measures have harmed the mental health of professionals working in the construction industry. Existing research has also indicated that demographic characteristics are leading variables causing differences in individual’s perceptions on mental health and psychosocial hazardous factors. Combining these, this research aims to compare the differences and similarities of the perceived mental health outcomes and psychosocial hazards among construction professionals with different demographic characteristics during the pandemic.

Using a questionnaire survey, data were collected from 531 construction professionals working in Chinese construction companies, which were analyzed quantitatively using mean score comparative analysis, Mann–Whitney U test and Kruskal–Wallis H test, as well as Spearman’s correlation analysis.

The results indicate that construction professionals with different ages, years of working experience and positions are exposed to different psychosocial hazards, resulting in different mental health conditions during the pandemic. Age and years of working experience are also strong predictors of the level of depression and anxiety experienced by construction professionals; that is, mental ill health tends to decrease with the increase of age and experience. Male department/unit heads, working in a company office environment for a private company and aged 31–40 years old with 11–20 years of working experience, tend to have the best mental health condition. In contrast, psychosocial hazards are more likely to produce the most serious impact on male site-based construction professionals working for a state-owned company, either with less than one year of working experience or in a senior management position.

Despite the significant contribution of the construction industry to the global economy, the differences and similarities of the mental health outcomes and psychosocial hazards among construction professionals with different demographic characteristics during the pandemic remain unknown. This research, therefore, reveals the mental health outcomes and psychosocial hazard impacts among different types of construction professionals during the pandemic. Specifically, this research unveils the important personal characteristics which are closely associated with poor mental health and the stronger impacts of psychosocial hazards on the mental health of construction professionals during the pandemic. The results are valuable for governments and construction companies to formulate targeted mental health intervention strategies during future public health emergencies.

This research aims to examine the effect of organisational culture on supply chain collaboration and firms’ competitive advantage in the garment industry in Vietnam.

Underpinned by the relational view and the organisational culture theories, the conceptual framework was proposed. This study obtained data from a survey of 192 managers in garment firms in Vietnam. A structural equation modelling was employed to examine the relationship between organisational culture, supply chain collaboration and competitive advantage.

There was a significant positive relationship between organisational culture in terms of group and development types and supply chain collaboration. Besides, the results revealed a significant positive relationship between supply chain collaboration and competitive advantage in terms of cost and differentiation. Furthermore, the cost competitive advantage was also found to have a significant positive effect on differentiation competitive advantage.

This study is perhaps one of the first empirical attempts to examine the relationship between organisational culture, supply chain collaboration and competitive advantage in the garment industry in Vietnam. Moreover, this study extends the application of the relational view and organisational culture theories in explaining these relationships in a new research context.

The high-load operation of the engine crankshaft causes severe wear and fatigue. This study aims to prepare in situ textures with effective density and study their wear mechanism on the surface of ductile cast iron, which optimizes the tribological properties of engine crankshafts and reduces wear.

A new method was proposed based on the hardness difference in graphite removal to form an in situ texture. The friction performance was evaluated using a combination of computational fluid dynamics and tribological testings. The influence of the texture characteristic parameters on the bearing capacity of the oil film was analyzed. The surface wear morphology was studied by scanning electron microscopy.

The texture density significantly affected the oil film bearing capacity. The surface texture can reduce the average friction coefficient (COF) by more than 35% owing to the oil film bearing and storage capacity. Specifically, the 13% texture density exhibited the lowest wear rate and COF under all three experimental conditions. The reduction in abrasive particles in the wear area of the textured surface indicates that the surface texture can improve the lubrication mechanism.

This study systematically explored the influence of the weight of each model parameter on tribological properties. Subsequently, focusing on the critical parameter (texture density), detailed tribological testings were carried out to reveal the specific effect of texture density on the wear mechanism under different working conditions, and the optimal texture density to achieve the optimal tribological performance was determined accordingly.

As the search engine platform, Baidu has already developed keyword advertising as one of its main business scopes, while in-feed advertising is emerging as another intelligent choice for the company. Our purpose is to validate the effectiveness of keyword and retargeted in-feed advertising on offline sales and whether the effectiveness of these two advertising strategies relies on keyword attributes work.

We utilize data from the ad campaigns of a prominent manufacturer within the machinery and equipment (hereinafter referred to as “the company”) on Baidu. To scrutinize the research hypotheses, we have employed linear regression models. Subsequently, we address potential endogeneity issues and use various techniques to ascertain the reliability of the results.

Empirical evidence indicates that both keyword and in-feed advertising enhance offline sales. Upon examining the moderating role of keyword attributes (specificity and length), we observe that specific keywords (price and word-of-mouth (WOM)) accelerate the boosting effect of advertising on sales; similarly, the longer the keywords, the more obvious the enhanced impact of advertising on sales. Moreover, the positive influence of specific keywords (price and WOM) on advertising effectiveness is more outstanding when the keywords are longer.

To our knowledge, no empirical investigation has yet to analyze keyword and retargeted in-feed advertising concurrently within the search engine context. Our research is the inaugural work to reveal that they serve as mutual substitutes regarding their impact on sales. Furthermore, this paper pioneers examining the moderating effects exerted by keyword attributes (specificity and length) on the effectiveness of these two ad types. The findings presented herein offer valuable insights into the harmonious coexistence and collaboration among companies, advertisers, users and search engine platforms.

This study delves into the intricate relationship between specific positive and negative emotions experienced by tourists during their vacations and the corresponding emotion regulation strategies they employ. Drawing from emotion regulation theory, we examine the nuanced impact of various strategies on tourists' emotional experiences, thereby advancing our understanding of emotion dynamics in the context of tourism.

Data were gathered through an online survey and travel diaries, subsequently analyzed using linear mixed-effects models.

Our findings underscore that emotion regulation strategies exert a significant influence on both positive and negative emotions. Furthermore, we identified that different strategies correlate uniquely with specific emotions. For instance, the deployment of Expressive Suppression, Savoring, and Stimulus Control strategies notably amplifies the intensity of joy.

This study recommends that tourism managers design experiences that evoke positive emotions through curated sensory cues, storytelling, and stress-free service offerings. Tourism managers should prioritize stress-free services, guide tourists in expressing themselves, and train service providers to manage emotions effectively, thus promoting positive emotional interactions and improving overall customer satisfaction.

Theoretically, this research enriches the emotion regulation literature by contextualizing it within the tourism domain, highlighting the differential effects of regulation strategies on diverse emotional experiences. From a practical standpoint, these insights can guide practitioners in crafting targeted marketing strategies and empower tourists with knowledge to select optimal strategies for enhancing their emotional well-being during vacations.

Timely intrusion detection in extensive traffic remains a pressing and complex challenge, including for Web services. Current research emphasizes improving detection accuracy through machine learning, with scant attention paid to the dataset’s impact on the capability for fast detection. Many datasets rely on flow-level features, requiring entire flow completion before determining if it constitutes an attack, reducing efficiency. This paper aims to introduce a new feature extraction method and construct a new security dataset that enhances detection efficiency.

This paper proposes a novel partial-flow feature extraction method that extracts packet-level features efficiently to reduce the high latency of flow-level extraction. The method also integrates statistical and temporal features derived from partial flows to improve accuracy. The method was applied to the original packet capture (PCAP) files utilized in creating the CSE-CIC-IDS 2018 dataset, resulting in the development of the WKLIN-WEB-2023 dataset specifically designed for web intrusion detection. The effectiveness of this method was evaluated by training nine classification models on both the WKLIN-WEB-2023 and CSE-CIC-IDS 2018 datasets.

The experimental results show that models trained on the WKLIN-WEB-2023 dataset consistently outperform those on the CSE-CIC-IDS 2018 dataset across precision, recall, f1-score, and detection latency. This demonstrates the superior effectiveness of the new dataset in enhancing both the efficiency and accuracy of intrusion detection.

This study proposes the partial-flow feature extraction method, creating the WKLIN-WEB-2023 dataset. This novel approach significantly enhances detection efficiency while maintaining classification performance, providing a valuable foundation for further research on intrusion detection efficiency.