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This paper aims to investigate the effect of coating microstructure, mechanical and oxidation property on the tribological behaviour of low-pressure plasma spraying (LPPS) tungsten carbide/cobalt (WC-Co) coatings.

WC-12Co and WC-17Co coatings were deposited via the LPPS spraying method. Tribological tests on the coatings were performed using a high-temperature ball-on-disc tribometer at temperatures from room temperature (RT, approximately 25 °C) up to 800 °C in ambient air.

WC-12Co coating contained brittle phases, pores and microcracks, which led to the low hardness, and finally promoted the splat delamination and the carbide debonding during wear. WC-17Co coating had higher cobalt content which benefited the coating to contain more WC particles, less brittle phases, pores and nearly no microcracks, and resulted in the high hardness and better wear resistance. Higher cobalt content also decelerated the oxidation rate of the coating and promoted the formation of cobalt oxides and CoWO4, which were able to maintain the load-bearing capacity and improve the tribological behaviour of the coating below 650°C. Above 650°C, the increase of oxidation degree and the decrease of mechanical property deteriorated the wear resistance of coatings.

The LPPS WC-Co coating with higher cobalt content had better tribological properties at different temperatures. The LPPS WC-Co coatings should not be used as wear-resistant coatings above 650 °C.

This paper introduces a novel method to improve building safety management by leveraging building information modeling (BIM) and adaptive information retrieval techniques. The integration aims to overcome the limitations of traditional safety management methods in connecting construction processes with risk management efficiently.

The proposed method involves developing industry foundation classes (IFC) ontologies and integrating them with a safety document ontology to form a comprehensive BIM-based safety context framework. Custom reasoning rules and an inference engine are constructed to enable automatic context-aware safety information retrieval. The methodology is demonstrated through an adaptive information retrieval system using job hazard analysis (JHA) documents.

The implementation of the BIM-based adaptive information retrieval system shows significant improvements in identifying and managing construction risks. By mapping job-specific risks to corresponding safety measures, the system enhances risk detection and management tailored to particular construction tasks. The results indicate a marked improvement in the precision and accuracy of safety assessments and recommendations, aligning them closely with planned construction activities and conditions.

This paper offers an innovative approach to construction safety management through the development of a BIM-facilitated context-aware information retrieval system. This approach provides a more intelligent and automated framework for identifying and managing risks in construction projects. By focusing on specific job steps and related risks, the system enhances the effectiveness and accuracy of safety measures, contributing to better overall building safety management.

The torque ripple and fault-tolerant capability are the two main problems for the switched reluctance motors (SRMs) in applications. The purpose of this paper, therefore, is to propose a novel 16/10 segmented SRM (SSRM) to reduce the torque ripple and improve the fault-tolerant capability in this work.

The stator of the proposed SSRM is composed of exciting and auxiliary stator poles, while the rotor consists of a series of discrete segments. The fault-tolerant and torque ripple characteristics of the proposed SSRM are studied by the finite element analysis (FEA) method. Meanwhile, the characteristics of the SSRM are compared with those of a conventional SRM with 8/6 stator/rotor poles. Finally, FEA and experimental results are provided to validate the static and dynamic characteristics of the proposed SSRM.

It is found that the proposed novel 16/10 SSRM for the application in the belt-driven starter generator (BSG) possesses these functions: less mutual inductance and high fault-tolerant capability. It is also found that the proposed SSRM provides lower torque ripple and higher output torque. Finally, the experimental results validate that the proposed SSRM runs with lower torque ripple, better output torque and fault-tolerant characteristics, making it an ideal candidate for the BSG and similar systems.

This paper presents the analysis of torque ripple and fault-tolerant capability for a 16/10 segmented switched reluctance motor in hybrid electric vehicles. Using FEA simulation and building a test bench to verify the proposed SSRM’s superiority in both torque ripple and fault-tolerant capability.

The purpose of this paper is to investigate the evolution of the permeability of spherical packing during cold compaction by pore-scale modeling.

The discrete element method (DEM) is used to generate spherical packing structure under different compressive pressures and the Lattice Boltzmann method (LBM) is adopted to calculate the permeability of each spherical assembly.

It is found that the decrease of the porosity is the main reason of the reduction in permeability in the initial compression stage, but its influence becomes insufficient in the late compression stages. Besides, two empirical formulas are obtained, which describe the relation between the permeability and the equivalent mean diameter and the variation of normalized permeability with compressive pressure, respectively.

In this study, the authors study the spherical particles and ignore the non-spherical effects. Besides, the classical contact model, the linear-spring-damping model, is used in DEM, so the plastic deformation cannot be considered.

The DEM and the LBM are well combined to study the compaction effects on permeability of spherical packing. Two simple expressions of the spherical packing structure with uniform diameter distribution are given for the first time.

The relationship between sex and tourism remains ambiguous in the tourism literature. Few studies have examined the underlying motivations behind sex-driven travel, and little is known about factors inhibiting tourists' procurement of commercial sex when traveling. Therefore, this study explored male Chinese tourists' perceived constraints during decision-making and developed a comprehensive scale to assess constraints to commercial sex consumption overseas.

Data were obtained from male Chinese tourists purchasing commercial sex while traveling overseas. This study involved a four-stage process as recommended by Churchill (1979) for scale development research. In Stage 1, preliminary items were generated through a comprehensive review of the constraints literature and in-depth interviews with 16 sex tourists, which generated an initial 26 items. During the second stage to purify the measurement items, six items were eliminated, resulting in 20 items. Stage 3 involved exploratory factor analysis (N = 275) to extract the scale's underlying factor structure. Results revealed a five-factor structure with sufficient evidence of internal reliability given Cronbach's alpha coefficients between 0.722 and 0.843. The final stage included confirmatory factor analysis (N = 259) to verify the scale's reliability and validity.

Ultimately, 20 items were developed to measure sex tourists' perceived constraints toward engaging in commercial sex services overseas based on five factors: structural constraints, intrapersonal constraints, interpersonal constraints, value conflicts and service supply–related constraints.

This study advances the scope of sex tourism research by verifying how these five constraints are independent, generalized and can influence the procurement of sexual services overseas. This study is the first in sex tourism research to explore the difficulties facing sex tourists. Results offer marketers important insight on how to better address these constraints while providing a safe and legal sex tourism experience.

The 2019 novel coronavirus (COVID-19) outbreak is projected to have adverse consequences on the global tourism and hospitality industry. This paper aims to examine how the outbreak may alter Chinese tourists’ lifestyle choices, travel behaviour and tourism preferences in the short and long term.

This paper is based on the synthesis of news broadcasted by several media outlets to be supported by an overview of the related literature on tourism marketing, tourism management and tourist behaviour. The authors’ experiences investigating trends in tourism and hospitality at the local and international level have also contributed to the study.

This paper predicts that COVID-19 will likely affect Chinese travellers’ consumption patterns, such as the growing popularity of free and independent travel, luxury trips and health and wellness tourism. New forms of tourism including slow tourism and smart tourism may also drive future tourism activities. Such changes are likely to force businesses to reconsider their service designs and distribution channels.

While Chinese and other potential visitors rethink how they travel, professionals, too, should reflect upon how to bring positive or negative changes to the tourism industry following this pandemic. Subsequent research should also consider how to mitigate the effects of similar public health crises in the future.

Recommendations for industry practitioners and policymakers focus on tailoring travel arrangements to tourists’ backgrounds. The suggestions may help to alleviate outbreak-related stress, offer travellers newly enriching experiences and partially mitigate the effects of COVID-19 on the tourism and hospitality industry. These recommendations can also apply more broadly to global tourist markets.

The COVID-19 outbreak has already brought significant impacts to nearly every society and industry. Tourism scholars and practitioners should carefully consider this tragedy and how it may inform industry and social practices. This and other public health crises represent sterling opportunities to view the industry holistically in terms of its effects on the environment, climate and travellers themselves.

This paper presumably represents a frontier study, critically examining the possible impacts of COVID-19 on Chinese travellers’ consumption patterns and how the tourism and hospitality industry may respond to such changes in the future.

预计2019年新型冠状病毒（COVID-19）爆发将对全球旅游和酒店业产生不利影响。本文预测, COVID-19可能会影响中国旅客的消费方式, 例如自由行, 豪华旅行以及健康与养生旅游的普及。包括慢速旅游和智能旅游在内的新型旅游形式也可能推动未来的旅游活动。这种变化可能促使企业重新考虑其服务设计和分销渠道。针对行业从业者和政策制定者的建议着重于根据游客的背景和需求量身定制旅行安排。我们的建议可能有助于减轻与疫情暴发导致的压力, 为旅行者提供全新的丰富体验, 并从一定程度减轻COVID-19对旅游业和酒店业的影响。这些建议还可以更广泛地应用于全球旅游市场。

COVID-19, 生活方式 旅游行为 灾后 集体主义倾向 中国

El impacto del COVID-19 se prevé importante en la actividad turística global y en la industria de la hostelería. Este artículo predice que el COVID-19 afectará con probabilidad alta los patrones de consumo de los viajeros chinos, incluyendo los cada vez más populares viajes comprados directamente por los consumidores chinos, en el segmento del lujo, y el turismo de salud y belleza. Nuevas formas de turismo, incluyendo el slow tourism, y el turismo responsable pueden convertirse en importantes tendencias de futuro igualmente. Dichos cambios llevarán con cierta seguridad a la industria a reconsiderar y adaptar su oferta de servicios en este sector, en particular su diseño y los canales de distribución utilizados. Las recomendaciones para la industria y los responsables de la política turística se alinean con una mayor proximidad de la oferta a los gustos cambiantes del consumidor. Dichas acciones ayudarán a reducir fricciones y fallos en la definición del negocio turístico, ofreciendo a los viajeros nuevas y enriquecedoras experiencias, así como podrán mitigar en parte los efectos adversos estimados del COVID-19, los cuales son relevantes a día de hoy. Así mismo, dichas recomendaciones son aplicables de una manera más global a la industria del turismo y la hospitalidad en el mercado mundial.

COVID-19, Estilo de vida, Conducta del viajero, Post-desastre, Orientacion collectivista, China

This study adopts the stimulus–organism–response model to examine relationships between the retail environment and customers’ emotions (i.e. pleasure and arousal), prestige sensitivity and word-of-mouth (WOM) intentions towards two luxury retailers: Luxury Brand A and Luxury Brand B.

Data were obtained from a sample of 135 Chinese customers who had purchased Luxury Brand A and 130 Chinese customers who had purchased Luxury Brand B directly from each retailer. The relationships between these two retailers were compared via partial least squares–Henseler’s multi-group analysis (MGA).

Findings indicated that pleasure and arousal did not play mediating roles between the retail environment and WOM intention for either customer group. Prestige sensitivity did not moderate customers’ emotions about Luxury Brand A or Luxury Brand B. Altruism served as a moderator between emotions (i.e. pleasure/arousal) and WOM intention regarding Luxury Brand B but was not a significant moderator for Luxury Brand A. Additionally, MGA suggested non-significant differences between Luxury Brands A and B. Implications for the luxury retail literature and luxury marketers are provided accordingly.

This study is one of the earliest to examine the moderating roles of price sensitivity and altruism on associations between the retail environment and customers’ emotions, prestige sensitivity, and WOM intentions in a luxury retail context. An MGA of customers for Luxury Brand A and Luxury Brand B was innovatively performed to evaluate the proposed framework.

This paper provides a thorough examination of the advancements and impacts of artificial intelligence (AI) on construction management (CM) over the past five years, particularly focusing on its role in mitigating prevalent challenges such as inefficiency and ensuring quality. By methodically reviewing and synthesizing the body of research conducted in this period, it underscores key contributions and breakthroughs in the application of AI within construction management (AICM). Additionally, the study aims to shed light on emerging trends and forecast future directions for technological innovation in the construction management sector.

Guided by the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework, this research conducts a bibliometric analysis of 176 relevant publications from the past five years. The analysis focuses on the adoption of AICM across three critical areas: construction equipment management, improvement of construction safety and construction cost optimization. Additionally, the study systematically identifies and examines 14 emerging themes within this domain, ensuring a comprehensive exploration aligned with PRISMA guidelines.

This manuscript summarizes recent research from the past five years in three key areas: construction equipment management, construction safety management and construction cost management within the realm of AICM. It identifies key gaps and outlines future research directions, including enhancing AI-driven equipment integration, developing sophisticated AI-based safety systems and optimizing cost management with advanced data analytics. These findings and directions are essential for steering the field toward greater digital innovation and sustainability.

This research provides a detailed analysis of the literature within the AICM domain, thoughtfully compiling significant findings and highlighting the importance of addressing user needs. The insights and recommendations shared aim to be beneficial for both academic researchers and industry professionals, contributing to the ongoing development of AICM as it moves toward a future characterized by digital innovation and sustainability.

This study introduces a novel approach to preventing construction quality problems by examining the complex interrelations among such issues. Recognizing the overlooked coupling between problems is essential, as it can exacerbate quality issues, triggering chain reactions that compromise project success. The research justifies its focus on these interrelations by highlighting the insufficiency of traditional quality management methods, which often fail to account for interconnected quality problems in the architecture, engineering and construction (AEC) industry.

At the core of this research is the establishment of a knowledge base for construction quality issues, marking a pioneering effort to systematically organize unstructured textual data on construction quality problems and their interconnections. This base serves as a platform for the subsequent application of advanced analytical techniques. Specifically, the study leverages preprocessing, text similarity algorithms and association rule mining to dissect and illuminate the nuanced coupling relationships among construction quality issues, a facet not thoroughly explored in prior research.

The innovative analytical methodology employed here reveals significant insights into the dynamics of construction quality issue coupling. These insights not only deepen the understanding of these complex interactions but also guide the development of targeted intervention strategies. The practical applicability and effectiveness of the proposed approach are demonstrated using selected textual materials as experimental evidence. The findings show that understanding and addressing these couplings can significantly mitigate potential chain reactions of defects, thus enhancing overall project quality.

The originality of this study lies in its threefold contribution: the creation of a dedicated knowledge base for construction quality issues, the application of novel analytical methodologies to decipher coupling relationships and the extension of text analysis techniques to the realm of construction quality problem prevention. Together, these innovations open new avenues for research and practice in construction management, offering a robust framework for the systematic identification and mitigation of quality issues in construction projects.

A zero-day vulnerability is a complimentary ticket to the attackers for gaining entry into the network. Thus, there is necessity to device appropriate threat detection systems and establish an innovative and safe solution that prevents unauthorised intrusions for defending various components of cybersecurity. We present a survey of recent Intrusion Detection Systems (IDS) in detecting zero-day vulnerabilities based on the following dimensions: types of cyber-attacks, datasets used and kinds of network detection systems.

Purpose: The study focuses on presenting an exhaustive review on the effectiveness of the recent IDS with respect to zero-day vulnerabilities.

Methodology: Systematic exploration was done at the IEEE, Elsevier, Springer, RAID, ESCORICS, Google Scholar, and other relevant platforms of studies published in English between 2015 and 2021 using keywords and combinations of relevant terms.

Findings: It is possible to train IDS for zero-day attacks. The existing IDS have strengths that make them capable of effective detection against zero-day attacks. However, they display certain limitations that reduce their credibility. Novel strategies like deep learning, machine learning, fuzzing technique, runtime verification technique, and Hidden Markov Models can be used to design IDS to detect malicious traffic.

Implication: This paper explored and highlighted the advantages and limitations of existing IDS enabling the selection of best possible IDS to protect the system. Moreover, the comparison between signature-based and anomaly-based IDS exemplifies that one viable approach to accurately detect the zero-day vulnerabilities would be the integration of hybrid mechanism.