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Damage of engineering structures is a nonlinear evolutionary process that spans across both material and structural levels, from mesoscale to macroscale. This paper aims to provide a comprehensive review of damage analysis methods at both the material and structural levels.

This study provides an overview of multiscale damage analysis of engineering structures, including its definition and significance. Current status of damage analysis at both material and structural levels is investigated, by reviewing damage models and prediction methods from single-scale to multiscale perspectives. The discussion of prediction methods includes both model-based simulation approaches and data-driven techniques, emphasizing their roles and applications. Finally, summarize the main findings and discuss potential future research directions in this field.

In the material level, damage research primarily focuses on the degradation of material properties at the macroscale using continuum damage mechanics (CDM). In contrast, at the mesoscale, damage research involves analyzing material behavior in the meso-structural domain, focusing on defects like microcracks and void growth. In structural-level damage analysis, the macroscale is typically divided into component and structural scales. The component scale examines damage progression in individual structural elements, such as beams and columns, often using detailed finite element or mesoscale models. The structural scale evaluates the global behavior of the entire structure, typically using simplified models like beam or shell elements.

To achieve realistic simulations, it is essential to include as many mesoscale details as possible. However, this results in significant computational demands. To balance accuracy and efficiency, multiscale methods are employed. These methods are categorized into hierarchical approaches, where different scales are processed sequentially, and concurrent approaches, where multiple scales are solved simultaneously to capture complex interactions across scales.

The bridge expansion joint (BEJ) is a key device for accommodating spatial displacement at the beam end, and for providing vertical support for running trains passing over the gap between the main bridge and the approach bridge. For long-span railway bridges, it must also be coordinated with rail expansion joint (REJ), which is necessary to accommodate the expansion and contraction of, and reducing longitudinal stress in, the rails. The main aim of this study is to present analysis of recent developments in the research and application of BEJs in high-speed railway (HSR) long-span bridges in China, and to propose a performance-based integral design method for BEJs used with REJs, from both theoretical and engineering perspectives.

The study first presents a summary on the application and maintenance of BEJs in HSR long-span bridges in China representing an overview of their state of development. Results of a survey of typical BEJ faults were analyzed, and field testing was conducted on a railway cable-stayed bridge in order to obtain information on the major mechanical characteristics of its BEJ under train load. Based on the above, a performance-based integral design method for BEJs with maximum expansion range 1600 mm (±800 mm), was proposed, covering all stages from overall conceptual design to consideration of detailed structural design issues. The performance of the novel BEJ design thus derived was then verified via theoretical analysis under different scenarios, full-scale model testing, and field testing and commissioning.

Two major types of BEJs, deck-type and through-type, are used in HSR long-span bridges in China. Typical BEJ faults were found to mainly include skewness of steel sleepers at the bridge gap, abnormally large longitudinal frictional resistance, and flexural deformation of the scissor mechanisms. These faults influence BEJ functioning, and thus adversely affect track quality and train running performance at the beam end. Due to their simple and integral structure, deck-type BEJs with expansion range 1200 mm (± 600 mm) or less have been favored as a solution offering improved operational conditions, and have emerged as a standard design. However, when the expansion range exceeds the above-mentioned value, special design work becomes necessary. Therefore, based on engineering practice, a performance-based integral design method for BEJs used with REJs was proposed, taking into account four major categories of performance requirements, i.e., mechanical characteristics, train running quality, durability and insulation performance. Overall BEJ design must mainly consider component strength and the overall stiffness of BEJ; the latter factor in particular has a decisive influence on train running performance at the beam end. Detailed BEJ structural design must stress minimization of the frictional resistance of its sliding surface. The static and dynamic performance of the newly-designed BEJ with expansion range 1600 mm have been confirmed to be satisfactory, via numerical simulation, full-scale model testing, and field testing and commissioning.

This research provides a broad overview of the status of BEJs with large expansion range in HSR long-span bridges in China, along with novel insights into their design.

The study aims to explore how healthcare workers (HCWs) navigate and experience time when caring for older cancer patients living with other illnesses.

This paper presents findings from a qualitative study of how HCWs conceptualise and navigate the temporal aspects of delivering personalised care to older people living with multimorbidity. Building on research from organisation studies and the sociology of time, we interviewed 19 UK HCWs about their experiences of delivering care to this patient group.

Our findings illustrate how the delivery of personalised care contradicts contemporary models for healthcare delivery defined by efficiency and standardisation. We found that HCWs engage with time as both a valuable commodity to be rationed and prioritised within a constrained context and as a malleable resource for managing workload and overcoming “turbulence” in the system. However, participants in this study also shared how the simultaneous multiplicity and lack of time had a profoundly personal impact on them through the emotional toll associated with “time debt” and “lost” time.

This research presents a unique analysis of how time is conceptualised and navigated in contemporary healthcare, offering valuable insights for policy improvement. We conclude that personalised models of healthcare are incompatible with many current temporal structures of treatment trajectories and work-practices, by nature of being centred around the person and not the system of delivery.

The purpose of the present study is to reveal the antecedents of the new green product success (NGPS) of small and medium-sized enterprises (SMEs) that manufacture in Türkiye with a middle-income economy. To this end, a research model for the serial mediating role of green human capital (GHC) and green adaptive ability (GAA) in the relationship between green mindfulness (GM) and NGPS was developed and tested. Determining the driving forces of NGPS is essential due to the contributions of SMEs manufacturing, especially in middle-income economies, such as Türkiye, to the national economy.

The present study used partial least squares structural equation modeling (PLS-SEM) to test the hypothesized relationships. The study hypotheses were tested in a sample of 396 manufacturing enterprises, qualified as SMEs in Türkiye with ISO 14001 certificates.

Empirical evidence indicates that GM, GHC and GAA act as significant antecedents of NGPS. Furthermore, empirical evidence demonstrates that GHC and GAA, in turn, mediate the relationship between GM and NGPS.

Managers, especially in manufacturing SMEs, should be aware that they need to develop intangible resources to improve NGPS. Increased investment in GM results in higher levels of GHC and GAA, which collectively leads to improved NGPS.

The current literature has not previously explained the mechanism of the relationship between GM and NGPS. The originality of the present study, which discusses the relevant research gap, is to develop an integrated framework to investigate how GM impacts NGPS, specifically for SMEs, and to test it empirically.

This study aims to investigate the “digitalization paradox” in manufacturing digital transformation, where significant investments in digital technology may not necessarily lead to increased returns. Specifically, it explores the intricate relationship between digital technology convergence, financial performance, productivity and technological innovation in listed Chinese manufacturing firms, drawing upon theories of digital innovation and knowledge networks.

Using a large panel data from 747 listed firms in China’s manufacturing sector and their 428,927 patents spanning from 2013 to 2022, this research first quantifies manufacturing firm-level digital technology convergence through patent network analysis. Furthermore, this study employs hierarchical regression analysis and the instrumental variable method to investigate the curvilinear relationship between digital technology convergence and financial performance. Furthermore, the moderating role of firms’ productivity and technological innovation is tested.

Three types of firm-level digital technology convergence (DTC) are delineated and quantified: local authority in digital convergence (DegreeDTC), convergence with heterogeneous digital knowledge (BetweenessDTC) and shortest-path convergence with digital technologies (ClosenessDTC, where a higher value signifies a more conservative and shorter path in adopting digital technologies). Network visualization shows that manufacturing firms' DTC has consistently increased over time. Contrary to traditional assumptions, our research reveals a U-shaped relationship between DTC (specifically, DegreeDTC and BetweenessDTC) and financial performance. This relationship is characterized by a negative correlation at lower levels and a positive one at higher levels. The joint effect of firms’ productivity and technological innovation significantly strengthens this relationship. These findings are robust across a series of robustness checks.

Our findings offer practical insights for both managers and policymakers. We recommend a balanced approach to digital innovation management within the technology convergence paradigm. Manufacturing firms can generate economic value by strategically choosing to either shrink or expand their digital technology application areas, thereby reducing uncertainties related to emerging convergent businesses. Additionally, the study underscores the synergistic strategy of combining innovation with productivity. Within the DTC business context, integrating productivity with technological innovation not only enhances cost flexibility but also improves problem-solution matching, ultimately amplifying synergistic benefits.

To the best of our knowledge, this is the first study to apply a digital technology co-occurrence network to unveil nuanced relationships in “DTC – finance performance” within the manufacturing sector. It challenges conventional thinking regarding the common positive effect of digital innovation and technological convergence. This study provides a comprehensive analysis of DTC, financial performance, productivity and technological innovation dynamics, as well as offers managerial implications for managers and policymakers.

• (1)

  We quantify manufacturing firm-level DTC through patent network analysis and find consistent increases over time.

• (2)

  A significant U-shaped relationship between DTC and financial performance, being negative at lower levels and positive at higher levels.

• (3)

  The joint effect of firms’ productivity and technological innovation reinforces this relationship by distributing costs and enhancing synergistic benefits.

• (4)

  We challenge existing literature by uncovering a complex relationship in “DTC – finance performance”, contrary to popular belief of a monotonic effect of digital innovation or technological convergence.

We quantify manufacturing firm-level DTC through patent network analysis and find consistent increases over time.

A significant U-shaped relationship between DTC and financial performance, being negative at lower levels and positive at higher levels.

The joint effect of firms’ productivity and technological innovation reinforces this relationship by distributing costs and enhancing synergistic benefits.

We challenge existing literature by uncovering a complex relationship in “DTC – finance performance”, contrary to popular belief of a monotonic effect of digital innovation or technological convergence.

This study aims to examine the conceptual and empirical operations of hospitality at its intersections with health care, which includes medical and senior care.

This study conducts a systematic review of literature on hospitality in health care published in hospitality, tourism and leisure journals spanning from 1990 to 2023. A total of 50 studies meeting the inclusion criteria are reviewed, providing insights into how hospitality is conceptualized, its practical implementation and the proposed outcomes in health-care settings.

Hospitality in health care is conceptualized by hospitality scholars in three main ways: as service functions, as a service exchange and as an organizational culture. There is a significant overlap between the notion of hospitality and the concept of person-centered care in gerontology and health-care literature. Also, hospitality contributes positively to patient/resident experiences, organizational performance and societal impacts.

The study is limited by its focus solely on the theoretical and practical aspects of hospitality in health care within hospitality, tourism and leisure journals, excluding relevant literature from gerontological and health-care journals.

Interdisciplinary research requires scholars from different disciplines to develop a common language and understanding of key concepts. This study presents the conceptual and practical domains of hospitality and its relevancy to health-care research and offers future directions to strengthen the interdisciplinary research between hospitality, health care and gerontology.

This study addresses a gap in understanding consumer retention in omnichannel retailing. It explores the impact of omnichannel capabilities on consumer retention by examining the roles of consumer empowerment, cross-channel integration, retailer uncertainty and consumer satisfaction.

Utilising the stimulus-organism-response theory, data from 338 Indian omnichannel consumers were analysed using SmartPLS V3 and Process Macro for SPSS. A structured questionnaire guided the investigation into the interconnected dynamics of omnichannel capabilities, consumer empowerment, cross-channel integration, retailer uncertainty and consumer satisfaction.

Findings suggest that omnichannel capabilities significantly and positively influence consumer retention, with a significant mediation impact on consumer empowerment and cross-channel integration. The results indicate that retailer uncertainty negatively moderates, whereas consumer satisfaction positively moderates the association between omnichannel capabilities and consumer retention. Moreover, the study unravels the mechanisms driving consumer retention in the omnichannel landscape.

This research pioneers unravelling the complexities of consumer retention in omnichannel retailing. It explores how consumer retention is enhanced through omnichannel capabilities. This study bridges a gap in existing research by examining the impact of omnichannel capabilities, consumer empowerment and cross-channel integration in omnichannel retailing. Therefore, this study provides innovative, unique and strategic adaptations in the retail industry.

This research aims to systematically review studies on significant risks for Critical Infrastructure Projects (CIPs) from selected top-tier academic journals from 2011 to 2023.

In this research, a three-step systematic literature review methodology was employed to analyse 55 selected articles on Critical Infrastructure Risks (CIRs) from well-regarded and relevant academic journals published from 2011 to 2023.

The findings highlight a growing research focus on CIRs from 2011 to 2023. A total of 128 risks were identified and grouped into ten distinct categories: construction, cultural, environmental, financial, legal, management, market, political, safety and technical risks. In addition, literature reviews combined with questionnaire surveys were more frequently used to identify CIRs than any other method. Moreover, oil and gas projects were the subjects most often explored in the reviewed papers. Furthermore, it was observed that publications from Iran, the USA and China dominated CIRs research, making significant contributions, accounting for 49.65% of the analysed articles.

This research specifically focuses on five types of CIPs (i.e. roadways, bridges, water supply systems, dams and oil and gas projects). Other CIPs like cyber-physical systems or electric power systems, were not considered in this research.

Governments and contracting firms can benefit from the findings of this study by understanding the significant risks associated with the execution of CIPs, irrespective of the nation, industry or type of project. The results of this investigation can offer construction professionals valuable insights to formulate and implement risk response plans in the early stages of a project.

As a novel literature review related to CIRs, it lays the groundwork for future research and deepens the understanding of the multi-faceted effects of these risks, as well as sets practical response strategies.

This work aims to develop a web platform for inspecting roof structures for technical assistance supported by drones and artificial intelligence. The tools used were HTML, CSS and JavaScript languages; Firebase software for infrastructure; and Custom Vision for image processing.

This study adopted the design science research approach, and the main stages for the development of the web platform include (1) creation and validation of the roof inspection checklist, (2) validation of the use of Custom Vision as an image recognition tool, and (3) development of the web platform.

The results of automatic recognition showed a percentage of 77.08% accuracy in identifying pathologies in roof images obtained by drones for technical assistance.

This study contributed to developing a drone-integrated roof platform for visual data collection and artificial intelligence for automatic recognition of pathologies, enabling greater efficiency and agility in the collection, processing and analysis of results to guarantee the durability of the building.

This work provides an overview of academic articles on the application of artificial intelligence (AI) in healthcare. It delves into the innovation process, encompassing a two-stage trajectory of exploration and development followed by dissemination and adoption. To illuminate the transition from the first to the second stage, we use prospect theory (PT) to offer insights into the effects of risk and uncertainty on individual decision-making, which potentially lead to partially irrational choices. The primary objective is to discern whether clinical decision support systems (CDSSs) can serve as effective means of “cognitive debiasing”, thus countering the perceived risks.

This study presents a comprehensive systematic literature review (SLR) of the adoption of clinical decision support systems (CDSSs) in healthcare. We selected English articles dated 2013–2023 from Scopus, Web of Science and PubMed, found using keywords such as “Artificial Intelligence,” “Healthcare” and “CDSS.” A bibliometric analysis was conducted to evaluate literature productivity and its impact on this topic.

Of 322 articles, 113 met the eligibility criteria. These pointed to a widespread reluctance among physicians to adopt AI systems, primarily due to trust-related issues. Although our systematic literature review underscores the positive effects of AI in healthcare, it barely addresses the associated risks.

This study has certain limitations, including potential concerns regarding generalizability, biases in the literature review and reliance on theoretical frameworks that lack empirical evidence.

The uniqueness of this study lies in its examination of healthcare professionals’ perceptions of the risks associated with implementing AI systems. Moreover, it addresses liability issues involving a range of stakeholders, including algorithm developers, Internet of Things (IoT) manufacturers, communication systems and cybersecurity providers.