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Stunting is a major multifactorial health problem in Indonesia that negatively impacts children. Among Southeast Asian countries, Indonesia had the highest prevalence of child stunting (0–59 months old). As Indonesia has also the largest population in Southeast Asia, it is crucial to assess measures to decrease the prevalence of stunting in the country. Therefore, this study aims to examine the prevalence and determinants of stunting among children 6–23 under two years old in Indonesia.

The study used a cross-sectional design using the national database. A total of 15,641 children aged 6–23 months were included in the study. A multivariate logistic regression was performed to identify the association between the dependent and independent variables.

The prevalence of stunting was 18% (95% CI = 17.5%–18.7%). This study found that children aged 12–23 months were the dominant factor in stunting (OR = 2.12, 95% CI = 1.92–2.36). Factors associated with stunting include being male (OR = 1.37, 95% CI = 1.26–1.49), low birth weight (LBW) (OR = 1.95, 95% CI = 1.68–2.27), short birth length (SBL) (OR = 1.82, 95% CI = 1.64–2.01), history of infection (OR = 1.15, 95% CI = 1.06–1.26) and lack of dietary diversity (OR = 1.13, 95% CI = 1.04–1.00) consumption of empty calorie drinks (OR = 1.11, 95% CI = 1.01–1.24), unimproved sanitation (OR = 1.16, 95% CI = 1.04–1.30), middle socioeconomic status (OR = 1.34, 95% CI = 1.16–1.55), low maternal education (OR = 1.51, 95% CI = 1.08–2.10) and living in a rural area (OR = 1.15, 95% CI = 1.06–1.26).

To the best of the authors’ knowledge, this is the first study to analyze the prevalence and determinants of stunting among children under two years old in Indonesia using a national which represented a population of interest.

Short-form video advertisements have recently gained popularity and are widely used. However, creating attractive short video advertisements remains a challenge for sellers. Based on the visual-audio perspective and signaling theory, this study investigated the impacts of three visual features (number of shots, pixel-level image complexity and vertical versus horizontal formats) and two audio features (speech rate and average spectral centroid) on user engagement behavior.

We conducted a field study on TikTok. To test our various hypotheses, we used regression analysis on 2,511 videos containing product promotion information posted by 60 sellers between January 1, 2020 and November 20, 2021.

For visual variables, the number of shots and pixel-level image complexity were found to have nonlinear (inverted U-shaped) relationships with user engagement behavior. The vertical video form was found to have a positive effect on comments and shares. In the case of audio variables, speech rate was found to have a significant positive effect on shares but not on likes and comments. The average spectral centroid was found to have significant negative influences on likes and comments.

This study provides specific suggestions for sellers who create short-form videos to improve user engagement behavior.

This study contributes to the literature on short-form video advertising by extending the potential drivers of user engagement behavior. Additionally, from a methodological perspective, it contributes to the literature by using computer vision and speech-processing techniques to analyze user behavior in a video-related context, effectively overcoming the limitations of the widely adopted survey method.

Prefabricated construction (PC) is increasingly recognized for its sustainability and is being vigorously promoted worldwide. However, its adoption in developing countries remains suboptimal, with existing studies predominantly focusing on policy frameworks or the impact of a single policy instrument. This study addresses this research gap by optimizing the path of PC promotion from the perspective of policy mixes.

The study employs fuzzy-set qualitative comparative analysis (fsQCA) in conjunction with necessary condition analysis, to explore effective policy configurations for PC promotion. A comprehensive collection of 171 PC-related policies issued by the Chinese government was analyzed using text mining to identify antecedent conditions of configuration. Data were further obtained through a questionnaire survey involving 263 valid responses, with fsQCA used to derive the optimal policy configurations.

The analysis identified six distinct combination paths for effective policy configurations. Land supply and governmental procurement were found to be core conditions, while fiscal and taxation financial measures emerged as marginal conditions prevalent across all paths. These findings suggest that land supply policies are particularly suitable for regions with limited land resources, such as Shanghai, while government procurement is more effective in regions like Xinjiang, where PC awareness is still developing.

In practice, the conclusions enable policymakers to clearly understand policy instruments, thereby finding differentiated pathways for promoting PC with comparable effects. The proposed recommendations help advance PC development effectively while reducing financial burden and minimizing resource waste. This provides important guidance for PC development across different regions or stages, helps address regional imbalances in PC development, and ultimately contributes to the broader goal of sustainable urban development.

This study significantly enriches the research on PC policy combinations by utilizing more comprehensive and robust data, thereby enhancing the universal applicability of the findings. The results provide valuable references for policymakers in different regions, helping to address regional imbalances in PC development and facilitating the construction industry’s transition towards greater intelligence and sustainability.

This paper analyzes the application of digital twin technology in the field of intelligent operation and maintenance of high-speed railway infrastructure from the perspective of top-level design.

This paper provides a comprehensive overview of the definition, connotations, characteristics and key technologies of digital twin technology. It also conducts a thorough analysis of the current state of digital twin applications, with a particular focus on the overall requirements for intelligent operation and maintenance of high-speed railway infrastructure. Using the Jinan Yellow River Bridge on the Beijing–Shanghai high-speed railway as a case study, the paper details the construction process of the twin system from the perspectives of system architecture, theoretical definition, model construction and platform design.

Digital twin technology can play an important role in the whole life cycle management, fault prediction and condition monitoring in the field of high-speed rail operation and maintenance. Digital twin technology is of great significance to improve the intelligent level of high-speed railway operation and management.

This paper systematically summarizes the main components of digital twin railway. The general framework of the digital twin bridge is given, and its application in the field of intelligent operation and maintenance is prospected.

Drawing on the theory of memory-dominant logic, this study aims to examine how the substantive staging of the servicescape, experience co-creation, experiential satisfaction and experience intensification affect experience memorability and hedonic well-being in the case of unmanned smart hotels.

An online survey was used, with the target respondents being hotel guests people aged 18 years and older who had been recent guests of the FlyZoo Hotel in Hangzhou, China. Data were collected online from 429 guests who had stayed in the hotel between April and June 2023. Data analysis was undertaken using structural equation modelling.

The results suggest that all the proposed four constructs are positive drivers of a memorable unmanned smart hotel experience. The relationship between the memorability of the hotel experience and hedonic well-being was found to be significant and positive.

Unmanned smart hotels should ensure that all smart technologies function effectively and dependably and offer highly personalised services to guests, allowing them to co-create their experiences. This will lead to the guest receiving a satisfying and memorable experience. To enable experience co-creation using smart technologies, unmanned smart hotels could provide short instructional videos for guests, as well as work closely with manufacturers and suppliers to ensure that smart technology systems are regularly updated.

This study investigates the antecedents and outcomes of a novel phenomenon and extends the concept of memorable tourism experiences to the context of unmanned smart hotels.

Innovation in financing processes, enabled by the advent of new technologies, has supported the development of alternative finance funding tools. In this context, the study analyses the growing importance of alternative finance instruments (such as equity crowdfunding, peer-to-peer (P2P) lending, venture capital, and others) in addressing the small and medioum enterprises' (SMEs) financing needs beyond traditional bank and market-based funding channels. By providing more flexible terms and faster approval times, these instruments are gradually reshaping the traditional bank-firm relationship.

To comprehensively understand this innovation shift in funding processes, the study employs a novel approach that merges three MCDA methods: Spherical Fuzzy Entropy, ARAS and TOPSIS. These methodologies allow for handling ambiguity and subjectivity in financial decision-making processes, examining the effects of multiple criteria, including interest rate, flexibility, accessibility, support, riskiness, and approval time, on the appeal of various financial alternatives.

The study’s results have significant theoretical and practical implications, supporting SMEs in carefully evaluate financing alternatives and enables banks to better identify the main “competitors” according to the “financial need” of the firm. Moreover, the rise of alternative finance, notably P2P lending, indicates a shift towards more efficient capital access, suggesting banks must innovate their funding channels to remain competitive, especially in offering flexible solutions for restructuring and high-risk scenarios.

The study advises top management that SMEs prefer traditional loans for their reliability and accessibility, necessitating banks to enhance transparency, innovate, and adopt digital solutions to meet evolving financing needs and improve customer satisfaction.

The study introduces a novel integration of Spherical Fuzzy TOPSIS, Entropy, and ARAS methodologies to face the complexities of financial decision-making for SME financing, addressing ambiguity and multiple criteria like interest rates, flexibility, and riskiness. It emphasizes the importance of traditional loans, the rising significance of alternative financing such as P2P lending, and the necessity for banks to innovate, thereby enriching the literature on bank-firm relationships and SME funding strategies.

This study aims to investigate the sealing performance of reciprocating seals under the effect of rubber abrasion using ABAQUS simulation software, and to propose a prediction framework based on a hybrid algorithm (GA-PSO-BPNN) to predict the leakage of reciprocating seals of downhole gauging instrumentation under different working condition parameters.

The authors combined the UMESHMOTION user program with the improved Archard wear model to investigate reciprocating seal performance. GA and a PSO were proposed as ways to enhance the BPNN’s predictive model.

The results show that the impact of fluid pressure fluctuations on the wear of the seal lip is more pronounced during the rapid wear phase compared to the steady wear phase. Similarly, variations in compression rate have a greater impact on seal lip wear at different stages of wear. The GA-PSO-BPNN prediction model outperforms the single-prediction model in terms of prediction accuracy.

The authors investigated sealing performance through simulation software and propose a GA-PSO-BPNN-based fault diagnosis method for rotating machinery. To verify the accuracy of the prediction model, a reciprocating sealing test platform for gauge work cylinders is constructed.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2024-0293/

The literature on knowledge management in sustainable supply chain (KMSSC) has witnessed significant growth in the past two decades. However, a scientometric review that consolidates the primary trends and clusters within this topic has been notably absent. This paper aims to scrutinize recent advancements and identify the intellectual underpinnings of KMSSC research conducted between 2002 and 2022.

The present review employs a scientometric analysis approach via visualization maps of prolific contributions, co-citation, co-occurrence and thematic networks to examine a total of 114 articles and conference papers on KMSSC.

Emerging research frontiers and hotspots are revealed and a state-of-the-art framework of KMSSC research structure is developed.

The review provides significant implications that guide KMSSC research and better inform sustainability decisions in the supply chain context.

To the best of the authors' knowledge, this is the first review to thoroughly synthesize the intersected domain of KMSSC using scientometric analysis.

Developing severe load spectrum of transport aircraft structures is crucial for enhancing the fatigue damage correlation between full-scale fatigue testing results and operational service. The lack of consensus on severe spectrum development methods for transport aircraft has prompted the current research, resulting in a proposed approach for a severe gust load spectrum based on the acceleration cumulative exceedance surface.

The measured load data were analyzed using a model based on the cumulative exceedance number surface to describe the variation in exceedance numbers. An improved sampling method based on multivariate Markov Chain Monte Carlo was employed to obtain the fleet fatigue damage distribution, enabling the determination of the severity of severe spectrum and the corresponding cumulative exceedance number surface, and a severe gust load spectrum was developed based on the surface.

The method that characterizes load spectrum variation using the cumulative exceedance surface minimizes the randomness of peak-trough pairs by incorporating the correlation of load spectrum peaks and troughs. This approach reduces the variation in fleet fatigue damage, thereby lowering the requirements for the severity of severe spectrum fatigue damage.

The proposed methodology extends from a one-dimensional curve to a two-dimensional surface, accounting for the correlation between peak and trough values to develop a severe spectrum. This approach more accurately describes the variation in acceleration cumulative exceedance numbers, directly benefiting fatigue damage calculation. This study provides valuable references for developing severe spectrum for transport aircraft.