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Peer review plays a crucial role in scientific writing and the publishing process, assessing the quality of research work. As the volume of paper submissions increases, peer review becomes increasingly burdensome, highlighting the importance of studying the duration of peer review. This study aims to explore the correlation between review aspect sentiment and the duration of peer review as well as the differences in this relationship across different disciplines and review rounds. Thus helping authors make targeted revisions and optimizations to their papers while reducing the duration of peer review, which enables authors’ research findings to reach the academic community and public domain more rapidly.

The study employs a two-step approach to understand the impact of review aspects on the duration of peer review. First, it extracts fine-grained aspects from peer review comments and uses sentiment classification models to classify the sentiment of each review aspect. Then, it conducts a correlation analysis between review aspect sentiment and the duration of peer review. Additionally, the study calculates sentiment scores for various review rounds to explore the differences in the impact of review aspect sentiment on the duration of peer review across different review rounds.

The study found that there is a weak but significant negative correlation between the sentiment of the review and the duration of peer review. Specifically, the aspect clusters, such as Evaluation & Result and Impact & Research Value, exhibit a relatively stronger correlation with the duration of peer review. Additionally, the correlation between review aspect sentiments and the duration of peer review varies significantly in different review rounds.

The significance of this study lies in connecting peer review comments text with the peer review process. By analyzing the correlation between review aspects and the duration of peer review, it identifies aspects that have a greater impact on the duration of peer review. This helps improve the efficiency of peer review from the perspectives of authors, reviewers and editors. Thus alleviating the burden of peer review and accelerating academic exchange and knowledge dissemination.

This chapter delves into how smart city innovations positively affect workforce efficiency, residents’ quality of life (QoL), and the delivery of services, particularly within the dynamic context of smart cities: innovation, development, transformation, and prosperity. It discusses the role of technologies like cyber-physical systems, the Internet of Things, and intelligent transport systems in creating efficient, sustainable urban spaces that benefit the workforce and the broader community. The chapter highlights strategies for improving urban environments, ensuring workforce well-being, and fostering sustainable growth by examining the interplay between these technologies and urban living. The narrative emphasizes the necessity of ongoing innovation, policy support, and workforce adaptation, underscoring the importance of tailoring smart city initiatives to regional needs for maximal impact on employee performance, QoL, and service delivery. Additionally, it introduces a comprehensive framework designed to guide the development of next-generation smart cities. This framework integrates advanced technologies for optimized urban management and service provision, directly linking to enhanced employee performance through improved urban infrastructure and services. The strategic application of this framework aims to elevate economic prosperity and societal well-being, ensuring workforce efficiency is central to the urban development agenda. The enhanced employee performance, catalyzed by smart city innovations, is pivotal in driving economic vibrancy, social inclusivity, and environmental sustainability, shaping the future of urban development. This analysis will offer valuable insights for smart cities research and development in the Gulf Region, suggesting pathways for implementing these concepts to address the region’s urbanization and development challenges.

This paper aims to reduce the cost of experiments required to test the efficiency of materials suitable for artificial tissue ablation by increasing efficiency and accurately forecasting heating properties.

A two-step numerical analysis is used to develop and simulate a bioheat model using improved finite element method and deep learning algorithms, systematically regulating temperature distributions within the hydrogel artificial tissue during radiofrequency ablation (RFA). The model connects supervised learning and finite element analysis data to optimize electrode configurations, ensuring precise heat application while protecting surrounding hydrogel integrity.

The model accurately predicts a range of thermal changes critical for optimizing RFA, thereby enhancing treatment precision and minimizing impact on surrounding hydrogel materials. This computational approach not only advances the understanding of thermal dynamics but also provides a robust framework for improving therapeutic outcomes.

A computational predictive bioheat model, incorporating deep learning to optimize electrode configurations and minimize collateral tissue damage, represents a pioneering approach in interventional research. This method offers efficient evaluation of thermal strategies with reduced computational overhead compared to traditional numerical methods.

The wavelet neural network (WNN) has the drawbacks of slow convergence speed and easy falling into local optima in data prediction. Although the artificial bee colony (ABC) algorithm has strong global optimization ability and fast convergence speed, it also has the drawbacks of slow speed while finding the optimal solution and weak optimization ability in the later stage.

This article uses an ABC algorithm to optimize the WNN and establishes an ABC-WNN analysis model. Based on the example of the Jinan Yuhan underground tunnel project, the deformation of the surrounding rock of the double-arch tunnel crossing the fault fracture zone is predicted and analyzed, and the analysis results are compared with the actual detection amount.

The comparison results show that the predicted values of the ABC-WNN model have a high degree of fitting with the actual engineering data, with a maximum relative error of only 4.73%. On this basis, the results show that the statistical features of ABC-WNN are the lowest, with the errors at 0.566 and 0.573, compared with the single back propagation (BP) neural network model and WNN model. Therefore, it can be derived that the ABC-WNN model has higher prediction accuracy, better computational stability and faster convergence speed for deformation.

This article uses firstly the ABC-WNN for the deformation analysis of double-arch tunnels. This attempt laid the foundation for artificial intelligence prediction in deformation analysis of multi-arch tunnels and small clearance tunnels. It can provide a new and effective way for deformation prediction in similar projects.

It evaluated the seismic vulnerability based on fewer factors by presenting the effectiveness of seismic and structural parameters. The proposed method first demonstrated the effect of earthquake ground motion inputs on predicting the slight, moderate, extensive and collapse limit states and confirmed the method’s efficiency. The fragility curves illustrated with the approach of the present study are compared with the traditional techniques, such as analytical methods.

Based on the different macro- and micro-structural characteristics and the earthquake records, achieving a certain relation from regression analysis using artificial neural networks (ANNs) is difficult. With this background in mind, the present study aimed to compare the proposed model of the considered bridge with the analytical and ANN results. After statistical analysis and estimation of the most effective factors in predicting responses from the proposed approach, two-parameter two- and three-dimensional fragility curves are extracted.

Due to the structural differences between horizontally curved bridges, the methodology does not require any classification of bridge classes to predict responses. For a specific L/R of the bridge, the parameters cumulative absolute velocity (CAV) and Sa (T1) can provide a good estimate of the seismic fragility curves, and the proposed approach with less parameter assignment also leads to good results. With less computational effort, fragility curves can be illustrated.

The proposed method demonstrated the ability to accurately estimate the occurrence and non-occurrence limit states while maintaining a low computational cost and the derivation of a curved bridge’s seismic fragility curve.

This study aims to investigate the potential of wood as a water-lubricated bearing material, determine the factors influencing the water-lubricated properties of wood and identify suitable alternatives to Lignum vitae.

Three resource-abundant wood species, Platycladus orientalis, Cunninghamia lanceolata and Betula platyphylla, were selected, and their properties were compared with those of Lignum vitae. The influencing mechanism of the tribological properties of different woods under water lubrication was thoroughly analyzed, in conjunction with the characterization and testing of mechanical properties, micromorphology and chemical composition.

The findings reveal that the mechanical properties and inclusions of wood are the primary factors affecting its tribological properties, which are significantly influenced by the micromorphology and chemical composition. The friction experiment results demonstrate that Lignum vitae exhibits the best tribological properties among the four wood species. The tribological properties of Platycladus orientalis are comparable to those of Lignum vitae, being only 17.1% higher. However, it is noted that higher mechanical properties can exacerbate the wear of the grinding pair.

The originality of this study lies in the combination of friction experiments and wood performance tests to identify the factors contributing to the superior water lubrication performance of wood, thereby guiding the application and improvement of different wood types in water-lubricated bearings.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2024-0284/

While top management teams (TMTs) play an important role in shaping firms’ strategic choices and performance outcomes, studies on green innovation have rarely considered the influence of the TMT demographics of firms and their suppliers. Drawing upon upper echelon theory, this study investigates the impact of buyer–supplier TMT misalignment on green innovation performance, along with potential moderators of this effect.

The empirical setting is Chinese-listed manufacturing firms that are present in both the Chinese Research Data Services Platform (CNRDS) database and the China Stock Market and Accounting Research (CSMAR) database. The study employs panel data regression methods on a dyadic dataset of 530 paired buyer–supplier firm-year observations over the period 2008–2019.

Buyer–supplier TMT misalignment in terms of functional background and educational level is negatively associated with buyer green innovation performance. This negative effect can, however, be mitigated by TMT tenure and long-term incentives in buyer firms.

By introducing the notion of TMT background misalignment to the supply chain, this study advances the understanding of relational TMT demographics in predicting organizational performance and extends upper echelon theory.

The volume of passenger traffic at metro transfer stations serves as a pivotal metric for the orchestration of crowd flow management. Given the intricacies of crowd dynamics within these stations and the recurrent instances of substantial passenger influxes, a methodology predicated on stochastic processes and the principle of user equilibrium is introduced to facilitate real-time traffic flow estimation within transfer station streamlines.

The synthesis of stochastic process theory with streamline analysis engenders a probabilistic model of intra-station pedestrian traffic dynamics. Leveraging real-time passenger flow data procured from monitoring systems within the transfer station, a gradient descent optimization technique is employed to minimize the cost function, thereby deducing the dynamic distribution of categorized passenger flows. Subsequently, adhering to the tenets of user equilibrium, the Frank–Wolfe algorithm is implemented to allocate the intra-station categorized passenger flows across various streamlines, ascertaining the traffic volume for each.

Utilizing the Xiaozhai Station of the Xi’an Metro as a case study, the Anylogic simulation software is engaged to emulate the intra-station crowd dynamics, thereby substantiating the efficacy of the proposed passenger flow estimation model. The derived solutions are instrumental in formulating a crowd control strategy for Xiaozhai Station during the peak interval from 17:30 to 18:00 on a designated day, yielding crowd management interventions that offer insights for the orchestration of passenger flow and operational governance within metro stations.

The construction of an estimation methodology for the real-time streamline traffic flow augments the model’s dataset, supplanting estimated values derived from surveys or historical datasets with real-time computed traffic data, thereby enhancing the precision and immediacy of crowd flow management within metro stations.

Integrating balance theory and social identify theory, this paper proposes a multilevel model to explain how abusive supervision climate of team impacts the relationship among team members as well as subordinates’ behavior towards their teammates, especially organizational citizenship behavior (OCB).

A survey was conducted to collect two-wave and multi-source data from 398 employees nested in 106 teams from Chinese high-technology companies. Hierarchical linear modeling was conducted to examine the theoretical model.

The results indicate that there is an inverted U-shape association between abusive supervision climate and subordinates’ OCB towards coworker; team member exchange (TMX) mediates their inverted U-shaped link. Furthermore, we confirm that coworker support plays a vitally moderating role upon the curvilinear link of abusive supervision climate (ASC)–TMX; specifically, when employees perceive low coworker support, negative relations between ASC and TMX will be stronger.

This study identifies team members’ advantageous and adverse relational response to shared threat of ASC and examines coworker support as a moderator of ASC, which provides valuable insights into when and why employees tend to cooperate with their teammates to jointly confront their leader’s abuse and highlights the importance of coworkers, thus enabling organizations to deeply understand the wider influences of ASC on interpersonal relationship between team members.

The purpose of this paper is to study the mechanochemical effect and self-growth mechanism of double-network (DN) gel and to provide a quasiperiodic model for rubber elasticity.

The chemical reaction kinetics is used to identify the mechanochemical transition probability of host brittle network and to explore the mechanical behavior of endosymbiont ductile network. A quasiperiodic model is proposed to characterize the cooperative coupling of host–endosymbiont networks using the Penrose tiling of a 2 × 2 matrix. Moreover, a free-energy model is formulated to explore the constitutive stress–strain relationship for the DN gel based on the rubber elasticity theory and Gent model.

In this study, a quasiperiodic graph model has been developed to describe the cooperative interaction between brittle and ductile networks, which undergo the mechanochemical coupling and mechanical stretching behaviors, respectively. The quasiperiodic Penrose tiling determines the mechanochemistry and self-growth effect of DNs.

It is expected to formulate a quasiperiodic graph model of host–guest interaction between two networks to explore the working principle of mechanical and self-growing behavior in DN hydrogels, undergoing complex mechanochemical effect. The effectiveness of the proposed model is verified using both finite element analysis and experimental results of DN gels reported in literature.