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In this study, machine learning (ML) algorithms were employed to predict the shear capacity and behavior of DCSWs.

In this study, ML algorithms were employed to predict the shear capacity and behavior of DCSWs. Various ML techniques, including linear regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost) and artificial neural network (ANN), were utilized. The ML models were trained using a dataset of 462 numerical and experimental samples. Numerical models were generated and analyzed using the finite element (FE) software Abaqus. These models underwent push-over analysis, subjecting them to pure shear conditions by applying a target displacement solely to the top of the shear walls without interaction from a frame. The input data encompassed eight survey variables: geometric values and material types. The characterization of input FE data was randomly generated within a logical range for each variable. The training and testing phases employed 90 and 10% of the data, respectively. The trained models predicted two output targets: the shear capacity of DCSWs and the likelihood of buckling. Accurate predictions in these areas contribute to the efficient lateral enhancement of structures. An ensemble method was employed to enhance capacity prediction accuracy, incorporating select algorithms.

The proposed model achieved a remarkable 98% R-score for estimating shear strength and a corresponding 98% accuracy in predicting buckling occurrences. Among all the algorithms tested, XGBoost demonstrated the best performance.

In this study, for the first time, ML algorithms were employed to predict the shear capacity and behavior of DCSWs.

Climate change caused by an increase in greenhouse gas emissions poses a threat to species on earth. Such emissions have been caused by activities that have increased the rate at which greenhouse emissions have occurred due to the burning of fossil fuels and industrial processes in recent decades. Without urgent intervention, the ability of earth’s citizens will be irrevocably altered. Hundreds of millions of people’s lives will effectively become extremely challenging. Deaths due to starvation, lack of water, storms and flooding will increase. The magnitude of the crisis confronting humanity has resulted in means the formation of what’s known as the ‘Net Zero’ target set by The Intergovernmental Panel on Climate Change (IPCC, 2024), a United Nations body consisting of global experts on climate change in 1994. This chapter explains why climate change has occurred, what its impact may be and how intervention by governments as well as all organisations and individuals catastrophe can be avoided. There is an overview of subsequent chapters contained in this book.

This conceptual paper aims to contribute to a better understanding of value creation and value capture with smart resources in the Internet of Things (IoT)-driven business models against the backdrop of an increasingly networked and connectivity-based environment. More specifically, the authors screen strategic management theories and adapt them to the specificities of new types of smart resources by focusing on a conceptual analysis of isolating mechanisms that enable value creation and value capture based upon different types of smart resources.

By adapting the state of the art of the contemporary resource-based discussion (resource-based view, dynamic capabilities view, relational view, resource-based view for a networked environment) to the context of IoT-driven business models, the paper typifies valuable intra- and inter-organisational resource types. In the next step, a discursive discussion on the evolution of isolating mechanisms, which are assumed to enable the translation of value creation into value appropriation, adapts the resource-based view for a networked environment to the context of IoT-driven business models.

The authors find that connectivity shapes both opportunities and challenges for firms, e.g. focal firms, in such business models, but it is notably social techniques that help to generate connectivity and transform inter-organisational ties into effective isolating mechanisms.

This paper lays a foundation for a theoretically underpinned understanding of how IoT can be exploited through designing economically sustainable business models. In this paper, research propositions are established as a point of departure for future research that applies strategic management theories to better understand business models that work with the digitisation and connectivity of resources on different levels.

A green innovation strategy is an important step for enterprises to balance economic and environmental. As the executors of strategic decisions, the attitude and capabilities of senior managers determine the effectiveness of implementing green innovation. Therefore, this paper aims to explore the relationship between executive compensation incentives and green innovation.

Based on the data of heavily polluting enterprises listed in China's A-share market from 2015 to 2020, this study constructs an OLS model with fixed effects of time and industry, and uses the mediation three-step method to verify the correlation between executive compensation incentives, innovation openness and green innovation. Meanwhile, the grouping regression was used to test the moderating effect of environmental regulation on executive compensation incentives.

The empirical results show that executive salary incentives promote green innovation and equity incentives inhibit green innovation; the openness breadth partially mediates the relationship between salary incentives, equity incentives and green innovation, while the openness depth only partially mediates the relationship between equity incentives and green innovation; and environmental regulation positively moderates executive incentives.

Due to sample selection and variable measurement, the study lacks certain generality. Therefore, future research needs to further analyze the internal factors affecting green innovation from multiple dimensions.

This study provides a new evidence for analyzing how executive compensation measures affect green innovation, and further enhances the mediating mechanism of open innovation.

This study has significant theoretical implications for examining the intra-firm factors that affect green innovation.

The purpose of this study is to enhance the safety and comfort of tourists in scenic areas undergoing renovation and transformation by developing a comprehensive safety assessment model that takes into account both internal and external factors affecting tourist and construction safety.

The research employs a multi-level tourist-construction interaction safety assessment index system, which is constructed through a deep analysis of factors such as the construction environment, tourist behavior and safety signs. The study utilizes game theory in conjunction with three main objective and subjective weight distribution methods to determine the weights of the index system, ensuring the objectivity and effectiveness of the assessment results. The cloud model and cloud generator are applied for the language transformation of the indicators, leading to a comprehensive assessment of construction safety.

The survey results indicate that the safety risks of the case project are relatively high, with limited impact of time segments on safety risks, and the risk level during weekends is slightly higher than on weekdays, but the difference is not significant. Among the reviewed influencing factors, compliance with safety signs and the proportion of people crossing construction areas are the factors with the highest risk level, representing a large number of tourists ignoring safety guidance and forcibly crossing construction areas, facing construction dangers, posing a great challenge to safety management.

This study offers a novel methodological approach to safety risk assessment in similar environments, contributing to the field by improving the systematicness and scientific nature of safety management. It provides a scientific assessment tool for the safety management of tourists in scenic area renovation projects, aiming to achieve the dual objectives of tourist safety and construction efficiency.

This paper proposes a framework to assist in managing predictive maintenance by detecting progressive surface wear on spur gears through the analysis of digital images of gear teeth using computer vision (CV) techniques.

An experimental setup was constructed to capture images of gear teeth using endoscopic cameras. The images were selected, pre-processed, stored in a database and used in the experimental study of the proposed framework. Three CV techniques were explored within the framework for detecting wear in spur gears: (1) edge detection; (2) gray level co-occurrence matrix (GLCM) combined with machine learning (ML) algorithms and (3) deep learning with convolutional neural networks (CNN).

The results showed 85% accuracy using the edge detection algorithm. Among the ML algorithms, accuracy was above 60% for the support vector machine (SVM) and above 70% for K-nearest neighbors (KNN). Principal component analysis (PCA) indicated that as the distance between the principal components increased, it characterized the formation and progression of surface wear on the gear teeth. With the CNN, an accuracy of 99.999981% was achieved in the training loss rate, with a classification accuracy rate (CAR) of 91.6666%, an F1 score of 90.9090% and a recall of 83.3334% during the testing phase.

This framework is applicable to a variety of gear systems and industrial contexts requiring predictive maintenance, making it a highly scalable solution for industry professionals.

This paper proposes a novel framework that considers various CV techniques to detect and assess the level of wear on spur gear surfaces. Moreover, the results provide guidelines for selecting the most appropriate method for detecting wear in gear systems.

Endurance time is an important factor limiting the progress of flapping-wing aircraft. In this study, this paper developed a prototype of a double-wing flapping-wing micro air vehicle (FMAV) that mimics insect-scale flapping wing for flight. Besides, novel methods for optimal selection of motor, wing length and battery to achieve prolonged endurance are proposed. The purpose of this study is increasing the flight time of double-wing FMAV by optimizing the flapping mechanism, wings, power sources, and energy sources.

The 20.4 g FMAV prototype with wingspan of 21.5 cm used an incomplete gear flapping wing mechanism. The motor parameters related to the lift-to-power ratio of the prototype were first identified and analyzed, then theoretical analysis was conducted to analyze the impact of wing length and flapping frequency on the lift-to-power ratio, followed by practical testing to validate the theoretical findings. After that, analysis and testing examined the impact of battery energy density and efficiency on endurance. Finally, the prototype’s endurance duration was calculated and tested.

The incomplete gear facilitated 180° symmetric flapping. The motor torque constant showed a positive correlation with the prototype’s lift-to-power ratio. It was also found that the prototype achieved the best lift-to-power ratio when using 100 mm wings.

A gear-driven flapping mechanism was designed, capable of smoothly achieving 180° symmetric flapping. Besides, factors affecting long-duration flight – motor, wings and battery – were identified and a theoretical flight duration analysis method was developed. The experimental result proves that the FMAV could achieve the longest hovering time of 705 s, outperforming other existing research on double-wing FMAV for improving endurance.

Thin cracks on the surface, such as those found in nuclear power plant concrete structures, are difficult to identify because they tend to be thin. This paper aims to design a novel segmentation network, called U-shaped contextual aggregation network (UCAN), for better recognition of weak cracks.

UCAN uses dilated convolutional layers with exponentially changing dilation rates to extract additional contextual features of thin cracks while preserving resolution. Furthermore, this paper has developed a topology-based loss function, called ℓ_cl Dice, which enhances the crack segmentation’s connectivity.

This paper generated five data sets with varying crack widths to evaluate the performance of multiple algorithms. The results show that the UCAN network proposed in this study achieves the highest F1-Score on thinner cracks. Additionally, training the UCAN network with the ℓ_cl Dice improves the F1-Scores compared to using the cross-entropy function alone. These findings demonstrate the effectiveness of the UCAN network and the value of incorporating the ℓ_cl Dice in crack segmentation tasks.

In this paper, an exponentially dilated convolutional layer is constructed to replace the commonly used pooling layer to improve the model receptive field. To address the challenge of preserving fracture connectivity segmentation, this paper introduces ℓ_cl Dice. This design enables UCAN to extract more contextual features while maintaining resolution, thus improving the crack segmentation performance. The proposed method is evaluated using extensive experiments where the results demonstrate the effectiveness of the algorithm.

This study examines the role of digital transformation intentions in enhancing the perceived resilience of firms, with a focus on China’s manufacturing sector. It investigates the mediating role of knowledge sharing and the moderating impact of transformational leadership.

A quantitative approach was employed, collecting data from 347 manufacturing firms. Participants included managers and MBA students involved in digital transformation projects. The study utilized statistical analysis to explore the relationships between digital transformation intentions, knowledge sharing, transformational leadership and perceived firm resilience.

The analysis reveals that knowledge sharing is a critical mediating factor between digital transformation intentions and perceived firm resilience. Additionally, transformational leadership significantly strengthens this relationship, highlighting its importance in the successful implementation of digital initiatives.

The study is geographically and sectorally limited to China’s manufacturing sector, which may affect the generalizability of the findings. Future research could explore other sectors and regions to validate and extend the results.

The findings underscore the necessity of integrating digital transformation initiatives with effective leadership and knowledge management practices. Firms that foster transformational leadership and facilitate knowledge sharing are better equipped to enhance their resilience in the face of global disruptions.

This research offers a deep understanding of how digital transformation intentions, mediated by knowledge sharing and supported by transformational leadership, contribute to perceived firm resilience. It provides valuable insights for both academic research and practical applications in the field of management.

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/