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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.

On the premise of verifying whether the platformization organization of DEEs is born, this work aims to explore the evolutionary process of the organizational structure of digital entrepreneurial enterprises (DEEs) and to further reveal the drivers of organizational structure evolution from the perspective of data resources.

The authors use a longitudinal two-case approach to analyze rich archival and interview data from two DEEs in China.

The findings reveal that the organizational structure of DEEs evolves from hierarchy, network and flatlization to platformization, that the drivers of evolution include building data flow channels, removing barriers of data flow and forming data rules. Meanwhile, the coordination devices in this process have gradually evolved from hierarchy to standard operating procedures, shared culture, norms, etc. to achieve a balance between commercial and creative success.

This work develops a framework for the evolution of organizational structure of DEEs from organization design theory lens and provide some management insights into the development of DEEs.

This study purposes a model based on competitive advantage theory, social identity theory and signaling theory that explores the relationship between GHRM and employer branding mediated by corporate environmental sustainability and organizational safety climate and employees experience as a moderator.

Data were gathered using a survey questionnaire from 329 employees working in this sector. Structural Equational Modeling was employed for data analysis through Smart PLS.

Results confirm that GHRM has a positive influence on corporate environmental sustainability and corporate environmental sustainability has a positive influence on organizational safety climate. Further, the results confirm that the organizational safety climate has a positive influence on employer branding. The results of partial least squares multi-group analysis show that difference between job experience influences employer branding. The results also lend support to the mediating effects of corporate environmental sustainability between GHRM and organizational safety climate, and the mediating effect of organizational safety climate between GHRM and employer branding.

The findings of the study guide policymakers and management of the textile industry to emphasize GHRM in order to make a working climate clean and safe. This working environment will be their competitive edge and a source of their organization branding.

HR literature has largely overlooked the physical work environment, instead focusing on psychological safety, for example (job stress, emotional exhaustion). This study presents a model demonstrating that a green work environment, fostered through GHRM practices enhances employer branding.

In the context of macroeconomic fluctuations and uncertainty in policy changes, it is essential to understand how companies adapt their environmental strategies and marketing tactics to ensure survival and growth. This study, therefore, examines the impact of perceived economic policy uncertainty on corporate greenwashing.

Based on panel data from listed companies on the Chinese A-share market between 2013 and 2022, this paper employs a high-dimensional fixed effects model to explore the impact of perceived economic policy uncertainty (PEPU) on corporate greenwashing behavior.

The results show that higher PEPU increases greenwashing, with agency costs and investor sentiment mediating the relationship. Corporate credit availability and managerial short-sightedness positively moderate this effect. Heterogeneity analysis reveals that non-state-owned enterprises in central and western regions, particularly those with weak environmental regulation and high pollution, are most impacted by PEPU.

This paper provides practical guidance for how to avoid the phenomenon of green reshuffle in economic and environmental policies and encourages enterprises to take more real and effective environmental protection measures.

These findings highlight the importance of considering corporate responses to policy uncertainty when formulating economic and environmental policies. They provide valuable insights for emerging economies in fostering genuine corporate environmental behavior and promoting sustainable development.

This study aims to investigate the tensile strength and compressive behaviour of two thermoplastic polyurethane (TPU) filaments produced via material extrusion (ME): TPU 95A and Reciflex (recycled).

Tensile strength and compressive behaviour are assessed. The influence of extrusion temperature and infill pattern on these properties is examined, supported by thermal characterization, surface morphology analyses and a comprehensive comparison with existing literature. An analytical method is presented for estimating the solid ratio of ME parts, using an ellipse model to describe the material bead geometry.

Reciflex is generally stiffer than TPU 95A in both tensile and compressive tests. Specimens loaded orthogonally in compression tests exhibited stiffer behaviour than those loaded parallelly, and higher tensile properties were typically observed when material beads were deposited parallel to the load direction. Unlike TPU 95A, Reciflex is sensitive to extrusion temperature variations.

By comparing recycled and virgin TPU filaments, this research addresses waste management concerns and advocates for environmentally sustainable production practices in the broadly used filament/based ME technique.

This study provides an extensive comparison of computed values with existing literature, offering insights into how different materials may behave under similar processing conditions. Given ongoing challenges in controlling melt flow during extrusion, these results may offer insights for optimizing the production of ME parts made with thermoplastic elastomers.

The subject of gender inequality has been approached by academics of various fields – psychologists, political scientists, developmental economists, feminists, sociologists, among others. Although there is a considerable amount of evidence indicating the presence of gender bias in various aspects of educational and work life in Muslim countries, not many studies have attempted to understand the female perspective during extra- and co-curricular activities in higher education institutions (HEIs) and their coping strategies.

A critical grounded theory approach was used.

The findings indicate that the role of female “religio-socio-cultural” stereotypes mutually reinforces each other to perpetuate an uneven playing field. Females have evolved many strategies to cope, including creation of sub-private spheres within larger public spheres and restricting their interactions to specific strata within the public sphere.

The study provides a female perspective regarding participation during extra- and co-curricular activities in a HEI in a Muslim majority context using critical grounded theory.

The 6D pose estimation is a crucial branch of robot vision. However, the authors find that due to the failure to make full use of the complementarity of the appearance and geometry information of the object, the failure to deeply explore the contributions of the features from different regions to the pose estimation, and the failure to take advantage of the invariance of the geometric structure of keypoints, the performances of the most existing methods are not satisfactory. This paper aims to design a high-precision 6D pose estimation method based on above insights.

First, a multi-scale cross-attention-based feature fusion module (MCFF) is designed to aggregate the appearance and geometry information by exploring the correlations between appearance features and geometry features in the various regions. Second, the authors build a multi-query regional-attention-based feature differentiation module (MRFD) to learn the contribution of each region to each keypoint. Finally, a geometric enhancement mechanism (GEM) is designed to use structure information to predict keypoints and optimize both pose and keypoints in the inference phase.

Experiments on several benchmarks and real robot show that the proposed method performs better than existing methods. Ablation studies illustrate the effectiveness of each module of the authors’ method.

A high-precision 6D pose estimation method is proposed by studying the relationship between the appearance and geometry from different object parts and the geometric invariance of the keypoints, which is of great significance for various robot applications.

The current study aims to investigate how young smartphone users in Bangladesh relate their brand experiences to brand loyalty. In addition, we want to visualize the direct and mediating effects of brand satisfaction, brand love and brand advocacy in our model.

The researchers examined the hypotheses by employing structural equation modeling (SEM) in AMOS and Decision Analyst STATS, version 2.0, with a sample size of 470 Bangladeshi smartphone users. The authors constructed the conceptual model by drawing upon both theoretical and empirical foundations. The researchers obtained data by utilizing an adopted and self-administered pre-structured questionnaire distributed via an online platform.

The results showed that brand experience greatly influences brand satisfaction, love, advocacy and loyalty, all of which have a significant impact on users’ brand loyalty across the country. The findings also suggested that the function of brand satisfaction as a critical mediator in the link between brand experience and brand loyalty was significant.

This experiment contributes to the body of knowledge by focusing on emotional brand attachments like brand satisfaction, love and advocacy and proposing that they can mediate experience and loyalty in the mobile market. The study also helps managers and executives better understand the primary drivers of smartphones, which are essential for generating and sustaining consumers’ happiness and loyalty in today’s highly competitive consumer market.

This research examines the influence of big data analytics (BDA) on the competitive performance of firms in the United Arab Emirates (UAE). Focused on the linkages of IT-enabled dynamic capabilities, managerial support, data driven culture, environmental uncertainty and supply chain resilience, the study aims to evaluate the mechanisms through which BDA contributes to competitive advantage.

This research employs an empirical investigation to address questions regarding the influence of BDA on the competitive performance of the supply chain industry in the UAE. The research involved the distribution of a structured questionnaire to the employees (n = 400) across diverse supply chain units in the UAE. The proposed framework was evaluated through SPSS and AMOS. Additionally, the researchers utilized the Process Macro to reveal the mediating and moderating dynamics.

The study finding emphasizes the impact of BDA on both supply chain resilience and competitive performance with IT-enabled capabilities playing a mediating role. Furthermore, managerial support was found to positively moderate the relationship between BDA and IT-enabled capabilities.

This study contributes to the improvisation of existing literature in the field providing an understanding of how study variables collectively influence competitive performance within the specific context of UAE firms.

The findings provide insights for industry practitioners, highlighting the strategic importance of integrating BDA into supply chain management to boost operational efficiency and sustain competitive performance.

The study findings provide opportunities for scholars as well as managers for optimizing their strategic developments to build sustainable competitive performance by processing data analytic processes, resilient activities and efficient managerial support.

Cracks are prevalent signs of pavement distress found on highways globally. The use of artificial intelligence (AI) and deep learning (DL) for crack detection is increasingly considered as an optimal solution. Consequently, this paper introduces a novel, fully connected, optimised convolutional neural network (CNN) model using feature selection algorithms for the purpose of detecting cracks in highway pavements.

To enhance the accuracy of the CNN model for crack detection, the authors employed a fully connected deep learning layers CNN model along with several optimisation techniques. Specifically, three optimisation algorithms, namely adaptive moment estimation (ADAM), stochastic gradient descent with momentum (SGDM), and RMSProp, were utilised to fine-tune the CNN model and enhance its overall performance. Subsequently, the authors implemented eight feature selection algorithms to further improve the accuracy of the optimised CNN model. These feature selection techniques were thoughtfully selected and systematically applied to identify the most relevant features contributing to crack detection in the given dataset. Finally, the authors subjected the proposed model to testing against seven pre-trained models.

The study's results show that the accuracy of the three optimisers (ADAM, SGDM, and RMSProp) with the five deep learning layers model is 97.4%, 98.2%, and 96.09%, respectively. Following this, eight feature selection algorithms were applied to the five deep learning layers to enhance accuracy, with particle swarm optimisation (PSO) achieving the highest F-score at 98.72. The model was then compared with other pre-trained models and exhibited the highest performance.

With an achieved precision of 98.19% and F-score of 98.72% using PSO, the developed model is highly accurate and effective in detecting and evaluating the condition of cracks in pavements. As a result, the model has the potential to significantly reduce the effort required for crack detection and evaluation.

The proposed method for enhancing CNN model accuracy in crack detection stands out for its unique combination of optimisation algorithms (ADAM, SGDM, and RMSProp) with systematic application of multiple feature selection techniques to identify relevant crack detection features and comparing results with existing pre-trained models.