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An alliance portfolio can help latecomer firms to acquire the necessary knowledge and resources to catch up with market leaders. However, how latecomer firms construct an alliance portfolio in terms of the nature of windows of opportunity has not been fully analyzed. This paper aims to explore how latecomer firms can build appropriate coalitions according to the nature of the window of opportunity to achieve technological catch-up in different catch-up phases.

Based on a longitudinal case study from 1984 to 2018 of Sunny Group, now a leading manufacturer of integrated optical components and products, this paper explores the process of technological catch-up of latecomer firms building different types of alliance portfolio in different windows of opportunity.

This paper finds that there is a sequence when latecomers build an alliance portfolio in the process of catch-up. When the uncertainty of opportunity increases, the governance mechanism of the alliance portfolio will change from contractual to equity-based. Also, latecomer firms build market-dominated and technology-dominated alliance portfolios to overcome their market and technology disadvantages, respectively.

These conclusions not only enrich the theory of latecomer catch-up from the perspective of windows of opportunity but also expand research on alliance portfolio processes from a temporal perspective.

The implementation of smart maintenance (SM) has greatly benefited facility managers, construction project managers and other stakeholders within the built environment. Unfortunately, its actualization for stakeholders in the built environment in the fourth industrial revolution (4IR) era remains a challenge. To reduce the challenge, this study aims at conducting a bibliometric analysis to unearth the critical success factors supporting SM implementation. The future direction and practice of SM in the construction industry were also explored.

A bibliometric approach was adopted for reviewing articles extracted from the Scopus database. Keywords such as (“smart maintenance“) OR (“intelligent maintenance”) OR (“technological maintenance”) OR (“automated maintenance”) OR (“computerized maintenance”) were used to extract articles from the Scopus database. The studies were restricted between 2006 and 2021 to capture the 4IR era. The initial extracted papers were 1,048; however, 288 papers were selected and analysed using VOSviewer software.

The findings revealed that the critical success factors supporting the implementation of SM in the 4IR era are collaboration, digital twin design, energy management system and decentralized data management system. Regarding the future practice of SM in the 4IR era, it was also revealed that SM is possible to evolve into maintenance 4.0. This will support the autonomous maintenance of infrastructures in the built environment.

The use of a single database contributed to the limitation of the findings from this study.

Despite the limitations, the findings of this study contributed to practice and research by providing stakeholders in the built environment with the direction of SM practice.

Stakeholders in the built environment have clamoured to implement SM in the 4IR era. This study provided the critical success factors for adopting SM, guaranteeing the 4IR era. It also provides the research trends and direction of SM practice.

Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.

This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.

In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 × 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.

The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.

This study focuses on the classification of targets with varying shapes using radar cross section (RCS), which is influenced by the target’s shape. This study aims to develop a robust classification method by considering an incident angle with minor random fluctuations and using a physical optics simulation to generate data sets.

The approach involves several supervised machine learning and classification methods, including traditional algorithms and a deep neural network classifier. It uses histogram-based definitions of the RCS for feature extraction, with an emphasis on resilience against noise in the RCS data. Data enrichment techniques are incorporated, including the use of noise-impacted histogram data sets.

The classification algorithms are extensively evaluated, highlighting their efficacy in feature extraction from RCS histograms. Among the studied algorithms, the K-nearest neighbour is found to be the most accurate of the traditional methods, but it is surpassed in accuracy by a deep learning network classifier. The results demonstrate the robustness of the feature extraction from the RCS histograms, motivated by mm-wave radar applications.

This study presents a novel approach to target classification that extends beyond traditional methods by integrating deep neural networks and focusing on histogram-based methodologies. It also incorporates data enrichment techniques to enhance the analysis, providing a comprehensive perspective for target detection using RCS.

The research explores the shift to Quality 4.0, examining the move towards a data-focussed transformation within organizational frameworks. This transition is characterized by incorporating Industry 4.0 technological innovations into existing quality management frameworks, signifying a significant evolution in quality control systems. Despite the evident advantages, the practical deployment in the Indian manufacturing sector encounters various obstacles. This research is dedicated to a thorough examination of these impediments. It is structured around a set of pivotal research questions: First, it seeks to identify the key barriers that impede the adoption of Quality 4.0. Second, it aims to elucidate these barriers' interrelations and mutual dependencies. Thirdly, the research prioritizes these barriers in terms of their significance to the adoption process. Finally, it contemplates the ramifications of these priorities for the strategic advancement of manufacturing practices and the development of informed policies. By answering these questions, the research provides a detailed understanding of the challenges faced. It offers actionable insights for practitioners and policymakers implementing Quality 4.0 in the Indian manufacturing sector.

Employing Interpretive Structural Modelling and Matrix Impact of Cross Multiplication Applied to Classification, the authors probe the interdependencies amongst fourteen identified barriers inhibiting Quality 4.0 adoption. These barriers were categorized according to their driving power and dependence, providing a richer understanding of the dynamic obstacles within the Technology–Organization–Environment (TOE) framework.

The study results highlight the lack of Quality 4.0 standards and Big Data Analytics (BDA) tools as fundamental obstacles to integrating Quality 4.0 within the Indian manufacturing sector. Additionally, the study results contravene dominant academic narratives, suggesting that the cumulative impact of organizational barriers is marginal, contrary to theoretical postulations emphasizing their central significance in Quality 4.0 assimilation.

This research provides concrete strategies, such as developing a collaborative platform for sharing best practices in Quality 4.0 standards, which fosters a synergistic relationship between organizations and policymakers, for instance, by creating a joint task force, comprised of industry leaders and regulatory bodies, dedicated to formulating and disseminating comprehensive guidelines for Quality 4.0 adoption. This initiative could lead to establishing industry-wide standards, benefiting from the pooled expertise of diverse stakeholders. Additionally, the study underscores the necessity for robust, standardized Big Data Analytics tools specifically designed to meet the Quality 4.0 criteria, which can be developed through public-private partnerships. These tools would facilitate the seamless integration of Quality 4.0 processes, demonstrating a direct route for overcoming the barriers of inadequate standards.

This research delineates specific obstacles to Quality 4.0 adoption by applying the TOE framework, detailing how these barriers interact with and influence each other, particularly highlighting the previously overlooked environmental factors. The analysis reveals a critical interdependence between “lack of standards for Quality 4.0” and “lack of standardized BDA tools and solutions,” providing nuanced insights into their conjoined effect on stalling progress in this field. Moreover, the study contributes to the theoretical body of knowledge by mapping out these novel impediments, offering a more comprehensive understanding of the challenges faced in adopting Quality 4.0.

As an advanced manufacturing method, additive manufacturing (AM) technology provides new possibilities for efficient production and design of parts. However, with the continuous expansion of the application of AM materials, subtractive processing has become one of the necessary steps to improve the accuracy and performance of parts. In this paper, the processing process of AM materials is discussed in depth, and the surface integrity problem caused by it is discussed.

Firstly, we listed and analyzed the characterization parameters of metal surface integrity and its influence on the performance of parts and then introduced the application of integrated processing of metal adding and subtracting materials and the influence of different processing forms on the surface integrity of parts. The surface of the trial-cut material is detected and analyzed, and the surface of the integrated processing of adding and subtracting materials is compared with that of the pure processing of reducing materials, so that the corresponding conclusions are obtained.

In this process, we also found some surface integrity problems, such as knife marks, residual stress and thermal effects. These problems may have a potential negative impact on the performance of the final parts. In processing, we can try to use other integrated processing technologies of adding and subtracting materials, try to combine various integrated processing technologies of adding and subtracting materials, or consider exploring more efficient AM technology to improve processing efficiency. We can also consider adopting production process optimization measures to reduce the processing cost of adding and subtracting materials.

With the gradual improvement of the requirements for the surface quality of parts in the production process and the in-depth implementation of sustainable manufacturing, the demand for integrated processing of metal addition and subtraction materials is likely to continue to grow in the future. By deeply understanding and studying the problems of material reduction and surface integrity of AM materials, we can better meet the challenges in the manufacturing process and improve the quality and performance of parts. This research is very important for promoting the development of manufacturing technology and achieving success in practical application.

This paper aims to examine the uniform diffracted fields from a perfectly magnetic conductive (PMC) surface with the extended theory of boundary diffraction wave (BDW) approach.

Miyamoto and Wolf’s symbolic expression of the vector potential was used in the extended theory of BDW integral. This vector potential is applied to the problem, and the nonuniform field expression found was made uniform. Here, the expression is made uniform, using the detour parameter with the help of the asymptotic correlation of the Fresnel function. The BDW theory for the PMC surface extended the diffracted fields, and the uniform diffracted fields were calculated.

The field expressions obtained were interpreted with the graphs numerically for different aperture radii and observation distances. It has been shown that the BDW is continuous behind the diffracting aperture. There does not exist any discontinuity at the geometrically light-to-shadow transition boundary, as is required by the theory.

The results were graphically compared with diffracted fields for other surfaces. As far as we know, the uniform diffracted fields from the circular aperture on a PMC surface were calculated for the first time with the extended theory of the BDW approach.

The objective of this research is to propose an immersive framework that integrates virtual reality (VR) technology with directives international safety training certification bodies to enhance construction safety training, which eventually leads to safer construction sites.

The adopted methodology combines expert insights and experimentation to maximize the effectiveness of construction safety training. The first step was identifying key considerations for VR models such as motion sickness prevention and adult learning theories. The second step was developing a game-like VR model for safety training, with multiple hazards and scenarios based on the considerations of the previous step. After that, safety experts evaluated the model and provided valuable feedback on its alignment with international safety training practices. Finally, the developed model is tested by senior students, where the testing format followed the Institution of Occupational Safety and Health (IOSH) working safely exam structure.

An advanced immersive VR safety training model was developed based on extensive lessons learned from the literature, previous work and psychology-informed adult learning theories. Model testing – through focus groups and hands-on experimentation – demonstrated significant benefit of VR in upgrading and complementing traditional training methods.

The findings presented in this paper make a significant contribution to the field of safety training within the construction industry and the broader context of immersive learning experiences. It also fosters further exploration into immersive learning experiences across educational and professional contexts.

Large class sizes are becoming the norm in higher education against concerns of dropping learning qualities. To maintain the standard of learning and add value, one of the common strategies is for the course convenor to proactively monitor student engagement with learning activities against their assessment outcomes and intervene timely. Learning analytics has been increasingly adopted to provide these insights into student engagement and their performance. This case study explores how learning analytics can be used to meet the convenor’s requirements and help reduce administrative workload in a large health science class at the University of New South Wales.

This case-based study adopts an “action learning research approach” in assessing ways of using learning analytics for reducing workload in the educator’s own context and critically reflecting on experiences for improvements. This approach emphasises reflexive methodology, where the educator constantly assesses the context, implements an intervention and reflects on the process for in-time adjustments, improvements and future development.

The results highlighted ease for the teacher towards the early “flagging” of students who may not be active within the learning management system or who have performed poorly on assessment tasks. Coupled with the ability to send emails to the “flagged” students, this has led to a more personal approach while reducing the number of steps normally required. An unanticipated outcome was the potential for additional time saving through improving the scaffolding mechanisms if the learning analytics were customisable for individual courses.

The results provide further benefits for learning analytics to assist the educator in a growing blended learning environment. They also reveal the potential for learning analytics to be an effective adjunct towards promoting personal learning design.

Compact and wideband antennas are the need of modern wireless systems that preferably work with compact, low-profile and easy-to-install devices that provide a wider coverage of operating frequencies. The purpose of this paper is to propose a novel compact and ultrawideband (UWB) microstrip patch antenna intended for high frequency wireless applications.

A square microstrip patch antenna was initially modeled on finite element method-based electromagnetic simulation tool high frequency structure simulator. It was then loaded with a rectangular slit and Koch snowflake-shaped fractal notches for bandwidth enhancement. The fabricated prototype was tested by using vector network analyzer from Agilent Technologies, N5247A, Santa Clara, California, United States (US).

The designed Koch fractal patch antenna is highly compact with dimensions of 10 × 10 mm only and possesses UWB characteristics with multiple resonances in the operating band. The −10 dB measured impedance bandwidth was observed to be approximately 13.65 GHz in the frequency range (23.20–36.85 GHz).

Owing to its simple and compact structure, positive and substantial gain values, high radiation efficiency and stable radiation patterns throughout the frequency band of interest, the proposed antenna is a suitable candidate for high frequency wireless applications in the K (18–27 GHz) and Ka (26.5–40 GHz) microwave bands.