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This study aims to explore the entrepreneur–venture nexus in transnational entrepreneurial ventures conducting operations in culturally similar countries. Theoretically, smaller cultural differences imply fewer challenges inherent to the liability of outsidership, thus facilitating cultural arbitrage activities. However, these minor differences can also hinder learning and increase competition with local firms, threatening the survival of ventures in the host country. To clarify this perspective, the research looks at how entrepreneurs’ resources affect venture survival, focusing on transnational entrepreneurs operating in countries that have relatively small cultural differences from their own.

This research is outlined by the capital approach in entrepreneurial opportunity development through a qualitative configurational design.

The results indicate that there are four strategies for achieving venture survivability for transnational entrepreneurship operations in culturally similar countries. This is reflected in both the core configurations associated with long-term venture survivability and in the model of venture survivability for cultural arbitrage opportunity development.

This study’s findings, which include the configurations and the model, speak to the burgeoning conversation of cultural arbitrage by illuminating venture survival strategies for international entrepreneurs operating in countries similar to their home countries. They are relevant for the international entrepreneurship discourse and the larger conversation in entrepreneurship, focusing on the entrepreneur–venture nexus.

This study aims to improve the sensitivity of magnetic detection. In this article, a multi-frequency modulation technique is used to increase the magnetic detection sensitivity of diamond nitrogen vacancy (NV) centers sensors.

In the field of magnetic detection, NV centers have corresponding advantages due to their unique long coherence property at room temperature. The important indicators for NV centers magnetometers are the magnetic detection sensitivity of the NV centers and the integration of the magnetometer. To solve this problem, the authors propose a multi-frequency modulated magnetic detection technique, using an integrated probe as well as a lock-in amplifier for the double enhancement of sensitivity as well as integration.

The following results can be obtained by processing and calculating the experimental data with an integrated lock-in amplifier circuit with an area of 27.50 cm² and a probe volume of 3.12 cm³. The multi-frequency modulation technique was used to increase the magnetic detection sensitivity of the NV centers from 8.59 nT/Hz1 / 2–2.42 nT/Hz1 / 2.

The authors propose a signal modulation technique with an integrated design, which achieves an improvement in the sensitivity of the sensor’s magnetic detection through practical testing.

The authors propose a signal modulation technique with an integrated design, which achieves an improvement in the sensitivity of the sensor’s magnetic detection through practical testing. This technique provides new research solution for the subsequent improvement of the magnetic detection sensitivity.

The aim of this study is to enhance the performance of cloth recommendation systems by proposing a hybrid adversarial network called Cloth-Net, which integrates Dense Vision Transformers for effective 2D-3D image classification.

Cloth-Net combines the strengths of adversarial networks with Dense Vision Transformers to process both 2D and 3D images for improved classification. The model was trained on a large-scale dataset of clothing images, using a hybrid adversarial approach that enhances both feature extraction and image classification accuracy. The methodology also includes data augmentation and transfer learning techniques to optimize the model’s generalization capability.

Experimental results demonstrate that Cloth-Net significantly outperforms traditional convolutional neural network-based methods in terms of accuracy, precision, and recommendation quality. The hybrid adversarial framework, together with Dense Vision Transformers, enables the model to better understand complex clothing images, leading to more accurate and personalized recommendations.

This study introduces a novel hybrid adversarial model, Cloth-Net, that uniquely combines Dense Vision Transformers with traditional adversarial networks for the first time in the context of 2D-3D image classification. The findings present a substantial improvement in the performance of cloth recommendation engines, making the proposed model valuable for both academic research and practical applications in fashion technology.

In a dynamic and uncertain business environment, it is necessary for companies to adapt to be capable of addressing the changing circumstances and ensure business continuity. Hence, companies are employing advanced Industry 4.0 (I4.0) technologies. This study aims to explore the role of advanced I4.0 technologies in facilitating companies’ development towards supply chain resilience (SCR).

The study adopted a structured approach of Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) to identify 234 articles. Under PRISMA, we adopted a three-phase methodology of planning, conducting and reporting the review results. The data of these articles were synthesized using a Scopus database to investigate the relationship between I4.0 technologies and SCR.

The study’s findings map the technologies appropriate in different supply chain nodes and bridge the literature gap in the intersection of I4.0 technologies and SCR. The study results indicate the role of I4.0 technologies role in achieving resilience in key functions of an organization through an I4.0 technologies-enabled sourcing, manufacturing, distribution and return resilience (I-SMDRR) framework. The I-SMDRR framework also depicts the relationship being moderated by internal and external risk factors.

The study offers a unique framework by mapping sourcing, manufacturing, distribution and return resilience through I4.0 technologies. Additionally, the review delineates the theory-mapped research gaps helpful for future researchers. In summary, this systematic review of the literature identifies the components that lead to developing SCR.

The aerodynamic load caused by high-speed train operation may lead to severe vibration of the pedestrian bridge, thus causing great safety hazards. Therefore, this study aims to investigate the aerodynamic loading characteristics of a pedestrian bridge when a high-speed train passes over the bridge, as well as to evaluate the vibration response of the aerodynamic loads on the bridge structure.

High-speed trains are operated at three different speeds. The aerodynamic pressure load characteristics of high-speed trains crossing a pedestrian bridge are investigated by combining a nonconstant numerical simulation method with a dynamic modeling test method, and the vibration response of the bridge is analyzed.

The results show that when a high-speed train passes through the pedestrian bridge, the pedestrian bridge interferes with the attenuation of the pressure around the train, so that the pressure spreads along the bridge bottom, and the maximum positive and negative pressure peaks appear in the center area of the bridge bottom, while the pressure fluctuations in the bridge entrance and exit areas are smaller and change more slowly, and the pressure attenuation of the bridge bottom perpendicular to the direction of the train’s operation is faster. In addition, the pressure fluctuation generated by the high-speed train will lead to a larger vertical response of the bridge structure in the mid-span position, and the main vibration frequency of the bridge structure ranges from 8 to 10 Hz, and the maximum value of the vertical deformation amplitude is located in the mid-span region of the bridge.

This paper analyzes the flow field distribution around the train and at the bottom of the bridge for the evolution of the flow field when the train passes through the bridge at high speed, and conducts a finite element dynamic analysis of the bridge structure to calculate the vibration response of the bridge when the train passes through at high speed, and to evaluate the comfort of the passengers passing through the high-speed railroad bridge.

Many waste-to-energy (WTE) plants are constructed and operated using the public-private partnership (PPP) mode in China. However, risk events of PPP WTE incineration projects sometimes occur. This study aims to clarify the relationship of risks in China's PPP WTE incineration projects and identify the key risks accordingly and risk transmission paths.

A risk list of PPP WTE incineration projects was obtained based on literature analysis. Moreover, a hybrid approach combining fuzzy sets, decision-making trial and evaluation laboratory (DEMATEL) and interpretive structural modeling (ISM) was developed to analyze the causality of risks, explore critical risks and reveal the risk transmission paths. The quantitative analysis process was implemented in MATLAB.

The results show that government decision-making risk, government credit risk, government supervision behavior risk, legal and policy risk, revenue and cost risk and management capacity risk are the critical risks of PPP WTE incineration projects in China. These critical risks are at different levels in the risk hierarchy and often trigger other risks.

Currently, there is a lack of exploration on the interaction between the risks of PPP WTE incineration projects. This study fills this gap by examining the key risks and risk transfer pathways of PPP WTE incineration projects from the perspective of risk interactions. The findings can help the public and private sectors to systematically understand the risks in PPP WTE incineration projects, thus enabling them to identify the risks that need to be focused on when making decisions and to optimize risk prevention strategies. The proposed hybrid approach can offer methodological ideas for risk analysis of other types of PPP projects.

Ergonomic practices significantly enhance productivity, reduce injuries and improve worker satisfaction in construction sites. However, a key issue in the Nigerian construction industry is the limited awareness and implementation of these practices. This study addresses this issue by raising awareness and driving ergonomic improvements to advance occupational health and safety standards.

A quantitative research method was employed, using close-ended questionnaires distributed to construction professionals, including architects, builders, engineers and quantity surveyors. The data collected were analyzed through statistical tools such as percentages, frequencies and mean scores. The Kruskal–Wallis H-test was used to determine significant differences in respondents’ opinions.

The study found that construction professionals have limited knowledge of ergonomic practices, and the implementation of these practices within the industry is minimal, raising concerns about occupational health and safety.

This article provides a comprehensive roadmap for developing economies like Nigeria to improve the understanding of ergonomic practices among construction professionals. It also outlines how collaborative efforts among key stakeholders, including higher education institutions, government, policymakers, lawmakers, financial institutions and professional bodies, can enhance the awareness and implementation of these innovative practices.

While extensive research has been conducted on ergonomic practices in developed countries, few studies have examined their awareness and implementation in developing nations, particularly Nigeria. This study addresses this gap from the perspective of construction professionals.

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.

This study aims to investigate how the supply chain (SC) experts from the UK manufacturing organisations (MO) interpret the drivers and barriers to adopting Industry 4.0 technologies from a technological, organisational and environmental (TOE) point of view. Furthermore, this study evaluates how adoption drivers and barriers influence innovative practices that support Industry 4.0 adoption.

A qualitative narrative inquiry strategy, involving nine semi-structured interviews with leading SC experts including group executives, global directors and vice presidents was adopted.

The people factor across the SC is found critical for successful Industry 4.0 adoption. It was also found that the firm size is an insignificant factor; rather, Industry 4.0 readiness in small and medium enterprises (SMEs) is driven by flexibility and agility, while larger organisations are driven by resource availability and scalability. Evidently, the culture and attitude of the MO tend to facilitate or hinder the Industry 4.0 adoption, therefore, innovative practices in building an expert team, establishing a systematic change management process, actively involving suppliers in the adoption process and continuously monitoring the adoption process were introduced.

By addressing the relationships between the drivers, barriers and innovative practices from a business perspective, a detailed Industry 4.0 adoption framework for the UK Manufacturing Supply Chains (MSC) was developed as a unique theoretical and practical contribution. This study also highlights the lessons learnt from applying Industry 4.0 in the UK context. The findings can also be informed in applying Industry 4.0 technologies in emerging market contexts.