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The paper aims to tackle the classification of Remote Sensing Images (RSIs), which presents a significant challenge for computer algorithms due to the inherent characteristics of clustered ground objects and noisy backgrounds. Recent research typically leverages larger volume models to achieve advanced performance. However, the operating environments of remote sensing commonly cannot provide unconstrained computational and storage resources. It requires lightweight algorithms with exceptional generalization capabilities.

This study introduces an efficient knowledge distillation (KD) method to build a lightweight yet precise convolutional neural network (CNN) classifier. This method also aims to substantially decrease the training time expenses commonly linked with traditional KD techniques. This approach entails extensive alterations to both the model training framework and the distillation process, each tailored to the unique characteristics of RSIs. In particular, this study establishes a robust ensemble teacher by independently training two CNN models using a customized, efficient training algorithm. Following this, this study modifies a KD loss function to mitigate the suppression of non-target category predictions, which are essential for capturing the inter- and intra-similarity of RSIs.

This study validated the student model, termed KD-enhanced network (KDE-Net), obtained through the KD process on three benchmark RSI data sets. The KDE-Net surpasses 42 other state-of-the-art methods in the literature published from 2020 to 2023. Compared to the top-ranked method’s performance on the challenging NWPU45 data set, KDE-Net demonstrated a noticeable 0.4% increase in overall accuracy with a significant 88% reduction in parameters. Meanwhile, this study’s reformed KD framework significantly enhances the knowledge transfer speed by at least three times.

This study illustrates that the logit-based KD technique can effectively develop lightweight CNN classifiers for RSI classification without substantial sacrifices in computation and storage costs. Compared to neural architecture search or other methods aiming to provide lightweight solutions, this study’s KDE-Net, based on the inherent characteristics of RSIs, is currently more efficient in constructing accurate yet lightweight classifiers for RSI classification.

This paper aims to explore the various sociotechnical dimensions of building information modelling (BIM)-induced changes associated with stakeholder management of projects.

This paper relies on grounded theory and data collection from two case studies – one in the public sector and one in the private sector – and is underpinned by Leavitt’s (1964) sociotechnical model.

Findings reveal four new dimensions of stakeholder management as being affected through BIM-induced changes: commitment; transparency; learning and experience; and stakeholder satisfaction, with these extending beyond the dimensions recognised in the existing literature. Another novelty lies in bringing to light the highly context-specific nature of BIM-induced changes pertinent to stakeholder management, with the two case studies demonstrating differences in these changes. Furthermore, a theoretical model of the causal impacts of various identified dimensions is presented, in which the sequence of changes and the causal associations between the identified dimensions are conceptualised.

Through Leavitt’s (1964) Diamond lens, the procedure of change and its evolutionary procedure for various components of the sociotechnical system of stakeholder management are theorised. The tentative conceptualisations presented offer a springboard from which to further investigate the episode of change pertinent to various dimensions of stakeholder management in BIM-enabled projects.

This study is aimed at making an inspection of the effects of collaborative innovation network characteristics on firm innovation performance, and the intermediary roles of knowledge transfer efficiency is taken into account.

This study used a convenient sampling method to obtain population and samples. Using data obtained by publishing online and paper questionnaires, and using on-site interviews in Anhui Province in the Yangtze River Delta region of China, descriptive analysis, regression analysis and correlation analysis are utilized to study the direct influence of collaborative innovation network characteristics on knowledge transfer efficiency as well as firm innovation performance, and the intermediary roles of knowledge transfer efficiency on firm innovation performance, respectively. In this study, 3,000 questionnaires were distributed to the employees of enterprises engaged in research and development (R&D) activities, of which 2,560 were valid. With the help of SPSS24.0 software, the reliability and validity of the questionnaire was analyzed.

The results are indicative of that network centrality and relationship strength positively affect knowledge transfer efficiency and firm innovation performance. Nevertheless, network scale has no significant correlation with knowledge transfer efficiency and enterprise innovation performance. In addition, knowledge transfer efficiency is an intermediary between collaborative innovation network characteristics and enterprise innovation performance, and positively affects enterprise innovation performance, which demonstrated that managers should take advantage of collaborative innovation network characteristics to elevate knowledge transfer efficiency because well-realized transferals of knowledge can help accelerate the coordination of resources in knowledge, and finally bring about the advancement of firm's innovation abilities and performance.

There are few previous studies that fully examined the relationships among collaborative innovation network characteristics, knowledge transfer efficiency and firm innovation performance. This paper developed previous researches on the relationships between collaborative innovation network characteristics, knowledge transfer efficiency and firm innovation performance. The mediation of knowledge transfer efficiency on the relationship between collaborative innovation network characteristics and firm innovation performance is analyzed. Further, studies on collaborative innovation network characteristics using data obtained from employees engaged in R&D activities are very limited in the literature. On account of that, the findings in this study may make sense to the innovation ability of innovative enterprise and expand the literature in the field of enterprise strategic management and knowledge management.

This analysis shows that collaborative innovation network characteristics have both positive and negative effects on firm innovation performance. Therefore, business managers should pay attention to their position in the collaborative innovation network and maintain the relationship strength with other innovation subjects. Special consideration should be given to the knowledge transfer of innovative enterprises, so as to improve firm innovation performance practically.

The study may provide additional understandings for researchers, government managers, universities and enterprises with regard to strategic management from the visual angle of innovation ecosystems. It is instrumental in the exploration of the mechanisms enabling firm innovation performance.

This chapter aims to discuss the challenges facing these countries building productive capacity for development. This chapter makes use of data published by international organizations as indicators for measuring the state of development in the Arab region. Several indicators are presented to compare Arab countries with other world regions. The use of data identifies some of the gaps that countries in the Arab region need to close to strengthen capacity building for development and fostering economic growth. The findings from the data presented reveal that the productive structure in most Arab countries remains weak to generate production linkages and provide incentives for investment in nonenergy sectors. The failure of the export-led growth model to diversify output and promote development in energy producing countries has increased the dependence of these countries on global trade. Fluctuations in commodity prices and uncertainty about global demand for energy have influenced the ability of the state to construct strategies for rapid transformation. Except for the energy sector, the productivity of nonoil sectors remains low reflecting inadequate incentives and ineffective entrepreneurial capabilities. The study examines the challenges for building productive capacity in the Arab world. It illustrates the failure of the led-export model and its inability to prompted economic diversification, especially in the Gulf countries. The study contributes to the literature on capacity building in the Arab world so that to encourage researchers and students of development conducting studies concerning the main development challenges facing these countries.

In recent decades, emerging market multinational enterprises (EMNEs) have predominantly adopted a big step internationalization strategy to expand their business overseas. This study aims to examine the effect of big step internationalization on the speed of subsequent foreign direct investment (FDI) expansion for EMNEs. The authors also investigate the potential boundary conditions.

The authors use the random effects generalized least squares (GLS) regression following a hierarchical approach to analyze the panel data set conducted by a sample of publicly listed Chinese firms from 2001 to 2012.

The findings indicate that implementing big step internationalization in the initial stages accelerates the speed of subsequent FDI expansion. Notably, the authors find that this effect is more pronounced for firms that opt for acquisitions as the entry mode in their first big step internationalization and possess a board of directors with strong political connections to their home country’s government. In contrast, the board of director’s international experience negatively moderates this effect.

This study provides insights into our scholarly and practical understanding of EMNEs’ big step internationalization and subsequent FDI expansion speed, which offers important implications for firms’ decision-makers and policymakers.

This study extends the internationalization theory, broadens the international business literature on the consequences of big step internationalization and deepens the theoretical and practical understanding of foreign expansion strategies in EMNEs.

The aim of this paper is to analyse the role of communities of practice (CoP) as knowledge-sharing tools in family small and medium-sized enterprises (SMEs). In this context, CoPs that jointly involve family and non-family members are expected to act as knowledge-sharing tools.

This paper employs a multiple case study methodology, analysing the cases of six small companies in different sectors and countries over a period of 8 years. Both primary and secondary data are used.

The results show the role CoPs play in involving family and non-family members in empowering knowledge-sharing initiatives. A CoP's role in knowledge sharing depends on the presence (or lack) of a family leader, the leadership approach, the degree of cohesion around shared approaches and values within the CoP, and the presence of multiple generations at work.

This paper contributes to the literature on knowledge sharing in family businesses, by exploring for the first time the role of the CoP as a knowledge-sharing tool, depending on families' involvement in the CoP.

Why is it that, despite repeated claims that digital-content firms and internet-based businesses can internationalize everywhere almost instantly, many seem unable to profitably expand outside their home markets? Why have emerging market firms (EMNEs) caught up with established developed-country multinationals (DMNEs) so much faster than expected? In this chapter, the author argues that the clue to these two puzzles lies in the realization that, contrary to the dominant view in the international business (IB) literature that focuses only on the intangibles exploited by DMNEs and assumes that these firms are free to unilaterally decide on their mode of entry and operation, doing business in a foreign country is only possible if intangibles are bundled with complementary local resources, usually held by local firms. Taking into account these complementary local resources and their owners makes it clear that DMNEs are not always free to choose their entry mode but must enlist the cooperation of local resource owners. The need of digital-content and internet-based firms for local complementary resources also explains why they sometimes experience problems when expanding abroad. Lastly, control of complementary local resources provides EMNEs with a home advantage against DMNEs competing with them in their home market. The author shows how EMNEs can capitalize on this advantage to obtain the intangibles they lack and need. The fact that these advantages are available on efficient global markets, while complementary local resources are not, explains the surprising speed of EMNE catch-up.

This paper aims to realize an in-situ quality inspection system rapidly for new injection molding (IM) tasks via transfer learning (TL) approach and automation technology.

The proposed in-situ quality inspection system consists of an injection machine, USB camera, programmable logic controller and personal computer, interconnected via OPC or USB communication interfaces. This configuration enables seamless automation of the IM process, real-time quality inspection and automated decision-making. In addition, a MobileNet-based deep learning (DL) model is proposed for quality inspection of injection parts, fine-tuned using the TL approach.

Using the TL approach, the MobileNet-based DL model demonstrates exceptional performance, achieving validation accuracy of 99.1% with the utilization of merely 50 images per category. Its detection speed and accuracy surpass those of DenseNet121-based, VGG16-based, ResNet50-based and Xception-based convolutional neural networks. Further evaluation using a random data set of 120 images, as assessed through the confusion matrix, attests to an accuracy rate of 96.67%.

The proposed MobileNet-based DL model achieves higher accuracy with less resource consumption using the TL approach. It is integrated with automation technologies to build the in-situ quality inspection system of injection parts, which improves the cost-efficiency by facilitating the acquisition and labeling of task-specific images, enabling automatic defect detection and decision-making online, thus holding profound significance for the IM industry and its pursuit of enhanced quality inspection measures.

This article takes into account object identification, enhanced visual feature optimization, cost effectiveness and speed selection in response to terrain conditions. Neither supervised machine learning nor manual engineering are used in this work. Instead, the OTV educates itself without instruction from humans or labeling. Beyond its link to stopping distance and lateral mobility, choosing the right speed is crucial. One of the biggest problems with autonomous operations is accurate perception. Obstacle avoidance is typically the focus of perceptive technology. The vehicle's shock is nonetheless controlled by the terrain's roughness at high speeds. The precision needed to recognize difficult terrain is far higher than the accuracy needed to avoid obstacles.

Robots that can drive unattended in an unfamiliar environment should be used for the Orbital Transfer Vehicle (OTV) for the clearance of space debris. In recent years, OTV research has attracted more attention and revealed several insights for robot systems in various applications. Improvements to advanced assistance systems like lane departure warning and intelligent speed adaptation systems are eagerly sought after by the industry, particularly space enterprises. OTV serves as a research basis for advancements in machine learning, computer vision, sensor data fusion, path planning, decision making and intelligent autonomous behavior from a computer science perspective. In the framework of autonomous OTV, this study offers a few perceptual technologies for autonomous driving in this study.

One of the most important steps in the functioning of autonomous OTVs and aid systems is the recognition of barriers, such as other satellites. Using sensors to perceive its surroundings, an autonomous car decides how to operate on its own. Driver-assistance systems like adaptive cruise control and stop-and-go must be able to distinguish between stationary and moving objects surrounding the OTV.

One of the most important steps in the functioning of autonomous OTVs and aid systems is the recognition of barriers, such as other satellites. Using sensors to perceive its surroundings, an autonomous car decides how to operate on its own. Driver-assistance systems like adaptive cruise control and stop-and-go must be able to distinguish between stationary and moving objects surrounding the OTV.

How does the interaction between time and knowledge affect the evolution of organizations? Past research in organizational evolution has mostly investigated time and knowledge as two separate variables. In contrast, theoretical perspectives integrating these variables are still seemingly scant. The authors believe that filling this literature gap needs attention. Thus, this study aims to contribute by developing a conceptual framework.

This is a conceptual study. The framework is centred on the concept of “co-evolutionary time”, which the authors explain through a business example from the tourism industry. Supported by a narrative-based style, from a methodological point of view the framework is featured by the attempt to synthesize specific, extant literature into new theoretical development.

As its main theoretical contribution, the co-evolutionary time suggests how firms can adapt in a way that, from an evolutionary perspective, proves fitting both in terms of contents and methods, thus opening possibilities for new long-term social construction and reconstruction. As its main practical contribution, co-evolutionary time can constitute not only a temporary source of organizational success and competitive advantage but also an agent of enduring change and long-term business survival.

As its main novelty, the framework is developed through merging two literature streams. In particular, the authors first consider the literature about time, with a focus on its objective and subjective dimensions. The authors then consider the literature about organizational evolution, with a focus on the co-evolutionary nature of the firm/environment relationship.