This study focuses on SME networks of design and high-tech companies in Southeast Netherland. By highlighting the personal networks of members across design and high-tech industries, the study attempts to identify the main brokers in this dynamic environment. In addition, we investigate whether specific characteristics are associated with these brokers. The main contribution of the paper lies in the fact that, in contrast to most other work, it is quantitative and that it focuses on brokers identified in an actual network (based on both suppliers and users of the knowledge infrastructure). Studying the phenomenon of brokerage provides us with clear insights into the concept of brokerage regarding SME networks in different fields. In particular we highlight how third parties contribute to the transfer and development of knowledge. Empirical results show, among others that the most influential brokers are found in the nonprofit and science sector and have a long track record in their branch.
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The energy management systems industry in the built environment is currently an important topic. Buildings use about 40% of the total global energy worldwide. Therefore, the energy management system’s sector is one of the most influential sectors to realize changes and transformation of energy use. New data science technologies used in building energy management systems might not only bring many technical challenges, but also they raise significant educational challenges for professionals who work in the field of energy management systems. Learning and educational issues are mainly due to the transformation of professional practices and networks, emerging technologies, and a big shift in how people work, communicate, and share their knowledge across the professional and academic sectors. In this study, we have investigated three different companies active in the building services sector to identify the main motivation and barriers to knowledge adoption, transfer, and exchange between different professionals in the energy management sector and explore the technologies that have been used in this field using the boundary-crossing framework. The results of our study show the importance of understanding professional learning networks in the building services sector. Additionally, the role of learning culture, incentive structure, and technologies behind the educational system of each organization are explained. Boundary-crossing helps to analyze the barriers and challenges in the educational setting and how new educational technologies can be embedded. Based on our results, future studies with a bigger sample and deeper analysis of technologies are needed to have a better understanding of current educational problems.
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Masonry structures represent the highest proportion of building stock worldwide. Currently, the structural condition of such structures is predominantly manually inspected which is a laborious, costly and subjective process. With developments in computer vision, there is an opportunity to use digital images to automate the visual inspection process. The aim of this study is to examine deep learning techniques for crack detection on images from masonry walls. A dataset with photos from masonry structures is produced containing complex backgrounds and various crack types and sizes. Different deep learning networks are considered and by leveraging the effect of transfer learning crack detection on masonry surfaces is performed on patch level with 95.3% accuracy and on pixel level with 79.6% F1 score. This is the first implementation of deep learning for pixel-level crack segmentation on masonry surfaces. Codes, data and networks relevant to the herein study are available in: github.com/dimitrisdais/crack_detection_CNN_masonry.
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This article examines the network structure, criminal cooperation, and external interactions of cybercriminal networks. Its contribution is empirical and inductive. The core of this study involved carrying out 10 case analyses on closed cybercrime investigations – all with financial motivations on the part of the offenders - in the UK and beyond. Each analysis involved investigator interview and access to unpublished law enforcement files. The comparison of these cases resulted in a wide range of findings on these cybercriminal networks, including: a common division between the scam/attack components and the money components; the presence of offline/local elements; a broad, and sometimes blurred, spectrum of cybercriminal behaviour and organisation. An overarching theme across the cases that we observe is that cybercriminal business models are relatively stable.
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Lifelong learning is necessary for nurses and caregivers to provide good, person-centred care. To facilitate such learning and embed it into regular working processes, learning communities of practice are considered promising. However, there is little insight into how learning networks contribute to learning exactly and what factors of success can be found. The study is part of a ZonMw-funded research project ‘LeerSaam Noord’ in the Netherlands, which aims to strengthen the professionalization of the nursing workforce and promote person-centred care. We describe what learning in learning communities looks like in four different healthcare contexts during the start-up phase of the research project. A thematic analysis of eleven patient case-discussions in these learning communities took place. In addition, quantitative measurements on learning climate, reciprocity behavior, and perceptions of professional attitude and autonomy, were used to underpin findings. Reflective questioning and discussing professional dilemma's i.e. patient cases in which conflicting interests between the patient and the professional emerge, are of importance for successful learning.
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Voortdurende maatschappelijke veranderingen en uitdagingen vragen om samenwerking en een leven lang ontwikkelen. Vaak gebeurt dit in learning communities (innovatieve leerwerkomgevingen) waar organisaties grensoverstijgend samenwerken aan complexe vraagstukken. Bruggenbouwers (brokers) hebben een sleutelpositie in het ontwikkelen van deze learning communities om mensen en organisaties met elkaar te verbinden. Een veelzijdige rol die zich moeilijk laat definiëren. Bovendien voorzien organisaties niet altijd bewust in ondersteuning en ontwikkeling van deze bruggenbouwers. Op basis van een mixed-methodsbenadering voorziet dit onderzoek in de behoefte van een generieke rolbeschrijving met zeven vaardigheden. Hierbij wordt de invloed van kennis, ervaring en persoonskenmerken belicht. Bruggenbouwers werken intersectoraal over grenzen van organisaties heen en ondersteunen betrokken professionals en organisaties in hun samenwerking door politiek bewust en strategisch te handelen. Zij stimuleren kennisdeling en vertalen kennis naar diverse betrokkenen en contexten en onderzoeken daarbij de beroepspraktijk systematisch. Deze rolbeschrijving en de gewenste ondersteuning hierin biedt concrete handvatten om bruggenbouwers beter te selecteren, te waarderen en ook gerichter te investeren in hun professionele ontwikkeling. Deze investering is van cruciaal belang omwille van de katalyserende werking van de rol als bruggenbouwer om het voortdurend leren en ontwikkelen bij organisaties mogelijk te maken
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Introduction: The social distancing restrictions due to the COVID-19 pandemic have changed students’ learning environment and limited their social interactions. Therefore, the objective of this study was to investigate the influence of the social distancing restrictions on students’ social networks, wellbeing, and academic performance. Methods: We performed a questionnaire study in which 102 students participated before and 167 students during the pandemic. They completed an online questionnaire about how they formed their five peer social networks (study-related support, collaboration, friendship, share information, and learn-from) out-of-class. We performed social network analysis to compare the sizes, structures, and compositions of students’ five social networks before and during the pandemic, between first- and second-year students, and between international and domestic students. Additionally, we performed Kruskal–Wallis H test to compare students’ academic performance before and during the pandemic. We performed thematic analysis to answers for two open-end questions in the online questionnaire to explore what difficulties students encountered during the COVID-19 pandemic and what support they needed. Results: The results showed that the size of students’ social networks during the pandemic was significantly smaller than before the pandemic. Besides, the formation of social networks differed between first- and second-year students, and between domestic and international students. However, academic performance did not decline during the COVID-19 pandemic. Furthermore, we identified three key areas in which students experienced difficulties and needed support by thematic analysis: social connections and interactions, learning and studying, and physical and mental wellbeing. Conclusion: When institutions implement learning with social distancing, such as online learning, they need to consider changes in students’ social networks and provide appropriate support.
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Anomaly detection is a key factor in the processing of large amounts of sensor data from Wireless Sensor Networks (WSN). Efficient anomaly detection algorithms can be devised performing online node-local computations and reducing communication overhead, thus improving the use of the limited hardware resources. This work introduces a fixed-point embedded implementation of Online Sequential Extreme Learning Machine (OS-ELM), an online learning algorithm for Single Layer Feed forward Neural Networks (SLFN). To overcome the stability issues introduced by the fixed precision, we apply correction mechanisms previously proposed for Recursive Least Squares (RLS). The proposed implementation is tested extensively with generated and real-world datasets, and compared with RLS, Linear Least Squares Estimation, and a rule-based method as benchmarks. The methods are evaluated on the prediction accuracy and on the detection of anomalies. The experimental results demonstrate that fixed-point OS-ELM can be successfully implemented on resource-limited embedded systems, with guarantees of numerical stability. Furthermore, the detection accuracy of fixed-point OS-ELM shows better generalization properties in comparison with, for instance, fixed-point RLS. © 2013 IEEE.
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Our study elucidates collaborative value creation and private value capture in collaborative networks in a context of sustainability. Collaborative networks that focus on innovative solutions for grand societal challenges are characterized by a multiplicity and diversity of actors that increase the complexity and coordination costs of collective action. These types of inter-organizational arrangements have underlying tensions as partners cooperate to create collaborative value and compete to capture or appropriate value on a private or organizational level, resulting in potential and actual value flows that are highly diffuse and uncertain among actors. We also observe that network participants capture value differentially, often citing the pro-social (e.g. community, belonging, importance) and extrinsic benefits of learning and reputation as valuable, but found it difficult to appropriate economic or social benefits from that value. Differential and asymmetric value appropriation among participants threatens continued network engagement and the potential collective value creation of collaborative networks. Our data indicates that networked value creation and capture requires maintaining resource complementarity and interdependency among network participants as the network evolves. We develop a framework to assess the relational value of collaborative networks and contribute to literature by unpacking the complexities of networked value creation and private value capture in collaborative networks for sustainability.
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