Distributed manufacturing systems and the internet of things : a case study

In order to stay competitive in today's global market, manufacturing companies need to be flexible. To ensure flexible production, shorten processing times, and reduce time-tomarket, companies are utilizing the distributed manufacturing system paradigm, wherein geographically distributed, local resources are used for product development and production. In this context, the Internet of Things (IoT) has emerged as a concept which uses existing communication technologies, such as local wireless networks and the Internet to ensure visibility of anything from anywhere and at any time. In the paper, a case study of applying the IoT to the manufacturing domain is discussed. A distributed agent-based system for virtual monitoring and control of 3-axis CNC milling machine tools is designed and developed. The machines' 3D models and process states are shown through a web interface in real-time. The potential and challenges of implementing this system and the basic building blocks for decentralized value creation are discussed

Structured authoring for AR-based communication to enhance efficiency in remote diagnosis for complex equipment

Remote diagnosis procedures are prone to communication errors due to varying levels of experience and knowledge between expert maintainers and technicians. These result in inefficiencies that delay the diagnosis process. The aim of the paper is to develop a Structured-Message Authoring framework for Augmented Reality (AR) Remote Communication (SMAARRC) and to evaluate its ability to enhance the efficiency of remote diagnosis services. The framework proposes a message structure and automatic AR content creation rules for it that enable data capture and sharing within a remote context. Laboratory experiments present an average time reduction of 56% for remote calls while maintaining same quality compared to traditional remote communication methods (phone calls and emails). Remote experts feedback evidence the usability and feasibility of this framework to work in real-life conditions

Analysing the correlation between social network analysis measures and performance of students in social network-based engineering education

Social network-based engineering education (SNEE) is designed and implemented as a model of Education 3.0 paradigm. SNEE represents a new learning methodology, which is based on the concept of social networks and represents an extended model of project-led education. The concept of social networks was applied in the real-life experiment, considering two different dimensions: (1) to organize the education process as a social network-based process; and (2) to analyze the students' interactions in the context of evaluation of the students learning performance. The objective of this paper is to present a new model for students evaluation based on their behavior during the course and its validation in comparison with the traditional model of students' evaluation. The validation of the new evaluation model is made through an analysis of the correlation between social network analysis measures (degree centrality, closeness centrality, betweenness centrality, eigenvector centrality, and average tie strength) and the grades obtained by students (grades for quality of work, grades for volume of work, grades for diversity of work, and final grades) in a social network-based engineering education. The main finding is that the obtained correlation results can be used to make the process of the students' performance evaluation based on students interactions (behavior) analysis, to make the evaluation partially automatic, increasing the objectivity and productivity of teachers and allowing a more scalable process of evaluation. The results also contribute to the behavioural theory of learning performance evaluation. More specific findings related to the correlation analysis are: (1) the more different interactions a student had (degree centrality) and the more frequently the student was between the interaction paths of other students (betweenness centrality), the better was the quality of the work; (2) all five social network measures had a positive and strong correlation with the grade for volume of work and with the final graThe authors wish to acknowledge the support of the Fundacao para a Ciencia e Tecnologia (FCT), Portugal, through the Grants "Projeto Estrategico-UI 252-2011-2012'' reference PEst-OE/EME/UI0252/2011, "Ph.D. Scholarship Grant'' reference SFRH/BD/85672/2012, and the support of Parallel Planes Lda.info:eu-repo/semantics/publishedVersio

Modulating Androgen Receptor-Driven Transcription in Prostate Cancer with Selective CDK9 Inhibitors

© 2020 The Authors Castration-resistant prostate cancers (CRPCs) lose sensitivity to androgen-deprivation therapies but frequently remain dependent on oncogenic transcription driven by the androgen receptor (AR) and its splice variants. To discover modulators of AR-variant activity, we used a lysate-based small-molecule microarray assay and identified KI-ARv-03 as an AR-variant complex binder that reduces AR-driven transcription and proliferation in prostate cancer cells. We deduced KI-ARv-03 to be a potent, selective inhibitor of CDK9, an important cofactor for AR, MYC, and other oncogenic transcription factors. Further optimization resulted in KB-0742, an orally bioavailable, selective CDK9 inhibitor with potent anti-tumor activity in CRPC models. In 22Rv1 cells, KB-0742 rapidly downregulates nascent transcription, preferentially depleting short half-life transcripts and AR-driven oncogenic programs. In vivo, oral administration of KB-0742 significantly reduced tumor growth in CRPC, supporting CDK9 inhibition as a promising therapeutic strategy to target AR dependence in CRPC. In the pursuit of hormone receptor modulators in prostate cancer, a potent, ultraselective CDK9 inhibitor is discovered. This study describes the most selective inhibitors of CDK9 known to date and provides compelling preclinical in vitro and in vivo support for CDK9 as a therapeutic target