Intelligent cloud manufacturing platform for efficient resource sharing in smart manufacturing networks

Abstract Modern manufacturing demands are characterized by high fluctuations with negative impact on resource efficiency. In this framework, Industry 4.0 key enabling technologies such as cloud manufacturing enable the sharing of distributed resources for effective use at industrial network level. In this work, an intelligent cloud manufacturing platform is proposed to increase resource efficiency in a manufacturing network through dynamic sharing of manufacturing services, including computational, software as well as physical manufacturing resources, that can be offered on demand according to a service-oriented paradigm. The cloud-based platform includes a database module where user input data are collected, an intelligent module for data processing, optimization and feasible solutions generation, and a decision support module for solutions evaluation and comparison. A case study demonstrates technical and economic advantages for industrial resource efficiency improvement

Cloud-based platform for intelligent healthcare monitoring and risk prevention in hazardous manufacturing contexts

This paper presents an intelligent cloud-based platform for workers healthcare monitoring and risk prevention in potentially hazardous manufacturing contexts. The platform is structured according to sequential modules dedicated to data acquisition, processing and decision-making support. Several sensors and data sources, including smart wearables, machine tool embedded sensors and environmental sensors, are employed for data collection, comprising information on offline clinical background, operational and environmental data. The cloud data processing module is responsible for extracting relevant features from the acquired data in order to feed a machine learning-based decision-making support system. The latter provides a classification of workers’ health status so that a prompt intervention can be performed in particularly challenging scenarios

A pandemic recap : lessons we have learned

On January 2020, the WHO Director General declared that the outbreak constitutes a Public Health Emergency of International Concern. The world has faced a worldwide spread crisis and is still dealing with it. The present paper represents a white paper concerning the tough lessons we have learned from the COVID-19 pandemic. Thus, an international and heterogenous multidisciplinary panel of very differentiated people would like to share global experiences and lessons with all interested and especially those responsible for future healthcare decision making. With the present paper, international and heterogenous multidisciplinary panel of very differentiated people would like to share global experiences and lessons with all interested and especially those responsible for future healthcare decision making.Non peer reviewe

A deep learning based-decision support tool for solution recommendation in cloud manufacturing platforms

Industry 4.0 key enabling technologies such as cloud manufacturing allow for the dynamic sharing of distributed resources for efficient use at industrial network level. Interconnected users, i.e. suppliers and customers, offer and request manufacturing services over a cloud manufacturing platform, where an intelligent engine generates a number of solutions based on functional and geometrical requirements. A high number of suppliers leads to a higher number of solutions available for customers increasing the decision-making complexity from a customer perspective. Recommendation systems play a crucial role in expanding the opportunities in decision-making processes under complex information environments. In this scope, this paper proposes the conceptualization and the development of a recommendation decision support tool to be implemented in a cloud manufacturing platform to assist customers in appropriately selecting manufacturing services with reference to sheet metal cutting operations. In terms of solution selection, a Deep Neural Network (DNN) paradigm is adopted to allow for the automatic learning of optimal solution recommendation list based both on customers past experiences and new choices. In this respect, a virtual interaction environment is firstly built for system pre-training. Subsequently, users' data are inputted in the pre-trained model to predict a recommendation list. This is then subject to user interaction, i.e. selection, which will be fed back into the model to update the training parameters. This paper concludes with a simulated case study reported to exemplify the proposed methodology for a variety of decision-making scenarios

Asymmetric dimerization of adenosine deaminase acting on RNA facilitates substrate recognition.

Adenosine deaminases acting on RNA (ADARs) are enzymes that convert adenosine to inosine in duplex RNA, a modification that exhibits a multitude of effects on RNA structure and function. Recent studies have identified ADAR1 as a potential cancer therapeutic target. ADARs are also important in the development of directed RNA editing therapeutics. A comprehensive understanding of the molecular mechanism of the ADAR reaction will advance efforts to develop ADAR inhibitors and new tools for directed RNA editing. Here we report the X-ray crystal structure of a fragment of human ADAR2 comprising its deaminase domain and double stranded RNA binding domain 2 (dsRBD2) bound to an RNA duplex as an asymmetric homodimer. We identified a highly conserved ADAR dimerization interface and validated the importance of these sequence elements on dimer formation via gel mobility shift assays and size exclusion chromatography. We also show that mutation in the dimerization interface inhibits editing in an RNA substrate-dependent manner for both ADAR1 and ADAR2

Owen Brown Village Center

Final project for ARCH407 (Spring 2016). University of Maryland, College Park.Students in the Master of Architecture program worked with representatives from the Howard County Department of Planning, the Columbia Association, and the Owen Brown Village board to come up with redevelopment schemes for Owen Brown Village Center. The semester began with the production of a series of base maps, which analyzed demographics, issues hydrology and the historical vision of James Rouse for the city of Columbia. Students also researched a series of built case study projects from around the world that provided inspiration and metrics for their design proposals at Owen Brown. Finally, each design team worked closely with a student from the Real Estate Development Capstone course to come up with program, square footages, and adjacencies for their design schemes.Howard Count