Harnessing Collaborative Technologies: Helping Funders Work Together Better

This report was produced through a joint research project of the Monitor Institute and the Foundation Center. The research included an extensive literature review on collaboration in philanthropy, detailed analysis of trends from a recent Foundation Center survey of the largest U.S. foundations, interviews with 37 leading philanthropy professionals and technology experts, and a review of over 170 online tools.The report is a story about how new tools are changing the way funders collaborate. It includes three primary sections: an introduction to emerging technologies and the changing context for philanthropic collaboration; an overview of collaborative needs and tools; and recommendations for improving the collaborative technology landscapeA "Key Findings" executive summary serves as a companion piece to this full report

A Smart Products Lifecycle Management (sPLM) Framework - Modeling for Conceptualization, Interoperability, and Modularity

Autonomy and intelligence have been built into many of today’s mechatronic products, taking advantage of low-cost sensors and advanced data analytics technologies. Design of product intelligence (enabled by analytics capabilities) is no longer a trivial or additional option for the product development. The objective of this research is aimed at addressing the challenges raised by the new data-driven design paradigm for smart products development, in which the product itself and the smartness require to be carefully co-constructed. A smart product can be seen as specific compositions and configurations of its physical components to form the body, its analytics models to implement the intelligence, evolving along its lifecycle stages. Based on this view, the contribution of this research is to expand the “Product Lifecycle Management (PLM)” concept traditionally for physical products to data-based products. As a result, a Smart Products Lifecycle Management (sPLM) framework is conceptualized based on a high-dimensional Smart Product Hypercube (sPH) representation and decomposition. First, the sPLM addresses the interoperability issues by developing a Smart Component data model to uniformly represent and compose physical component models created by engineers and analytics models created by data scientists. Second, the sPLM implements an NPD3 process model that incorporates formal data analytics process into the new product development (NPD) process model, in order to support the transdisciplinary information flows and team interactions between engineers and data scientists. Third, the sPLM addresses the issues related to product definition, modular design, product configuration, and lifecycle management of analytics models, by adapting the theoretical frameworks and methods for traditional product design and development. An sPLM proof-of-concept platform had been implemented for validation of the concepts and methodologies developed throughout the research work. The sPLM platform provides a shared data repository to manage the product-, process-, and configuration-related knowledge for smart products development. It also provides a collaborative environment to facilitate transdisciplinary collaboration between product engineers and data scientists

Advancing Data Collection, Management, and Analysis for Quantifying Residential Water Use via Low Cost, Open Source, Smart Metering Infrastructure

Urbanization, climate change, aging infrastructure, and the cost of delivering water to residential customers make it vital that we achieve a higher efficiency in the management of urban water resources. Understanding how water is used at the household level is vital for this objective.Water meters measure water use for billing purposes, commonly at a monthly, or coarser temporal resolutions. This is insufficient to understand where water is used (i.e., the distribution of water use across different fixtures like toilets, showers, outdoor irrigation), when water is used (i.e., identifying peaks of consumption, instantaneous or at hourly, daily, weekly intervals), the efficiency of water using fixtures, or water use behaviors across different households. Most smart meters available today are not capable of collecting data at the temporal resolutions needed to fully characterize residential water use, and managing this data represents a challenge given the rapidly increasing volume of data generated. The research in this dissertation presents low cost, open source cyberinfrastructure (datalogging and data management systems) to collect and manage high temporal resolution, residential water use data. Performance testing of the cyberinfrastructure demonstrated the scalability of the system to multiple hundreds of simultaneous data collection devices. Using this cyberinfrastructure, we conducted a case study application in the cities of Logan and Providence, Utah where we found significant variability in the temporal distribution, timing, and volumes of indoor water use. This variability can impact the design of water conservation programs, estimations and forecast of water demand, and sizing of future water infrastructure. Outdoor water use was the largest component of residential water use, yet homeowners were not significantly overwatering their landscapes. Opportunities to improve the efficiency of water using fixtures and to conserve water by promoting behavior changes exist among participants

Development of a wireless sensor network for agricultural monitoring for Internet of Things (IoT)

Monitoring of the agricultural environment has become an important area of control and protection which provides real-time system and control communication with the physical world. This thesis focuses on Development ofa wireless Sensor Network for agricultural monitoring for Internet of things (IoT) to monitor environmental condition. Among the various technologies for Agriculture monitoring, Wireless Sensor Networks (WSNs) are perceived as an amazing one to gather and process information in the agricultural area with low-cost and low-energy consumption. WSN is capable of providing processed field data in real time from sensors which are physically distributed in the field. Agriculture and farming are one of the industries which have a late occupied their regards for WSNs, looking for this financially acute innovation to improve its production and upgrade agribusiness yield standard. Wireless Sensor Networks (WSNs) have pulled in a lot consideration in recent years.The proposed system uses WSN sensors to capture and track information pertaining to crop growth condition outside and inside greenhouses. 6LowPAN network protocol is used for low power consumption and for transmitting and receiving of data packets.This thesis introduces the agricultural monitoring system's hardware design, system architecture, and software process control. Agriculture monitoring system set-up is based on Contiki OS while device testing is carried out using real-time farm information and historical dat

Elektronický systém pro podporu provádění klinických studií s možností zpracování dat pomocí umělé inteligence

An increasing amount of data are collected through wearable devices during ambulatory, and long-term monitoring of biological signals, adoption of persuasive technology and dynamics of clinical trials information sharing - all of that changes the possible clinical intervention. Moreover, more and more smartphone apps are hitting the market as they become a tool in daily life for many people around the globe. All of these applications are generating a tremendous amount of data, that is difficult to process using traditional methods, and asks for engagement of advanced methods of data processing. For recruiting patients, this calls for a shift from traditional methods of engaging patients to modern communication platforms such as social media, that are providing easy access to up- to-date information on an everyday basis. These factors make the clinical study progression demanding, in terms of unified participant management and processing of connected digital resources. Some clinical trials put a strong accent on remote sensing data and patient engagement through their smartphones. To facilitate this, a direct participant messaging, where the researchers give support, guidance and troubleshooting on a personal level using already adopted communication channels, needs to be implemented. Since the...Objem dat, který je generován nositelnými zařízeními v průběhu ambulatorního i dlouhodobého snímání biologických signálů, adopce pervazivních technologií a dynamika předávání informací v rámci klinických studií - to vše mění způsoby, kterým mohou prováděny klinické studie. Více a více aplikací, které přicházejí na trh se stávají pomůckou v denním životě lidí po celém světě. Všechny tyto aplikace produkují obrovské množství dat, jež je obtížné zpracovat tradičními metodami, a vyvstává tak nutnost využití pokročilých metod. Je také možné sledovat odvrat od tradičních metod náboru pacientů, k moderním komunikačním platformám jako sociální sítě, které usnadňují přístup k aktuálním informacím. Tyto faktory činí postup v klinické studii náročným s ohledem na management účastníků studie a zpracování informací ze zdrojů dat. Některé klinické studie kladou velký důraz na sběr dat ze senzorů a zapojení pacientů do studie prostřednictvím jejich mobilních telefonů. Pro usnadnění tohoto přístupu, je nutné využít přímou komunikací s pacientem, kdy administrátoři studie poskytují podporu a pomáhají řešit problémy, které se mohou v průběhu studie vyskytnout, a to za pomocí moderních komunikačních platforem a elektronických zpráv vedených přímo s účastníkem studie. Celý tento postup je nicméně časově náročný, a je...Centre for Practical Applications Support and Spin-off Companies of the 1st Faculty of Medicine Charles UniversityCentrum podpory aplikačních výstupů a spin-off firem 1. LF UK1. lékařská fakultaFirst Faculty of Medicin

D7.5 FIRST consolidated project results

The FIRST project commenced in January 2017 and concluded in December 2022, including a 24-month suspension period due to the COVID-19 pandemic. Throughout the project, we successfully delivered seven technical reports, conducted three workshops on Key Enabling Technologies for Digital Factories in conjunction with CAiSE (in 2019, 2020, and 2022), produced a number of PhD theses, and published over 56 papers (and numbers of summitted journal papers). The purpose of this deliverable is to provide an updated account of the findings from our previous deliverables and publications. It involves compiling the original deliverables with necessary revisions to accurately reflect the final scientific outcomes of the project