This study explores how journalists in highspeed newsrooms gather information, how gathering activities are temporally structured and how reliability manifests itself in information-gathering activities.
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The number of light commercial vehicles in cities is growing, which puts increasing pressure on the liveability of cities. Light electric freight vehicles (LEFV) and cargo bikes can offer a solution, as they occupy less space, can be manoeuvred easily and does not emit tailpipe pollutants. This paper presents the results of the first half-year of the LEVV-LOGIC project (2016-2018), aimed at exploring the potential of LEFVs for various urban freight flows. Delivery characteristics, trends, practical examples and the judgement of experts are combined to assess the potential of LEFVs for seven major urban freight flows. The preliminary analysis concludes that every urban freight flow has a certain level of potential for using LEFV. In particular parcel and food deliveries have high potential; however, deliveries related to services and the last phase of construction work can also be switched to LEFV. In comparison, non-food deliveries to retail establishments and the collection of waste collection have less potential. Though the latter can change when recycling standards become higher.
DOCUMENT
Being a hub and a hotspot for many people from all over the world means that new dynamics have entered Amsterdam and other cities in the last few years. Money flows pass through the city, and we often do not know anymore whose money this is, where it comes from or where it goes. At the same time, the development of cryptocurrencies and parallel money cultures all contribute to the opaqueness of the future of the financial world.To better understand the current dynamics that will affect our financial future, the City of Amsterdam has taken the initiative to organize an international conference in collaboration with the Institute of Network Cultures from the Amsterdam University of Applied Science: Flying Money – Investigating illicit Financial Flows in the City.This publication contains the results of the conference, along with relevant academic and other articles ensuing from the conference.
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National forestry Commission (SBB) and National Park De Biesbosch. Subcontractor through NRITNational parks with large flows of visitors have to manage these flows carefully. Methods of data collection and analysis can be of help to support decision making. The case of the Biesbosch National Park is used to find innovative ways to figure flows of yachts, being the most important component of water traffic, and to create a model that allows the estimation of changes in yachting patterns resulting from policy measures. Recent policies oriented at building additional waterways, nature development areas and recreational concentrations in the park to manage the demands of recreation and nature conservation offer a good opportunity to apply this model. With a geographical information system (GIS), data obtained from aerial photographs and satellite images can be analyzed. The method of space syntax is used to determine and visualize characteristics of the network of leisure routes in the park and to evaluate impacts resulting from expected changes in the network that accompany the restructuring of waterways.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
