This study evaluates the maximum theoretical exposure to radiofrequency (RF) electromag- netic fields (EMFs) from a Fifth-generation (5G) New Radio (NR) base station (BS) while using four commonly used mobile applications: YouTube for video streaming, WhatsApp for voice calls, Instagram for posting pictures and videos, and running a Video game. Three factors that might affect exposure, i.e., distance of the measurement positions from the BS, measurement time, and induced traffic, were examined. Exposure was assessed through both instantaneous and time-averaged extrapolated field strengths using the Maximum Power Extrapolation (MPE) method. The former was calculated for every measured SS-RSRP (Secondary Synchronization Reference Signal Received Power) power sample obtained with a sampling resolution of 1 second, whereas the latter was obtained using a 1-min moving average applied on the applications’ instantaneous extrapolated field strengths datasets. Regarding distance, two measurement positions (MPs) were selected: MP1 at 56 meters and MP2 at 170 meters. Next, considering the measurement time, all mobile application tests were initially set to run for 30 minutes at both MPs, whereas the video streaming test (YouTube) was run for an additional 150 minutes to investigate the temporal evolution of field strengths. Considering the traffic, throughput data vs. both instantaneous and time-averaged extrapolated field strengths were observed for all four mobile applications. In addition, at MP1, a 30-minute test without a User Equipment (UE) device was conducted to analyze exposure levels in the absence of induced traffic. The findings indicated that the estimated field strengths for mobile applications varied. It was observed that distance and time had a more significant impact than the volume of data traffic generated (throughput). Notably, the exposure levels in all tests were considerably lower than the public exposure thresholds set by the ICNIRP guidelines.INDEX TERMS 5G NR, C-band, human exposure assessment, mobile applications, traffic data, maximum extrapolation method, RF-EMF.
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The Amsterdam Sensor Lab is part of the Amsterdam University of Applied Sciences (AUAS) and its goal is to develop application specific sensor systems for applied research. In order to (anonymously) measure, for instance, traffic without influencing people’s behaviour, a pressure sensing sub-tile is under development. It can be placed under a regular (0.3*0.3 m) tile in the pavement and, hence, cannot be seen by the public. Applications may range from evaluating the behaviour of pedestrians in crowds or on large open areas, to measuring the mechanical stress on bridges due to lorry traffic. The resulting data may be valuable to social scientists and municipal decision makers.A preliminary demonstration model has been realized that can detect: weight (pressure), direction, and a speed estimate of pedestrians and cyclists, by measuring the direction and velocity of pressure changes. Data communication is wireless, e.g., via Bluetooth™, to a Raspberry Pi™ or computer for calibration and visualization of the data. The demonstration model has been working satisfactorily for about half a year in the corridors of the AUAS.Pressure changes are measured with strain gauges using low-noise analogue instrumentation amplifiers and digitized with a 16 bit effective resolution. Current consumption is about 50 mA, the minimal detectable pressure is ca. 10 N and the maximal pressure ca. 1500 N. The data is refreshed every 2 ms.New electronics for a second version of the sub-tile (under development) make it possible to detect the tiny signal of a 0.3 gram rubber object falling from a 10 cm height. Investigations and development are going on to increase the measurement range from this low-level (impulse) pressure up to a pressure of about 500 kN, and configuring multiple sub-tiles to a wireless sensor network, thus paving the way to a (smart) sensing pavement. Apart from that, possibilities to give an estimate of the kind of traffic using artificial intelligence will be investigated.
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In the city of Amsterdam commercial transport is responsible for 15% of vehicles, 34% of traffic’s CO2 emissions and 62% of NOx emissions. The City of Amsterdam plans to improve traffic flows using real time traffic data and data about loading and unloading zones. In this paper, we present, reflect, and discuss the results of two projects from the Amsterdam University of Applied Sciences with research partners from 2016 till 2018. The ITSLOG and Sailor projects aim to analyze and test the benefits and challenges of connecting ITS and traffic management to urban freight transport, by using real-time data about loading and unloading zone availability for rerouting trucks. New technologies were developed and tested in collaboration with local authorities, transport companies and a food retailer. This paper presents and discusses the opportunities and challenges faced in developing and implementing this new technology, as well as the role played by different stakeholders. In both projects, the human factor was critical for the implementation of new technologies in practice.
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We present a novel anomaly-based detection approach capable of detecting botnet Command and Control traffic in an enterprise network by estimating the trustworthiness of the traffic destinations. A traffic flow is classified as anomalous if its destination identifier does not origin from: human input, prior traffic from a trusted destination, or a defined set of legitimate applications. This allows for real-time detection of diverse types of Command and Control traffic. The detection approach and its accuracy are evaluated by experiments in a controlled environment.
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Limited data is available on the size of urban goods movement and its impact on numerous aspects with respect to livability such as emissions and spatial impact. The latter becomes more important in densifying cities. This makes it challenging to implement effective measures that aim to reduce the negative impact of urban good movement and to monitor their impact. Furthermore, urban goods movement is diverse and because of this a tailored approach is required to take effective measures. Minimizing the negative impact of a heavy truck in construction logistics requires a different approach than a parcel delivery van. Partly due to a lack of accurate data, this diversity is often not considered when taking measures. This study describes an approach how to use available data on urban traffic, and how to enrich these with other sources, which is used to gain insight into the decomposition (number of trips and kilometers per segment and vehicle type). The usefulness of having this insight is shown for different applications by two case studies: one to estimate the effect of a zero-emission zone in the city of Utrecht and another to estimate the logistics requirements in a car-free area development.
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Abstract The Government of the Netherlands wants to be energy neutral by 2050 (Rijksoverheid, sd). A transition towards non-fossil energy sources also affects transport, which is one of the industries significantly contributing to CO2 emission (Centraal Bureau Statistiek, 2019). Road authorities at municipalities and provinces want a shift from fossil fuel-consuming to zero-emission transport choices by their inhabitants. For this the Province of Utrecht has data available. However, they struggle how to deploy data to positively influence inhabitants' mobility behavior. A problem analysis scoped the research and a survey revealed the gap between the province's current data-item approach that is infrastructure oriented and the required approach that adopts traveler’s personas to successfully stimulate cycling. For this more precisely defined captured data is needed and the focus should shift from already motivated cyclists to non-cyclers.
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PURPOSE: To study the preliminary effects and feasibility of the “Traffic Light Method for somatic screening and lifestyle” (TLM) in patients with severe mental illness. DESIGN AND METHODS: A pilot study using a quasi-experimental mixed method design with additional content analyses of lifestyle plans and logbooks. FINDINGS: Significant improvements were found in body weight and waist circumference. Positive trends were found in patients’ subjective evaluations of the TLM. The implementation of the TLM was considered feasible. PRACTICE IMPLICATIONS: The TLM may contribute to a higher quality of care regarding somatic screening and lifestyle training.
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Personal data is increasingly used by cities to track the behavior of their inhabitants. While the data is often used to mainly provide information to the authorities, it can also be harnessed for providing information to the citizens in real-time. In an on-going research project on increasing the awareness of motorists w.r.t. the environmental consequences of their driving behavior, we make use of sensors, artificial intelligence, and real-time feedback to design an intervention. A key component for successful deployment of the system is data related to the personal driving behavior of individual motorists. Through this outset, we identify challenges and research questions that relate to the use of personal data in systems, which are designed to increase the quality of life of the inhabitants of the built environment.
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Liveability along four streets in Hanoi, Vietnam is assessed. Hanoi is a rapidly growing metropolis characterised by high levels of personal motorized traffic. Two high traffic volume streets and two low traffic volume streets were studied using a mixed methods approach, combining the collection and analysis of quantitative and qualitative data on traffic volumes and liveability perceptions of its residents. The research methodology for this study revisits part of the well-known Liveable Streets study for San Francisco by Appleyard et al. (1981). A Principal Component Analysis (PCA) shows that residents on both low traffic volume streets experience less traffic hazard and stress, including noise and air pollution, than neighbouring high traffic volume streets. In line with Appleyard, the study shows that low traffic volume streets were rated more liveable than high traffic volume streets. In contrast to Appleyard, however, the study also shows that traffic volumes are not correlated with social interaction, feeling of privacy and sense of home, which is likely caused by the high levels of collectivism typical for Vietnam. Finally, the study indicates a strong residential neighbourhood type dissonance, where a mismatch exists between preferences for living in peaceful and quiet streets and the actual home location of residents.
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ackground and aim – Driven by new technologies and societal challenges, futureproof facility managers must enable sustainable housing by combining bricks and bytes into future-proof business support and workplace concepts. The Hague University of Applied Sciences (THUAS) acknowledges the urgency of educating students about this new reality. As part of a large-scale two-year study into sustainable business operations, a living lab has been created as a creative space on the campus of THUAS where (novel) business activities and future-proof workplace concepts are tested. The aim is to gain a better understanding amongst students, lecturers, and the university housing department of bricks, bytes, behavior, and business support. Results – Based on different focal points the outcomes of this research present guidelines for facility managers how data-driven facility management creates value and a better understanding of sustainable business operations. In addition, this practice based research presents how higher education in terms of taking the next step in creating digitized skilled facility professionals can add value to their curriculum. Practical or social implications – The facility management profession has an important role to play in the mitigation of sustainable and digitized business operations. However, implementing high-end technology within the workplace can help to create a sustainable work environment and better use of the workplace. These developments will result in a better understanding of sustainable business operations and future-proof capabilities. A living lab is the opportunity to teach students to work with big data and provides a playground for them to test their circular workplace, business support designs, and smart building technologies.
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