Objective: Approximately 50 % of Dutch community-dwelling older adults does notmeet protein recommendations. This study assesses the effect of replacing lowprotein foods with protein-rich alternatives on the protein intake of Dutchcommunity-dwelling older adults. Design: The Dutch National Food Consumption Survey—Older Adults 2010–2012(DNFCS-OA) was used for scenario modelling. Dietary intake was estimated basedon two 24-h recalls. Commonly consumed products were replaced by comparableproducts rich in protein (scenario 1), foods enriched in protein (scenario 2) and acombination of both (scenario 3). Replacement scenarios were confined to partici-pants whose dietary protein intake was < 1·0 g/kg BW/d (n 391). Habitual proteinintake of all older adults was estimated, adjusting for effects of within-person varia-tion in the 2-d intake data. Setting: A simulation study based on the DNFCS-OA. Participants: 727 Dutch community-dwelling older adults aged 70þ. Results: Mean protein intake of the total population increased from 1·0 to 1·2 g/kgBW/d (scenarios 1 and 2) and to 1·3 g/kg BW/d (scenario 3). The percentage ofparticipants with intakes of ≥ 1·0 g/kg BW/d increased from 47·1 % to 91·4 %,90·2 % and 94·6 %, respectively, in scenarios 1, 2 and 3. The largest increases inprotein intake were due to replacements in food groups: yoghurt, cream dessertsand pudding, potatoes, vegetables and legumes and non-alcoholic beverages andmilk in scenario 1 and bread; yoghurt, cream desserts and pudding and soups inscenario 2. Conclusions: This simulation model shows that replacing low protein foods withcomparable alternatives rich in protein can increase the protein intake of Dutchcommunity-dwelling older adults considerably. Results can be used as a basisfor nutritional counselling.
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Air transportation has grown in an unexpected way during last decades and is expected to increase even more in the next years. Traffic growth tendencies forecast an expansion in the demand and greater aviation connectivity, but also higher workload to the different airspace users, especially for airport and services. Therefore, it is essential to employ strategies designed to use efficiently valuable corporate resource. Airport authorities around the world are investing in large capital projects, including new or improved runways, terminal expansions, and entirely new airports. However, this effort is sometimes limited due to their geographic location. In this work, two main objectives are pursued: first, to highlight the importance of the industry by exposing the current situation and future trends all over the world focusing in the Mexican industry; and second, to introduce a simulation model which can be used as a decision making tool for the upcoming demand. The analysis of the scenarios illustrates how to develop strategies to cope with the different airspace user's needs.
MULTIFILE
This paper focuses on the use of discrete event simulation (DES) as a decision support tool for airport land use development. As a study case, Querétaro Airport (Mexico) is used, due to its rapid growth and the different services it offers. The SIMIO® software was used to carry out a macro-level simulation of the airport’s processes, considering generic process times, flight types and demand schedules. The resulting strategic simulation model can be used to diagnose the current growth situation, analyse the airport's growth potential, and evaluate different expansion scenarios using the available land, including the expansion of the terminal building, cargo operations or MRO. The arrival and departure of aircraft (commercial, cargo, maintenance, aviation school and private aviation) at the airport were simulated to detect bottlenecks for different expansion scenarios, that aim to find an optimal balance between the growth options in the different airport grounds. The objective is to compare the potential growth of different layout expansion possibilities. Preliminary results indicate that land use options have a great impact on the growth potential of the airport and some general aviation activities, such as the aviation school, are interfering with the potential growth of other activities at Querétaro Airport.
In the Glasgow declaration (2021), the tourism sector promised to reduce its CO2 emissions by 50% and reduce them to zero by 2050. The urgency is felt in the sector, and small steps are made at company level, but there is a lack of insight and overview of effective measures at global level.This study focuses on the development of a necessary mix of actions and interventions that the tourism sector can undertake to achieve the goal of a 50% reduction in greenhouse gases by 2030 towards zero emissions by 2050. The study contributes to a better understanding of the paths that the tourism sector can take to achieve this and their implications for the sector. The aim of the report is to spark discussion, ideas and, above all, action.The study provides a tool that positively engages the sector in the near and more distant future, inspires discussion, generates ideas, and drives action. In addition, there will be a guide that shows the big picture and where the responsibilities lie for the reduction targets. Finally, the researchers come up with recommendations for policymakers, companies, and lobbyists at an international and European level.In part 1 of the study, desk research is used to lay the foundation for the study. Here, the contribution of tourism to global greenhouse gas emissions is mapped out, as well as the image and reputation of the sector on climate change. In addition, this section describes which initiatives in terms of, among other things, coalitions and declarations have already been taken on a global scale to form a united front against climate change.In part 2, 40 policies and measures to reduce greenhouse gas emissions in the sector are evaluated in a simulation. For this simulation, the GTTMdyn simulation model, developed by Paul Peeters from BUAS, is used which works on a global scale and shows the effect of measures on emissions, tourism, transport, economy, and behaviour. In this simulation, the researchers can 'test' measures and learn from mistakes. In the end one or more scenarios will; be developed that reach the goals of 50% reduction in 2030 and zero emissions in 2050. In part 3, the various actions that should lead to the reduction targets are tested against the impacts on the consequences for the global tourism economy, its role in providing leisure and business opportunities and the consequences for certain destinations and groups of industry stakeholders. This part will be concluded with two workshops with industry experts to reflect on the results of the simulation.Part 4 reports the results of the study including an outline of the consequences of possibly not achieving the goal. With this, the researchers want to send a warning signal to stakeholders who may be resistant to participating in the transition.
Automated driving nowadays has become reality with the help of in-vehicle (ADAS) systems. More and more of such systems are being developed by OEMs and service providers. These (partly) automated systems are intended to enhance road and traffic safety (among other benefits) by addressing human limitations such as fatigue, low vigilance/distraction, reaction time, low behavioral adaptation, etc. In other words, (partly) automated driving should relieve the driver from his/her one or more preliminary driving tasks, making the ride enjoyable, safer and more relaxing. The present in-vehicle systems, on the contrary, requires continuous vigilance/alertness and behavioral adaptation from human drivers, and may also subject them to frequent in-and-out-of-the-loop situations and warnings. The tip of the iceberg is the robotic behavior of these in-vehicle systems, contrary to human driving behavior, viz. adaptive according to road, traffic, users, laws, weather, etc. Furthermore, no two human drivers are the same, and thus, do not possess the same driving styles and preferences. So how can one design of robotic behavior of an in-vehicle system be suitable for all human drivers? To emphasize the need for HUBRIS, this project proposes quantifying the behavioral difference between human driver and two in-vehicle systems through naturalistic driving in highway conditions, and subsequently, formulating preliminary design guidelines using the quantified behavioral difference matrix. Partners are V-tron, a service provider and potential developer of in-vehicle systems, Smits Opleidingen, a driving school keen on providing state-of-the-art education and training, Dutch Autonomous Mobility (DAM) B.V., a company active in operations, testing and assessment of self-driving vehicles in the Groningen province, Goudappel Coffeng, consultants in mobility and experts in traffic psychology, and Siemens Industry Software and Services B.V. (Siemens), developers of traffic simulation environments for testing in-vehicle systems.
Based on the model outcomes, Houtlaan’s energy transition will likely result in congestion and curtailmentproblems on the local electricity grid within the next 5-7 years, possibly sooner if load imbalance between phasesis not properly addressed.During simulations, the issue of curtailment was observed in significant quantities on one cable, resulting in aloss of 8.292 kWh of PV production per year in 2030. This issue could be addressed by moving some of thehouses on the affects cable to a neighboring under-utilized cable, or by installing a battery system near the end ofthe affected cable. Due to the layout of the grid, moving the last 7 houses on the affected cable to the neighboringcable should be relatively simple and cost-effective, and help to alleviate issues of curtailment.During simulations, the issue of grid overloading occurred largely as a result of EV charging. This issue can bestbe addressed by regulating EV charging. Based on current statistics, the bulk of EV charging is expected to occurin the early evening. By prolonging these charge cycles into the night and early morning, grid overloading canlikely be prevented for the coming decade. However, such a control system will require some sort of infrastructureto coordinate the different EV charge cycles or will require smart EV chargers which will charge preferentiallywhen the grid voltage is above a certain threshold (i.e., has more capacity available).A community battery system can be used to increase the local consumption of produced electricity within theneighborhood. Such a system can also be complemented by charging EV during surplus production hours.However, due to the relatively high cost of batteries at present, and losses due to inefficiencies, such a systemwill not be financially feasible without some form of subsidy and/or unless it can provide an energy service whichthe grid operator is willing to pay for (e.g. regulating power quality or line voltage, prolonging the lifetime of gridinfrastructure, etc.).A community battery may be most useful as a temporary solution when problems on the grid begin to occur, untila more cost-effective solution can be implemented (e.g. reinforcing the grid, implementing an EV charge controlsystem). Once a more permanent solution is implemented, the battery could then be re-used elsewhere.The neighborhood of Houtlaan in Assen, the Netherlands, has ambitious targets for reducing the neighborhood’scarbon emissions and increasing their production of their own, sustainable energy. Specifically, they wish toincrease the percentage of houses with a heat pump, electric vehicle (EV) and solar panels (PV) to 60%, 70%and 80%, respectively, by the year 2030. However, it was unclear what the impacts of this transition would be onthe electricity grid, and what limitations or problems might be encountered along the way.Therefore, a study was carried out to model the future energy load and production patterns in Houtlaan. Thepurpose of the model was to identify and quantify the problems which could be encountered if no steps are takento prevent these problems. In addition, the model was used to simulate the effectiveness of various proposedsolutions to reduce or eliminate the problems which were identified
