This research concerning the experience and future of zoos was carried out from 2011-2012 and takes regional ideas concerning Zoo Emmen as well as global visions into account. The research focuses partly on Zoo Emmen, its present attractions and visitors while also comparing and contrasting visions on the future in relationship to other international zoos in the world. In this way, remarkable experiences and ideas will be identified and in the light of them, it can serve as inspiration for stakeholders of zoos at large. The main research subject is a look at the future zoos in view of: The Zoo Experience – an international experience benchmark; The Zoo of the Future – a Scenario Planning approach towards the future; The virtual zoo - zoo’s in the internet domain.
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By use of a literature review and an environmental scan four plausible future scenarios will be created, based on the research question: How could the future of backpack tourism look like in 2030, and how could tourism businesses anticipate on the changing demand. The scenarios, which allow one to ‘think out of the box’, will eventually be translated into recommendations towards the tourism sector and therefore can create a future proof company strategy.
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This report provides the global community of hospitality professionals with critical insights into emerging trends and developments, with a particular focus on the future of business travel. Business travellers play a pivotal role within the tourism industry, contributing significantly to international travel, GDP, and business revenues.In light of recent disruptions and evolving challenges, this forward-looking study aims not only to reflect on the past but, more importantly, to anticipate future developments and uncertainties in the realm of business travel. By doing so, it offers strategic insights to help hospitality leaders navigate the ever-evolving landscape of the industry.Key findings from the Yearly Outlook include:• Recovery of International Travel: By 2024, international travel arrivals have surpassed 2019 levels by 2%, signalling a full recovery in the sector. In Amsterdam, there was a 13% decrease in business traveller numbers, offset by an increase in the average length of stay from 2.34 to 2.71 days. Notably, more business travellers opted for 3-star accommodations, marking a shift in preferences.• Future of Business Travel: The report outlines a baseline scenario that predicts a sustainable, personalised, and seamless business travel experience by 2035. This future will likely be driven by AI integration, shifts in travel patterns—such as an increase in short-haul trips, longer stays combining business and leisure—and a growing focus on sustainability.• Potential Disruptors: The study also analyses several potential disruptors to these trends. These include socio-political shifts that could reverse sustainability efforts, risks associated with AI-assisted travel, the decline of less attractive business destinations, and the impact of global geopolitical tensions.The Yearly Outlook provides practical recommendations for hospitality professionals and tourism policymakers. These recommendations focus on building resilience, anticipating changes in business travel preferences, leveraging AI and technological advancements, and promoting sustainable practices within the industry.
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Due to the existing pressure for a more rational use of the water, many public managers and industries have to re-think/adapt their processes towards a more circular approach. Such pressure is even more critical in the Rio Doce region, Minas Gerais, due to the large environmental accident occurred in 2015. Cenibra (pulp mill) is an example of such industries due to the fact that it is situated in the river basin and that it has a water demanding process. The current proposal is meant as an academic and engineering study to propose possible solutions to decrease the total water consumption of the mill and, thus, decrease the total stress on the Rio Doce basin. The work will be divided in three working packages, namely: (i) evaluation (modelling) of the mill process and water balance (ii) application and operation of a pilot scale wastewater treatment plant (iii) analysis of the impacts caused by the improvement of the process. The second work package will also be conducted (in parallel) with a lab scale setup in The Netherlands to allow fast adjustments and broaden evaluation of the setup/process performance. The actions will focus on reducing the mill total water consumption in 20%.
Production processes can be made ‘smarter’ by exploiting the data streams that are generated by the machines that are used in production. In particular these data streams can be mined to build a model of the production process as it was really executed – as opposed to how it was envisioned. This model can subsequently be analyzed and stress-tested to explore possible causes of production prob-lems and to analyze what-if scenarios, without disrupting the production process itself. It has been shown that such models can successfully be used to diagnose possible causes of production problems, including scrap products and machine defects. Ideally, they can even be used to model and analyze production processes that have not been implemented yet, based on data from existing production pro-cesses and techniques from artificial intelligence that can predict how the new process is likely to be-have in practice in terms of data that its machines generate. This is especially important in mass cus-tomization processes, where the process to create each product may be unique, and can only feasibly be tested using model- and data-driven techniques like the one proposed in this project. Against this background, the goal of this project is to develop a method and toolkit for mining, mod-elling and analyzing production processes, using the time series data that is generated by machines, to: (i) analyze the performance of an existing production process; (ii) diagnose causes of production prob-lems; and (iii) certify that a new – not yet implemented – production process leads to high-quality products. The method is developed by researching and combining techniques from the area of Artificial Intelli-gence with techniques from Operations Research. In particular, it uses: process mining to relate time series data to production processes; queueing networks to determine likely paths through the produc-tion processes and detect anomalies that may be the cause of production problems; and generative adversarial networks to generate likely future production scenarios and sample scenarios of production problems for diagnostic purposes. The techniques will be evaluated and adapted in implementations at the partners from industry, using a design science approach. In particular, implementations of the method are made for: explaining production problems; explaining machine defects; and certifying the correct operation of new production processes.
Road freight transport contributes to 75% of the global logistics CO2 emissions. Various European initiatives are calling for a drastic cut-down of CO2 emissions in this sector [1]. This requires advanced and very expensive technological innovations; i.e. re-design of vehicle units, hybridization of powertrains and autonomous vehicle technology. One particular innovation that aims to solve this problem is multi-articulated vehicles (road-trains). They have a smaller footprint and better efficiency of transport than traditional transport vehicles like trucks. In line with the missions for Energy Transition and Sustainability [2], road-trains can have zero-emission powertrains leading to clean and sustainable urban mobility of people and goods. However, multiple articulations in a vehicle pose a problem of reversing the vehicle. Since it is extremely difficult to predict the sideways movement of the vehicle combination while reversing, no driver can master this process. This is also the problem faced by the drivers of TRENS Solar Train’s vehicle, which is a multi-articulated modular electric road vehicle. It can be used for transporting cargo as well as passengers in tight environments, making it suitable for operation in urban areas. This project aims to develop a reverse assist system to help drivers reverse multi-articulated vehicles like the TRENS Solar Train, enabling them to maneuver backward when the need arises in its operations, safely and predictably. This will subsequently provide multi-articulated vehicle users with a sustainable and economically viable option for the transport of cargo and passengers with unrestricted maneuverability resulting in better application and adding to the innovation in sustainable road transport.
