In this paper, we present the challenges, failures and successes on urban freight transportation. We first identify the various involved stakeholders with their interests. Then we evaluate a large number of urban freight transport initiatives and identify lessons learned, which are distinguished in policy, logistics and technology based views. Further, we present a vision for urban freight transportation, which is not only based on the lessons learned, but also on actual market research reports and recent findings.
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A presentation on indicative characteristics of successful and unsuccessful research call proposals.
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Differentiating perfectionistic strivings and perfectionistic concerns, the present study examined how perfectionism predicts what coping strategies people use, when dealing with failures, and how perfectionism and coping influence people's satisfaction. A sample of 149 students completed daily reports for 3-14 days, reporting the most bothersome failure they experienced during the day, what strategies they used to cope with the failure, and how satisfied they felt at the end of the day. Multilevel regression analyses showed that perfectionistic concerns predicted more frequent use of self-blame, less frequent use of active coping and acceptance, and higher satisfaction at the end of the day, whereas perfectionistic strivings predicted less frequent use of self-blame and higher satisfaction. Although positive reframing, acceptance, and humor predicted higher satisfaction for all students, further analyses showed that positive reframing coping was particularly helpful for students high in perfectionistic concern. The findings suggest that accommodative coping strategies are generally helpful in dealing with personal failures, with positive reframing being a coping strategy that works particularly well for people high in perfectionistic concerns (who are prone to dissatisfaction) to achieve higher satisfaction at the end of the day.
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In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process. To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry. This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. In several case studies these products are tested and further improved. This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.
Rotating machinery, such as centrifugal pumps, turbines, bearings, and other critical systems, is the backbone of various industrial processes. Their failures can lead to significant maintenance costs and downtime. To ensure their continuous operation, we propose a fault diagnosis and monitoring framework that leverages the innovative use of acoustic sensors for early fault detection, especially in components less accessible for traditional vibration-based monitoring strategies. The main objective of the proposed project is to develop a fault diagnosis and monitoring framework for rotating machinery, including the fusion of acoustic sensors and physics-based models. By combining real-time monitoring data from acoustic sensors with an understanding of first principles, the framework will enable maintenance practitioners to identify and categorize different failure modes such as wear, fatigue, cavitation, reduced flow, bearing damage, impeller damage, misalignment, etc. In the initial phase, the focus will be on centrifugal pumps using the existing test set-up at the University of Twente. Sorama specializes in acoustic sensors to locate noise sources and will provide acoustic cameras to capture sound patterns related to pump deterioration during various operating conditions. These acoustic signals will then be correlated with the different failure modes and mechanisms that will be described by physics-based models, such as wear, fatigue, cavitation, corrosion, etc. Furthermore, a recently published data set by the Dynamics Based Maintenance research group that includes vibration analysis data and motor current analysis data of various fault scenarios, such as mentioned above, will be used as validation. The anticipated outcome of this project is a versatile framework for a physics-informed acoustic monitoring system. This system is designed to enhance early fault detection significantly, reducing maintenance costs and downtime across a broad spectrum of industrial applications, from centrifugal pumps to turbines, bearings, and beyond.
Een zestal lectoren richt het Platform Inzet van Technologie voor Gezondheid en Welzijn op. Het overkoepelende doel van dit platform is doorontwikkeling, adoptatie en duurzame implementatie van reeds ontwikkelde technologie voor (gezondheids)zorg en welzijn. De zorgconsument wordt gezien als belangrijkste gebruiker van eHealth-technologie. Hun wensen staan daarom voorop. Daarnaast spelen huidige en toekomstige zorgprofessionals een belangrijke rol in het behalen van het overkoepelende doel evenals (private) ontwikkelaars van eHealth. Het platform heeft subdoelen geformuleerd op drie gebieden, te weten kennis & onderzoek, onderwijs & praktijk en visievorming & zichtbaarheid. Deze zijn eveneens gericht op de zorgconsument (bijvoorbeeld als het gaat om het verhogen van eigen regie en zelfmanagement), gericht op zorgprofessionals (bijvoorbeeld als het gaat om kwaliteit, doelmatigheid en continuïteit van zorg) en op andere stakeholders zoals bedrijven die technologieën ontwikkelen en willen opschalen. Het platform kent een open karakter. Dit uit zich onder meer in het feit dat uitbreiding van het platform vast agendapunt van de stuurgroep van het platform is, in het actief uitnodigen van relevante stakeholders op diverse bijeenkomsten (zoals inspiratiesessies en symposia) en in het vrij beschikbaar stellen van producten van het platform (zoals een publicatie met best practices and good failures en diverse 'white papers'). Het platform legt nadruk op het delen van kennis over technologie voor gezondheid en welzijn en op afstemming van onderzoekagenda's en ontwikkelingen met collega-platforms en -netwerken. Daarnaast ligt de nadruk op co-creatie met gebruikers van de technologie én op samenwerking met een diversiteit aan partners, waaronder, naast de genoemde zorgconsumenten en hun vertegenwoordigers, kennisinstellingen en private partners. Het eerste jaar wordt gezien als opbouwfase. Hierin wordt het netwerk versterkt, de visie en de plannen aangescherpt en gestart met financiering zoeken voor concrete activiteiten. In de loop van de tijd verschuift de nadruk naar samenwerking in (onderzoeks)projecten. Hiermee zorgt het platform niet alleen voor financiële middelen maar ook voor draagkracht en verbinding tussen partijen voor toekomstige samenwerking.
