Mobile Rapid DNA technology is close to being incorporated into crime scene investigations, with the potential to identify a perpetrator within hours. However, the use of these techniques entails the risk of losing the sample and potential evidence, because the device not only consumes the inserted sample, it is also is less sensitive than traditional technologies used in forensic laboratories. Scene of Crime Officers (SoCOs) therefore will face a ‘time/success rate trade-off’ issue when making a decision to apply this technology.In this study we designed and experimentally tested a Decision Support System (DSS) for the use of Rapid DNA technologies based on Rational Decision Theory (RDT). In a vignette study, where SoCOs had to decide on the use of a Rapid DNA analysis device, participating SoCOs were assigned to either the control group (making decisions under standard conditions), the Success Rate (SR) group (making decisions with additional information on DNA success rates of traces), or the DSS group (making decisions supported by introduction to RDT, including information on DNA success rates of traces).This study provides positive evidence that a systematic approach for decision-making on using Rapid DNA analysis assists SoCOs in the decision to use the rapid device. The results demonstrated that participants using a DSS made different and more transparent decisions on the use of Rapid DNA analysis when different case characteristics were explicitly considered. In the DSS group the decision to apply Rapid DNA analysis was influenced by the factors “time pressure” and “trace characteristics” like DNA success rates. In the SR group, the decisions depended solely on the trace characteristics and in the control group the decisions did not show any systematic differences on crime type or trace characteristic.Guiding complex decisions on the use of Rapid DNA analyses with a DSS could be an important step towards the use of these devices at the crime scene.
Developers of charging infrastructure, be it public or private parties, are highly dependent on accurate utilization data in order to make informed decisions where and when to expand charging points. The Amsterdam The Amsterdam University of Applied Sciences in close cooperation with the municipalities of Amsterdam, Rotterdam, The Hague, Utrecht and the metropolitan region of Amsterdam developed both the back- and front-end of a decision support tool. This paper describes the design of the decision support tool and its DataWareHouse architecture. The back-end is based on a monthly update of charging data with Charge point Detail Records and Meter Values enriched with location specific data. The design of the front-end is based on Key Performance Indicators used in the decision process for charging infrastructure roll-out. Implementing this design and DataWareHouse architecture allows all kinds of EV related companies and cities to start monitoring their charging infrastructure. It provides an overview of how the most important KPIs are being monitored and represented in the decision support tool based on regular interviews and decision processes followed by four major cities and a metropolitan region in the Netherlands.
In 2010, the Dutch Probation Service introduced a digital decision support system for case management plans, a so called fourth generation risk/needs assessment instrument. Does this system help probation officers in determining the appropriate interventions that should prevent recidivism for each individual client? And to what extent are the case management plans based on existing theories on desistance from crime? These are the central questions in the thesis of Jacqueline Bosker of the University of Applied Sciences Utrecht: ‘Linking Theory and Practice in Probation – Structured decision support for case management plans’. “Further development is still needed, but we can conclude that the use of digital decision support helps improve the quality of case management plans.” Bosker concluded in her research that this form of decision support enhances the quality of the case management plans. “In the most practical sense, it helps a probation officer not to overlook certain measures that might be applicable for the client considering the risks and needs. The plans also correspond better to the offenders’ goals and focus more on strengthening social bonds. Over the years, desistance from crime has been studied and researched. This knowledge should be used in practice. A decision support system helps linking theory and practice.”
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Aanleiding: De elektrische auto wordt steeds populairder en er zijn inmiddels meer dan 5.000 openbare en 5.000 semiopenbare oplaadpunten in Nederland. Professionals bij gemeenten, energiebedrijven, laadpuntexploitanten en netbeheerders missen echter de instrumenten waarmee zij tot onderbouwde besluitvorming omtrent de plaatsing en het aantal laadpunten kunnen komen. De belangrijkste vragen die ze hebben, hebben betrekking op beschikbaarheid en gebruik van de laadinfrastructuur (effectiviteit van de infrastructuur), en het sluitend krijgen van de businesscase (kostenefficiëntie). Doelstelling Het project wil bijdragen aan een van de grote uitdagingen rond elektrisch rijden: het ontwikkelen van een effectieve en kostenefficiënte laadinfrastructuur, gedragen door een sluitende businesscase. Het onderzoek bestaat uit het iteratief ontwikkelen van wiskundige voorspel- en simulatiemodellen voor de uitrol en het gebruik van de laadinfrastructuur. De projectdeelnemers toetsen deze modellen in de praktijk met concrete interventies in door de consortiumpartners geboden proeftuinen. De voorspellingen en simulaties worden vervolgens toegankelijk gemaakt voor de professionals bij gemeenten en bedrijven. Studenten ontwikkelen daarvoor instrumenten zoals kennisdashboards en decision-supportsystemen. Overige deelnemers kunnen bij het project aanhaken door casussen in te brengen die de studenten uitwerken met behulp van een datagedreven productontwikkelingsproces. Beoogde resultaten Concrete resultaten van dit project zijn onder andere: " een set gevalideerde en generiek toepasbare voorspel- en simulatiemodellen; " 10 uitgevoerde casestudies waarin concrete simulaties worden uitgevoerd en adviezen voor ketenpartijen worden gedestilleerd; " minimaal 3 experimenten waarin concrete interventies zijn uitgevoerd en geëvalueerd; " 3 geteste (kennis)dashboards voor te selecteren partijen; " 3 gerealiseerde datagedreven producten/services; " 3 concrete en geteste decision-supportsystemen voor nader te selecteren ketenpartijen.
The COVID19 pandemic highlighted the vulnerability in supply chain networks in the healthcare sector and the tremendous waste problem of disposable healthcare products, such as isolation gowns. Single-use disposable isolation gowns cause great ecological impact. Reusable gowns can potentially reduce climate impacts and improve the resilience of healthcare systems by ensuring a steady supply in times of high demand. However, scaling reusable, circular isolation gowns in healthcare organizations is not straightforward. It is impeded by economic barriers – such as servicing costs for each use – and logistic and hygiene barriers, as processes for transport, storage and safety need to be (re)designed. Healthcare professionals (e.g. purchasing managers) lack complete information about social, economic and ecological costs, the true cost of products, to make informed circular purchasing decisions. Additionally, the residual value of materials recovered from circular products is overlooked and should be factored into purchasing decisions. To facilitate the transition to circular procurement in healthcare, purchasing managers need more fine-grained, dynamic information on true costs. Our RAAK Publiek proposal (MODLI) addresses a problem that purchasing managers face – making purchasing decisions that factor in social, economic and ecological costs and future benefits from recovered materials. Building on an existing consortium that developed a reusable and recyclable isolation gown, we design and develop an open-source decision-support tool to inform circular procurement in healthcare organizations and simulate various purchasing options of non-circular and circular products, including products from circular cascades. Circular procurement is considered a key driver in the transition to a circular economy as it contributes to closing energy and material loops and minimizes negative impacts and waste throughout entire product lifecycles. MODLI aims to support circular procurement policies in healthcare organizations by providing dynamic information for circular procurement decision making.
The postdoc candidate, Sondos Saad, will strengthen connections between research groups Asset Management(AM), Data Science(DS) and Civil Engineering bachelor programme(CE) of HZ. The proposed research aims at deepening the knowledge about the complex multidisciplinary performance deterioration prediction of turbomachinery to optimize cleaning costs, decrease failure risk and promote the efficient use of water &energy resources. It targets the key challenges faced by industries, oil &gas refineries, utility companies in the adoption of circular maintenance. The study of AM is already part of CE curriculum, but the ambition of this postdoc is that also AM principles are applied and visible. Therefore, from the first year of the programme, the postdoc will develop an AM material science line and will facilitate applied research experiences for students, in collaboration with engineering companies, operation &maintenance contractors and governmental bodies. Consequently, a new generation of efficient sustainability sensitive civil engineers could be trained, as the labour market requires. The subject is broad and relevant for the future of our built environment being more sustainable with less CO2 footprint, with possible connections with other fields of study, such as Engineering, Economics &Chemistry. The project is also strongly contributing to the goals of the National Science Agenda(NWA), in themes of “Circulaire economie en grondstoffenefficiëntie”,”Meten en detecteren: altijd, alles en overall” &”Smart Industry”. The final products will be a framework for data-driven AM to determine and quantify key parameters of degradation in performance for predictive AM strategies, for the application as a diagnostic decision-support toolbox for optimizing cleaning &maintenance; a portfolio of applications &examples; and a new continuous learning line about AM within CE curriculum. The postdoc will be mentored and supervised by the Lector of AM research group and by the study programme coordinator(SPC). The personnel policy and job function series of HZ facilitates the development opportunity.
