The labor productivity of construction projects is low. This urges construction companies to increase their labor efficiency, particularly when demands grow and labor is scarce. This blog introduces an overview that helps practitioners identify causes of low productivity to find and eliminate the root causes.
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Reporting of research findings is often selective. This threatens the validity of the published body of knowledge if the decision to report depends on the nature of the results. The evidence derived from studies on causes and mechanisms underlying selective reporting may help to avoid or reduce reporting bias. Such research should be guided by a theoretical framework of possible causal pathways that lead to reporting bias. We build upon a classification of determinants of selective reporting that we recently developed in a systematic review of the topic. The resulting theoretical framework features four clusters of causes. There are two clusters of necessary causes: (A) motivations (e.g. a preference for particular findings) and (B) means (e.g. a flexible study design). These two combined represent a sufficient cause for reporting bias to occur. The framework also features two clusters of component causes: (C) conflicts and balancing of interests referring to the individual or the team, and (D) pressures from science and society. The component causes may modify the effect of the necessary causes or may lead to reporting bias mediated through the necessary causes. Our theoretical framework is meant to inspire further research and to create awareness among researchers and end-users of research about reporting bias and its causes.
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Overtourism is a much-debated topic in academic literature and among policy makers. The discussions have led to insights into the different aspects of overtourism, but the focus has largely remained on finding solutions for the direct, and often short-term, effects of (over)tourism faced by cities "in general", rather than identifying and solving the underlying causes and tensions. To take this needed step, it is essential to be aware that the causes, tensions and impacts related to (over)tourism, the interaction between the city's stakeholders as well as the issues outside tourism that a city is facing are all context specific. A way to identify the underlying causes and tensions in a specific city is to utilize the Smart City Hospitality Framework. The framework, which merges the concepts of sustainable development and city hospitality, provides a diversity of lenses to frame specific tensions that fit within a local context and, as a result, support a contextualized analysis of impacts and intervention strategies of city tourism. In this chapter we utilize the framework to analyse the role of tourism in three European cities (Gothenburg, Darmstadt and Warsaw), each with a different relation to tourism. A deliberate choice was made here to not focus on major tourism cities that are commonly associated with overtourism, to highlight how tensions related to overtourism are also appearing in cities where "in general" there still seems to be room for an increase in tourism numbers. The cases make clear that, also in these cities, the problematic relationship between tourism and the liveability of cities for local stakeholders, as well as the lack of equality with regard to the distribution of benefits and disadvantages, (start to) harm the sustainability of urban tourism development. The cases also highlight the disillusionment of people with the extent to which their voice is heard and taken seriously. In the discussion we identify a number of avenues for further research and experimentation.
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Alcohol use disorder (AUD) is a pattern of alcohol use that involves having trouble controlling drinking behaviour, even when it causes health issues (addiction) or problems functioning in daily (social and professional) life. Moreover, festivals are a common place where large crowds of festival-goers experience challenges refusing or controlling alcohol and substance use. Studies have shown that interventions at festivals are still very problematic. ARise is the first project that wants to help prevent AUD at festivals using Augmented Reality (AR) as a tool to help people, particular festival visitors, to say no to alcohol (and other substances). ARise is based on the on the first Augmented Reality Exposure Therapy (ARET) in the world that we developed for clinical treatment of AUD. It is an AR smartphone driven application in which (potential) visitors are confronted with virtual humans that will try to seduce the user to accept an alcoholic beverage. These virtual humans are projected in the real physical context (of a festival), using innovative AR glasses. Using intuitive phone, voice and gesture interactions, it allows users to personalize the safe experience by choosing different drinks and virtual humans with different looks and levels of realism. ARET has been successfully developed and tested on (former) AUD patients within a clinical setting. Research with patients and healthcare specialists revealed the wish to further develop ARET as a prevention tool to reach people before being diagnosed with AUD and to extend the application for other substances (smoking and pills). In this project, festival visitors will experience ARise and provide feedback on the following topics: (a) experience, (b) awareness and confidence to refuse alcohol drinks, (c) intention to use ARise, (d) usability & efficiency (the level of realism needed), and (e) ideas on how to extend ARise with new substances.
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.
Despite their various appealing features, drones also have some undesirable side-effects. One of them is the psychoacoustic effect that originates from their buzzing noise that causes significant noise pollutions. This has an effect on nature (animals run away) and on humans (noise nuisance and thus stress and health problems). In addition, these buzzing noises contribute to alerting criminals when low-flying drones are deployed for safety and security applications. Therefore, there is an urgent demand from SMEs for practical knowledge and technologies that make existing drones silent, which is the main focus of this project. This project contributes directly to the KET Digital Innovations\Robotics and multiple themes of the top sectors: Agriculture, Water and Food, Health & Care and Safety. The main objective of this project is: Investigate the desirability and possibilities of extremely silent drone technologies for agriculture, public space and safety This is an innovative project and there exist no such drone technology that attempts to reduce the noises coming from drones. The knowledge within this project will be converted into the first proof-of-concepts that makes the technology the first Minimum Viable Product suitable for market evaluations. The partners of this project include WhisperUAV, which has designed the first concept of a silent drone. As a fiber-reinforced 3D composite component printer, Fiberneering plays a crucial role in the (further) development of silent drone technologies into testable prototypes. Sorama is involved as an expert company in the context of mapping the sound fields in and around drones. The University of Twente is involved as a consultant and co-developer, and Research group of mechatronics at Saxion is involved as concept developer, system and user requirement verifier and validator. As an unmanned systems innovation cluster, Space53 will be involved as innovation and networking consultant.
