Social dominance in cattle is important when resources are scarce and competitive situations occur such as in a queue in front of an automated milking system (AMS). We aimed to 1) create and validate an algorithm to automatically register waiting time in front of an AMS (WT_AMS) for individual cows and 2) study the effect of dominance on observed WT_AMS. Our research took place on a commercial dairy farm in the Netherlands housing 110 Holstein Friesian dairy cows and operating a two stand GEA M1one AMS. Cows were fitted with a NEDAP SmartTag Neck that included cow location. Fifteen one-hour-long observation periods took place during which three researchers noted the time an animal came into a preselected open waiting area in front of the AMS and the time of either leaving the waiting area or entering the AMS. During the time an animal was in the waiting area dominance behavior performed or received by the focal animal was registered. An algorithm was developed to determine the WT_AMS based on location data. WT_AMS for observations and algorithm were strongly correlated (Spearman's rank-order correlation: r=0.828; p=0.000; n=112). A weak negative correlation was found between dominance and waiting time in front of the AMS (r=- 0.248; p=0.01; n=66). 1n conclusion, the algorithm can be used to automatically assess WT_AMS accurately, and dominance behavior was found to have a small effect on waiting time in front of the AMS. More research is needed to determine the effect, for instance, of disease on individual WT_AMS.
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Objective: To systematically describe changes in pain and functioning in patients with osteoarthritis (OA) awaiting total joint replacement (TJR), and to assess determinants of this change. Methods: MEDLINE®, EMBASE, CINAHL® and Cochrane Database were searched through June 2008. The reference lists of eligible publications were reviewed. Studies that monitored pain and functioning in patients with hip or knee OA during the waiting list for TJR were analyzed. Data were collected with a pre-specified collection tool. Methodological quality was assessed and a best-evidence analysis was performed to summarize results. Results: Fifteen studies, of which two were of high quality, were included and involved 788 hip and 858 knee patients (mean age 59-72 and main wait 42-399 days). There was strong evidence that pain (in hip and knee OA) and self-reported functioning (in hip OA) do not deteriorate during a
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Productivity in construction is relatively low compared to other industries. This is particularly true for labour productivity. Problems that contribute to low labour productivity are often related to unorganised workspace, and inefficient organisation of work, materials and equipment. In terms of time use, site workers spend time on various activities including installing, waiting, walking etc. In lean production terms time use should be value adding and not wasteful or non-value adding. The study reported in this paper has endeavoured to measure the time use and movement applying an automated data system. The case study reflected a limited application to a specific kind of activity, namely doors installation. The study investigated time use and movements based on interviews and on automated detection of workforce. The interviews gave insights in the time build-up of work and value-added time use per day. The automated tracking indicated time intervals and uninterrupted presence of site workers on work locations giving indications of value adding time. The time measurements of the study enable comparison of time use categories of site workers. The study showed the data system calculated the same amounts of productive and value adding time one would expect based on the organisation and characteristics of the work. However, the discussion of the results underlined that the particular characteristics of individual projects and types of team work organisation may well have an impact on productivity levels of workers. More application and comparative studies of projects and further development and extension of the automated data system should be helpful.
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Youth care is under increasing pressure, with rising demand, longer waiting lists, and growing staff shortages. In the Netherlands, one in seven children and adolescents is currently receiving youth care. At the same time, professionals face high workloads, burnout risks, and significant administrative burdens. This combination threatens both the accessibility and quality of care, leading to escalating problems for young people and families. Artificial intelligence (AI) offers promising opportunities to relieve these pressures by supporting professionals in their daily work. However, many AI initiatives in youth care fail to move beyond pilot stages, due to barriers such as lack of user acceptance, ethical concerns, limited professional ownership, and insufficient integration into daily practice. Empirical research on how AI can be responsibly and sustainably embedded in youth care is still scarce. This PD project aims to develop practice-based insights and strategies that strengthen the acceptance and long-term adoption of AI in youth care, in ways that support professional practice and contribute to appropriate care. The focus lies not on the technology itself, but on how professionals can work with AI within complex, high-pressure contexts. The research follows a cyclical, participatory approach, combining three complementary implementation frameworks: the Implementation Guide (Kaptein), the CFIR model (Damschroder), and the NASSS-CAT framework (Greenhalgh). Three case studies serve as core learning environments: (1) a speech-to-text AI tool to support clinical documentation, (2) Microsoft Copilot 365 for organization-wide adoption in support teams, and (3) an AI chatbot for parents in high-conflict divorces. Throughout the project, professionals, clients, ethical experts, and organizational stakeholders collaborate to explore the practical, ethical, and organizational conditions under which AI can responsibly strengthen youth care services.
Mycelium biocomposites (MBCs) are a fairly new group of materials. MBCs are non-toxic and carbon-neutral cutting-edge circular materials obtained from agricultural residues and fungal mycelium, the vegetative part of fungi. Growing within days without complex processes, they offer versatile and effective solutions for diverse applications thanks to their customizable textures and characteristics achieved through controlled environmental conditions. This project involves a collaboration between MNEXT and First Circular Insulation (FC-I) to tackle challenges in MBC manufacturing, particularly the extended time and energy-intensive nature of the fungal incubation and drying phases. FC-I proposes an innovative deactivation method involving electrical discharges to expedite these processes, currently awaiting patent approval. However, a critical gap in scientific validation prompts the partnership with MNEXT, leveraging their expertise in mycelium research and MBCs. The research project centers on evaluating the efficacy of the innovative mycelium growth deactivation strategy proposed by FC-I. This one-year endeavor permits a thorough investigation, implementation, and validation of potential solutions, specifically targeting issues related to fungal regrowth and the preservation of sustained material properties. The collaboration synergizes academic and industrial expertise, with the dual purpose of achieving immediate project objectives and establishing a foundation for future advancements in mycelium materials.
Lastmile.info contributes to livable urban environments and efficient deliveries. LastMile.info is set to become the essential platform for finding and monitoring all the necessary information so that you can optimally plan and execute the final stage of the route during store deliveries:> Clear overview of regulations (such as restrictions and time windows)> Shorter waiting times: reduced financial and environmental burden> Greater driver satisfaction thanks to insight into delivery locations
