26 research outputs found
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Evaluating the Use of Daily Care Notes Software for Older People with Dementia
There has been little research to investigate the impact of software to support the care for older people with dementia care. This article reports the evaluation of software adapted to support one key person-centered task for the care of older residents with dementia – recording and sharing daily care notes. The evaluation on the dementia wing of 1 residential home for over 6 months revealed that use of the software on mobile devices carried by the carers increased the number and volume of daily care notes recorded, but only for the types of content that were already being recorded by carers. Carers reported more advantages that resulted from daily care notes once in digital form than from the documenting task, as well as barriers to the use of mobile digital software to record daily care notes
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Using mobile devices and apps to support reflective learning about older people with dementia
There has been little research to develop computing technologies to support the care of people with dementia, in spite of the growing challenges that the condition poses for society. To design such technologies, an existing model of computer-support reflective learning was instantiated with findings from a pre-design study in one residential home. The result was a mobile device running an adapted enterprise social media app to support person-centred care. Evaluations of the device and app in two residential homes revealed that use of the app both motivated and increased different styles of care note recording, but little reflective learning was identified or reported. The results suggest the need for more comprehensive and flexible computer-based support for reflective learning about residents in their care – and new designs of this more comprehensive support are also introduced
Integrative analysis of multimodal mass spectrometry data in MZmine 3
3 Pág.We thank Christopher Jensen and Gauthier Boaglio for their contributions to the MZmine codebase. We thank Jianbo Zhang and Zachary Russ for their donations to MZmine development. The MZmine 3 logo was designed by the Bioinformatics & Research Computing group at the Whitehead Institute for Biomedical Research. T.P. is supported by Czech Science Foundation (GA CR) grant 21-11563M and by the European Union’s Horizon 2020 research and innovation programme under Marie Skłodowska-Curie grant agreement 891397. Support for P.C.D. was from US NIH U19 AG063744, P50HD106463, 1U24DK133658 and BBSRC-NSF award 2152526. T.S. acknowledges funding by Deutsche Forschungsgemeinschaft (441958208). M. Wang acknowledges the US Department of Energy Joint Genome Institute ( https://ror.org/04xm1d337 , a DOE Office of Science User Facility) and is supported by the Office of Science of the US Department of Energy operated under subcontract No. 7601660. E.R. and H.H. thank Wen Jiang (HILICON AB) for providing the iHILIC Fusion(+) column for HILIC measurements. M.F., K.D. and S.B. are supported by Deutsche Forschungsgemeinschaft (BO 1910/20). L.-F.N. is supported by the Swiss National Science Foundation (project 189921). D.P. was supported through the Deutsche Forschungsgemeinschaft (German Research Foundation) through the CMFI Cluster of Excellence (EXC-2124 — 390838134 project-ID 1-03.006_0) and the Collaborative Research Center CellMap (TRR 261 - 398967434). J.-K.W. acknowledges the US National Science Foundation (MCB-1818132), the US Department of Agriculture, and the Chan Zuckerberg Initiative. MZmine developers have received support from the European COST Action CA19105 — Pan-European Network in Lipidomics and EpiLipidomics (EpiLipidNET). We acknowledge the support of the Google Summer of Code (GSoC) program, which has funded the development of several MZmine modules through student projects. We thank Adam Tenderholt for introducing MZmine to the GSoC program.Peer reviewe
Mining sequencing to control blend quality
The extraction of ore from a mine must be scheduled to meet specific order production targets or `builds'. A number of physical, logical, and capacity constraints affect this planning. There is uncertainty in the process due to the imprecision of our knowledge of the mine's content until extracted from the ground. At the 2016 mathematics-in-industry study group workshop in Australia, Schneider Electric presented a project to consider mine scheduling. This paper reports on the assorted modelling approaches: exploration of sample data; considerations of the physically feasible mining sequences; the construction of a mixed integer program; a general heuristic strategy for dealing with different levels of uncertainty; and a build simulation. These provide promising avenues for further research on mine sequencing and related problems.
References M. Menabde, G. Froyland, P. Stone, G. Yeates (2005), ``Mining schedule optimisation for conditionally simulated orebodies'', in Dimitrakopoulos R. (ed.) Orebody modelling and strategic mine planning: uncertainty and risk management models, Australasian Inst. Mining and Metallurgy, pp. 343–357, ISBN:1-920806-42-3 M. Ibrahimov, A. Mohais, S. Schellenberg, Z. Michalewicz (2014). ``Scheduling in iron ore open-pit mining'', The International Journal of Advanced Manufacturing Technology, 72(5–8), 1021–1037. doi:10.1007/s00170-014-5619-8 L. Caccetta, S. P. Hill (2003) ``An Application of Branch and Cut to Open Pit Mine Scheduling'' Journal of Global Optimization, 27, 349–365. doi:10.1023/A:102483502218