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  1. A model-based bone milling state identification method via force sensing for a robotic surgical system
    Al-Abdullah KI, Lim CP, Najdovski Z, Yassin W
    Int J Med Robot, 2019 Jun;15(3):e1989.
    PMID: 30721570 DOI: 10.1002/rcs.1989
    BACKGROUND: This paper presents a model-based bone milling state identification method that provides intraoperative bone quality information during robotic bone milling. The method helps surgeons identify bone layer transitions during bone milling.

    METHODS: On the basis of a series of bone milling experiments with commercial artificial bones, an artificial neural network force model is developed to estimate the milling force of different bone densities as a function of the milling feed rate and spindle speed. The model estimations are used to identify the bone density at the cutting zone by comparing the actual milling force with the estimated one.

    RESULTS: The verification experiments indicate the ability of the proposed method to distinguish between one cortical and two cancellous bone densities.

    CONCLUSIONS: The significance of the proposed method is that it can be used to discriminate a set of different bone density layers for a range of the milling feed rate and spindle speed.

  2. Fulltext A Review of Spatial Analysis Application in Childhood Malnutrition Studies
    Shamsuddin AS, Mohd Abu Bakar WA, Syed Ismail SN, Jaafar NH, Mohd Yassin W, Norhizat M
    Malays J Med Sci, 2022 Oct;29(5):24-38.
    PMID: 36474532 DOI: 10.21315/mjms2022.29.5.4
    Approximately 230 million children under 5 years old of age suffer from malnutrition and over half of the children below 5 years old deaths are due to malnutrition nowadays. To gain a better understanding of this problem, the application of spatial analysis has risen exponentially in recent years. In this review, the present state of information on the use of spatial analysis in childhood malnutrition studies was evaluated using four databases of digital scientific journals: ScienceDirect, Scopus, PubMed and CINAHL. We chose 2,278 articles from the search results and a total of 27 articles met our criteria for review. The following information was extracted from each article: objective of study, study area, types of malnutrition, subject, data sources, computer software packages, spatial analysis and factors associated with childhood malnutrition. A total of 10 spatial analysis methods were reported in the reviewed articles and the Bayesian geoadditive regression model was the most common method applied in childhood malnutrition studies. This review highlights the importance of the application of spatial analysis in determining the geographic distribution of malnutrition cases, hotspot areas and risk factors correlated with childhood malnutrition. It also provides implications for strategic initiatives to eradicate all forms of malnutrition.
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