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Dry matter intake (DMI) determination is essential for effective management of meat goats, especially in optimizing feed utilization and production efficiency. Unfortunately, farmers often face challenges in accurately predicting DMI which leads to wastage of feed and an increase in the cost of production. This investigation aimed to predict DMI in Black Bengal goats by using body weight (BW), body condition score (BCS), average daily gain (ADG), and metabolic body weight (MBW) by applying an artificial neural network (ANN) model.

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Quantitative phase imaging (QPI) has become a valuable tool in the field of biomedical research due to its ability to quantify refractive index variations of live cells and tissues. For example, three-dimensional differential phase contrast (3D DPC) imaging uses through-focus images captured under different illumination patterns deconvoluted with a computed 3D phase transfer function (PTF) to reconstruct the 3D refractive index. In conventional 3D DPC with semi-circular illumination, partially spatially coherent illumination often diminishes phase contrast, exacerbating inherent noise, and can lead to a large number of zero values in the 3D PTF, resulting in strong low-frequency artifacts and deteriorating imaging resolution.

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