Purpose Fingertip injuries are common in industrial production activities as well as in domestic work. Loss of pulp hampers daily life activities. Functional and aesthetic aspects are important in fingertip reconstruction. The bone is usually exposed along with soft tissue loss. Therefore to reconstruct the pulp flap with adequate bulk is required. Methods We reported a case series of 12 patients with the injury over the volar aspect of distal phalanx of the index or middle finger. In all cases, laterally based thenar flap was chosen. The flap donor site was closed primarily in most of cases, while 4 patients required skin graft. The flap was detached between 2–3 weeks. Functional assessment was done using static and dynamic 2-point discrimination and range of motion at each joint. The aesthetic outcome was assessed through questionnaire. The results were analyzed using the unpaired t -test (SPSS version 21). Results Partial necrosis occurred in 2 cases while rest of flaps survived successfully. Static 2-point discrimination ranged from 6–10 mm, mean 8.6 mm; and dynamic 2-point discrimination ranged from 8–10 mm, mean 8.9 mm. The mean satisfaction score was (4.0 ± 0.55). Conclusion Thenar flap is a good choice for reconstruction of the finger pulp as it provides the bulk with good functional and aesthetic outcome.

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Natural fiber-reinforced polymer (NFRP) composites are increasingly considered in the industry for creating environmentally benign product alternatives. The complex structure of the fibers and their random distribution within the matrix basis impede the machinability of NFRP composites as well as the resulting product quality. This article investigates a smart process monitoring approach that employs acoustic emission (AE)-elastic waves sourced from various plastic deformation and fracture mechanisms-to characterize the variations in the NFRP machining process. The state-of-the-art analytic tools are incapable of handling the transient dynamic patterns with long-term correlations and bursts in AE and how process conditions and the underlying material removal mechanisms affect these patterns. To address this gap, we investigated two types of the bidirectional gated recurrent deep learning neural network (BD-GRNN) models, viz., bidirectional long short-term memory and bidirectional gated recurrent unit to predict the process conditions based on dynamic AE patterns. The models are tested on the AE signals gathered from orthogonal cutting experiments on NFRP samples performed at six different cutting speeds and three fiber orientations. The results from the experimental study suggest that BD-GRNNs can correctly predict (around 87 % accuracy) the cutting conditions based on the extracted temporal-spectral features of AE signals.