RESULTS: The relationship between dimensionless moisture content and shrinkage of sweet potato in terms of volume, surface area, perimeter and illuminated area was found to be linearly correlated. The results also demonstrated that the shrinkage of sweet potato based on computer vision and backscattered optical parameters is affected by the product thickness, drying temperature and drying time. A multilayer perceptron (MLP) artificial neural network with input layer containing three cells, two hidden layers (18 neurons), and five cells for output layer, was used to develop a model that can monitor, control and predict the shrinkage parameters and moisture content of sweet potato slices under different drying conditions. The developed ANN model satisfactorily predicted the shrinkage and dimensionless moisture content of sweet potato with correlation coefficient greater than 0.95.
CONCLUSION: Combined computer vision, laser light backscattering imaging and artificial neural network can be used as a non-destructive, rapid and easily adaptable technique for in-line monitoring, predicting and controlling the shrinkage and moisture changes of food and agricultural crops during drying. © 2017 Society of Chemical Industry.
Methods: This open label comparative design study randomized health professional clinicians to receive "practice points" on tendinopathy management via Twitter or Facebook. Evaluated outcomes included knowledge change and self-reported changes to clinical practice.
Results: Four hundred and ninety-four participants were randomized to 1 of 2 groups and 317 responders analyzed. Both groups demonstrated improvements in knowledge and reported changes to clinical practice. There was no statistical difference between groups for the outcomes of knowledge change (P = .728), changes to clinical practice (P = .11) or the increased use of research information (P = .89). Practice points were shared more by the Twitter group (P
MATERIALS AND METHODS: An auricular prosthesis, a complete denture, and anterior and posterior crowns were constructed using conventional methods and laser scanned to create computerized 3D meshes. The meshes were optimized independently by four computer-aided design software (Meshmixer, Meshlab, Blender, and SculptGL) to 100%, 90%, 75%, 50%, and 25% levels of original file size. Upon optimization, the following parameters were virtually evaluated and compared; mesh vertices, file size, mesh surface area (SA), mesh volume (V), interpoint discrepancies (geometric similarity based on virtual point overlapping), and spatial similarity (volumetric similarity based on shape overlapping). The influence of software and optimization on surface area and volume of each prosthesis was evaluated independently using multiple linear regression.
RESULTS: There were clear observable differences in vertices, file size, surface area, and volume. The choice of software significantly influenced the overall virtual parameters of auricular prosthesis [SA: F(4,15) = 12.93, R2 = 0.67, p < 0.001. V: F(4,15) = 9.33, R2 = 0.64, p < 0.001] and complete denture [SA: F(4,15) = 10.81, R2 = 0.67, p < 0.001. V: F(4,15) = 3.50, R2 = 0.34, p = 0.030] across optimization levels. Interpoint discrepancies were however limited to <0.1mm and volumetric similarity was >97%.
CONCLUSION: Open-source mesh optimization of smaller dental prostheses in this study produced minimal loss of geometric and volumetric details. SculptGL models were most influenced by the amount of optimization performed.
Materials and Methods: This is a single-center quasi-experimental study involving 100 patients seen in the outpatient department with knee osteoarthritis. They were randomly (computer generated) allocated into two arms (high frequency [H-F] or low frequency [L-F]). H-F is set at 100 Hz and L-F is set at 4 Hz. A baseline assessment is taken with the visual analog score (VAS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Oxford Knee Score, and Lequesne index. They were instructed to self-administer the TENS therapy as per protocol and followed up at the 4th and 12th week to be reevaluated on the above scores.
Results: The final results show that both H-F and L-F groups showed improvement in all parameters of the VAS, WOMAC index, Oxford Knee Score, and Lequesne index (73%). Only the pain component of Lequesne index, activities of daily living component of Lequesne index, total Lequesne index, and pain component of WOMAC index shows a statistically significant difference, favoring the H-F group. The H-F group yields a faster result; however, with time the overall effect remains the same in both groups.
Conclusion: Both H-F and L-F groups show improvement in all the component of Lequesne index, Oxford Knee Score, WOMAC index, and VAS with no statistical difference between the two groups. Although H-F yields a faster result, not everyone is able to tolerate the intensity. Therefore, the selection of H-F or L-F should be done on case basis depending on the severity of symptoms, patient's expectation, and patient's ability to withstand the treatment therapy. Based on this 12th week follow-up, both groups will continue to improve with time. A longer study should be conducted to see it this improvement will eventually plateau off or continue to improve until the patient is symptom free.