Computationally efficient model to predict the deformations of a cellular foot orthotic

Background Foot orthotics (FOs) are frequently prescribed to provide comfortable walking for patients. Finite element (FE) simulation and 3D printing pave the way to analyse, optimize and fabricate functionally graded lattice FOs where the local stiffness can vary to meet the therapeutic needs of each individual patient. Explicit FE modelling of lattice FOs with converged 3D solid elements is computationally prohibitive. This paper presents a more computationally efficient FE model of cellular FOs. Method The presented FE model features shell elements whose mechanical properties were computed from the numerical homogenization technique. To verify the results, the predictions of the homogenized models were compared to the explicit model's predictions when the FO was under a static pressure distribution of a foot. To validate the results, the predictions were also compared with experimental measurements when the FO was under a vertical displacement at the medial longitudinal arch. Results The verification procedure showed that the homogenized model was 46 times faster than the explicit model, while their relative difference was less than 8% to predict the local minimum of out-of-plane displacement. The validation procedure showed that both models predicted the same contact force with a relative difference of less than 1%. The predicted force-displacement curves were also within a 90% confidence interval of the experimental measurements having a relative difference smaller than 10%. In this case, using the homogenized model reduced the computational time from 22 h to 22 min. Conclusion The presented homogenized model can be therefore employed to speed up the FE simulation to predict the deformations of the cellular FOs

Quantum Random Number Generator Based on LED

Quantum Random Number Generators Produce random numbers based on the intrinsic probability nature of quantum mechanics, making them true random number generators. In this paper, we design and fabricate an embedded QRNG that produces random numbers based on fluctuations of spontaneous emission in a LED. Additionally, a new perspective on the randomness of the recombination process in a LED is introduced that is consistent with experimental results. To achieve a robust and reliable QRNGm we compare some usual post processing methods and select the best one for a real time device. This device could pass NIST tests, the output speed is 1 Mbit per S and the randomness of the output data is invariant in time and different temperatures

Supervised Machine Learning for Estimation of Total Suspended Solids in Urban Watersheds

Machine Learning (ML) algorithms provide an alternative for the prediction of pollutant concentration. We compared eight ML algorithms (Linear Regression (LR), uniform weighting k-Nearest Neighbor (UW-kNN), variable weighting k-Nearest Neighbor (VW-kNN), Support Vector Regression (SVR), Artificial Neural Network (ANN), Regression Tree (RT), Random Forest (RF), and Adaptive Boosting (AdB)) to evaluate the feasibility of ML approaches for estimation of Total Suspended Solids (TSS) using the national stormwater quality database. Six factors were used as features to train the algorithms with TSS concentration as the target parameter: Drainage area, land use, percent of imperviousness, rainfall depth, runoff volume, and antecedent dry days. Comparisons among the ML methods demonstrated a higher degree of variability in model performance, with the coefficient of determination (R2) and Nash–Sutcliffe (NSE) values ranging from 0.15 to 0.77. The Root Mean Square (RMSE) values ranged from 110 mg/L to 220 mg/L. The best fit was obtained using the AdB and RF models, with R2 values of 0.77 and 0.74 in the training step and 0.67 and 0.64 in the prediction step. The NSE values were 0.76 and 0.72 in the training step and 0.67 and 0.62 in the prediction step. The predictions from AdB were sensitive to all six factors. However, the sensitivity level was variable

Supervised Machine Learning for Estimation of Total Suspended Solids in Urban Watersheds

Machine Learning (ML) algorithms provide an alternative for the prediction of pollutant concentration. We compared eight ML algorithms (Linear Regression (LR), uniform weighting k-Nearest Neighbor (UW-kNN), variable weighting k-Nearest Neighbor (VW-kNN), Support Vector Regression (SVR), Artificial Neural Network (ANN), Regression Tree (RT), Random Forest (RF), and Adaptive Boosting (AdB)) to evaluate the feasibility of ML approaches for estimation of Total Suspended Solids (TSS) using the national stormwater quality database. Six factors were used as features to train the algorithms with TSS concentration as the target parameter: Drainage area, land use, percent of imperviousness, rainfall depth, runoff volume, and antecedent dry days. Comparisons among the ML methods demonstrated a higher degree of variability in model performance, with the coefficient of determination (R2) and Nash–Sutcliffe (NSE) values ranging from 0.15 to 0.77. The Root Mean Square (RMSE) values ranged from 110 mg/L to 220 mg/L. The best fit was obtained using the AdB and RF models, with R2 values of 0.77 and 0.74 in the training step and 0.67 and 0.64 in the prediction step. The NSE values were 0.76 and 0.72 in the training step and 0.67 and 0.62 in the prediction step. The predictions from AdB were sensitive to all six factors. However, the sensitivity level was variable

Surrogate optimization of a lattice foot orthotic

Background: Additive manufacturing enables to print patient-specific Foot Orthotics (FOs). In FOs featuring lattice structures, the variation of the cell’s dimensions provides a locally variable stiffness to meet the therapeutic needs of each patient. In an optimization problem, however, using explicit Finite Element (FE) simulation of lattice FOs with converged 3D elements is computationally prohibitive. This paper presents a framework to efficiently optimize the cell’s dimensions of a honeycomb lattice FO for flat foot condition. Methods: We built a surrogate based on shell elements whose mechanical properties were computed by the numerical homogenization technique. The model was submitted to a static pressure distribution of a flat foot and it predicted the displacement field for a given set of geometrical parameters of the honeycomb FO. This FE simulation was considered as a black-box and a derivative-free optimization solver was employed. The cost function was defined based on the difference between the predicted displacement by the model against a therapeutic target displacement. Results: Using the homogenized model as a surrogate significantly accelerated the stiffness optimization of the lattice FO. The homogenized model could predict the displacement field 78 times faster than the explicit model. When 2000 evaluations were required in an optimization problem, the computational time was reduced from 34 days to 10 hours using the homogenized model rather than explicit model. Moreover, in the homogenized model, there was no need to re-create and re-mesh the insole’s geometry in each iteration of the optimization. It was only required to update the effective properties. Conclusion: The presented homogenized model can be used as a surrogate within an optimization framework to customize cell’s dimensions of honeycomb lattice FO in a computationally efficient manner

Effect of Gargling with Honey and Lemon Water on Cough, Sore Throat, and Hoarseness Following Endotracheal Extubation: A Clinical Trial Study

Background and purpose: Cough, hoarseness, and sore throat are complications of intubation. The aim of this study was to evaluate the effect of gargling with honey and lemon water on cough, sore throat, and hoarseness after extubation of endotracheal tube following surgery. Materials and methods: A clinical trial was carried out in 110 patients undergoing surgery in Neyshabur 22 Bahman Hospital, Iran 2020. They were selected using convenience sampling and randomly allocated to experimental or control group. In experimental group, 6 hours after surgery, 30 cc of honey lemon water was gargled, three times, every two hours and then swallowed. The control group received routine care. Postoperative sore throat, cough, and hoarseness were assessed before the intervention, and 12 and 24 hours after extubation of endotracheal tube. Data were analyzed by independent t-test and repeated measures analysis of variance. Results: Findings showed no significant difference between the two groups in cough and sore throat 12 hours after extubation, but the score for hoarseness was lower in experimental group (P= 0.05). Twenty-four hours after extubation, cough (P= 0.001), hoarseness (P= 0.006), and sore throat (P= 0.023) were significantly lower in experimental group. The passage of time was found to significantly affect all three variables (P<0.001). Conclusion: Considering the positive effects of gargling with honey and lemon water in reducing the complications of endotracheal tube and no side effects, it is recommended to be used after extubation. (Clinical Trials Registry Number: IRCT20150511022218N4

Synthesize of heterostructure TiO2 by simultaneous doping of double silver and phosphate to degradation of methylene blue under visible light

Abstract Photocatalysts show great potential as compounds for restoring contaminated water and wastewater resources. The study aims to synthesize a composite with high photocatalytic potential under visible light to photodegrade the organic pollutants. Ag/Ag3PO4@ TiO2 were synthesized by doping Ag and Ag3PO4 on TiO2. The composite was characterized using X-ray diffraction analysis (XRD), diffuse reflectance spectroscopy, Field emission scanning electron microscopy, and Energy-dispersive X-ray spectroscopy analyses, and its photodegradation ability was investigated by methylene blue. Utilization of pure TiO2 yielded a removal efficiency that was merely half of the efficiency achieved when using modified particles, owing to the reduction of TiO2 s band gap from 3.2 to 1.94 eV. In addition to its enhanced photocatalytic performance under visible light, the synthesized Ag/Ag3PO4@TiO2 photocatalyst demonstrated remarkable efficiency in removing dyes such as methylene blue from aqueous solutions. The removal efficiency at pH less than 7 in 50 ppm methylene blue solution using 3 g/l photocatalyst over 45 min visible light irradiation was approximately 90 percent. Under sunlight, photocatalytic reactions exhibited an efficiency of over 95 percent within 45 min. It can be concluded that the simultaneous introduction of metallic (Ag) and nonmetallic (PO4 3−) dopants significantly increases the efficiency of electron–hole recombination suppression in the photocatalyst and also decreases the band gap