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Abstract Gastric Function has been successfully estimated by gastric electrical impedance tomography (gEIT) Suit with dual-step fuzzy clustering. The gEIT Suit which are made of elastic cloth with dual-planar electrodes and compact data acquisition (DAQ) system measures gastric impedance Z to visualize the gastric conductivity distribution σ. The dual-step fuzzy clustering extracts the clustered gastric conductivity distribution k σ, which accurately estimates the gastric function. The gEIT Suit with dual-step fuzzy clustering are applied to eight healthy persons during liquid meal consumption to estimate the gastric function under gastric accommodation phase of 200, 400 and 600 mL based on the gastric emptying phase. As the results, the gEIT Suit successfully estimate the gastric function. By the measured impedance Z, the subjects have a mean temporal impedance $$\overline{\Delta \mathbf{Z} }$$ Δ Z ¯ = − 9.27 [Ohm] and p-value of that $$\overline{\mathbf{Z} }$$ Z ¯ p(Z) = 0.0013[–]as the t-test result. In the case of gastric conductivity distribution σ, the subjects have a value of spatial mean conductivity distribution ⟨σ⟩ = 0.23[–] and p-value of that ⟨σ⟩ p(σ) = 0.0140[–]. Lastly, in the case gastric volume V, subjects have a gastric volume V = 12.44 [%] and p-value p(V) = 0.0664[–].

High-speed and accurate meat composition imaging method has been proposed based on mechanically-flexible electrical impedance tomography (mech-f-EIT) with k-nearest neighbor and fuzzy k-means machine learning approaches. This proposed method has four stages which are 1) estimation of meat boundary shape ∂Ω by mech-f-EIT for base data, 2) approximation of Jacobian matrix J* by k-nearest neighbor (k-NN) algorithm under ∂Ω for high speed, 3) clustering of meat composition kσ (fat k = 1, lean k = 2, bone k = 3) by fuzzy k-means algorithm based on the reconstructed meat conductivity distribution σ for high accuracy, and 4) edge detection of meat composition kΩ by Canny algorithm for sharp edge. This method is qualitatively evaluated by using two agar phantoms, a cow's lower leg and three lamb's lower legs. As the results, mech-f-EIT estimates ∂Ω with total mean boundary error 〈ẽb〉 = 4.81 %. This method achieves high-speed approximation of J* with total mean speed-up performance 〈s̃p〉 = 4.51 times as compared with the computation time of standard J; nonetheless, total mean cross correlation between J* and J is accurate 〈c̃c〉 = 0.92. Moreover, this method clusters the kσ with total mean area error 〈ẽa〉 = 4.49 %. Furthermore, this imaging method detects sharply the meat composition edges kΩ between fat and lean (k = 1 - 2) and between lean and bone (k = 2 - 3) with total mean edge error 〈ẽe〉 =6.90 %.

This study analyzed the contributions of Balinese female entrepreneurs in the culinary industry in promoting local food to support tourism in Bali. Research was undertaken in the area of Bali province and data were obtained from observations, interviews and study of literature supported by the theory of invented tradition. Eight Balinese warrior of culinaries were interviewed supported by food bloggers, literature reviews and documentation. Data were analyzed based on descriptive qualitative. The results show that Balinese female entrepreneurs have: (1) popularized Balinese food, (2) preserved the existence of Balinese food, (3) strengthened the Balinese culinary identity, and (4) supported tourism in Bali.

A sectorial sensitivity matrix using a K-means clustering algorithm has been proposed for electrical capacitance tomography (ECT) for accurate determination of permittivity distribution in the inclusion within the background area. The proposed sectorial sensitivity matrix clusters the pixel position of the inclusion area to minimize the soft field effect that leads to image artifacts and blurred images. The standard sensitivity matrix is nonlinear thus it is not easy to eliminate image artifacts due to the noise signal and nonlinear electric field propagation. The performance of the sectorial sensitivity matrix is evaluated through numerical simulation and experimental studies. From the results, the sectorial sensitivity matrix improves the reconstructed images with a 42.92% and 67.12% lower root mean square error RMSE, 1.66% and 16.16% higher correlation coefficient CC, and 21.37% lower image error IE values in the simulation and experimental studies respectively as compared to the standard sensitivity matrix. The sectorial sensitivity matrix has an advantage of high performance even when the signal to noise ratio in an ECT system is low. Under such conditions, the present normalization method is incapable of calculating the normalized capacitance of the inclusion due to the low ratio of permittivity of the inclusion to that of the background. [ABSTRACT FROM AUTHOR]