Quantitative Pore Characterization of Polyurethane Foam with Cost-Effective Imaging Tools and Image Analysis: A Proof-Of-Principle Study.

This study investigated the pore characterization of polyurethane (PU) foam as a necessary step in water filtration membrane fabrication. Porous material characterization is essential for predicting membrane performance, strength, durability, surface feel, and to understand the transport mechanisms using modeling and simulations. Most existing pore characterization techniques are relatively costly, time-consuming, subjective, and have cumbersome sample preparations. This study focused on using three relatively inexpensive imaging systems: a black box, Canon camera (EOS760D), and LaserJet scanner (M1132 MFP). Two standard, state-of-the-art imaging systems were used for comparison: a stereomicroscope and a scanning electron microscope. Digital images produced by the imaging systems were used with a MATLAB algorithm to determine the surface porosity, pore area, and shape factor of the polyurethane foam in an efficient manner. The results obtained established the compatibility of the image analysis algorithm with the imaging systems. The black box results were found to be more comparable to both the stereomicroscope and SEM systems than those of the Canon camera and scanner imaging systems. Indeed, the current research effort demonstrates the possibility of substrate characterization with inexpensive imaging systems.

Estimating cocoa bean parameters by FT-NIRS and chemometrics analysis.

Rapid analysis of cocoa beans is an important activity for quality assurance and control investigations. In this study, Fourier transform near infrared spectroscopy (FT-NIRS) and chemometric techniques were attempted to estimate cocoa bean quality categories, pH and fermentation index (FI). The performances of the models were optimised by cross-validation and examined by identification rate (%), correlation coefficient (Rpre) and root mean square error of prediction (RMSEP) in the prediction set. The optimal identification model by back propagation artificial neural network (BPANN) was 99.73% at 5 principal components. The efficient variable selection model derived by synergy interval back propagation artificial neural network regression (Si-BPANNR) was superior for pH and FI estimation. Si-BPANNR model for pH was Rpre=0.98 and RMSEP=0.06, while for FI was Rpre=0.98 and RMSEP=0.05. The results demonstrated that FT-NIRS together with BPANN and Si-BPANNR model could successfully be used for cocoa beans examination.

Response of okra based on electrophysiological modeling under salt stress and re-watering / Resposta do okra com base em modelagem eletrofisiológica sob stress de sal e re-watering

In this study, two okra cultivars, Chinese green and Chinese red were used to assess the water status and growth parameters subjected to salt stress by adding NaCl and CaCl2 with same proportion in Hoagland culture solution at levels of 0%, 0.6%, 1.2%, 1.8% and re-watering at levels of 0.6-0%, 1.2-0.6%, 1.8-1.2%. The measured water potential and physiological capacitance values were used to calculate leaf tensity. Salt stress significantly reduced growth and water status parameters. Chinese green showed more reduction as compared to Chinese red but at 1.8% salt stress reduction of both cultivars were almost same. Re-watering had given a positive response for both cultivars to recover from higher salt stress. Dry weight, physiological capacitance, leaf tensity and salts concentration levels models gave predicting re-watering levels in percentage, also gave values of dilute irrigation point for Chinese red 9.05 or 10.00 ds m-1 and Chinese green 6.67 or 5.66 ds m-1. At resulted dilution points, plants of both cultivars were under high salt stress, which emphasized the need to re-water or dilution of salts for the survival of plants. The most effective predicting re-watering level and dilute irrigation point of both cultivars were found in same regime, so these models findings were very credible and meaningful. Higher dilute irrigation value of Chinese red indicates its more tolerance ability than Chinese green. Model's equations also gave direct irrigation point of Chinese red 1.32 or 1.62 ds m-1 and Chinese green 2.07 or 0.38 ds m- 1. It was concluded that predicting re-watering levels, dilute and direct irrigation point help to get maximum production using saline water resources.

Neste estudo, foram utilizados dois cultivares de quiabo, verde chinês e vermelho chinês para avaliar o estado da água e parâmetros de crescimento submetidos a estresse salino, adicionando NaCl e CaCl2 com a mesma proporção em solução de cultura de Hoagland a níveis de 0%, 0,6%, 1,2% , 1,8% e re-irrigação a níveis de 0,6-0%, 1,2-0,6%, 1,8-1,2%. O potencial de água medido e os valores de capacitância fisiológica foram utilizados para calcular a tensão das folhas. O estresse com sal reduziu significativamente os parâmetros de crescimento e de estado da água. O verde chinês mostrou mais redução em comparação ao vermelho chinês, mas em 1,8% a redução do estresse salino de ambas as cultivares foi quase a mesma. Re-rega tinha dado uma resposta positiva para ambas as cultivares para recuperar de maior sal estresse. Os valores de peso seco, capacitância fisiológica, tensão da folha e níveis de concentração de sais mostraram predizer níveis de irrigação em porcentagem, também apresentaram valores de ponto de irrigação diluído para vermelho chinês 9,05 ou 10,00 ds m-1 e verde chinês 6,67 ou 5,66 ds m-1. Nos pontos de diluição resultantes, as plantas de ambas as cultivares estavam sob alto estresse salino, o que enfatizou a necessidade de re-água ou diluição de sais para a sobrevivência das plantas. O nível de irrigação mais eficiente e o ponto de irrigação diluído de ambas as cultivares foram encontrados no mesmo regime, portanto, esses resultados foram muito confiáveis e significativos. Maior valor de irrigação diluída de vermelho chinês indica a sua capacidade de tolerância mais do que verde chinês. As equações do modelo também deram ponto de irrigação direta de vermelho chinês 1,32 ou 1,62 ds m-1 e verde chinês 2,07 ou 0,38 ds m-1. Concluiu-se que a previsão dos níveis de rega, o ponto de irrigação diluído e direto ajudam a obter a máxima produção usando recursos hídricos salinos.