Applications of Box–Behnken experimental design coupled with artificial neural networks for biosorption of low concentrations of cadmium using Spirulina (Arthrospira) spp

Psychological Stress Identification and Evaluation Method Based on Mobile Human-Computer Interaction Equipment

Since the 1980s, the research of artificial neural networks in the field of artificial intelligence has become more and more common. It accepts nonlinear parallel processing, has strong learning and flexibility, and can be used for influencing factor analysis. The ideal power values and triggers are obtained in the Hopfield network model using genetic algorithm, which best avoids the drawbacks of the Hopfield network model instillation learning method. Through the BP of mobile human-computer interaction equipment, hereditary, genetic algorithms, and Hi-PLS regression method in the artificial neural network, the psychological pressure of college students is identified, evaluated, and predicted from three dimensions such as learning, life, and personal events. This makes it possible to understand the current physical and mental conditions of the students in a timely manner, guide to relieve anxiety and fear, and reach a safe psychological level. The three test results are less than 1%, which has high research significance and value.

Monitoring Microalgal Growth of Chlorella minutissima with a New All Solid-state Contact Nitrate Selective Sensor

As third generation feedstock, microalgae are microorganisms that can grow only in the optimum conditions. There are parameters including the concentration of macro and microelements in nutrient solution, pH, temperature, and light intensity that have significant impact on microalgal growth. In recent years, various sensing devices has been developed for sensitive measurement of these parameters during microalgal growth. In this study, a new potentiometric nitrate selective sensor was developed to indicate the nitrate uptake of microalgae and the effect of nitrate nutrient on microalgal growth, specifically, and this sensor was successfully applied to determine nitrate concentration in medium during microalgal growth. Moreover, the effects of nitrate, carbonate and phosphate concentration in the growth medium on biomass production of Chlorella minutissima were investigated by using Box-Behnken design method, and optimum conditions were determined for the highest biomass production of microalgae. As a result of the experiments, it was seen that the highest C. minutissima production was achieved using the medium consist of 2.63 g/L NaNO₃ , 0.35 g/L Na₂ CO₃ and 0.4 g/L KH₂ PO_4. Statistically, it was observed that there was a proportional relationship between the microalgae production and investigated parameters such as carbon, nitrogen and phosphate amounts of culture mediums. The electrode showed a wide linear range between 1.0×10^-1 and 5.0×10^-5 M with a detection limit of the 5×10^-6 M and the response time was found as 10 s. The results showed that developed nitrate selective sensor could be successfully applied for continuous measurement of nitrate in microalgal productions at reduced cost. This article is protected by copyright. All rights reserved.

A Generalized Method for Modeling the Adsorption of Heavy Metals with Machine Learning Algorithms

Applications of machine learning algorithms (MLAs) to modeling the adsorption efficiencies of different heavy metals have been limited by the adsorbate–adsorbent pair and the selection of specific MLAs. In the current study, adsorption efficiencies of fourteen heavy metal–adsorbent (HM-AD) pairs were modeled with a variety of ML models such as support vector regression with polynomial and radial basis function kernels, random forest (RF), stochastic gradient boosting, and bayesian additive regression tree (BART). The wet experiment-based actual measurements were supplemented with synthetic data samples. The first batch of dry experiments was performed to model the removal efficiency of an HM with a specific AD. The ML modeling was then implemented on the whole dataset to develop a generalized model. A ten-fold cross-validation method was used for the model selection, while the comparative performance of the MLAs was evaluated with statistical metrics comprising Spearman’s rank correlation coefficient, coefficient of determination (R2), mean absolute error, and root-mean-squared-error. The regression tree methods, BART, and RF demonstrated the most robust and optimum performance with 0.96 ⫹ R2 ⫹ 0.99. The current study provides a generalized methodology to implement ML in modeling the efficiency of not only a specific adsorption process but also a group of comparable processes involving multiple HM-AD pairs.