Spectrometry is practical for rapid analysis of fruit maturity. RBF LSSVR model is effective for Vis-NIR calibration. However, nonlinear effect of the high-dimensional spectrum data influences the model prediction accuracy. A fuzzy optimization strategy was proposed to improve performance of RBF LSSVR models in Vis-NIR determination of pomelo maturity. A linguistic iterative mode was introduced. The input variables were features extracted by fuzzy transform PCA. The output was a polynomial formula. In application, the maturity of pomelo was recorded as indices of L*, a* and b*. The fuzzy transformed data was input to the fuzzy optimized LSSVR model for calibration, validation and test. Parameter uncertainty was also investigated to examine the advantage of the proposed strategy. Results showed that the fuzzy optimized model gained appreciate predictions on the indices. This strategy is effective for implementation of Vis-NIR LSSVR models and expected for rapid prediction of fruit maturity.

IoT devices typically stream data such as sensor values to other devices including cloud-based services. Analyzing these streams for cyber-attacks is a challenging task. This is due to the infinite nature of stream-based datatypes. Analyzing streams can require additional real-time processing and computational performance capabilities. In this article, we focus on how concept drifts affect Botnet cyber-attack detection in IoT scenarios. To reveal the result, we incorporate the concept drift analysis to detect cyber-attacks on the Bot-IoT dataset, which consists of legitimate and simulated IoT network traffics, together with various types of attacks. We designed subdatasets of the Bot-IoT to ensure the concept drift occurs that eventually complete the experiments. The detection accuracies improved 15%–26% compared with the classification models without concept drift analysis. We also gain superior performance results by comparing confusion matrices when concept drift analysis is ongoing. We propose a technique featuring a dynamic sliding window based on the residual projection to perform concept drift analysis. During the process of finding concepts in data streams, the sample number is updated dynamically by comparing the anomalous quantity obtained by the residual projection method in the current window to the previous one. In addition to the Bot-IoT dataset, our method is also applied to two popular synthetic datasets SEA Concept and UG-2C-5D. The results demonstrate the effectiveness of our method with respect to the false alarm rate, misses, and average delay.

A polymer optical fiber SPR sensor for detecting Cu²⁺ ion concentration in water is proposed. The sensor employs a simple side-polish structure and realizes the detection of Cu²⁺ ion concentration by employing the chitosan (CS)/polyacrylic acid (PAA) bilayer film on the gold film of the optical fiber surface. The structure of the fiber probe is optimized, and the sensing performances for the Cu²⁺ ion detection are analyzed experimentally. The experimental results demonstrate that the sensor exhibits a high sensitivity of 465.539 nm/ppm for the Cu²⁺ ion detection in the concentration range of 0–0.04 ppm. And it has a fast response speed and good selectivity for Cu²⁺ ions. The sensor has the advantages of simple structure and low cost, and has potential applications in the field of heavy metal detection.

Reactive transport models (RTMs) are essential tools to describe and integrate a wide range of physical and biogeochemical processes in natural and engineered porous media. However, the high computational cost often limits their applications for many practical purposes. These challenges mainly stem from the solution of a set of coupled partial differential equations (PDEs), describing multicomponent transport and geochemical reactions along with their multilevel coupling across different spatial and temporal scales. To mitigate this issue, we propose and develop a surrogate modeling approach, hidden reactive transport neural network (HRTNet). The proposed model relies on a flexible architecture based on two networks, which share a common loss function and allows incorporating both data‐driven and physics‐chemistry‐informed contributions. We consider pyrite oxidation in a 1‐D geochemically heterogeneous domain as a model example to investigate and demonstrate the capability of HRTNet as well as to analyze the performance of the developed surrogate modeling approach. HRTNet was trained based on the training data generated from the process‐based reactive transport simulations, and, successively, the trained model was used as a surrogate to predict the behavior of the reactive transport system. The results reveal that the predictions obtained by the trained surrogate model agree well with those from mechanistic RTMs in a considerably reduced computation time. Furthermore, the physics‐ and chemistry‐informed learning was promising to achieve a good generalization capability, because HRTNet could predict the desired spatio‐temporal dynamics for a wide range of initial concentrations beyond the training data sets.