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The batch process generally covers high nonlinearity and two-directional dynamics: time-wise dynamics, which correspond to inherently time-varying dynamics resulting from the slowly varying underlying driving forces within each batch duration; and batch-wise dynamics, which are associated with different operating modes among different batches. However, most existing dynamic nonlinear monitoring methods cannot extract the slowly varying underlying driving forces of the nonlinear batch process and rarely tackle the batch-wise dynamic characteristics among batch runs. In order to address these issues, a new monitoring scheme based on two-directional dynamic kernel slow feature analysis (TDKSFA) is developed by combining kernel SFA with a global modelling strategy. In the TDKSFA method, kernel SFA is integrated with the ARMAX time series model to mine the nonlinear and timewise dynamic properties within a batch run due to its capability of extracting the slowly varying underlying driving forces. Furthermore, the global modelling strategy is presented to handle the batch-wise dynamics among batches by calculating the total average kernel matrix of all training batches. After the slow features are extracted, Hotelling's T 2 and SPE statistics are built to detect faults. To solve the issue of fault variable nonlinear identification, a novel nonlinear contribution plot inspired by the pseudo-sample variable projection trajectories in the TDKSFA model is further proposed to identify fault variables. Finally, the feasibility and effectiveness of the TDKSFA-based fault diagnosis strategy is demonstrated through a numerical system and the penicillin fermentation process. K E Y W O R D S batch process, fault diagnosis, slow feature analysis, two-directional dynamics 1 | INTRODUCTION Batch processes are becoming increasingly important in the pharmaceutical, semiconductor, polymer, and biochemical industries to manufacture high value-added products. However, it is difficult to establish a batch process monitoring model due to the high nonlinearity in time-wise and the time-varying dynamics both time-wise

Herein, we disclose a strategy for the asymmetric dearomatization of N-arylacyl indoles via a palladium-catalyzed tandem Heck/carbonylation, leading to an array of indoline-3carboxylates bearing vicinal C2-aza-quaternary and C3 tertiary stereocenters in high yields and excellent enantio-and diastereoselectivities. This study is an important advance in the field of asymmetric carbonylation and enantioselective dearomatization reactions.

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Lonicera japonica Thunb is a commonly used Chinese herbal medicine, which belongs to the family Caprifoliaceae. The active components varied greatly during bud development. Research on the variation of the main active components is significant for the timely harvesting and quality control of Lonicera japonica. In this study, the attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) combined with the chemometric method was performed to investigate the variability of different harvesting periods of Lonicera japonica. The preliminary characterization from ATR-FTIR fingerprints showed various characteristic absorption peaks of the main active components from the different harvesting times, such as flavonoids, organic acids, iridoids, and volatile oils. Additionally, principal component analysis (PCA) scatter plots showed that there was a clear clustering trend in the samples of the same harvesting period, and the samples of the different harvesting periods could be well distinguished. Finally, further analysis by the orthogonal partial least-squares discriminant analysis (OPLS-DA) showed that there were regular changes in flavonoids, phenolic acids, iridoids, and volatile oils in different harvesting periods. Therefore, ATR-FTIR, as a novel and convenient analytical method, could be applied to evaluate the quality of Lonicera japonica.

Indoor occupancy detection is essential for energy efficiency control and Coronavirus Disease 2019 traceability. The number and location of people can be accurately identified and determined through classroom surveillance video analysis. This information is used to manage environmental equipment such as HVAC and lighting systems to reduce energy use. However, the mainstream one-stage YOLO algorithm still uses an anchor-based mechanism and couples detection heads to predict. This results in slow model convergence and poor detection performance for densely occluded targets. Therefore, this paper proposed a novel decoupled anchor-free VariFocal loss convolutional network algorithm DFV-YOLOv5 for occupancy detection to tackle these problems. The proposed method uses the YOLOv5 algorithm as a baseline. It uses the anchor-free mechanism to reduce the number of design parameters needing heuristic tuning. Afterwards, to reduce the coupling of the model, speed up the model's convergence ability, and improve the model detection performance, the detection head is decoupled based on the YOLOv5 model. It can resolve the conflict between classification and regression tasks. In addition, we use the VariFocal loss to assign more weights to difficult data points to optimize the class imbalance problem and use the training target q to measure positive samples, treating positive and negative samples asymmetrically. The total loss function is redesigned, the L 1 loss is increased, and the ablation experiment verifies the effect of the improved loss. By applying a hybrid activation function of the sigmoid linear unit and rectified linear unit, we improved the model's nonlinear representation and reduced the model's inference time. Finally, a classroom dataset was constructed to validate the occupancy detection performance of the model. The proposed model was compared with mainstream target detection models regarding average mean precision, memory allocation, execution time, and the number of parameters on the VOC2012, CrowdHuman and self-built datasets. The experimental results show that the method significantly improves the detection accuracy and robustness, shortens the inference time, and proves the practicality of the algorithm in occupancy detection compared with the mainstream target detection model and related variants of the model.