Under the international background of the transformation and promotion of manufacturing, the Chinese government proposed the “Made in China 2025” strategy, which focused on the improvement of a quality-based innovation ability. Moreover, predicting manufacturing quality is one of the crucial measures for quality management. Accurate prediction is closely related to the feature learning of manufacturing processes. Therefore, two categories of intelligent learning approaches, i.e., shallow learning and deep learning, are investigated and compared for manufacturing quality prediction in this paper. Specifically, the feed forward neural network (FFNN) with one hidden layer and the least squares support vector machine (LSSVM) with no hidden layers are selected as the representatives for shallow learning, and the deep restricted Boltzmann machine (DRBM) and the stack autoencoder (SAE) are chosen as the representatives for deep learning. The manufacturing data is collected from a competition about manufacturing quality control in the Tianchi Data Lab of China. The experiments show that the deep framework overwhelms the shallow architecture in terms of mean absolute percentage error, root-mean-square error, and threshold statistics. In addition, the prediction results also indicate that the performances depend on the length of the training data. That is, the bigger the sample size is, the better the performance is.

Accurately forecasting a battery’s remaining useful life (RUL) plays an important role in the prognostics and health management of rechargeable batteries. An effective forecast is reported using a particle filter (PF), but it currently suffers from particle degeneracy and impoverishment deficiencies in RUL evaluations. In this paper, an inheritance PF is developed to predict lithium-ion battery RUL for the first time. A battery degradation model is first mapped onto a PF problem using the genetic algorithm (GA) framework. Then, a Lamarckian inheritance operator is designed to improve the light-weight particles by heavy-weight ones and thus to tackle particle degeneracy. In addition, the inheritance mechanism retains certain existing information to tackle particle impoverishment. The performance of the inheritance PF is compared with an elitism GA-based PF. The former has fewer tuning parameters than the latter and is less sensitive to tuning parameters. Both PFs are applied to the prediction of lithium-ion battery RUL, which is validated using capacity degradation data from the NASA Ames Research Center. The experimental results show that the inheritance PF method offers improved RUL prediction and wider applications. Further improvement is obtained with one-step ahead prediction when the charging and discharging cycles move along.

Objective: The aim of this work was to assess the effect of ergothioneine (ESH)-enriched mushroom extract on oxidative stability, volatile compounds, and sensory quality of emulsified sausage.Methods: The ESH content was determined by high performance liquid chromatography. The antioxidant activity of Flammulina velutipes (F. velutipes) extract was determined through radical-scavenging activity of 1,1 diphenyl-2-picryl-hydrazyl, 2,2-azinobis (3-ethylbenzothiazoline-6-sulfonic acid) and hydroxyl radicals. Four different groups of emulsified sausage were manufactured: control, no antioxidants; BHA, 0.01% butylated hydroxyanisole; EEME, 0.8% ESH-enriched mushroom (F. velutipes) extract; AE, 0.012% authentic ESH, after storage for 14 days (at 4°C), the quality of sausage including oxidative stability (2-thiobarbituric acid reactive substances and protein carbonyls content), volatile compounds and sensory quality were studied.Results: It was demonstrated that adding ESH-enriched F. velutipes extract to sausage could effectively prevent lipid and protein oxidation, and its efficacy was equivalent with 0.01% BHA. During meat processing, the ESH mainly contributed to the antioxidative activity of F. velutipes extract. The flavor and sensory attributes of emulsified sausage were improved through adding ESH-enriched F. velutipes extract.Conclusion: Accordingly, the extract of F. velutipes contained high-level of ESH and could be a good antioxidant candidate for processed meat production.

Accurate and rapid identification of toxic wild mushrooms is critical for food safety and public health, yet existing visual detection methods face severe limitations due to high interspecies morphological similarity, complex field backgrounds, and drastic scale variations. To address these challenges, we propose YOLO-SD, an improved multiscale detection model based on YOLOv11. A lightweight Simple Attention Module (SimAM) is embedded after the backbone’s final C2PSA layer to reweight deep semantic features and highlight discriminative regions. Meanwhile, the first C3k2 module in the neck is replaced by an optimized C3k2 with an improved Dilation-wise Residual Module (DWR) structure (denoted as C3k2_DWR), which adopts dual dilation branches, partial depthwise convolutions and a learnable scaling coefficient to enhance multiscale feature extraction with low parameter overhead. Ablation experiments verify the significant complementary effect between the two modules, whose combination achieves 0.883 mean Average Precision at IoU 0.5 (mAP@50), outperforming both single-module versions and the 0.865 YOLOv11 baseline. Comparative results show YOLO-SD reaches 89.3% Precision and 80.8% Recall, superior to mainstream YOLO variants while maintaining high inference efficiency. This work provides an effective intelligent identification solution for early warning of wild toxic mushroom hazards, contributing to agricultural ecological safety and food chain risk management.