In order to solve the problem of existing diagnostic methods for chronic gastritis which are complex and traumatic, a novel noninvasive method for diagnosis of chronic gastric based on e‐nose and deep convolutional neural network is proposed. Firstly, in order to collect samples, a respiratory gas sampling device was established and the response curve of respiratory gas is generated. Then, a deep convolutional neural network for the diagnosis of chronic gastritis is proposed to recognize and classify the respiratory gas response curve. The DCNN model attained good results with accuracy, sensitivity, and specificity of 85.00%, 90.00%, and 80.00%, respectively, for chronic gastric prediction. The proposed method provides a new way for the clinical auxiliary diagnoses of chronic gastric.

Automatic detection of fabric defects is important in textile quality control, particularly in detecting fabrics with multifarious patterns and colors. This study proposes a fabric defect detection system for fabrics with complex patterns and colors. The proposed system comprises five convolutional layers designed to extract features from the original images effectively. In addition, three fully connected layers are designed to classify the fabric defects into four categories. Using this system, the detection accuracy is improved, and the depth of the model is shortened simultaneously. Optimal detection rates for testing dirty marks, clip marks, broken yams, and defect-free were 88.01%, 90.15%, 98.01%, and 97.73%, respectively. The experimental results show that the proposed method is effective, feasible, and has significant potential for fabric defect detection.

Numerous conflicting objectives exist in the engineering field, and resolving these conflicts to reduce costs constitutes a problem that demands top-priority consideration. A model for tool wear and a multi-quadratic regression model for milling forces were developed to accurately predict the trends of wear on the rake face of the milling tool and the variations in milling forces. The influence of milling parameters (spindle speed, n; feed rate, v_f; axial milling depth, a_p) on both the wear of the rake face and milling force was analyzed by means of orthogonal experiments. The findings indicated that the impact of these parameters on the wear ranked in the following order: n > v_f > a_p. In contrast, for milling force, F, the ranking was a_p > v_f > n. Utilizing MATLAB’s genetic algorithm, an optimization procedure was conducted with multiple objectives including the wear of the rake face, milling force, and material removal rate; subsequently, a Pareto optimal solution set was generated for milling parameters based on practical processing requirements.

Rolling element bearing monitoring under operating condition shifts remains difficult because fault signatures are transient, fault data are scarce, and degradation trends may depend on load and speed. This study evaluates a condition-aware DANN-LSTM framework for joint fault diagnosis and RUL prediction. A one-dimensional CNN extracts vibration features, a gradient reversal branch aligns condition-related distributions for fault classification, and an LSTM models chronological degradation features without direct adversarial regularization. The model jointly optimizes classification, condition-discrimination, and RUL losses. Experiments on public bearing datasets show high class-wise identification rates, a validation accuracy of 0.989, and an RUL RMSE of 7.9. Controlled ablation indicates that moderate condition alignment improves transfer classification while preserving useful degradation ordering for RUL prediction. The framework offers a practical data-driven baseline for bearing condition monitoring under controlled condition shifts.

To achieve the multi-objective collaborative optimization of milling processes, orthogonal experiments are conducted to develop prediction models for vibration acceleration and milling force, and range analysis together with variance analysis are adopted to reveal the sensitivity of each milling parameter to machining performance. Taking low vibration, small milling force and high material removal rate (MRR) as optimization objectives, the Multi-Objective Whale Optimization Algorithm (MOWOA) is employed to tackle this multi-criteria optimization problem, and a set of Pareto non-dominated solutions with balanced trade-offs are acquired. By integrating the weight assignment of the Analytic Hierarchy Process (AHP) with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), comprehensive decision-making for all candidate schemes is implemented in accordance with practical machining requirements of users, and the optimal milling process parameters are determined. The results indicate an inherent trade-off among machining efficiency, milling load and machine tool vibration. An increase in the material removal rate will inevitably lead to simultaneous rises in milling force and machine tool vibration magnitude. The optimal combination of process parameters screened to meet comprehensive multi-objective requirements is spindle speed n = 12,000.00 r/min, feed rate v_f = 1048.26 mm/min, and axial milling depth a_p = 3.00 mm.