Distillation columns are vital for substance separation and purification in various industries, where malfunctions can lead to equipment damage, compromised product quality, production interruptions, and environmental harm. Early fault detection using AI-driven methods like deep learning can mitigate downtime and safety risks. This study employed a lab-scale distillation column to collect passive acoustic signals under normal conditions and three potential faults: flooding, dry tray, and leakage. Signal processing techniques were used to extract acoustic features from low signal-to-noise ratios and weak time-domain characteristics. A deep learning-based passive acoustic feature recognition method was then applied, achieving an average accuracy of 99.03% on Mel-frequency cepstral coefficient (MFCC) spectrogram datasets. This method demonstrated robust performance across different fault types and limited data scenarios, effectively predicting and detecting potential faults in distillation columns.

The glass relics are precious material evidence of the early trade and cultural exchange between the East and the West. To explore the cultural differences and trade development between early China and foreign countries, it is extremely important to classify glass cultural relics. Despite their similar appearances, Chinese glass contains more lead, while foreign glass contains more potassium. In view of this, this paper proposes a joint Daen-LR, ARIMA-LSTM, and MLR machine learning algorithm (JMLA) for the analysis and identification of the chemical composition of ancient glass. We separate the sampling points of ancient glass into two systems: lead-barium glass and high-potassium glass. Firstly, an improved logistic regression model based on a double adaptive elastic network (Daen-LR) is used to select variables with both Oracle and adaptive classification characteristics. Secondly, the ARIMA-LSTM model was used to establish the correlation curve of chemical composition before and after weathering and to predict the change in chemical composition with weathering. Thirdly, combining the data processed by the above two methods, a multiple linear regression model (MLR) is used to classify unknown glass products. It was shown that the sample obtained by this processing method has a very good fit. In comparison with other similar types of models like Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), and Random Forests based on classification and regression trees (CART-RF), the classification accuracy of JMLA is 97.9% on the train set. The accuracy rate on the test set reached 97.6%. The results of the research demonstrate that JMLA can improve the accuracy of the glass type classification problem, greatly enhance the research efficiency of archaeological staff, and gain a more reliable result.

The fault detection of the chemical equipment operation process is an effective means to ensure safe production. In this study, an acoustic signal processing technique and a k-nearest neighbor (k-NN) classification algorithm were combined to identify the running states of the distillation columns. This method can accurately identify various fluid flow states in distillation columns, including normal and flooding states. First, the acoustic signals were collected under normal and abnormal states in an experimental distillation column. Then, the method of dual-domain feature extraction was used to extract the features such as the energy ratio and linear prediction coefficient (LPC). Moreover, the extracted feature parameters were analyzed and compared in a general way. Finally, the k-NN model was used to classify the acoustic signals. The results show that this method had high identification accuracy and provided an important reference for further research.

With the rapid expansion of drones in last‐mile delivery, high energy consumption has become a critical challenge, affecting both operational efficiency and sustainability. Among the various factors influencing unmanned aerial vehicle (UAV) energy consumption, payload weight plays a significant role in determining power usage. However, most existing studies assume known payload conditions, while in real‐world applications, payload is often unknown, limiting the accuracy of energy consumption prediction and optimization. To address this issue, this study proposes a long short‐term memory (LSTM)‐Kolmogorov–Arnold network (KAN) energy consumption modeling approach, integrating deep learning with prior knowledge of quadrotor dynamics to enhance UAV energy prediction under unknown load conditions. The proposed framework consists of a load prediction model based on an improved LSTM network and an energy consumption model employing the KAN for power estimation, effectively capturing the impact of payload variations on energy consumption. Additionally, a particle swarm optimization (PSO) algorithm is applied to dynamically adjust flight parameters, further improving energy efficiency. Experimental validation on a customized quadrotor platform demonstrates that the proposed model achieves R ² values of 0.9852 and 0.9235 for load and power prediction, respectively, indicating high predictive accuracy. When combined with PSO, the approach enhances energy efficiency by 23.14%–28.21% under various payload conditions. This study provides an effective solution for UAV energy consumption prediction and optimization, contributing to improved operational efficiency and the sustainability of drone‐based logistics.

To address the unclear coupling mechanism between bubble detachment behavior and acoustic characteristics in gas–liquid two-phase flow, this paper systematically studied bubble behavior and acoustic characteristics under different conditions by building a high-precision synchronous measurement system, combining acoustic signal analysis and bubble dynamics observation. The influence mechanism of liquid surface tension, dynamic viscosity, and orifice diameter on the critical gas flow velocity of bubble flow transition was analyzed, and a flow pattern classification criterion system was established. The experimental results showed that the bubble flow state could be divided into three states according to the characteristics of the acoustic signals: discrete bubble flow, single-chain bubble flow, and dual-stage chain bubble flow. The liquid surface tension and dynamic viscosity had no significant effect on the critical gas flow velocity of the transition from discrete bubble flow to single-chain bubble flow, but significantly increased the critical gas flow velocity of the transition from single-chain bubble flow to dual-stage chain bubble flow. The increase in the orifice diameter reduced the critical gas flow velocity of the two types of flow transition. In addition, the Weber number (We) and Galileo number (Ga) were introduced to construct a quantitative classification system of flow pattern, which provided theoretical support for the optimization of industrial gas–liquid two-phase flow.

In clinical medicine, jawbone cysts are a common dental disease, and their symptoms are similar to other dental diseases, making diagnosis difficult. To address this issue, this paper proposes a cyst detection system based on Alex Net to achieve the detection of cysts on dental radiographs using a CBCT dataset. The system can detect potential cyst lesions and locations in a timely manner to assist doctors in diagnosis. The improved model achieves an average accuracy of 83.5% and a maximum accuracy of 99.9%, achieving a high cyst recognition rate on existing datasets. In addition, the extensive image enhancement techniques introduced in the Alex Net model also improve the performance of the model. The experimental results show that compared to Res Net and VGG Net, both networks are not ideal for the classification of jawbone cysts, and may not be able to effectively extract key features from medical images, resulting in low classification accuracy. Therefore, it is important to choose a suitable deep learning model for the diagnosis of specific dental diseases. In future research, it is possible to further explore how to combine multiple deep learning models to improve the accuracy of diagnosis of dental diseases such as jaw cysts. In addition, improving data preprocessing and enhancing techniques can further improve the generalization ability of the model. In summary, by combining deep learning and clinical medicine concepts and methods, more effective auxiliary diagnostic systems can be developed to improve the accuracy and efficiency of dental disease diagnosis.