Advanced Engineering Informatics最新文献

{"title":"A novel discriminative joint adversarial network for quantitatively detecting wheel polygonization of heavy-haul locomotives across variable running conditions","authors":"Maoyong Dong,&nbsp;Shiqian Chen,&nbsp;Hongbing Wang,&nbsp;Wanming Zhai","doi":"10.1016/j.aei.2026.104377","DOIUrl":"10.1016/j.aei.2026.104377","url":null,"abstract":"<div><div>Timely quantitative detection of wheel polygonal wear is of great significance for railway maintenance and improving the train running quality. However, existing deep learning-based detection methods struggle with speed variation-induced feature distribution shifts, exhibiting weak transferability and failing to achieve quantitative diagnosis of wheel defects. To address these issues, a novel discriminative joint adversarial network (NDJAN) for polygonal fault detection under varying running speeds is proposed in this paper. A multi-branch parallel ResNet is first developed to extract sensitive features from raw signals using shortcut connections, which can preserve critical wear amplitude-related information and alleviate gradient vanishing problems. Then, a two-level discriminative feature fusion (TLDFF) scheme is designed with a hybrid attention mechanism and lightweight depthwise separable convolutions. The former is employed to amplify discriminative features, while the latter achieves intelligent fusion of multi-branch features through learnable weighting coefficients, ensuring optimal integration of complementary information from different branches. Finally, an implicit-explicit joint distribution alignment (IEJDA) strategy is presented to address fundamental transfer distribution discrepancies under variable operating conditions. This module accomplishes global distribution matching and fine-grained adaptation of decision boundaries by acting on the feature layer and regression decision layer, respectively. Both dynamics simulations and field tests are carried out to demonstrate that the proposed NDJAN approach can effectively and accurately detect the polygonal wear amplitudes.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104377"},"PeriodicalIF":9.9,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reinforcement learning-based hyper-heuristic algorithm for multi-warehouse and multi-machine agricultural machinery operation scheduling problem considering soil conditions and carbon emissions","authors":"Tengfei Wu ,&nbsp;Lanyue Zhang ,&nbsp;YingLu He ,&nbsp;Liangcheng Zhou ,&nbsp;Yiming Chen ,&nbsp;Qing Yuan ,&nbsp;Xingyun Duan ,&nbsp;Xiaorong Lv","doi":"10.1016/j.aei.2026.104346","DOIUrl":"10.1016/j.aei.2026.104346","url":null,"abstract":"<div><div>Multi-machine coordinated scheduling systems are a key technology for efficient production in unmanned farms and play an important role in advancing agricultural informatization. However, existing agricultural machinery scheduling studies often neglect cross-regional operational requirements in China’s hilly and mountainous areas and overlook the influence of soil moisture on machinery performance. To address these challenges, this study formulates a multi-warehouse and multi-machine agricultural machinery scheduling model and develops a tri-objective optimization framework to simultaneously minimize travel distance, operation time, and carbon emissions. A reinforcement learning-based hyper-heuristic algorithm (RLHHA) is proposed, which adopts a five-layer encoding scheme tailored to the problem structure and integrates eight customized low-level heuristics with a Q-learning controller. Hypervolume and spacing metrics are employed as state features to guide the adaptive selection of heuristics, thereby improving the accuracy and stability of scheduling decisions. Extensive experiments are conducted on six benchmark instances of different scales. Comparative results with three classical algorithms and two advanced hybrid algorithms demonstrate that the proposed RLHHA achieves superior performance in terms of solution accuracy, convergence quality, and robustness. The results indicate that the proposed model and algorithm can effectively support accurate, reliable, and sustainable decision-making for cross-regional agricultural machinery scheduling in real-world scenarios.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104346"},"PeriodicalIF":9.9,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generative AI-driven data augmentation and object-guided vision-language reasoning for PPE compliance analysis in work-at-height","authors":"Wenyu Xu ,&nbsp;Wen Yi ,&nbsp;Yi Tan","doi":"10.1016/j.aei.2026.104364","DOIUrl":"10.1016/j.aei.2026.104364","url":null,"abstract":"<div><div>PPE compliance is a fundamental prerequisite for ensuring safety in work-at-height. Although computer vision has advanced PPE detection, challenges remain in dataset scarcity that limits generalization and in weak semantic reasoning that hinders reliable compliance verification. To address these limitations, this paper presents a generative AI-driven data augmentation and an object-guided vision-language model (VLM) to analyze PPE compliance in work-at-height. Safety standards on work-at-height and PPE (e.g., GB 80-2016, GB 2811-2019) are formalized via ChatGPT 4o into a variable pool and structured prompts, which are used as inputs to text-to-image (T2I) generation model for generating a synthetic dataset. Object detection model is employed to detect PPE elements, and the structured outputs of object detection model are integrated with VLM, enabling vision-language reasoning that combines object detection with natural language understanding. Experimental results demonstrate that DALL·E 3 produces a more realistic synthetic dataset than other image generation models, with the hybrid dataset significantly improving detection performance ([email protected]=88.5%, Small Object [email protected]=75.8%). Using YOLOv11 detections as structured inputs, Qwen2.5-VL-7B achieves reliable compliance reasoning (CRA=87.6%, SC=0.83, EQ=4.2), and these advances are consolidated in an integrated platform supporting automated reporting and interactive analysis. This framework enhances work-at-height safety by alleviating data scarcity through generative augmentation and strengthening PPE compliance reasoning.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104364"},"PeriodicalIF":9.9,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatio-temporal motion-aware intelligent robotic grasping with velocity estimation for moving objects","authors":"Qing Jiao ,&nbsp;Weifei Hu ,&nbsp;Tingjie Wang ,&nbsp;Geyu Shao ,&nbsp;Ning Tang ,&nbsp;Jiayi Wang ,&nbsp;Long Fang","doi":"10.1016/j.aei.2026.104367","DOIUrl":"10.1016/j.aei.2026.104367","url":null,"abstract":"<div><div>Dynamic grasping capabilities, i.e., grasping moving objects in unstructured environments, could render robotic systems more competitive in both industrial and daily life applications. However, previous studies mostly relied on restrictive assumptions, such as static objects subject to slight perturbations or pre-learned object motion patterns, which severely limited adaptability to unknown trajectories. While recent learning-based methods relax these assumptions, they prioritize object or grasp tracking to ensure smooth robot motion over future grasp pose prediction. The scarcity of dynamic grasp datasets further hinders the advancement of learning-based methods. To address these challenges, this paper presents a moving-object grasp prediction method based on Conv-T (Convolutional Transformer), a hierarchical architecture that fuses spatiotemporal features for motion-aware dynamic grasping. By integrating velocity estimation, this method models the dynamics of the latent motion trajectories from time-series depth images to predict future grasp poses. The Conv-T is built based on a proposed <strong>SL</strong>iding <strong>W</strong>indow <strong>M</strong>ulti-head <strong>S</strong>elf-<strong>A</strong>ttention (SLW-MSA) mechanism, which balances computational efficiency with performance by integrating the properties of convolutional operations and self-attention mechanisms. Additionally, a dynamic grasp dataset generation pipeline combining data synthesis with data expansion techniques is developed to efficiently embed temporal motion cues into the training data. The proposed method is validated on the constructed dynamic grasp datasets as well as in simulated and real‐world robotic environments. Experimental results demonstrate that our Conv-T-based method not only outperforms state-of-the-art networks on datasets but also exhibits superior robustness compared to other baselines when grasping moving objects.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104367"},"PeriodicalIF":9.9,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generalized envelope nonlinear Gini index-gram guided two-stage chirp mode decomposition for shield machine main bearing fault diagnosis","authors":"Gang Shi,&nbsp;Chengjin Qin,&nbsp;Pengcheng Xia,&nbsp;Zhinan Zhang,&nbsp;Chengliang Liu","doi":"10.1016/j.aei.2026.104354","DOIUrl":"10.1016/j.aei.2026.104354","url":null,"abstract":"<div><div>The main bearing is the core mechanical equipment of shield machine, known as the heart of the shield machine, mainly applied to support the rotation of cutterhead system. The health status of main bearing has significant impact on the stable and safe construction for shield machine. However, main bearing has very low rotational speed of only 1 rpm to 5 rpm, and the structure is very complex, with a diameter even exceeding 10 m. The fault features for main bearing vibration signals are relatively weak. Therefore, the main bearing fault diagnosis is a challenging task. To tackle the problem, we develop a novel generalized envelope nonlinear Gini index-gram guided two-stage chirp mode decomposition (GENGI-TSCMD) for fault diagnosis of shield machine main bearing in this article. The proposed method consists of optimized demodulated frequency band (ODFB) selection and fault frequency extraction two key parts. We firstly developed GENGI-gram for ODFB extraction, which adopting stepwise increasing segmentation and signal frequency spectrum trend to divide the signal’s frequency bands, and obtain two grams. A new GENGI fault pulse identifier was constructed to select ODFB in these two grams. GENGI can availably characterize fault pulse features and suppress noise interference by combining generalized envelope and nonlinear weights based on Gini index. For fault frequency extraction, we proposed TSCMD by establishing bandwidth guided adaptive chirp mode decomposition (BACMD) and fault frequency extraction mode decomposition (FEMD). BACMD is adopted to extract sub-signals in each demodulation frequency band of GENGI-gram. FEMD is applied to decompose each order fault frequency sub-signals of ODFB signal’s envelope. In this way, the main bearing’s fault type can be precisely diagnosed. BACMD derives the bandwidth calculation formula for sub-signal, which can extract sub-signals with specific frequency bandwidth. FEMD constructs a novel adaptive signal decomposition model, which can effectively decompose each order fault frequency sub-signal with narrow frequency band. Then we use actual main bearing fault vibration signals to verify the proposed method. The experimental results show that proposed GENGI-TSCMD can availably filter out various types of strong noise disturbance, and precisely extract each order fault frequency component. Moreover, proposed method has superior performance than current signal processing fault diagnosis methods.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104354"},"PeriodicalIF":9.9,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implicit coordination through environment modification: Multi-agent RL approach for adaptive door placement in evacuation scenarios","authors":"Isabelle Fitkau,&nbsp;Timo Hartmann","doi":"10.1016/j.aei.2026.104359","DOIUrl":"10.1016/j.aei.2026.104359","url":null,"abstract":"<div><div>In evacuation modeling, architectural configurations are typically treated as fixed constraints, limiting the exploration of adaptive design strategies that respond dynamically to occupants’ movement patterns. This research investigates environment-mediated coordination in a prototypical multi-agent reinforcement learning (MARL) setting. Using Proximal Policy Optimization (PPO), a navigating agent learns escape trajectories, while a design agent modifies door placement solely based on observed movement, without explicit communication. The system was evaluated in a multi-room environment to explore how complementary strategies evolve when navigation performance and immediate floor plan changes are coupled. The results demonstrate successful implicit coordination, with substantial reductions in episode length as the door controller agent progressively shifted door placements towards positions that facilitated faster escape paths. Navigation trajectories converged on paths that maximized the reward of the navigating agent, while door placement strategies minimized evacuation times. Therefore, although allowing architectural changes during episode runtime, the results still show emerging adaptive behaviors of both agents and demonstrate the feasibility of coordinating navigation and design actions through dynamic environment modification. This approach paves the way for adaptive architectural design systems that can dynamically respond to user behavior patterns, with potential applications in performance-based building design tools and in emergency planning optimization.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104359"},"PeriodicalIF":9.9,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DuoTransFormer for nonlinear seismic response prediction: Cover large, focus local, and enforce law","authors":"Xinyi Hu ,&nbsp;Congzhen Xiao ,&nbsp;Zhiqiang Zhang ,&nbsp;Jie Gao","doi":"10.1016/j.aei.2026.104375","DOIUrl":"10.1016/j.aei.2026.104375","url":null,"abstract":"<div><div>Deep learning-based methods have recently been applied to structural seismic response prediction, yet existing models still face challenges in capturing strongly nonlinear behaviors and exhibit limitations in multi-scale temporal modeling and inter-variable dependencies. Based on these observations, we propose <span><math><mi>DuoTransformer</mi></math></span>, a novel dual-view Transformer backbone that integrates a <span><math><mi>Trans</mi></math></span>posed Trans<span><math><mi>former</mi></math></span> Encoder treating each variable as a token and a Patch <span><math><mi>Transformer</mi></math></span> Encoder for local temporal patterns, thereby enabling multi-scale modeling of both coarse-grained and fine-grained temporal dependencies. Inspired by response characteristics such as period elongation and stiffness degradation, the Physical Module is designed following a phenomenon-driven design paradigm. Furthermore, DuoTransFormer decouples the prediction process into two stages: the Physical Module, which hard-codes structural dynamics and outputs three parallel data chains as physics-informed feature sequences, and the Transformer Module, which takes them as inputs. Two proof-of-concept experiments are first conducted to validate the effectiveness of the proposed method. Subsequently, extensive evaluations on two open-access structural models, including a 42-story case study building and a 632-m real-world building, demonstrate that DuoTransFormer outperforms state-of-the-art time-series forecasting models (e.g., PatchTST, iTransformer) while achieving substantially lower computational cost than finite element simulations. The model-agnostic Physical Module can be flexibly combined with all baselines to deliver substantial performance gains.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104375"},"PeriodicalIF":9.9,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-driven modelling of unloading hours using explainable gradient boosting models","authors":"Celal Cakiroglu ,&nbsp;Najat Almasarwah ,&nbsp;Mehmet Hakan Özdemir ,&nbsp;Batin Latif Aylak ,&nbsp;Manjeet Singh ,&nbsp;Muhammet Deveci","doi":"10.1016/j.aei.2026.104353","DOIUrl":"10.1016/j.aei.2026.104353","url":null,"abstract":"<div><div>Unloading processes denote the extraction of finished goods and raw materials from transport units and their subsequent conveyance to designated locations. The efficiency of unloading processes is vital in supply chain and logistics management, regarded as an essential component. Delays in unloading operations result in numerous challenges, including heightened operational expenses, diminished labour efficiency, and supply chain bottlenecks. Consequently, it is essential to ascertain unloading times beforehand to mitigate these challenges, resulting in diminished idle time, enhanced overall efficiency, and optimized scheduling. Therefore, precise prediction of unloading times is critically significant. The novelty of this study lies in the application of machine learning techniques to improve operational efficiency by accurately predicting unloading time. To that end, this study employed LightGBM and XGBoost to predict the unloading time in a real case. The unloading time can be predicted with <span><math><msup><mrow><mi>R</mi></mrow><mo>2</mo></msup></math></span> score greater than 0.99 utilizing both models. Subsequently, the SHapley Additive exPlanations (SHAP) methodology was used to ascertain how each input feature contributed to the model’s output. The load of leg significantly influences the unloading time more than the gross weight of truck and the leg distance.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104353"},"PeriodicalIF":9.9,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RLLM-SS: A knowledge-guided simplex search method integrating large language model and reinforcement learning for injection molding quality control","authors":"Haipeng Zou ,&nbsp;Xinyu Li ,&nbsp;Yongkuan Yang ,&nbsp;Ke Yao ,&nbsp;Xiangsong Kong ,&nbsp;Zhijiang Shao ,&nbsp;Furong Gao","doi":"10.1016/j.aei.2026.104372","DOIUrl":"10.1016/j.aei.2026.104372","url":null,"abstract":"<div><div>In injection molding (IM), product quality and process stability are highly dependent on the setting of key process parameters, making efficient parameter tuning essential for achieving reliable and consistent production. However, the tuning process is traditionally guided by expert experience and trial-and-error methods, which often lead to low efficiency and prolonged adjustment cycles. To address this challenge, we propose a knowledge-guided simplex search method that integrates a large language model (LLM) with the Soft Actor–Critic (SAC) reinforcement learning algorithm in a collaborative optimization framework, called RLLM-SS. In RLLM-SS, a quasi-gradient mechanism leverages historical data to dynamically estimate the step size and gradient compensation direction of the simplex search method. These estimated variables, integrated with domain knowledge, are encoded into structured prompts that guide the injection molding quality LLM in dynamically adjusting simplex coefficients through natural language reasoning. This enables the simplex search to overcome fixed-coefficient limitation and avoid local optima to the maximum extent. To mitigate the drawbacks of the LLM, such as its tendency to generate hallucinated outputs and lack of memory of past tuning adjustments, a SAC-based evaluation module is introduced. It assigns rewards based on optimization performance, thereby reinforcing effective strategies and fostering continuous policy improvement when similar conditions recur. Experimental evaluations first verified LLM-SS on standard high-dimensional benchmark functions, confirming its effectiveness in complex search spaces, and were then conducted on an injection molding quality simulation platform built on a neural network trained with practical IM process data. Results show that RLLM-SS outperforms several advanced methods, reducing the average number of iterations by 27.6% and the final Euclidean distance to the target quality curve by 68.3%. It also maintains strong robustness under Gaussian noise perturbations.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104372"},"PeriodicalIF":9.9,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An adaptive spatial–temporal encoder with gated multi-convolutions for remaining useful life prediction","authors":"Wen Liu ,&nbsp;Jyun-You Chiang ,&nbsp;Yi Li ,&nbsp;Haobo Zhang","doi":"10.1016/j.aei.2026.104369","DOIUrl":"10.1016/j.aei.2026.104369","url":null,"abstract":"<div><div>Accurate estimation of Remaining Useful Life (RUL) is essential for safe and economical operation of turbofan engines. This paper introduces an Adaptive Spatial-Temporal Encoder with Gated Multi-Convolutions (GMC-ASTE), a novel approach that simultaneously models temporal dynamics and inter-sensor relationships to enhance RUL prediction accuracy. The methodology employs a multi-scale gated convolution module to extract refined features from raw multi-sensor data, effectively reducing noise while retaining transient signals. These features are subsequently processed through an adaptive spatial–temporal encoder, which utilizes graph attention mechanisms to dynamically adjust sensor connections and multi-head temporal attention to capture long-term dependencies parallelly. Extensive experiments on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) benchmark demonstrate that GMC-ASTE achieves superior performance, with the lowest Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Scoring metrics across all four sub-datasets. The results confirm the effectiveness and interpretability of the proposed model, providing an advanced framework that improves engine prognostic theory and offering airlines a practical tool to reduce downtime and maintenance costs.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104369"},"PeriodicalIF":9.9,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}