Computers and Artificial Intelligence is a double-blind peer-reviewed, open-access journal dedicated to publishing high-quality research in the fields of computer science and artificial intelligence. The journal provides an essential platform for researchers, practitioners, and educators to share innovative ideas, findings, and advancements that are shaping the future of these rapidly evolving fields.
Design of a Cross-border Trade Logistics Route Optimization System Based on Graph Neural Networks
Against the backdrop of global digital trade development and the deepening of the Belt and Road Initiative, the complexity and dynamism of cross-border trade logistics networks continue to increase. Traditional route optimization methods are struggling to meet the core requirements of intelligent logistics, such as real-time response, multi-constraint coordination, and green and low-carbon development, thus becoming a bottleneck for the intelligent upgrading of cross-border logistics. Therefore, this paper designs a cross-border trade logistics path optimization system based on Graph Neural Network (GNN). Firstly, it refines the “departure–transit–customs clearance–destination” four-level structure of cross-border logistics, and constructs a logistics network graph model that integrates multiple dimensions of attributes. It then proposes the GCN-PPO hybrid optimisation algorithm, which uses GCN to extract network topology and spatio-temporal features, and combines this with PPO to enable multi-objective dynamic decision-making regarding cost, timeliness and environmental sustainability. Finally, a system with four core modules was developed based on Python and PyTorch. Numerical examples were designed based on simulated data to verify the effectiveness of the method. The results show that, compared with Dijkstra and NSGA-II algorithms, the system reduces transportation costs by 17.8% ± 1.2%, improves delivery timeliness by 23.5% ± 1.5%, reduces carbon emission intensity by 9.2% ± 0.6%, and achieves an order fulfillment rate of 96.8% ± 0.5%. This system has transcended the limitations of traditional methods and achieved intelligent and dynamic optimization of cross-border logistics routes. It provides a reusable technical solution for the intelligent, green and resilient upgrade of cross-border trade logistics.
cross-border trade logistics, route optimisation, graph neural networks, multi-objective decision-making, green logistics
Copyright (c) 2026 Xianglin Chang (Author) https://creativecommons.org/licenses/by/4.0
The Technological Development and Application Frontiers of Social Media Sentiment Analysis: A Ten Year Review (2016-2026)
Social media sentiment analysis technology has been rapidly iterative in the past decade, but the existing research lacks a systematic review of its technology evolution and application context. This paper reviews the relevant literature from 2016 to 2026 from two dimensions of technology paradigm and application scenarios. The technical level has experienced four paradigm iterations from the explicit emotion classification of SVM and TextCNN, to the implicit emotion detection led by BiLSTM+Attention and BERT, to the multimodal fusion model, and to the generative reasoning of the big language model. The application level has been deeply embedded in public opinion governance, brand insight, financial forecasting and public health monitoring. The research points out that the lack of cross domain generalization, short text noise interference and model bias are the core bottlenecks. In the future, people should build a cross language unified model and a lightweight multimodal framework, and establish a fairness evaluation system.
social media, emotional analysis, deep learning, large language model, multimodal fusion
Copyright (c) 2026 Zilin Li (Author) https://creativecommons.org/licenses/by/4.0
Application of Thermal Imagery Technology for Fault Detection of Large Electrical Equipment and Microelectronic components
With the increasing integration level and power of electrical equipment, their working conditions are becoming increasingly harsh, leading to a higher risk of failure and a growing demand for reliable nondestructive testing (NDT) methods. Thermal imagery, as one of the fully developed method of visualized temperature detection, realizes non-contact error detection by recording infrared radiation from the surface of objects. This paper examines the application of thermal imaging technology for fault detection in large electrical equipment and microelectronic components. The study reviews the fundamental principles and key parameters of thermal imaging, then compares its use across the two scales. For large equipment, thermal imaging effectively handles complex structures and high-voltage environments, offering large-area coverage and non-contact real-time monitoring, though environmental factors may affect accuracy. For microelectronic components, despite limitations in resolution and penetration, thermal imaging provides valuable visualized thermal distribution data for packaging failure analysis. Overall, thermal imaging proves to be an effective and adaptive NDT method, with future improvements in detector performance expected to further enhance its capabilities.
thermal imagery technology, fault detection, large electrical equipment microelectronic components
Copyright (c) 2026 Zhenghan Ma (Author) https://creativecommons.org/licenses/by/4.0
Traffic Flow Prediction System Based on Spatiotemporal Graph Neural Network
Urban traffic congestion poses a persistent challenge to sustainable mobility, demanding intelligent forecasting systems capable of capturing both the complex spatial layout of road networks and the dynamic evolution of traffic patterns over time. Traditional statistical and deep learning methods often fail to jointly model these interdependent spatiotemporal characteristics, particularly when road topology is incomplete, outdated, or oversimplified. To address this gap, we propose a novel spatiotemporal graph neural network architecture specifically designed for short-term traffic flow prediction. Our framework introduces an adaptive graph learning module that dynamically infers meaningful connectivity relationships among traffic sensors—not relying on fixed geographic or distance-based assumptions—but instead leveraging real-time traffic correlations and node-level embeddings. This learned structure is then integrated into a hierarchical encoder that combines spectral graph convolution with dilated causal temporal convolutions, enabling effective modeling of both localized spatial interactions and multi-scale temporal dependencies across varying prediction horizons. A lightweight, shared-weight decoder supports flexible multi-step forecasting without sacrificing accuracy or efficiency. We rigorously evaluate our approach on two large-scale, real-world traffic datasets—PeMSD4 and PeMSD8—covering diverse urban environments and sensor densities. Experimental results demonstrate consistent and statistically significant improvements over state-of-the-art baselines, achieving more than a twelve percent reduction in mean absolute error for fifteen-minute forecasts. Ablation studies confirm the critical role of adaptive graph construction and hierarchical temporal modeling, while visualization of learned graphs reveals interpretable, physically plausible connectivity patterns aligned with actual road infrastructure and traffic behavior. The system shows strong deployment potential, balancing predictive performance with practical considerations such as inference speed and compatibility with edge-computing platforms used in modern traffic management centers.
traffic flow prediction, graph neural networks, adaptive graph learning, spatiotemporal modeling, urban mobility, short-term forecasting, intelligent transportation systems
Copyright (c) 2026 Zhengxu Luan, Huan Wang, Miao Wang, Yitong Chen, Xinxin Chao, Xinyue Cao, Zhiyong Pan (Author) https://creativecommons.org/licenses/by/4.0
Research Evolution and Hotspots Analysis of Disinformation Induced by Generative Artificial Intelligence (2020–2025)
The ubiquitous integration of social media in our day to day life has one inevitable consequence, namely the serious threat of artificial intelligence (AI)-produced fake news and misinformation. Generative models can nowadays generate natural looking text with high quality. They are used in many different settings, and the consequences affect everyone which from individual users up through organizations, institutions, and societies. Monitoring where that research is going is not just an academic exercise, which requires identifying real hotspots. The data set for this analysis consists of 571 papers in English published by the Web of Science Core Collection, exclusively in 2020-2025 period. Multidimensional quantitative data of the literature analysis are acquired by CiteSpace and VOSviewer. It can be seen that there is a significant increase in publication after 2023. Currently, the United States and China are dominant in world production. Oddly enough, while overall macro-level productivity is high, institutions barely collaborate with each other at the micro-level. The thematic focus has shifted. The earlier works were largely chasing computer vision tasks such as detecting deepfakes; today’s literature is heavy on controlling multimodal disinformation generated by LLMs. This trend is already seeping into risky domains, especially health care and journalism. Even with the emergence of new trends, fundamental knowledge here still resides on generative adversarial networks, residual networks, and preliminary deepfake evaluations. Finally, our study provides insight into the landscape of what is known within the discipline. Policy makers and scientists have a data-driven reference point from which to identify gaps in research and design policies that truly integrate science.
generative artificial intelligence, disinformation, deepfake, large language models, bibliometrics, network analysis
Copyright (c) 2026 Hancheng Zheng (Author) https://creativecommons.org/licenses/by/4.0
Automatic Detection of Epileptic EEG Signals Based on Channel Attention and Bidirectional Temporal Modeling
Epilepsy is a common chronic neurological disease. As an important tool for epilepsy diagnosis and disease assessment, electroencephalogram (EEG) can reflect the abnormal discharge activity of brain neurons. However, traditional EEG interpretation relies heavily on expert manual analysis, which has problems such as time-consuming, strong subjectivity and low efficiency. To improve the automation level of seizure detection, this paper proposes a pyramid-type one-dimensional convolutional neural network model (SE-P1D-LSTM) that integrates a channel attention mechanism and a Bidirectional Long Short-Term Memory (BiLSTM) network. The method first preprocesses the EEG signal through overlapping sliding window and Z-Score standardization, and combines Gaussian noise disturbance and random amplitude scaling for data augmentation; Then use the pyramidal one-dimensional convolutional structure to extract local temporal features, and adaptively strengthen the key channel information through SEBlock; Finally, a BiLSTM is introduced to model the long-range temporal dependencies in EEG signals to realize the automatic classification of normal and seizure EEG signals. The experiment was carried out based on the public epilepsy EEG dataset of the University of Bonn in Germany, and the performance of the model was evaluated using 10-fold cross-verification. The results show that the method in this paper has achieved an accuracy rate of 99.87%, a sensitivity of 99.87% and a specificity of 99.87% in the two-classification task, which is better than a variety of comparative models. The research results show that SE-P1D-LSTM can effectively extract the discriminative characteristics of epilepsy EEG signals, and has good classification performance and application potential in the automatic detection task of seizures.
epileptic seizure detection, EEG signals, deep learning, channel attention, bidirectional long short-term memory (BiLSTM), one-dimensional convolutional neural network (1D-CNN)
Copyright (c) 2026 Xien Gao (Author) https://creativecommons.org/licenses/by/4.0
FPGA-Based Bilinear Interpolation Image Scaler with Q8 Fixed-Point Arithmetic and Hardware-Software Co-Verification
Image scaling serves as a critical performance bottleneck in real-time video pipelines powering multi-resolution embedded display systems. The design integrates a dual ping-pong line buffer architecture to deliver four pixels per clock cycle, paired with a digital differential analyzer (DDA) for precise sub-pixel coordinate generation. A four-stage pipelined interpolation core optimized for Xilinx Digital Signal Processing 48 (DSP48) slices achieves one-pixel-per-cycle throughput with only six cycles of end-to-end latency. Hardware-software co-verification conducted on ModelSim SE-64 demonstrates perfect reconstruction of constant-color images and 25.8 dB Peak Signal-to-Noise Ratio (PSNR) for gradient images at 4x4 identity scaling. Resource estimates for a Xilinx Artix-7 XC7A35T indicate the design occupies merely 3.8% of available Look-Up Tables (LUTs) and 6.7% of DSP48 slices, making it exceptionally suitable for low-power embedded applications.
FPGA, bilinear interpolation, image scaling, fixed-point arithmetic, line buffer
Copyright (c) 2026 Sirui Wang (Author) https://creativecommons.org/licenses/by/4.0
Reliability Evaluation of Large Language Models for Social Media Sentiment Annotation: An Empirical Study Based on Model Agreement and Downstream Tasks
Driven by booming social media and user-generated texts, sentiment analysis stands as a core natural language processing task, yet large-scale high-quality data labeling comes with steep costs and practical barriers. This work assesses how dependable large language models are for sentiment tagging, alongside how their labeled outputs shape subsequent classification effects. We build a dataset containing 1,543 Chinese entertainment comment snippets scraped from Bilibili. Under unified prompting, three LLMs—DeepSeek, Qwen and Doubao—produce zero-shot sentiment tags, while 500 sampled entries receive manual annotation to form authoritative benchmark labels. Cohen’s Kappa is adopted to quantify human-model annotation consistency, and TF-IDF features are input to Logistic Regression, Linear SVM and Random Forest for downstream classification evaluation. Among the three models, Doubao achieves the highest human-label consistency with a κ value of 0.800, exceeding Qwen (κ=0.746) and DeepSeek (κ=0.636). Classifiers trained on Doubao’s labels obtain optimal Macro-F1 values of 0.8125 (SVM) and 0.8174 (Random Forest). Obvious performance discrepancies exist across LLMs; high-quality annotations significantly boost downstream classification accuracy, which highlights the importance of selecting competent LLMs for sentiment labeling tasks.
large language models (LLMS), sentiment analysis, user-generated content, zero-shot learning
Copyright (c) 2026 Wenjing Pi, Changxian He (Author) https://creativecommons.org/licenses/by/4.0
Big Data-Based Analysis of Urban Traffic Congestion: A Case Study of Beijing
As urbanization continues to reshape large cities, traffic congestion remains a persistent challenge for urban transportation systems. Using Beijing as a case study, this paper examines the spatiotemporal evolution of urban traffic congestion from a deep learning perspective based on multi-source data. The results suggest that traffic congestion in Beijing displays a pronounced “dual-peak” pattern associated with daily commuting activities. Compared with the pre-pandemic period, weekday travel demand has generally recovered and in some cases exceeded previous levels, whereas holiday travel remains relatively subdued, accompanied by increasingly concentrated travel behavior. Among the models considered, Long Short-Term Memory (LSTM) performs particularly well in capturing nonlinear variations and temporal dependencies in traffic flow, leading to improved prediction accuracy. Between 2020 and 2025, the congestion index experienced a trajectory of decline, recovery, and subsequent stabilization, a pattern that appears to be associated with the gradual implementation of intelligent traffic management measures. These findings contribute to a better understanding of recent changes in urban traffic dynamics and may offer useful insights for future traffic planning and governance.
urban traffic congestion, big data analysis, deep learning, LSTM
Copyright (c) 2026 Zihan Zhou (Author) https://creativecommons.org/licenses/by/4.0
Research on the Application of Generative AI in Immersive Tasks for RPG Games
In recent years, generative artificial intelligence has developed rapidly, supported by improvements in computing resources, data availability, and model architecture design. The introduction of the Transformer architecture in 2017 provided a key technical basis for later large-scale language models. The release of ChatGPT in 2022 further increased the visibility and adoption of generative AI systems, contributing to their wider application beyond research settings. At present, research in this area has increasingly involved multimodal models and more efficient deployment strategies. In the field of role-playing games (RPGs), many existing task systems are still based on pre-designed scripts, with relatively fixed task structures, limited narrative variation, and constrained interaction patterns between non-player characters (NPCs). These characteristics limit the flexibility of gameplay experience to some extent and make it difficult to fully support diverse player interactions. This paper reviews studies on generative AI applications in RPGs since 2020, focusing on three aspects: narrative generation, dynamic task design, and NPC dialogue systems. It further summarizes current research developments, discusses issues such as content controllability, experience consistency, and computational cost, and outlines several research directions, including multimodal task modeling and personalization mechanisms.
generative AI, RPG games, immersive tasks
Copyright (c) 2026 Weixuan Zhou (Author) https://creativecommons.org/licenses/by/4.0
Review of Applications of Machine Learning in Optoelectronic Information Science
With the rapid advancement of artificial intelligence technology, machine learning—as one of its core branches—has demonstrated immense application value in the field of optoelectronic information science. This article systematically reviews the research progress on the practical applications of machine learning in several sub-fields of optoelectronic information science. It covers core areas such as the design and optimization of optoelectronic materials, photoelectric detection and imaging, optical communication systems, photonic device design, and photoelectron spectroscopy analysis. It also analyzes the major challenges faced in the current application process, such as data quality and model interpretability. Future research directions are outlined, providing a systematic reference basis for researchers in related fields.
machine learning, optoelectronic information science, optoelectronic materials, optical communication, photonic devices
Copyright (c) 2026 Dongchen Huang (Author) https://creativecommons.org/licenses/by/4.0
The pricing of green bonds has emerged as a critical issue of international scholarly and policy interest amid the rapid expansion of the global green bond market. Existing empirical research, however, remains constrained by limited sample coverage and superficial model comparisons, leaving substantial gaps in the understanding of the key determinants of green bond yields. This study examines a comprehensive sample of 7,062 Chinese green bonds issued between 2016 and 2024, employing three machine learning algorithms—Random Forest (RF), XGBoost, and Support Vector Machine (SVM)—to predict green bond coupon rates. The empirical results indicate that XGBoost achieves the strongest predictive performance, followed closely by Random Forest, whereas SVM exhibits comparatively weaker performance. Feature importance analysis reveals that macroeconomic factors—particularly year-on-year GDP growth rate, year-on-year M2 growth rate, and year-on-year CPI growth rate—constitute the most critical determinants of green bond pricing. Extensive robustness tests, encompassing alternative train-test split ratios and cross-validation settings, confirm that these findings remain stable across different experimental configurations. This study offers timely theoretical support and practical guidance for investors and regulatory authorities in green bond market decision-making.
green bond pricing, machine learning, random Forest, XGBoost
Copyright (c) 2026 Haokai Huang (Author) https://creativecommons.org/licenses/by/4.0
A Review of Multimodal Large Language Models: Fusion Mechanisms and Capability Evolution
With the continuous advancement of large language models, MLLMs have gradually emerged as a major research focus in the field of artificial intelligence. Although existing studies have reviewed relevant progress from different perspectives, such as multimodal learning, vision-language models, and domain-specific applications, there is still a lack of systematic analysis of the intrinsic relationships among fusion mechanisms, capability formation, and application expansion in MLLMs. This paper reviews the principal technical paradigms of multimodal fusion, including early fusion, intermediate fusion, late fusion, and hybrid fusion, and compares the structural characteristics and applicable scenarios of different fusion approaches. Building on this analysis, the paper further explores the development of MLLMs from the perspectives of vision-language understanding, multimodal content generation, multimodal interaction and agent-oriented tasks, as well as domain-specific applications, thereby outlining the evolutionary trajectory of their core capabilities and broader application trends.
multimodal large language models, fusion mechanisms, multimodal understanding, multimodal generation
Copyright (c) 2026 Zibo Xu, Shuqiang Gao (Author) https://creativecommons.org/licenses/by/4.0
An Optimization Study of TFHE Encryption Algorithm Based on tiny-DNN
Privacy leakage, malicious tampering, and incompleteness of big data have become severe challenges for deep learning. Meanwhile, fully homomorphic encryption algorithms suffer from increased energy consumption and insufficient precision. To address these issues, this paper proposes a dual-path heterogeneous privacy-preserving inference mechanism. First, data is divided into private and non-private categories using a privacy segmentation coefficient. Private data is placed in the TFHE encryption domain, while a tiny-DNN-based secure black-box is employed to protect non-private data. The zero-error nonlinear activation mechanism of the MUX logic gate in TFHE is used to train the deep learning framework. Subsequently, a cross-domain fixed-point homomorphic adder fuses the two types of data to obtain the ciphertext result formed by end-to-end heterogeneous data. This approach ensures the prediction accuracy of deep learning while reducing the network latency of big data classification. Experimental results show that the proposed system reduces end-to-end inference latency to 970 ms while maintaining model accuracy. Security analysis demonstrates that the system possesses strong robustness against attacks, thereby providing confidentiality and integrity protection for real-time inference of big data.
deep learning framework, fully homomorphic encryption, data security, MUX logic gate
Copyright (c) 2026 Penghui Liu (Author) https://creativecommons.org/licenses/by/4.0
A Review of Obstacle Avoidance Path Planning Technologies for Intelligent Vehicles
As a core component of autonomous driving systems, obstacle avoidance path planning technology for intelligent vehicles aims to achieve safe and efficient navigation in dynamic environments through the coordination of multiple modules. This paper systematically reviews the current state of research on the overall architecture and key algorithms of this technology, focusing on the technical developments of the three core modules: perception, decision-making, and control. In the realm of environmental perception, technologies such as multimodal fusion, V2X collaboration, and vehicle–road–cloud integration have significantly enhanced the robustness and global awareness of perception; the path decision-making module encompasses diverse developments ranging from classical graph search algorithms to intelligent optimization methods, balancing the real-time performance and optimality of decision-making algorithms; and the motion control section summarizes the trend toward integrating deterministic methods with heuristic strategies, thereby enhancing the accuracy and adaptability of trajectory tracking. The review notes that while current technologies have progressed in modular collaboration, future breakthroughs are still needed in areas such as end-to-end learning and lightweight deployment to drive the system toward higher reliability.
intelligent vehicles, obstacle avoidance, environmental perception, decision-making algorithms, motion control
Copyright (c) 2026 Yuefan Min (Author) https://creativecommons.org/licenses/by/4.0
LLM-Based Zero-Code Control for Robotic Arms: Representative Methods, Challenges, and Future Trends
With the rapid evolution of Large Language Models (LLMs), significant advancements have emerged in natural language processing, cross-modal reasoning, and complex task planning, heralding a paradigm shift in robotic manipulation. Traditional robotic arm control demands specialized programming and extensive engineering expertise, creating a high barrier to entry. This paper systematically reviews recent advancements in LLM-based zero-code implementation technologies for robotic arms, which enable direct control through natural language and substantially reduce development costs and operational complexity. We establish a clear technical classification framework, analyzing the performance characteristics and applicability of various methods such as direct mapping approaches (SayCan, Code as Policies, ProgPrompt) and feedback-driven architectures (Inner Monologue). We also explore multimodal fusion techniques (RT-2, PaLM-E, VIMA, VoxPoser) and their application across diverse scenarios including education, home services, and manufacturing. Furthermore, we identify key challenges—including natural language parsing accuracy, robustness of multimodal fusion, long-horizon planning, and safety—and propose future research directions aimed at fostering the integration of embodied intelligence with natural language interaction. This study clarifies the research trajectory in this field, offering theoretical support for the broader popularization and deployment of robotic technology.
Large Language Models (LLMs), robotic arms, zero-code control, natural language interaction, embodied AI
Copyright (c) 2026 Jiajun He (Author) https://creativecommons.org/licenses/by/4.0
Multidimensional Performance Comparison and Evolutionary Trends of Typical Transmission Systems for Underactuated Robotic Hands
This review focuses on the transmission systems of underactuated robotic hands. It aims to compare the dynamic characteristics of three mainstream approaches—linkage, tendon-driven, and flexible structures—to reveal their performance bottlenecks and provide guidance for design optimization. Based on relevant theoretical and experimental data, the study comparatively analyzes key indicators such as force transmission efficiency, degrees of freedom, and environmental adaptability. The findings indicate that linkage transmission offers high stiffness and superior precision, making it suitable for heavy-load scenarios and widely used in aerospace applications. However, its bulky structure affects dynamic response speed. Tendon-driven transmission is lightweight and compliant, producing almost no rigid impact, which makes it ideal for biomimetic grasping and widely applied in medical rehabilitation due to its high safety. Nevertheless, it has insufficient lateral stiffness. Flexible structures exhibit strong adaptability to complex environments and are advantageous for handling fragile objects, such as in agricultural picking tasks, but suffer from low control precision and response lag. In summary, no universal solution exists. Future development should involve task-specific trade-offs in selection, exploration of hybrid mechanisms, or the integration of rigid-flexible coupled hybrid actuation systems.
underactuated robotic hand, transmission system, rigid-flexible coupling, nonlinear control
Copyright (c) 2026 Songyi Wang (Author) https://creativecommons.org/licenses/by/4.0
Empirical Research on the Influence of Image Enhancement Technology on Image Classification Performance Based on Deep Learning
To address the problem of insufficient systematic evaluation of image preprocessing technologies, this study adopts the control variable method and quantitative analysis to systematically evaluate the influence of three types of image enhancement technologies (destructive enhancement, conservative enhancement, and combined enhancement) on the performance of an image classification model based on ResNet-50 feature extraction and SVM classification. The CIFAR-10 dataset is used with a sample size of 5,000 images. The experimental results show that destructive enhancement brings an average accuracy decrease of 21.24%, Unsharp Mask in conservative enhancement brings an increase of 3.64%, and Wavelet→Unsharp in combined enhancement brings an increase of 2.26%. The impact of image enhancement on classification performance varies significantly: appropriate enhancement effectively improves model performance, while excessive enhancement impairs it. The findings provide a basis for technology selection in practical applications.
image enhancement, deep learning, image classification, ResNet-50, transfer learning
Copyright (c) 2026 Zhuofan Jia (Author) https://creativecommons.org/licenses/by/4.0
Development and Validation of a Scale for Assessing the Effectiveness of Generative AI-Enhanced English Writing Learning Among Chinese University Students
This study, grounded in the Technology Acceptance Model (TAM) and student engagement theory, aims to develop a psychometric instrument for assessing the effectiveness of generative AI-enhanced English writing learning among Chinese university students. Drawing upon established scales from domestic and international literature, the initial scale was adapted to the specific context of generative AI-assisted English writing, comprising three dimensions—AI Use Attitude, Learning Engagement, and Learning Outcomes—with 42 items. Following expert review, the scale was administered to university students across 28 provinces in China, yielding 282 valid responses. Data were analyzed using SPSS Statistics 27, employing item analysis, exploratory factor analysis (EFA), and reliability and validity testing. Item analysis, based on critical ratio (CR), corrected item-total correlation (CITC), communality, and factor loading, resulted in the deletion of six items (Q8, Q15, Q27, Q35, Q39, Q42), retaining 36 items in the final scale. Exploratory factor analysis supported a three-dimensional structure: “AI Use Attitude—Learning Engagement—Learning Outcomes.” Reliability analysis demonstrated Cronbach's α coefficients exceeding 0.87 for all dimensions and the overall scale, with a split-half reliability of 0.947. Validity testing confirmed satisfactory content validity and structural validity. The findings indicate that the scale possesses robust psychometric properties and practical feasibility, providing a valid instrument for evaluating the effectiveness of generative AI-enhanced English writing instruction.
generative AI, English writing, learning effectiveness, scale development, reliability and validity
Copyright (c) 2026 Jingyu Wang (Author) https://creativecommons.org/licenses/by/4.0
A Review of Deep Learning-Based 15Visual Inspection Techniques for Chip Surface Defects
With the continuous improvement of semiconductor chip integration, chip surface defects have a serious impact on chip performance and reliability. Traditional detection methods such as manual inspection and traditional machine vision have problems such as strong subjectivity, reliance on manual feature design, and poor adaptability, which are difficult to meet the high-precision detection needs of advanced chips. Deep learning has shown obvious advantages in automatic feature extraction, small target detection and complex background adaptation, and has gradually become the core technology of chip surface defect visual inspection. This paper systematically combs the types and imaging characteristics of chip surface defects, classifies and summarizes deep learning-based detection methods from four aspects: classification, object detection, segmentation and Transformer architecture, and analyzes the key technical difficulties such as multi-scale defects, complex texture interference and data imbalance in the detection process. On this basis, the current challenges in detection accuracy, cross-process generalization, data annotation and industrial deployment are discussed, and the future development trends such as multi-modal fusion, weakly supervised learning, model lightweight and intelligent quality control closed-loop are prospected. The review aims to provide theoretical reference and technical support for the research and engineering application of chip surface defect detection based on deep learning.
chip surface defect, deep learning, visual inspection, defect detection, semiconductor manufacturing
Copyright (c) 2026 Borui Cui, Yuhuai Yin, Yujing Xiao, Sijia Wang, Xiao Xiao (Author) https://creativecommons.org/licenses/by/4.0
The Application and Future Advancement of Agricultural Harvesting Robot Technology
Against the trend of a reduction in agricultural labour and the scale of development of intelligent agricultural machinery, the traditional way of harvesting by hand is often ineffective and costly. The agricultural harvesting robot, endowed with technologies such as machine vision, deep learning, intelligent control, flexible manipulation, is the technical means of the intelligence automation harvesting of fruit and vegetables. In recent years, with the progress of multi-sensor fusion and three-dimensional visual perception technologies, harvesting robots' performance of the recognition of targets, positioning and grasping stability have been greatly improved. The harvesting robot has been preliminarily applied in harvesting crops such as tomatoes, strawberries and apples. In this paper, the development of agricultural harvesting robot is introduced and focused on its technical problems such as fruit recognition and fruit localization, path planning and flexible grasping. It also summarizes existing challenges, such as limited environmental flexibility and high cost, and provides insights into future trends of development.
agricultural harvesting robots, intelligent perception, deep learning, flexible manipulation, smart agriculture
Copyright (c) 2026 Yutian Chen (Author) https://creativecommons.org/licenses/by/4.0
Research on Oracle Bone Inscription Detection and Recognition Algorithm Based on YOLO11-ViT
Oracle bone inscriptions are the earliest mature writing system discovered in China and constitute an important historical source for the origin of Chinese characters and traditional Chinese culture. However, oracle bone rubbing images are usually affected by manual carving variations, long-term underground burial, imbalanced sample distributions, severe noise interference and highly similar character structures, which considerably restrict recognition accuracy. To address these challenges, this paper proposes a two-stage oracle bone inscription detection and recognition model integrating YOLO11-ViT. First, the images are preprocessed using grayscale conversion, Otsu binarization, Gaussian denoising and morphological optimization. Second, YOLO11m is employed to accurately detect and localize oracle bone characters in the rubbing images. Finally, a Vision Transformer model is used to classify the cropped single-character regions. Experiments are conducted on the dataset provided by the 2024 14th MathorCup Mathematical Application Challenge. The results show that the proposed method achieves an mAP@0.5 of 0.92 in the detection stage and an accuracy of 97.60%, a recall of 0.96 and an F1 score of 0.97 in the recognition stage, outperforming the compared methods.
oracle bone inscription recognition, yolo11, vision transformer, object detection, image preprocessing
Copyright (c) 2026 Zhiyuan Lu (Author) https://creativecommons.org/licenses/by/4.0
Research on Cross-lingual Causal Representation Learning Based on Multi-dimensional Semantic Disentanglement
This paper focuses on several key challenges in cross-lingual natural language processing, including unstable semantic transfer, the entanglement of language-specific features, and insufficient interpretability of learned representations. To address these issues, the study introduces the necessity of combining multi-dimensional semantic disentanglement with causal representation learning. A cross-lingual causal representation learning framework is proposed, covering semantic dimension decomposition, causal factor modeling, language-invariant representation extraction, semantic intervention mechanisms, and cross-lingual transfer optimization. Finally, the paper summarizes the experimental findings and discusses the theoretical and practical significance of the proposed approach.
semantic disentanglement, causal representation learning, cross-lingual transfer, language-invariant representation, natural language processing
Copyright (c) 2026 Wenwen Zhao (Author) https://creativecommons.org/licenses/by/4.0
A Real-Time Multi-Target Capture Framework Based on a Single PTZ Camera
To address the problems of insufficient target details and low multi-target capture efficiency in large-scale surveillance scenes, this paper proposes a real-time multi-target capture framework based on a single PTZ (Pan-Tilt-Zoom) camera. The proposed method integrates object detection, PTZ parameter estimation, and path scheduling to achieve rapid sequential capture of multiple targets. First, an object detection algorithm is used to locate targets in the scene. Then, the corresponding PTZ control parameters are estimated according to the pixel coordinates of each target, and feasible capture regions are constructed. Finally, a scheduling strategy combining greedy path planning and local optimization is adopted to generate a low-cost capture sequence for rapid PTZ switching among multiple targets. Experimental results demonstrate that the proposed framework can effectively reduce PTZ motion time and improve multi-target capture efficiency while maintaining stable operation in real-world environments. The proposed framework requires no additional hardware and has good practical deployment value for intelligent surveillance applications.
PTZ camera, multi-target capture, path planning, object detection, intelligent surveillance
Copyright (c) 2026 Yi Tian, Yang Yang (Author) https://creativecommons.org/licenses/by/4.0
Stochastic Gradient Descent and the Law of Large Numbers: A Probabilistic Analysis of Convergence
Stochastic Gradient Descent (SGD) is one of the most fundamental optimization algorithms in modern large-scale machine learning. However, by relying on only a minuscule number of random samples to estimate gradients at each iteration, it inevitably introduces stochastic noise. This paper aims to delve into the mathematical essence of how this highly randomized algorithm achieves stable convergence from a probabilistic perspective. The paper first rigorously reviews the theoretical foundations and mathematical proofs of the Weak and Strong Laws of Large Numbers. Subsequently, it constructs a probabilistic model for stochastic gradients and thoroughly analyzes the update mechanism of the SGD algorithm. The study demonstrates that the stochastic gradients in single iterations of SGD form a sequence of independent and identically distributed random vectors, whose mathematical expectation is the true full-batch gradient. Based on the Law of Large Numbers, the sample mean of these stochastic gradients converges asymptotically to the true gradient in probability (or almost surely), thereby effectively averaging out and dissipating the random noise during long-term iterations. The conclusion indicates that the effectiveness of SGD is mathematically equivalent to “unbiased estimation combined with the Law of Large Numbers,” and its overall optimization behavior asymptotically approximates standard, noise-free gradient descent.
stochastic gradient descent, law of large numbers, convergence analysis, unbiased estimation, machine learning optimization
Copyright (c) 2026 Haoting Chai (Author) https://creativecommons.org/licenses/by/4.0
Advances in Deep Learning-Based Face Deblurring Methods
The task of face deblurring is to restore a clear, realistic and identity-preserving high-quality face image from a given blurred face image. This paper systematically expounds the methods of face deblurring based on deep learning from five stages: traditional methods based on physical models, early deep learning methods based on end-to-end regression, methods based on generative adversarial networks (GAN) and prior embedding, methods based on the Transformer architecture, and methods based on diffusion models, following the development of technology. By sorting out the core ideas, key technologies and representative works of each stage, it reveals the clear evolution of face deblurring research from “pixel-level image restoration” to “identity-constrained generative modeling”. In addition, it introduces the commonly used datasets for face deblurring, and makes predictions and prospects for the problems to be solved and future research directions in face deblurring research. Face deblurring is a research hotspot in the field of computer vision, and more high-quality algorithms will be proposed in the future, and it is developing in a more diversified direction.
face deblurring, deep learning, identity preservation, diffusion model
Copyright (c) 2026 Haochen Wang (Author) https://creativecommons.org/licenses/by/4.0
An Integrated Learning-Based Model for Intelligent Classification of Logistics Claims Risk and Prediction of Payout Amounts
In recent years, the rapid development of the express and logistics industry has led to a continuous increase in shipment volume, accompanied by a growing number of claims disputes and payment risks. The intelligent automation of the claims settlement process has become a crucial direction for enterprises to reduce costs and improve efficiency. For Problem 1, this paper proposes a claims risk labeling model based on quantile distribution analysis and monotonicity constraints. The method employs a combined strategy of equal-frequency binning, quantile estimation, PAVA monotonic regression, and PCHIP continuous interpolation to ensure the classification boundaries are smooth and align with business logic. The results show that the proportions for the three classified risk groups are: Reasonable Claims at 85.12%, Elevated Claims at 12.19%, and Severe Overclaims at 2.70%. To address problem 2, this paper proposes a Stacking ensemble learning-based model for predicting the actual claim payment amount in logistics compensation. By performing feature selection, enhanced feature engineering, and target variable transformation on historical waybill data, we employed multiple machine learning algorithms, including CatBoost, MLP neural networks, XGBoost, and LightGBM, and combined them using a Stacking ensemble strategy to optimize prediction performance. For problem 3, this paper proposes a risk labeling classification model based on an MLP model to determine the risk level category of shipping orders. To address the severe class imbalance in the dataset, the SMOTE oversampling method was employed to balance the sample distribution. At the model level, a CatBoost classifier, a neural network (MLP), and a Support Vector Machine (SVM) were trained separately, and their performance was compared via cross-validation. The results indicate that the MLP model performed best, achieving a weighted F1-Score of 0.9098 and a training accuracy of 95.55%, significantly outperforming CatBoost and SVM. This demonstrates the model's strong discriminative capability.
logistics claims risk identification, stacking ensemble learning, MLP model, classification evaluation
Copyright (c) 2026 Jiali Du (Author) https://creativecommons.org/licenses/by/4.0
Research on Multi-Sensor Fusion Technology, Application Scenarios, and Future Prospects Review
To better acquire information and understand actual situations, multi-sensor technology has been widely applied in various industries. It represents a significant advancement over single sensors and is currently a core method for perceiving complex environments. However, issues such as inconvenient information collection, improper data processing, and insufficient environmental adaptability persist. This paper systematically elaborates on the technical background of multi-sensor fusion technology, its core algorithms including Kalman filtering and Bayesian estimation, as well as its applications in robotics, intelligent monitoring, and healthcare. It also discusses existing problems and corresponding solutions, and provides an outlook on the future application of sensors in fields like quantum technology and bionics, as well as potential emerging industries.
multi-sensor fusion, fusion algorithm, robot, intelligent recognition, healthcare
Copyright (c) 2026 Feiyang Zhao (Author) https://creativecommons.org/licenses/by/4.0
Mesh Simplification Method Based on Implicit Geometric Constraints
To address the lack of continuous geometric constraints in traditional quadric error metrics (QEM)-based mesh simplification under high simplification ratios, this paper proposes a 3D mesh simplification method with implicit geometric constraints. First, TetWeave is used to perform implicit modeling on the original high-resolution mesh, from which a continuous implicit reference field is constructed, together with query modules for implicit deviation and implicit normal evaluation at candidate collapse points. Then, within the classical edge-collapse framework, the implicit geometric deviation is introduced as an additional constraint term in a normalized composite cost, while implicit normal consistency is employed as a legality criterion for collapse execution. Finally, experiments on multiple models are conducted to analyze the influence of different weight settings on simplification error and computational cost. The results show that the constructed implicit query module can reliably reflect the geometry of the original continuous surface, and that the normalized implicit constraint has a significant influence on candidate edge ranking and the final Hausdorff distance. With an appropriate weight setting, the proposed method improves the preservation of the original surface geometry, although it also introduces additional computational overhead. The proposed framework provides a feasible way to incorporate continuous implicit geometric supervision into classical edge-collapse simplification.
mesh simplification, quadric error metrics, implicit geometric constraint, edge collapse, Hausdorff distance
Copyright (c) 2026 Yangyang Qiu (Author) https://creativecommons.org/licenses/by/4.0
A Review of Three Types of Neural Networks: Convolutional, Recurrent, and Recursive
Artificial neural networks are a core technology in the field of nonlinear system modeling and intelligentization. Convolutional, Recurrent, and Recurrent Neural Networks, as their three core branches, each have unique structural and performance characteristics, and related research urgently needs systematic review and integration. This article adopts the methods of literature combing and comparative analysis to comprehensively sort out the development vein of the three types of neural networks, analyze their structural characteristics and applicable scenarios, explore the technical pain points and optimization paths of various networks, and look forward to the future development direction. The innovation of this research lies in the research results of the development and application of the three types of neural networks in the system, clearly defining the technical characteristics, differences between advantages and disadvantages and integration application modes of each network, and clarifying their respective optimization directions. The study found that the three types of neural networks have their own advantages and disadvantages, complementary adaptation, and have irreplaceable application value in computer vision, natural language processing and other fields. In the future, its development needs to focus on optimizing its own technical defects, improving the ability of model generalization, further expand the application boundaries through deep integration with other network models and learning mechanisms, and promote the application of intelligent technology in complex scenarios.
convolutional neural networks, recurrent neural networks, deep learning, network structure, feature extraction, temporal data, model fusion
Copyright (c) 2026 Zheng Tang (Author) https://creativecommons.org/licenses/by/4.0