Artificial Intelligence-enabled vocational education brings new demands for compound talents in the process of intelligent training, but it still faces many challenges in the process of integrating Artificial Intelligence and vocational education. In order to promote the deep integration of Artificial Intelligence and vocational education and meet the needs of economic development and so‐ ciety for diversified technical and skilled talents, on the basis of summarizing the mechanism of action and analyzing the practical problems, this paper puts forward the combination of Artificial Intelligence and professional curriculum construction, giving full play to the potential of teachers to use intelligent technology, and promoting the development of students' personality based on Artifi‐ cial Intelligence to implement the application of Artificial Intelligence in vocational education. 

With the rapid development of AI large models, the demand for computing power has sharply increased. Focusing on the pain points of low utilization of computing power resources and security concerns caused by sensitive data leaving the park, this ar‐ ticle proposes a solution based on storage and lossless network technology. By constructing an intelligent computing experimental network and adopting an innovative mode of simultaneous transmission and training, real-time data processing and rapid model up‐ dates have been achieved. The experimental results show that this scheme significantly improves the efficiency of computing power usage, reduces single task training time by 50%, increases data transmission rate to 7.3 Gbps, and can still maintain efficient opera‐ tion under 200 KM distance conditions. However, challenges such as data security and privacy protection still need to be addressed in the future. This study provides new ideas and methods for addressing the computing power demand in the era of large models.

The application of various AI products or technologies in the field of education will drive a more profound digital transfor‐ mation and innovation in education, which is an inevitable requirement for advancing high-quality education development in the era of Artificial Intelligence. The intervention of AI large language model technology in higher vocational education has overturned the traditional paradigms and methods of educational technology intervention in this field from multiple dimensions such as precise modeling, intelligent interaction, emotional identification, and has facilitated the transformation and sublimation of the educational model in higher vocational education. However, the inherent weaknesses of AI large language models also pose potential risks and challenges to higher vocational education and teaching in areas such as ideology, values, scientific ethics, and more. Therefore, while integrating AI large language model technology and seizing the opportunity for high-quality development in higher vocational educa‐ tion, it is also crucial to establish design norms for technology application. This study puts forward the construction method of inte‐ grating AI technology into the curriculum system of higher vocational education from three dimensions: curriculum content, learning mode and curriculum environment. On the basis of analyzing the potential risks, this paper implements the path of promoting the digital transformation of higher vocational education through top-level design, content construction, audit algorithm and strengthen‐ ing supervision by implementing AI large language model.

As the informatization process continues to advance, digital natives have become a major component of society. The widespread application of digital technologies in education has transformed their learning styles and knowledge acquisition, leading to a growing phenomenon of technology dependency. Based on grounded theory, this study conducts in-depth interviews with 31 digital natives and implemented three-level coding using NVivo11 to identify the underlying mechanisms that lead to technology dependency in the learning process and construct an attribution model of technology dependency. The model identifies factors contributing to technology dependency at the individual, school, and societal levels, including technological convenience, teacher encouragement, and peer pressure. It also identifies potential cognitive, behavioral, and emotional impacts of technology dependency, including reduced independent learning, weakened critical thinking, and distracted attention. To address these impacts, intervention strategies are proposed at the school and societal levels, including increased awareness and supervision, as well as psychological counseling and intervention, to help digital natives reduce their technology dependency and develop appropriate behavior habits using technology.

A multi-sensor fusion model based on Bidirectional Long Short-Term Memory (BiLSTM) and gated attention mechanism is proposed to address the challenges of heterogeneous data fusion and insufficient real-time performance in multi-sensor human activity recognition. This method captures bidirectional long-term dependencies through BiLSTM and employs gated attention to achieve adaptive weighting of multi-source features, effectively enhancing feature representation capability and fusion efficiency. Experimental results demonstrate that the proposed approach achieves a highest accuracy of 95.3%,in addition, under a comparable lightweight setting, the proposed model exhibits lower parameter count and faster inference latency than the Transformer baseline, thereby achieving a more favorable balance between accuracy and efficiency.Ablation studies further confirm the critical role of BiLSTM and the attention mechanism in improving performance. This research provides a solution that balances recognition accuracy and inference efficiency for behavior recognition in complex scenarios, showing strong potential for practical applications.

Under the background of the "Double High Plan" construction, the disconnect between higher vocational talent cultivation and industrial technology iteration urgently needs to be addressed. Based on the "Five Golds" (elite majors, high-quality courses, outstanding teachers, advanced practice bases, and innovative teaching materials) integrated resource optimization framework, and relying on the reform practice of the Internet of Things major at Suzhou Polytechnic University, this study explores the construction of an educational ecosystem featuring "integration of industry and education, and integration of certificates and curricula". The model aims to achieve deep coupling between professional chains and industrial chains, dynamic synchronization between course content and technological development, and effective integration of faculty capabilities with industry practices. Practical results demonstrate that this approach enhances the alignment of students' occupational competence with enterprise demands, providing a replicable pathway for the digital transformation of higher vocational education. As a foundational project for high-quality development in vocational education, the "Five Golds" initiative requires systematic integration and dynamic adaptation mechanisms to drive vocational education from adaptive reform toward innovative leadership^[1].

To address the importance of global background information and local detail features in rain streak removal, as well as the limitations of deep learning models in handling high-resolution images, we propose ClearMamba, a spatio-temporal state-space model with global context fusion. This model achieves pixel-level feature refinement through spatio-temporal feature modeling and global context integration, significantly enhancing single-image deraining performance. Experiments on datasets such as DID-Data demonstrate the superiority of this algorithm, with a peak signal-to-noise ratio (PSNR) improvement of approximately 4.9% compared to methods like PReNet. Furthermore, we systematically evaluate the empowering potential of deraining models for downstream visual tasks (e.g., object detection), achieving top performance. The proposed method offers a practical solution for optimizing intelligent visual systems in rainy environments.

Online peer assessment provides new theoretical and technical means for the development of critical thinking, and peer evaluation and peer interaction are the main activities implemented by online peer assessment. The article proposes a model of online peer assessment for critical thinking development around peer evaluation and peer interaction, uses social network and epistemic network analysis methods, and explores the development process of critical thinking in online peer assessment from the temporal dimension and role dimension, respectively, using the interactive comments as the analysis content. The findings show that (1) critical thinking shows a progressive and continuous development process in online peer assessment; (2) peer interaction is the main driver for critical thinking development; (3) critical thinking has progressive differences in different stages and groups of online peer assessment; and (4) critical thinking has a significant structural pattern. The article reveals the development process of critical thinking from the perspective of online peer assessment, and provides a theoretical basis and practical reference for improving the quality of online peer assessment and the development level of critical thinking.

The "Curriculum-Training-Competition-Innovation-Industry"(CTCI²) innovation and entrepreneurship practice teaching model, driven by digital-intelligent empowerment and competitions in higher vocational computer-related disciplines, aims to effectively integrate digital-intelligent technologies, skills competitions, and innovation/entrepreneurship practices. Its goal is to cultivate high-level technical and skilled talents equipped with innovative thinking and precise career orientation, capable of adapting to the new-era industrial environment. This model explores the construction of a five-in-one innovation and entrepreneurship practice teaching framework based on digital-intelligent empowerment and competition-drive, the establishment of a digital-human interactive CTCI² innovation platform, and the development of an integrated educational effectiveness evaluation system. It seeks to propel higher vocational computer education into a new stage characterized by enhanced quality and excellence, value-added empowerment, and ultimately achieve high-quality development in vocational education.

The Linux laboratory course is a fundamental core course for computer science and information related majors, characterized by strong practicality and complex operational chains. Traditional evaluation systems mainly rely on students' submitted lab reports, which are result-oriented, lacking process analysis and contextual awareness, making it difficult to comprehensively and dynamically reflect students’ actual abilities and learning status. To this end, this paper proposes an evaluation system for Linux laboratory courses that integrates knowledge graphs with multimodal perception. It integrates key technologies such as code behavior analysis, speech and facial emotion recognition, system operation trajectory extraction, and knowledge graph reasoning. The system takes multimodal inputs—including student behavior logs, oral defense speech, facial expressions, operation flows, and error types—and applies focal contrastive learning and a Neo4j-based knowledge graph model to achieve comprehensive scoring and personalized feedback. Experimental results demonstrate that the proposed system significantly improves evaluation accuracy, consistency, and the stimulation of students’ learning motivation, providing a new paradigm and technological support for teaching reform in Linux and other system-oriented laboratory courses.

Aiming at the core pain points of current car owners, such as scattered maintenance records and lack of targeted reminders, and combining with the diversified development trend of China's automobile aftermarket in 2024, an intelligent record and recommendation system for automobile maintenance is designed and implemented. With intelligent recording and accurate reminders as the core, the system constructs a three-layer architecture consisting of a perception layer, a data layer, and an application layer: The perception layer collects image documents, integrates YOLOv8 to locate and correct document areas, and uses OCR (Optical Character Recognition) technology to extract VIN (Vehicle Identification Number) codes. The data layer adopts MySQL master-slave architecture, Redis cache, and Alibaba Cloud OSS (Object Storage Service) to store information such as vehicle files and maintenance records. The application layer generates accurate reminders based on three-dimensional factors and dynamically iterates the system message reminder strategy in combination with user feedback.Test results show that the recognition accuracy of key information in system documents reaches 99.5%, and the record query response time is less than 1 second. The system can effectively reduce manual entry operations, solve the problems of low efficiency of traditional maintenance management and lack of targeted reminders, and provide car owners with a lightweight and highly user-friendly one-stop maintenance management service.

Aiming at the problems existing in the current teaching of database course, such as single teaching mode, disjointed theory and practice, and one-sided evaluation method, this paper puts forward a project-driven teaching mode based on constructivism theory. This model takes "supermarket management system" as the teaching project carrier, and designs and implements the whole process teaching scheme covering project start-up, exploration construction and achievement evaluation, aiming at guiding students to actively construct database knowledge through active exploration and collaborative communication in real situations. Teaching practice shows that this model can effectively improve students' ability of problem solving, teamwork and knowledge transfer, and provides an effective path with both theoretical support and practical value for the teaching reform of database course.

Traditional anti-bullying interventions in schools rely primarily on proactive reports from teachers and students, together with periodic inspections and surveillance by administrators. These approaches are untimely reactive and labor-intensive, which makes timely detection and response to bullying incidents difficult. A deep-learning-based campus-bullying monitoring system that provides robust protection for campus security and safeguards the mental health of students. The system integrates YOLOv8 for object detection and 3D ResNet for action recognition as its core algorithms, while leveraging servo-driven dynamic tracking as its enabling hardware technology. By combining modules for object detection, action analysis, and dynamic tracking, we construct a comprehensive and intelligent monitoring framework. By processing and analyzing live surveillance streams in real time, the system can rapidly and accurately identify potential bullying behaviors and promptly issue early-warning notifications so that administrators can intervene without delay. In addition, the system includes the “Glimmer Haven” mini-program, which offers emotional support and psychological solace to victims, alleviating their mental distress. An AI-powered chatbot further assists students and faculty in understanding and addressing campus-bullying issues, thereby fostering a safer and more harmonious school environment.

With the rapid development of technologies such as cloud computing and artificial intelligence (AI), the education sector is undergoing profound changes. As an essential component of the information technology major, the teaching reform and practice of Linux technology courses are of great significance for cultivating high-quality talents that meet the demands of the new era. This paper focuses on the teaching innovation in the field of information technology and deeply explores the teaching reform paths and practical methods of Linux technology courses in a private cloud environment, empowered by AI technology. By analyzing the existing problems in the current teaching of this course, it expounds on the necessity and feasibility of the cloud-intelligence integrated teaching model. It elaborates on specific measures such as building a teaching environment based on a private cloud platform, optimizing teaching content and resources with AI assistance, and innovating teaching methods and means. Through the analysis of actual teaching cases, it compares the teaching effects before and after the reform, aiming to provide beneficial references and inspirations for improving the teaching quality of Linux technology courses and cultivating high-quality talents that adapt to the demands of the times.

Focusing on the prediction of customers' new car model purchase behavior, this paper carries out a comparative analysis on the performance of four classification algorithms: Logistic Regression (LR), K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GaussianNB), and Support Vector Machine (SVM). Based on data related to car consumption, following data preprocessing, hyperparameter tuning is performed for each model. For Logistic Regression, the regularization parameters and penalty terms are optimized. For the KNN algorithm, the number of neighbors is adjusted, and an appropriate distance measurement method is selected. For Gaussian Naive Bayes, the adaptability of the feature independence assumption is verified, and corresponding fine-tuning is carried out. Optimization is performed on key parameters of SVM, including the kernel function and regularization coefficient. After training and testing evaluation, the SVM demonstrates outstanding comprehensive performance after tuning, with an AUC reaching 0.965 and a prediction accuracy of 93%. Both KNN and Gaussian Naive Bayes achieve an AUC of 0.963 and an accuracy of 93%, while Logistic Regression has an AUC of 0.955 and an accuracy of 91%. The research shows that SVM has significant advantages in fitting nonlinear data, is suitable for the car consumption prediction scenario, and provides powerful data support and decision basis for enterprises to develop targeted marketing strategies.

On-site observation in the marine environment is required to obtain real and reliable data for research on marine microorganisms. This article presents an automatic seawater sample collection and distribution system for ships based on the NI CompactRIO. The system is deployed on scientific research vessels and establishes connections with instruments such as the winch system at the stern of the vessel and microbial flow image analyzer, under the control of the management system, achieving fully automatic or semi-automatic extraction, filtration, transportation, and automatic cleaning of the entire pipeline of seawater samples. The system can also monitor various water quality parameters such as temperature, salinity, dissolved oxygen, conductivity, chlorophyll, etc. online and upload to the system for storage. The system is designed based on a reliable and stable embedded hardware system, which is conducive to long-term operation in marine environments, maximizing the preservation of the original appearance of seawater samples, and ensuring the authenticity and effectiveness of analysis and detection data.

This article aims to explore an effective evaluation path that adapts to the educational goals of vocational colleges that combine moral and technical education. It organically integrates the CIPP evaluation model with the concept of "dual core of morality and technology", and constructs a three-dimensional evaluation system that covers professional qualities, skills, and career development capabilities. Take the course of Responsive Web Development for example, it conducts evaluations through four stages: background, input, process, and outcome, and combines blended teaching to achieve full-cycle data collection and dynamic optimization. Practice shows that this system can enhance students' technical abilities and professional qualities, providing theoretical and practical paths for the implementation of "dual core of morality and technology" in professional courses in vocational colleges.

In response to the disconnection between traditional electronic technology experiment teaching content and complex engineering problems, this paper restructures the experimental courses in a task-chain-based hierarchical manner to enhance students' practical abilities and comprehensive professional competencies. During the restructuring process, the curriculum design adopts the perspective of engineers' professional qualities and technical skills, promoting the deep integration of experimental content with engineering demands. The experimental content has been reorganized into five progressive levels: basic experimental projects, compound experimental projects, comprehensive experimental projects, AI-enhanced innovative experimental projects, and real competition questions from electronic design contests. Simultaneously, the course promotes innovation and reform in teaching methods and evaluation systems, moving away from a single assessment model toward a multidimensional framework that integrates theoretical knowledge, practical skills, innovative thinking, and teamwork. Through systematic reform, the electronic technology experiment course not only enhances students' hands-on abilities, but also consistently strengthens their comprehensive competencies in analyzing and solving engineering problems. Ultimately, these efforts lay a solid foundation for cultivating high-quality, interdisciplinary, and application-oriented talent in private universities, effectively bridging the gap between talent development and industry needs.

Based on the Outcome-based Education (OBE) concept, this study addresses key challenges in Computer Introduction teaching in private colleges and universities, such as significant student heterogeneity, strong practical preferences, and insufficient learning autonomy, then constructs a "Three-in-One" curriculum reform framework. The framework integrates: (1) a three-tier teaching organization mechanism ("standard instruction-tiered training-personalized tutoring") to unify core knowledge standardization and personalized learning paths; (2) a trinity task system ("professional cognition-vocational skills-research methods") that combines tool application with disciplinary thinking; (3) a three-phase motivation system ("task-driven-team interaction-flipped certification") to activate learning engagement. Teaching practice demonstrates that this framework significantly enhances students' knowledge mastery, tool application skills, and career planning awareness. The study provides a replicable reform approach for introductory computer courses in private universities, offering practical guidance for cultivating application-oriented computing professionals.

In response to the challenges faced by application-oriented universities in cultivating software engineering major, such as inadequate practical skills, limited innovation capabilities, and narrow interdisciplinary perspectives, this paper systematically investigates the development pathway for a "first-class undergraduate majors" grounded in Outcome-based Education (OBE). Using Daqing Normal University's software engineering major as a case study, this paper elaborates on specific measures and outcomes of major construction from three key areas: refining training objectives, optimizing the "three-stage progressive" curriculum system, and enhancing industry-education integration and collaborative education mechanisms. Through these comprehensive professional development initiatives, the overall strength of the major has significantly improved, with a clear focus on serving the petroleum and petrochemical industries. Consequently, students' professional competencies and employability have been notably enhanced. The OBE-based approach to professional construction effectively addresses the mismatch between talent supply and industrial demand, providing a replicable and scalable model for similar universities.

This paper explores the development of courses, resources, and textbooks of Network Fundamentals and Application under the integration of work, study, competition, and certification(WSCC) framework. The aim is to enhance the effectiveness of vocational education and technical skills training by organically integrating job training (work), course instruction (study), skills competitions (competition), and professional qualification certification (certification). Based on research into industry enterprises, examination points from skills competitions, and content from junior network engineer certification programs, this study proposes principles for modular curriculum design and emphasizes the importance of multimedia teaching resources, experimental training resource construction, and the integration of online and offline resources. An analysis of three years of teaching practice indicates that this model significantly improves students' theoretical knowledge and practical operational skills, thereby enhancing their employability. Additionally, a comprehensive system for monitoring teaching quality and an effective feedback mechanism have been established to ensure continuous improvement in educational quality.

Cloud computing and virtualization technologies are developing rapidly in the computer field. Docker container technology has become the focus of the research direction. As the traffic load of cloud data center changes at any time, network congestion may frequently occur, resulting in network equipment resource tension, and the decline of application throughput, increase of packet loss and delay, affecting the communication performance and the quality of the entire cloud platform server. Aiming at these problems, this paper improves the network congestion by transferring the Docker container mounted on the physical machine, and a network-aware Docker container rescheduling algorithm is proposed to improve the communication capability of the physical machine to improve the global communication efficiency. The algorithm improves the communication capability between physical machines by migrating fewer virtual machines to improve the overall communication performance of the entire data center.

As an important part of road traffic, bridges need to be regularly inspected and maintained during their service life. Among them, the crack width of bridges is an important inspection index. Traditional crack width detection requires special vehicles to occupy fixed lanes, which affects traffic and incurs high costs. In this paper, a rotor UAV is used as the platform, and a distance sensor and relevant protective measures are added. The image information of bridge cracks is collected through close proximity photography. After image processing, correction, and calibration with professional instruments, the crack width is calculated. Through the detection of the actual crack width and comparison with the traditional detection method, it is shown that the UAV close proximity photography method can meet the identification requirements for the crack width of bridges larger than 0.2 mm, realizes low-cost detection, and has certain application prospects.

This study focuses on optimizing third-party model integration and GPU acceleration strategies in the Mediapipe framework. As an open-source mobile AI framework developed by Google, Mediapipe achieves low-latency, high-precision real-time processing on mobile devices through its pipeline architecture. However, the framework exhibits significant limitations in supporting third-party model integration. To address this issue, we propose an innovative model integration layer design and successfully implement three models: YOLOv11, YOLOv11-Pose, and RTMPose. Regarding GPU acceleration strategies, this research explores two key aspects: model inference parameter optimization and inference result parsing, proposing a comprehensive performance optimization solution. Experimental results demonstrate that on the Android platform, this integration solution achieves significant improvements in model execution efficiency while maintaining excellent deployment convenience.

Traditional gesture recognition methods are often susceptible to interference from factors such as lighting conditions, obstructions, and inconvenient equipment carrying. This paper proposes a sensorless gesture recognition algorithm based on a multi-branch CNN-GRU framework (SGMCG). The method firstly processes Channel State Information (CSI) signals to construct a Body-coordinate Velocity Profile (BVP) dataset. Subsequently, multiple parallel CNN branches with varying kernel sizes extract spatial features of gestures. Each branch is followed by GRU networks to capture temporal dynamics. The temporal features from all branches are then fused and classified via a Softmax layer. Experimental results demonstrate that SGMCG achieves higher average recognition accuracy on both numerical and interactive gesture datasets compared to Widar3.0, GRU, and LSTM models, while also exhibiting superior robustness.

The S14 Global Finals serve as a significant cultural touchstone and emotional anchor for LPL fans. This article aims to delve into and examine the emotional responses and social interactions of audiences during the tournament. Take the bullet comments data of the 2024 League of Legends Global Finals for example, this article conducts word frequency analysis, word cloud visualization, and topic analysis based on LDA model, and uses SnowNLP for emotional semantic analysis. Research shows that fans pay attention to game results and game characters, and engage in in-depth discussions on tactics and performance related topics. Based on LDA theme analysis, combined with perplexity calculation and pyLDAvis visualization display, the optimal number of themes is 4, and 4 themes related to the competition are identified. In addition, the emotion analysis ratio chart and histogram show the emotional interaction behind the bullet screen. Although the 2024 League of Legends Global Finals ended with the defeat of the all-Chinese class BLG, the fans' emotions were relatively depressed, but the overall emotional tendency was still relatively positive.

Multi-label feature selection improves the performance of learning models by eliminating irrelevant features. However, most existing methods assume that the labels in the training set only contain simple logical values and that all relevant labels have the same effect on instances. In addition, in practical applications, the influence of different labels on instances may vary. Based on this, this paper proposes a feature selection method based on fuzzy neighborhood information entropy and mutual discriminant index. Firstly, the original multi-label datasets are transformed into label distribution datasets by using label enhancement technology. Then, the neighborhood information entropy is used to quantify the similarity relationship between samples in the label space. Finally, the feature space and the label space are combined by using the fuzzy neighborhood mutual discriminant index to identify the feature subset with significant discrimination ability. Experiments on six datasets comprehensively show that the classification performance of this algorithm is superior to that of other algorithms.

This paper proposes an unsupervised log anomaly detection method based on an improved variational autoencoder generative adversarial network (VAE-GAN) to address the issues of instability and interdependence in log sequence data. The proposed model combines the advantages of GAN and VAE by embedding the temporal convolutional network module into the encoder, decoder, and discriminator, effectively capturing the distribution of log sequence data and optimizing the sequence mapping in the latent space, thereby achieving high-precision reconstruction of normal log sequences. The model continuously improves the reconstruction ability of the variational autoencoder through adversarial training mechanism, enabling it to identify abnormal patterns in the log more accurately. The experimental results show that compared with other unsupervised methods, this method has better performance on public log datasets.

Aiming at the two problems of poor real-time performance and calculation accuracy caused by insufficient processor computing power and limited effective bits when implementing traditional integrated navigation simulation algorithms on hardware platforms built with MEMS inertial devices and industrial-grade processors, this study quantifies the noise and error magnitudes of MEMS inertial devices, simplifies Kalman filter error equations, and adopts UD decomposition algorithm. The optimized algorithm achieves 17% improvement in computational speed, effectively demonstrating its validity. Ultimately it is implemented on a low-cost attitude measurement system composed of MEMS devices. Vehicle tests verify that the heading angle accuracy reaches 0.5° and horizontal attitude angle accuracy reaches 0.2°, successfully resolving the issues of real-time navigation data output and computational precision.

In the current context of information and innovation, in order to meet the needs of information and innovation development, the "Tripartite Education Reforms" of the course of Computer Network Technology in vocational colleges are urgently needed. This article analyzes the problems of theoretical and practical disconnection and lack of teaching resources in the teaching process of Computer Network Technology. It proposes to empower the "Three Education Reform" with virtual simulation technology, introduces the advantages and effectiveness of Huawei eNSP virtual simulation technology in "Three Education Reform", and elaborates on the specific application of virtual simulation technology in course teaching with a typical teaching case. Practice has proven that the application of eNSP virtual simulation technology effectively enhances students' learning interest, practical ability, and innovation ability, expands their learning space and time, and promotes the improvement of teaching quality.

This paper focuses on the three-order evolution of educational agents, including tool embedding, agent symbiosis, and infrastructure, and constructs a theoretical framework for the evolution of educational agents, and deeply explores the connotation and dynamic mechanism of its paradigm transition. The driving force of paradigm transition comes from the synergistic effect of technological iteration, educational demand upgrading and institutional innovation, and the transitional contradiction between different paradigms is the explicit game between technical logic and educational law. In the end, the ultimate form of educational agents will realize the ecological revolution of the education system from technology empowerment to intelligent endogenous, promote the qualitative change of educational equity from resource balance to ability equality, and its development will eventually blur the boundary between technology and civilization, and become an accelerator for human cognitive evolution.

Anomaly detection in borehole strain data as earthquake precursors is of great significance for earthquake prediction. This paper takes the borehole strain time series provided by the Jiangsu Earthquake Agency as experimental data. Firstly, a preprocessing method combining trend separation and residual denoising is used to effectively extract the long-term trends and critical fluctuation information from the data, thereby reducing noise interference. Subsequently, an LSTM-Attention model is designed and its performance is compared with three other models: LSTM, CNN, and AutoEncoder. The comparison results indicate that the LSTM-Attention model outperforms the other models in terms of MSE, MAE, and RMSE. Finally, this model is used for anomaly detection. Experimental validation demonstrates that the LSTM-Attention model can accurately capture anomalies in the data for the task of anomaly detection in borehole strain precursors of earthquakes.

The current face expression recognition methods mainly focus on the spatial domain when facing scenes such as emotional computing, human-computer interaction, intelligent monitoring. However, the spatial domain method has the problem of being difficult to deal with the noise, while the frequency domain processing is limited to the details that cannot be well taken into account in the global. In order to solve this problem, a multi-channel face expression recognition method based on frequency domain low-pass filtering and Gabor feature fusion is proposed, which is able to combine the global structural information of the image in the low-pass frequency domain filtering and the global detail information in the Gabor filtering through channel fusion under the premise of small changes in network overhead, so as to make up for the defects of the conventional processing methods, and ultimately improve the face expression recognition model. Finally, the accuracy of the face expression recognition model is improved. The experimental results show that the method improves the accuracy by 0.78% and 1.86% on the publicly available face expression recognition datasets FER2013 and RAF-DB, respectively. It is also demonstrated by means of ablation experiments that this combination method can make up for the defects of each other to a certain extent, which shows that this fusion method has a better effect.

Aiming at the low degree of industry-education matching, lagging technology iteration, single dimension of practice evaluation and other core problems currently prevailing in network engineering majors of applied undergraduate colleges and universities, we aim to build a four-stage spiral practice teaching system empowered by "industry-university-research-use" in-depth synergy and generative AI technology, and to explore ways to solve the problem of disjointed technology supply in traditional practice teaching. By integrating the resources of the government, universities and enterprises, a four-stage progression path of "basic experiment, comprehensive practical training, project battle and industrial application" is designed, forming a closed loop of ability cultivation. This study proposes a new engineering practice teaching paradigm driven by generative AI, which provides a replicable technical solution and collaborative mechanism for similar specialties.

With the development of information technology, mainframes still play a pivotal role in civil aviation information systems. The host database is a file-based database composed of multiple database files. It often occurs that the space of a certain database file is abundant, while the space of individual Freespace Record is insufficient, which has an adverse impact on business. FSMonitor is a system used to monitor the Freespace Record space of host database files. It introduces W8236 encoding, RLE compression, and Checksum verification to transfer the FS$SYS file, which records the usage of database files, from the host system to the open system. The file is parsed in the open system for database file usage prediction and intelligent alarm, so as to ensure sufficient database space and the stable operation of the system. At the same time, it reduces the high-cost resource consumption of mainframes.

In the application of phase shifters, phase shifters with filtering characteristics can be employed to select signals at the receiving frequency and achieve stable phase shift values within the phase shift bandwidth. This paper is based on the structure of a transmission line ultra-wideband (UWB) phase shifter and utilizes the principles of filtering phase shift values and the closed-form expression for the phase shift slope to design, simulate (both circuit and electromagnetic), and test a filtering-type UWB phase shifter. The measured center frequency of the designed filtering-type UWB phase shifter is 6.85 GHz, capable of achieving phase shifts of 22.5° (with a phase shift bandwidth of 66.4%), 45° (with a phase shift bandwidth of 103.7%), and 90° (with a phase shift bandwidth of 93%). The results demonstrate that the fabricated filtering-type UWB phase shifter closely aligns with the simulation outcomes, exhibiting characteristics of ultra-wideband performance, filtering capability, and a simple structure, indicating its potential for practical applications.

To address the issue of low detection accuracy in smart classroom behavior detection due to dense student populations, mutual occlusion, and large differences in target scales, an improved YOLO11-based classroom behavior detection model, ADU-YOLO11, is proposed. Firstly, the partial convolutional downsampling layers are replaced with the Adown downsampling module to reduce model complexity and minimize the loss of key feature information. Secondly, the dynamic detection head DyHead (Dynamic Head) is adopted, which enhances detection capabilities through scale, spatial, and task-aware attention. Finally, the UIoU (Unified-IoU) loss function is used to optimize the training convergence speed and improve the regression accuracy of the predicted bounding boxes. Experimental results show that ADU-YOLO11 achieves mAP50 and mAP50-95 improvement of 1.1% and 1.6% respectively, along with a precision increase of 2.7% and a recall increase of 0.6% compared to the original YOLO11n on the STBD-08 student-teacher behavior dataset. Moreover, it outperforms other object detection algorithms, demonstrating its effectiveness and superiority in smart classroom behavior detection.

Traditional speaker recognition methods based on convolutional neural networks (CNNs) often fail to capture long-term temporal dependencies and full-frequency information, limiting their robustness in complex acoustic environments. To address this, we propose LTEB-CAM++, a hybrid architecture combining a Context-Aware Masking Network (CAM++) with a Lightweight Transformer Encoder Block (LTEB). The LTEB module, inserted between CAM++'s FCM and D-TDNN layers, leverages Nyström-based self-attention to efficiently model global speech contexts (up to 10s), while the D-TDNN module extracts fine-grained local features. Trained on fused MFCC-FBANK features, LTEB-CAM++ reduces EERand minDCF by 7.98% and 12.58%, respectively, on CN-Celeb versus the CAM++ baseline, demonstrating superior efficiency and discriminability.

The surface defects of steel rails are difficult to detect due to irregular shapes, scale differences, and background complexity, and the existing YOLOv5s model has limitations such as insufficient bounding box positioning accuracy and weak multi-scale feature extraction ability. This article proposes an improved detection method for YOLOv5s. Firstly, to address the issue of positioning deviation, the GIoU loss function is used to enhance the robustness of bounding box regression; Secondly, embedding CBAM attention mechanism to enhance the focus of defect area features; Finally, to address multi-scale defect detection, the BiFPN structure is introduced to achieve bidirectional weighted fusion. The experiment shows that the improved model can be applied to the dataset of surface defects on steel rails mAP@0.5 reaching 81.8%, an increase of 5.3% compared to the benchmark model, significantly improving the reliability of detection under complex working conditions and providing an effective solution for surface defect detection of rails.

With the increasingly widespread application of voice evidence in judicial cases, grassroots public security organs are facing practical difficulties such as a shortage of voice testing professionals and disconnect between technical capabilities and case needs. As the main battlefield for cultivating public security talents, public security colleges undertake the dual tasks of serving practical needs and leading technological innovation. In order to adapt to the new situation, face new challenges, and better cultivate students' awareness of using voice to serve cases and their ability to use voice testing methods to serve cases, public security colleges and universities invite public security practical experts, judicial appraisal experts, and technical experts in the speech recognition industry to build a set of Forensic Speech Analysis course teaching system oriented towards case needs, focusing on teaching content, teaching resources, practical teaching, teaching methods, teaching assessment system, and teacher team. This paper aims to cultivate high-quality applied voice inspection professionals who understand the profession, can apply, can innovate and have professional ethics.