In recent years, the application of 3D Gaussian splatting technology in novel view synthesis has made remarkable progress, achieving significant improvements in rendering speed and efficiency. Unlike the previous Neural Radiance Fields (NeRF), which are based on implicit representations, 3D Gaussian splatting models scenes using a set of Gaussian ellipsoids and rasterizes these ellipsoids onto images to achieve efficient rendering. This explicit representation not only enhances rendering efficiency but also provides crucial support for subsequent tasks such as dynamic reconstruction, geometric editing, and physical simulation. This review aims to outline its development trajectory, analyze key technologies, and explore current applications and challenges, providing a comprehensive reference for researchers in related fields. The study further indicates that the next phase of development for 3D Gaussian sputtering technology will shift from pursuing single-point breakthroughs in rendering speed and static quality to addressing system-level challenges such as scalability, dynamic realism, generalization capability, and interactive intelligence. This advancement will provide core support for major application fields including digital twins, mixed reality, and autonomous systems.

Frequent flood disasters pose a serious threat to people's lives and property safety. Rapid and accurate monitoring of flood inundation areas and their dynamic changes is crucial for disaster emergency response and assessment. To meet this demand, this study uses the flood disaster in Zhuozhou City, Hebei Province, from July to August 2023 as a study case. Utilizing Sentinel-2 multi-temporal optical imagery, we conduct the identification and dynamic change monitoring of floodwater bodies. By integrating spectral reflectance with various water body indices, we construct comprehensive identification features and separately developed Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM) models for water body extraction. The results indicate that the RF model exhibits a water body identification accuracy exceeding 98.84% across different time phases, with a Kappa coefficient greater than 0.97. Furthermore, its stability and efficiency surpass those of the SVM and ANN models. Based on the time-series monitoring results obtained from RF, the complete process of "rapid inundation - slow recession" during the Zhuozhou flood disaster is revealed. During the recession phase, due to topographical and drainage conditions, some areas experience persistent waterlogging. This study combines the accessibility of Sentinel-2 data with the efficiency of the RF model, offering a high-precision and high-efficiency emergency monitoring solution for flood disasters.

In the C language knowledge question answering system, text similarity calculation is very important, but traditional methods, such as cosine similarity and editing distance, have problems such as inability to deeply understand semantics and difficulty in dealing with language complexity. To this end, this study proposes a C-knowledge text similarity calculation model (BMHA) that fuses bidirectional long short-term memory network (BiLSTM) and attention mechanism. The model takes advantage of the context-aware ability and attention mechanism of BiLSTM to focus on key information to dig deep into text semantics. Experimentally, on the platform built based on the TensorFlow framework, the F1 value of the BMHA model in the semantic matching task is 92.1%, which is 19.7% and 24.5% higher than that of the traditional LSTM and CNN benchmark models, respectively. It shows that its performance is excellent, which provides a new method for text similarity calculation in the field of programming education, and is expected to promote the development of related fields.

For the problem that finite element modeling mostly uses half manual, even pure manual to conduct vertebra segmentation, this paper proposes an automatic segmentation algorithm. This algorithm firstly marks seed points in segmentation region with the multi-resolution composite feature vector based point landmark algorithm, then grows the segmented regions using the method of region growing, finally achieves the automatic segmentation. Experimental validation using 120 spinal MRI images demonstrates that the proposed method achieves an average similarity coefficient of 90.02% for segmentation results, with a maximum of 95.28%. The segmentation process requires only 5.0 minutes, representing over a tenfold improvement in efficiency compared to manual segmentation and significantly outperforming traditional graph theory-based segmentation methods and U-Net algorithm. Conclusions indicate that the proposed algorithm enables efficient and accurate automatic segmentation of vertebral bodies, demonstrating good stability and application potential. It provides a reliable foundation for subsequent 3D reconstruction and finite element analysis.

To address the issues of feature redundancy and noise interference caused by modal heterogeneity in multimodal sentiment analysis, this paper proposes an adaptive collaborative perception model based on multi-task joint learning (AdaSP-MTL). The AdaSP-MTL framework decouples unimodal and multimodal sentiment tasks and constructs a heterogeneous feature disentanglement space to enhance modality-specific modeling. A dynamic gated fusion module based on inter-modal cosine similarity is designed to adaptively allocate fusion weights, thereby suppressing cross-modal noise propagation. Furthermore, the model integrates low-level local features extracted by CNNs with high-level sequential features captured by BiGRUs to form multi-scale sentiment representations. Experimental evaluations on the SIMS, MOSI, and MOSEI datasets demonstrate that AdaSP-MTL comprehensively outperforms existing baseline models in sentiment classification tasks, validating its advantages in effectively modeling modal heterogeneity, suppressing noise interference, and constructing robust sentiment representations.

To solve the long detection time in traditional soil nutrient testing, the shortcomings of transmission distance limitation in currently available portable equipment, and the susceptibility to detection precision degradation caused by temperature interference, a cloud-based portable sensor for the detection of basic soil nutrients is introduced in this paper. The sensor uses STM32F407 as the main control chip and employs a 485 type, five-pin, four-parameter soil sensor, which can measure the temperature, moisture, pH value and NPK at the same time, and realizes the data acquisition of the four parameters through the Modbus RTU protocol. The FS-MCore-F800 series highly integrated 4G Cat.1 module is adopted to implement real-time remote transmission of detection data and remote monitoring functions. Through testing, the device offers extensive signal coverage and stable communication under weak signal conditions. It supports integrated detection of multiple sensors via RS-485, while reducing moisture measurement errors through a random forest-based temperature compensation model. The cloud platform enables multi-terminal access, and the device features excellent operability, presenting broad promotion prospects in smart agriculture.

To address the limitations of traditional micro-landscape systems, which rely on manual maintenance and lack ecological self-regulatory capabilities, an intelligent micro-landscape control system based on the K210 chip has been designed. This system incorporates adaptive environmental regulation and human-machine affective interaction functionalities. By integrating sensors for real-time monitoring of temperature, humidity, and light intensity, it autonomously controls misting and lighting to maintain ecological equilibrium within the micro-landscape. Utilizing Wi‑Fi technology, the system enables remote transmission and storage of environmental data. Innovatively, the KPU + YOLO‑K210 model has been incorporated to achieve facial expression recognition, allowing the system to perceive user emotions and dynamically adjust the ambient atmosphere, thereby delivering personalized experiences such as stress relief, aesthetic enhancement, and anti‑fatigue effects. Experimental results demonstrate that the YOLO‑K210 model achieves a recognition accuracy of 0.874, with all system functions operating stably and reliably. These findings validate the feasibility of establishing bidirectional "environment-human" interaction in intelligent micro-landscape systems and provide a referential framework for their systematic design.

In view of the requirement that efficiency and reusability for signal processing modules in software-defined radio (SDR) systems, this paper designs and implements a reconfigurable signal processing platform based on DSP and FPGA. The platform adopts a layered architecture, enabling flexible waveform configuration and efficient management through hardware resource isolation and modular design. Waveform software loading is performed on the DSP side, where the FPGA configuration file is loaded using the Slave SelectMap method, combined with EDMA (Enhanced Direct Memory Access) based data transfer to accelerate the system reconfiguration process. Test results demonstrate that the proposed design achieves excellent real-time performance, stability, and scalability, providing valuable insights for the design of signal processing platforms in SDR systems.

Evaluating students' vocational abilities is a key point in assessing the quality of talent cultivation in New Engineering disciplines in higher vocational colleges. However, at present, there are relatively few studies on the vocational ability assessment of New Engineering students. Based on the COMET capability model and typical work tasks, this study establishes a vocational ability assessment system for students majoring in New Engineering. A total of 244 students from a certain vocational college are selected as the assessment subjects. Based on three ability levels and 24 ability indicators, evaluation indicators and scoring standards are formulated. Open-ended comprehensive test questions, background situation questionnaires and answering motivation questionnaires are designed. The assessment results show that 80.4% of the students have reached the process ability level, while only 5.3% have reached the design ability level. Students lack environmental protection awareness and innovation awareness when completing tasks. This research will provide a scientific reference for the reform of talent cultivation models in colleges and universities, and help them optimize their curriculum systems, teaching models and evaluation systems.

In response to the demand for highly skilled talents in the new generation of information technology industry, and to address issues such as the disconnection between industry and education, the singularity of evaluation methods, and insufficient integration of ideological and political education in computer courses in higher vocational education, a classroom teaching model featuring "dual-line guidance, three-stage progression, six E's linkage, and multi-dimensional evaluation" has been constructed, using the Windows Server operating system management course as the carrier. This model integrates value shaping and skill cultivation, aligns with job standards based on the three-stage abilities of "network construction, network management, and network maintenance", creates real work scenarios through the "six E's" teaching process, and establishes a multi-subject whole-process evaluation system covering knowledge, abilities, and qualities. Practice has shown that this model effectively synchronizes course content with industrial technology, integrates teaching processes with work tasks, and coordinates educational effectiveness with social services, demonstrating good application effectiveness and promotion value.

Addressing the prominent issues in traditional Python Programming courses, such as dynamic adjustment, practical feedback, and teaching precision, this paper constructs a "1+1+N" teaching AI intelligent platform. Guided by the educational philosophy of "integrating intelligence and education, tripartite collaboration", and based on the "one body, two wings" architecture, the platform achieves precise support and dynamic optimization throughout the teaching process. Practical results demonstrate that this reform significantly enhances students' programming skills, learning engagement, and course satisfaction, promotes the transformation of teachers' roles towards advanced education, and establishes a data-driven, teacher-student collaborative teaching ecosystem. This provides a replicable practical pathway for curriculum reform in applied undergraduate institutions.

Aiming at the problems of high learning difficulty, disconnect between theory and practice, and insufficient interdisciplinary adaptability in Artificial Intelligence general education courses in higher vocational colleges, this study proposes the "Intelligence and Application Integration" dual-driven teaching philosophy. It constructs a "Five-Dimensional Integrated" curriculum framework consisting of "Basic Introduction - Theoretical Support - New Technology Application - Tool Application - Comprehensive Inspiration". Taking intelligent tool empowerment as the practical carrier and intelligent thinking leadership as the cognitive orientation, the study integrates cutting-edge technologies such as large models and agents, and aligns with emerging professional demands like AI trainers. This approach effectively enhances the intelligent thinking level and interdisciplinary application ability of students from different majors, providing a systematic teaching solution to solve the current curriculum dilemmas.

With the rapid development of Artificial Intelligence technology, programming education is transforming from traditional grammar instruction to cultivating computational thinking and practical problem-solving abilities. Python, as a widely adopted programming language, urgently needs innovative teaching methods. While project-based learning aligns with the inherent needs of programming education, it still faces challenges in practice, such as limited project selection, high student cognitive load, and simplistic evaluation methods. The emergence of Generative Artificial Intelligence offers a new path to address these issues. Take DeepSeek as an example, this paper systematically constructs a Python project-based teaching model empowered by Generative Artificial Intelligence. Focusing on three stages—project introduction, activity exploration, and results presentation—it elaborates on the collaborative mechanism between teachers, students, and GenAI in six stages: project selection, planning, activity exploration, project creation, results sharing, and activity evaluation. It provides a systematic theoretical framework and instructional design reference for Python project-based teaching supported by generative artificial intelligence, laying the foundation for subsequent empirical research and practical exploration.

This paper constructs an intelligent teaching evaluation system for electrical and electronic experimental course group based on the "6E" instructional model, integrating evaluation mechanisms into the five teaching stages - Elicit, Engage, Explore, Explain, and Elaborate, to form a closed-loop feedback structure. Then an intelligent evaluation platform for the "Three Electrical Courses" experimental curriculum cluster has been developed to support process-oriented and personalized evaluation. Practice has shown that the system effectively enhances students' comprehensive abilities and innovation competence, while providing an effective evaluation pathway and decision-making support for the reform of experimental teaching.

Case-based practical teaching methods are widely used in the teaching of Artificial Intelligence Technology courses, but there are common problems such as low student participation, serious "toy-like" of cases, and disconnection from actual talent demand. To address these issues, this paper explores a project-case-guided practical teaching method oriented by talent market demand and aimed at promoting the subjective initiative of students. Based on typical and currently mainstream artificial intelligence technologies, the method integrates demands and resources from multiple sources, including scientific research projects, national Class-A competitions, and enterprises, to design a three-level, full-process project case system of "basic-advanced-practical". Through phased and step-by-step guidance of cases, it enables students to independently learn theoretical and practical knowledge of the course, enhancing their hands-on abilities while improving their competitiveness in industry employment.