Frontiers in Computing and Intelligent Systems
Frontiers in Computing and Intelligent Systems (FCIS) is a peer-reviewed journal published by Darcy & Roy Press. The mission of the journal is to provide an academic forum for discussing the issues of computing and intelligent systems across the world. All articles published are rigorously and fast reviewed meeting the Journal Quality standards.
Automatic Recognition of Concrete Cracks in Industrial and Civil Buildings Based on CNN + SVM/Random Forest
Concrete cracks are a key indicator for evaluating the safety of industrial and building structures Civil buildings. Traditional manual detection methods have low efficiency, strong subjectivity, and high labor costs, which cannot meet the needs of large-scale automated monitoring Significant progress has been made in learning based crack detection algorithms, with most existing models For the development of transportation infrastructure (bridges, pavements, tunnels), due to significant differences, the performance will decrease by 15% to 25% when directly migrated to building scenarios In terms of lighting, background texture, and crack morphology. In addition, currently over 95% of research Only focusing on binary classification, unable to provide crack width and Severity is crucial for engineering practice. Although pixel level segmentation methods can Implementing crack quantification relies on extremely expensive manual labeling (every 10-20 minutes) Images make large-scale applications impractical. To address these challenges, this article proposes an automatic crack recognition scheme for lightweight concrete used in industrial and civil buildings, combined with CNN feature extraction based on SVM/random forest classifier. This scheme utilizes CNN Superior generalization of hierarchical feature extraction and SVM/random forest in small samples Plot. The four pre trained CNN backbone networks (VGG16, ResNet50, MobileNetV3, EfficientNet Lite) are System comparison and introduction of ZCA whitening and anti distillation to enhance Has robustness to complex interferences such as shadows and stains. For edge deployment (Smartphones, drones, Raspberry Pi), full frame lightweighting, and model compression Implemented support vector pruning and parameter quantization. It is crucial to have coarse particles By mining the severity of cracks, innovative classification of crack severity was achieved without pixel level segmentation The implicit mapping between CNN features and crack width greatly reduces annotation costs. Experiments on public datasets (SDNET2018, Crack500) and self built industrial/civilian datasets Constructing a crack dataset shows that this approach increased F1 scores by 6% in complex situations Interference scene. The optimized MobileNetV3+compressed SVM model only achieves 92 KB, single image inference time of 0.8 ms on Raspberry Pi 4B, crack severity classification The accuracy rate is 87%. These results fully meet the requirements of practical engineering, Efficient and deployable solutions for building health assessment.
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Research on the Path Optimization Model for Low-Altitude Logistics of Heavy-Load Drones Based on Genetic Algorithm
Low-altitude logistics could indicate a significant application scenario within the broader low-altitude economy. Moreover, the findings may suggest that heavy-payload unmanned aerial vehicles demonstrate considerable potential in emergency delivery, remote-area supply, and medical distribution contexts. However, the evidence appears to show that route planning remains constrained by payload capacity, flight range, task point distribution, and energy consumption. In light of these key constraints, the study may suggest that a multi-task-point delivery scenario provides the relevant framework for constructing a route optimization model. Route optimization shows payload, range, task demands matter. Furthermore, the results could indicate that minimization of total flight distance serves as the critical optimization objective within this established model. Additionally, the significant evidence may suggest that a genetic algorithm based on natural-number encoding, order crossover, swap mutation, and a penalty function appears to support robust simulation experiments. Nevertheless, the important findings might demonstrate that the results show the optimal total flight distance obtained by the genetic algorithm could indicate superiority over those generated by the random routing method and the nearest-neighbor algorithm. Notwithstanding these results, the evidence may suggest that the algorithm also appears to support reductions in delivery time and estimated energy consumption. Genetic algorithm outperforms alternatives. Thus, the study may suggest that the findings provide a useful reference for route planning and scheduling decisions in low-altitude logistics.
Review of Threshold Voltage Modeling for Nitride Recessed-Gate FinFETs under Thermal Effects
The nitride-based (for instance, GaN) recessed gate FinFET has potential applications in the high frequency and high power sectors owing to its large bandgap properties and excellent channel controllability. Nevertheless, the problem of self-heating caused by the downsizing of the devices results in the drift of threshold voltage. This paper systematically reviews the current research status of threshold voltage modeling for nitride recessed-gate FinFETs under thermal effects. First, key domestic and international achievements in self-heating effect characterization, recessed-gate technology, and threshold voltage modeling are summarized. Second, the regulation rules of structural parameters and heat dissipation schemes on thermally induced threshold voltage drift are generalized, establishing key quantitative relationships: every 10 nm increase in recess depth causes a positive threshold voltage shift of 30–50 mV, and a high-thermal-conductivity substrate can reduce the hot-spot temperature in the channel by 20–30 K. Finally, deficiencies in current research are analyzed, including insufficient revelation of thermoelectric coupling mechanisms, lack of multiphysics modeling, and inadequate quantification of process-induced thermal damage. This paper points out that future efforts should focus on thermoelectric co-modeling and optimization of thermal management structures, providing theoretical support and engineering references for accurate threshold voltage modeling and device structure optimization of nitride recessed-gate FinFETs under thermal effects.
Review on Portal Intelligent Authentication Algorithm Based on Artificial Intelligence
The Portal authentication system constitutes a core component of modern network authentication systems. The vast majority of conventional authentication schemes rely on static username-password authentication mechanisms, which inherently suffer from security vulnerabilities. Furthermore, mandatory periodic password updates and high password complexity requirements degrade user experience significantly. This paper delivers a comprehensive and integrated analysis of state-of-the-art intelligent authentication algorithms for network access authentication, and thoroughly dissects three technical implementation frameworks: user behavior modeling, anomalous access behavior detection, and authentication decision optimization. The research focuses on elaborating the fundamental principles and practical deployment details within real authentication systems for temporal behavior modeling based on Long Short-Term Memory (LSTM), integrated anomaly detection architecture, and decision optimization strategies. It systematically organizes the algorithm execution workflow and simulation testing schemes. Ultimately, this paper discusses and analyzes the prevailing technical bottlenecks and prospective development trends of high-performance intelligent authentication algorithms.
An EKF-Based Satellite-Visual-Inertial Integrated Navigation Algorithm for Agricultural Machinery
In order to overcome the limitations of relying on a single sensor in a complex agricultural environment, this paper studies the integration of navigation parameters across satellite, visual and inertial platforms. The different characteristics of these sensors are analyzed in detail to establish the state equation. Based on these mathematical models, an extended Kalman filter (EKF) is designed. Through state estimation and measurement update of different data sets, the efficient synthesis of Global Navigation Satellite System (GNSS), visual recognition module and Inertial Navigation System (INS) information is realized. In addition, MATLAB is used for comparative simulation. The results show that the proposed multi-sensor integrated framework has higher accuracy and robustness than single GNSS and dual GNSS / INS systems. Among them, the east position error is constrained between-0.42 ~ 0.37 m, and the north position error is constrained between-0.22 ~ 0.26 m.
The Third-Generation Cross-Institutional Encrypted Collaboration: A Review of MPC-TEE Fusion in Medical Privacy AI
With the increasingly stringent global data protection regulations and the growing demand for cross-institutional collaboration, traditional privacy computing technologies can hardly meet the requirements for efficient, secure and scalable collaboration. The third-generation cross-institutional encryption technology takes the fusion of Secure Multi-Party Computation (MPC) and Trusted Execution Environment (TEE) as the core, and better balances security and performance through the collaborative design of software and hardware. In the fields of healthcare, financial services, the Internet of Things and others, MPC-TEE fusion technology can realize efficient collaboration while ensuring data privacy, further promoting the industrialization of privacy AI. However, this technology still faces challenges such as side-channel attacks, insufficient standardization and high costs. This paper systematically reviews the latest research progress of MPC-TEE fusion technology, deeply analyzes its technical foundation, fusion principles and core architecture design, compares and analyzes the application performance and scenario adaptability of mainstream technical frameworks in the medical field, and discusses the security threats faced by the technology and multi-level defense mechanisms. It also looks forward to the development trends and research directions under the fusion of post-quantum cryptography, edge computing and other technologies, providing a reference for the large-scale implementation, optimization and upgrading of this technology in the field of medical privacy AI.
An Experimental Study on E-commerce Customer Churn Prediction Based on the Explainable XGBoost Algorithm
The e-commerce industry has entered a phase of deepening market penetration, with high customer churn rates becoming a key issue constraining business development. Addressing the shortcomings of traditional churn prediction models—such as insufficient accuracy, poor interpretability, and failure to account for the heterogeneity of customer group needs—this paper proposes a framework for e-commerce customer churn prediction that integrates RFM value segmentation, the XGBoost algorithm, and SHAP interpretability analysis. Using a publicly available e-commerce dataset as the experimental sample, the RFM model was employed to segment customers into four value groups. Differentiated prediction models were constructed for each group, and SHAP was utilised to perform three-level interpretability analysis: global, group-level and local. Experimental results indicate significant heterogeneity in the churn drivers across the high-value, medium-value, potential-value and low-value customer sub-models. This study effectively addresses the ‘black box’ problem of models, offering enhanced interpretability and greater operational value, thereby providing support for e-commerce enterprises in making refined customer retention and operational decisions.
Research on Traffic Risk Prediction Model Based on Multimodal Spatiotemporal Fusion Deep Learning
Aiming at the problem that single-modal features are difficult to depict the complex spatiotemporal evolution law in urban traffic risk prediction, a multimodal spatiotemporal deep learning model fusing Multi-view Graph Convolutional Network (GCN), Transformer and Temporal Convolutional Network (TCN) is proposed to realize binary traffic risk prediction at the 10×10 grid level. Based on the traffic dataset of Area C in New York City (NYC), the study integrates grid time series, static features and multi-type adjacency matrix data. Strategies such as outlier processing, dynamic threshold binarization and feature normalization are adopted to solve the problems of data quality and imbalance between positive and negative samples. Meanwhile, gradient clipping, weighted loss and early stopping strategies are combined to ensure the stability of model training. Experimental results show that the model achieves an F1 score of 0.745, an AUC-ROC of 0.798 and an AUC-PR of 0.756 on the test set. It can effectively capture the spatial correlation and temporal dependence features of traffic risks, maintain good prediction performance in the unbalanced data scenario, and meet the actual business needs of urban traffic risk prediction.
Lightweight Traffic Object Detection Model Based on Improved YOLOv5n
Aiming at the problems of the lightweight YOLOv5n model in complex traffic scenarios, such as easy missed detection of small objects, low recognition accuracy of occluded objects, and loss of feature fusion information, this paper proposes an improved detection model with multi-module collaborative optimization. Firstly, the original CSPDarknet53 backbone network is replaced and fine-tuned with the HGNetV2 lightweight structure to enhance the feature extraction ability for small-scale and occluded objects. Secondly, the Detect_MBConv module is introduced to reconstruct the detection head, reducing computational overhead based on depthwise separable convolution. Meanwhile, Dysample dynamic upsampling is used to replace traditional interpolation to improve the quality of cross-scale feature fusion. Experiments are conducted based on the BDD100K traffic dataset. The results show that the improved model achieves 86.24% mAP@0.5, 98 FPS inference speed, and only 2.23 M parameters. Compared with the original model, the accuracy is significantly improved. The model can be effectively applied to edge low-computing devices such as vehicle-mounted terminals and surveillance cameras, meeting the requirements of real-time and high-precision detection in actual traffic scenarios.
Research on Agricultural Machinery Development and Grain Yield in Heilongjiang Province Based on Differential Privacy
With the continuous acceleration of the pace of agricultural informatization construction, the need for quantitative analysis of the improvement of agricultural machinery equipment level and the enhancement of grain production capacity is becoming increasingly urgent. Based on the relevant index data such as grain output, total power of agricultural machinery, the number of large and medium-sized agricultural tractors, and the number of combine harvesters in the Heilongjiang Statistical Yearbook from 2004 to 2024, this paper incorporates Laplacian noise of different scales to achieve differential privacy protection and selects a gradient privacy budget <!-- [if gte mso 9]><xml> <o:OLEObject Type="Embed" ProgID="Equation.DSMT4" ShapeID="_x0000_i1025" DrawAspect="Content" ObjectID="_1844421157"> </o:OLEObject> </xml><![endif]--> ranging from 0.1 to 5.0. Explore the impact of different privacy budgets on the statistical analysis effect of agricultural production data from multiple perspectives. Research shows that in terms of the statistical data of agricultural machinery development and grain output in Heilongjiang Province, differential privacy has a distinct critical threshold: when <!-- [if gte mso 9]><xml> <o:OLEObject Type="Embed" ProgID="Equation.DSMT4" ShapeID="_x0000_i1026" DrawAspect="Content" ObjectID="_1844421158"> </o:OLEObject> </xml><![endif]-->, information such as agricultural machinery and grain output was seriously distorted, and no meaningful statistical conclusions could be drawn regarding the relationship between agricultural machinery development and grain output. When <!-- [if gte mso 9]><xml> <o:OLEObject Type="Embed" ProgID="Equation.DSMT4" ShapeID="_x0000_i1027" DrawAspect="Content" ObjectID="_1844421159"> </o:OLEObject> </xml><![endif]-->, it was a turning point when data availability began to improve, and to a certain extent, it could reflect the main connection between the development of agricultural machinery and grain output. When <!-- [if gte mso 9]><xml> <o:OLEObject Type="Embed" ProgID="Equation.DSMT4" ShapeID="_x0000_i1028" DrawAspect="Content" ObjectID="_1844421160"> </o:OLEObject> </xml><![endif]-->, it is possible to obtain as accurate research results as possible on the impact of agricultural machinery development on grain production while ensuring privacy and security, achieving the best balance between data availability and privacy protection.
Optimal LQR Controller Design for Wheel Legged Robots Using Genetic Algorithm
Wheel-legged robots are typical nonlinear underactuated systems, whose balance control is commonly achieved by a Linear Quadratic Regulator (LQR). However, the performance of the LQR controller for such robots heavily depends on the selection of the state weighting matrix Q and the control weighting matrix R, which in practice relies highly on expert heuristics and often fails to achieve an efficient trade-off among multiple performance objectives. To address this issue, this paper proposes a systematic automatic tuning method for LQR controllers of wheel-legged robots. By embedding engineering constraints into the fitness function in the form of penalty terms, the proposed method can stably generate feasible controllers that achieve favorable multi-performance trade-offs without manual trial‑and‑error. On this basis, for variable leg‑length conditions, a grid‑based offline solution of the Riccati equation combined with polynomial fitting is employed to extend the optimal weightings obtained at the nominal leg length to the entire leg‑length range via gain scheduling, enabling online continuous adaptation of the feedback gain with respect to leg length. Simulation results on a nonlinear model demonstrate that the controller automatically optimized by the genetic algorithm (GA) exhibits good dynamic response and robustness under various scenarios, including balancing control, fall recovery, and terrain adaptation, thereby validating the effectiveness of the proposed method.
Generative AI Applications in Advertising
This paper aims to investigate the application of generative artificial intelligence (GenAI) models in advertising, particularly how their use affects the quality of advertising content and consumers' perceptions of AI-generated advertisements across different industries. In this paper, a curated AI-Generated Advertisement Dataset is used to conduct a content-level analysis, aiming to examine existing AI-generated advertisements based on their contents. Statistics and correlation analysis are applied to assess the quality AI-generated advertisements in four evaluation categories: Visual Quality, Style Consistency, Semantic Accuracy, and Creativity. The other, Xiaohongshu dataset, is used for user engagement and sentiment analysis. Two regression models are applied to the dataset to understand how people react and interact with AI-generated contents. This paper suggests a necessary improvement for the current development of GenAI models. That is, paying more attention to improving users’ attitudes/perceptions toward AI-generated advertisements, leading to strengthened authenticity and reliability of advertisements. This suggestion is especially critical for industries where consumers value these qualities more, to enhance the overall effectiveness of AI applications in advertising. This paper addresses the real-world impact of GenAI model’s application in advertising, offering new marketing lenses on how AI-generated ads can gain more consumers’ attention and trust.
Key Metrology Requirements and Industrial Benefit Assessment for the 6G Communication Era
As research into 6G enters a critical window, Terahertz (THz) communication has emerged as a core enabling technology for the 6G physical layer, owing to its immense bandwidth and potential for integrated sensing and communication. However, the extreme frequency band introduces significant challenges for the existing metrological framework, including device non-linearity, near-field propagation effects, and measurement uncertainty in ultra-wideband signals. From the perspective of national metrological infrastructure supporting industrial development, this paper systematically explores the key metrology requirements for 6G THz communication across device characterization, spatial propagation, and system integration. Furthermore, it provides a quantitative assessment of the economic value that precise metrology contributes to shortening R&D cycles, enhancing manufacturing yields, and optimizing deployment costs. The research demonstrates that establishing a standardized and traceable 6G communication metrology system is a strategic cornerstone for overcoming commercialization bottlenecks, dismantling technical trade barriers, and achieving high-quality industrial development.
Perturbing Localization of Electron Distribution in Thermally Activated Delayed Fluorescence Emitters for Efficient Red Electroluminescence
To compete with the severe non-radiative decay in long-wavelength emission, fast radiative decay is significantly required. Due to good rigidity and strong electron-donating ability, phenoxazine (PXZ) is a promising donor group to be applied in long-wavelength thermally activated delayed fluorescence emitters. However, PXZ-based molecules show highly twisted geometry, which reduces the oscillator strength and limits their performance. Herein, we demonstrate that the oscillator strength can be enhanced without compromising the molecular rigidity by strategically increasing localization of electron distribution in PXZ-based emitters. The corresponding red organic light-emitting diode showed a maximum external quantum efficiency of 11.7%, demonstrating an improvement of 39% compared with the control group.
Bridging the Lunar Lifeline: A Multi-Modal Resilience and Optimization Framework for Large-Scale Interplanetary Logistics
Building a lunar colony for 100,000 inhabitants, transporting almost 100 million metric tons of infrastructure in the past, would bring logistical and engineering challenges that had never been encountered before. This paper makes a multi-modal optimization framework, which integrates the two modes of transportation of the space elevator system SES and conventional rockets. This model uses hybrid integer linear programming MILP, stochastic perturbation analysis, and Monte-Carlo simulation to calculate the optimal resource allocation scheme under the three constraints of cost, duration, and elasticity. Logistics of various life support resources, including water management, we use a phased delivery schedule and a strategic safety reserve. The strata black carbon radiative forcing model, we use it directly to do environmental impact assessment work. The hybrid SES-rocket transportation strategy can compress the entire construction period to 139.5 years, maintaining a stable supply capacity even in the case of system failure, and significantly reducing the total amount of carbon emissions. The main work of this paper is several aspects, the first is a multi-scenario transportation optimization design for lunar logistics, the second is a random modeling method for non-ideal operations, the third is the life support planning and ISRU integration scheme, the fourth is the complete environmental impact analysis content.
Study on the Ignition Capacity of Overloaded BVV Wires to Combustibles
To clarify the ignition characteristics of BVV copper wires on different types of combustibles under overload conditions and provide theoretical support for electrical fire investigation and fire safety, this study took 1.5mm² BVV copper wires as the research object. Under the overload condition of 6 times the rated current (108A), the ignition capacity and phenomena of the wires on debris materials (paper scraps, wood chips), fiber materials (cotton yarn, polyester), and foam materials(polyurethane foam, polystyrene foam)were systematically studied through different contact methods including single-sided contact, full wrapping, half wrapping, and 5mm spacing. Key parameters such as temperature change, smoke emission time, ignition time, and combustion duration were collected using thermocouples, high-speed cameras and other equipment. The experimental results show that: for debris materials, the ignition risk of spaced contact is the highest, and wood chips are more easily ignited than paper scraps with stronger combustion persistence, for fiber materials, the ignition risk of full wrapping contact is the highest, and cotton yarn has a lower ignition point and more intense fire, being more easily ignited than polyester, for foam materials, double-sided contact (full wrapping) is more dangerous, and polystyrene foam burns more violently and tends to sustain combustion, posing higher risks than polyurethane foam. The differences in ignition characteristics of different types of combustibles are closely related to their ignition points, thermal conductivity, structural properties, as well as heat accumulation and oxygen supply caused by contact methods.
Structural Design and Simulation Research of an Air-Ground Amphibious UAV
Aiming at the problems that traditional unmanned aerial vehicles (UAV) can only operate in the air. An air‑ground amphibious UAV platform with variable structure and dual‑domain traffic capacity is designed to integrate the advantages of aerial flight and ground driving. An integrated wheel‑arm structure with four steering gear motors independently controlled and gear transmission integrated is innovative designed. The mechanical model of the frame is established and optimized, and strength verification is carried out to verify the rationality and safety margin of the structure. Under hovering condition, the maximum stress of the frame is 3.002MPa and the maximum displacement is 7.956×10⁻³ mm. After the connector is fixed with the wheel, the maximum stress is 1.325MPa and the maximum displacement is 2.956×10⁻² mm. Under static ground condition, the maximum stress of the wheel set is 0.5118MPa and the maximum displacement is 1.559×10⁻³ mm. The stress of each component is far lower than the allowable stress of carbon fiber and structural steel, meeting the strength and stiffness design requirements. Finally, prototype tests show that the platform can stably realize aerial hovering, ground driving and mode switching, with good air‑ground dual‑domain traffic capacity. The amphibious UAV designed in this study has both good aerial flight performance and ground mobility, providing a technical reference for the engineering realization of lightweight and high‑reliability unmanned platforms in logistics distribution, emergency rescue and other scenarios.
Performance Trade-off and System Evolution Analysis of Wireless Power Supply and Signal Transmission Technology for Implantable Biosensor Systems
As more and more wireless battery-free implants are required in such areas as brain-computer interfaces and precision medicine, it is important to choose an adequate energy and data link. In order to meet this requirement, this research systematic compare three popular wireless technologies in the implantable bioelectronic devices: electromagnetic coupling, radio frequency radiation and ultrasound. This paper first develops a compact evaluation framework based on three fundamental dimensions: power supply efficiency and safety, communication performance and robustness, and system integration with miniaturization. Next, we perform detailed analysis of the principles behind each technology, its inherent limitations and state-of-the-art system architectures with their optimization approaches. The comparative study indicates that electromagnetic coupling is superior in terms of system integration density and intelligent power management, radio frequency radiation has excellent interference resistance as a dedicated communication link in hybrid systems, and ultrasound technology has great potential in deep-tissue safety and receiver miniaturization. This work not only presents a clear performance matrix for technology selection for different implantation scenarios but also gives directions towards dust-sized integration, heterogeneous fusion and adaptive networking.
Research and Design of Ball Sports Training Robots
The ball sports training robot belongs to the field of robotics. It can autonomously identify the balls on the field, plan the path, move to the ball's position through an omnidirectional movement chassis, perform the automatic ball retrieval task, and then conduct secondary identification to determine the position of the trainee. It can autonomously adjust the direction and angle of the omnidirectional pan-tilt unit, change the rotational speed of the active roller, thereby adjusting the serving trajectory, and automatically serve the ball. There is no need for manual ball retrieval and serving. It can effectively replace manual practice, with high training efficiency and strong applicability.
A Survey on Synchronous Control Architectures for Data Transmission Delay in Networked Control Systems
Networked Control Systems (NCS) are widely applied in industrial automation, remote control and other fields. However, data transmission delay has severely affected the stability and real-time performance of systems, becoming a core bottleneck restricting their development. This paper systematically reviews the synchronous control architectures and key technologies for data transmission delay. Firstly, it analyzes the generation mechanism, characteristics of data transmission delay in NCS and its impact on system stability. Secondly, it elaborates the design ideas of current mainstream synchronous control architectures from three aspects: synchronous architecture principle, controller coordination method and delay compensation scheme. Thirdly, it summarizes the typical simulation platforms and performance verification methods, and compares the control effects of different strategies. Finally, it concludes the limitations of existing research and prospects the future development direction in complex network environments. This paper aims to provide a systematic technical reference for the theoretical deepening and engineering application of NCS.
Design and Implementation of a Smart Scenic Area Tourist Behavior Safety Monitoring System based on HiSilic
Inspired by the technical capability of the HiSilicon Hi3516DV300 camera to efficiently run the YOLOv3-tiny model on embedded devices, this solution offers significant advantages of high accuracy, low latency, and low power consumption, providing a valuable reference for the application of lightweight edge intelligence devices in scenic area monitoring. This study analyzes and designs a smart scenic area tourist behavior safety monitoring system based on an “edge-cloud” collaborative architecture. The hardware terminal adopts the HiSilicon Hi3516DV300 smart camera, equipped with an optimized YOLOv3-tiny lightweight object detection model accelerated by TensorRT, to achieve local real-time detection of human bodies and key targets within the scenic area. Simple tracking and trajectory analysis are combined to complete crowd flow statistics and density warning. The model is also tailored for head/body detection and deployed at the edge for real‑time counting. On the cloud side, the Alibaba Cloud IoT platform provides device management, data storage, visual analytics, and remote interaction functionalities.
Research on the Construction and Application of Risk Prediction Model for University Management Students with Learning Difficulties Based on Machine Learning
The state has proposed to promote educational equity and the all-round development of students, but the existence of "students with learning difficulties" is contrary to the requirements of all-round development. The construction of a risk prediction model for students with learning difficulties is a teaching mode that can help students identify learning difficulties in advance, stimulate their interest in learning, reduce cognitive load, improve learning efficiency, and cultivate students' abilities and core literacy. This paper takes strengthening the education of the management major in universities as the core, and aims to promote educational equity and facilitate the all-round development of "students with learning difficulties". It proposes the construction and application research of a risk prediction model for students with learning difficulties in university management majors based on machine learning. In response to the problem of slow convergence speed in traditional machine learning This paper proposes an analysis model for the causes of learning difficulties among college management students based on the conjugate gradient method in machine learning, in order to predict the formation reasons of the difficulties of college management students. The factor quantification adopts the fuzzy comprehensive evaluation method. The experimental simulation results show that the system model shortens the training time, and the error between the predicted value and the actual value is zero. This model can be applied to the learning difficulties of English in higher vocational colleges Analysis and prediction of causes.
Kidney Tumor Image Segmentation Algorithm based on Deep Learning
This paper focuses on the segmentation algorithm of kidney tumor image based on deep learning, expounds the research significance of the subject, combs the research status at home and abroad in this field, and defines the research methods and technical routes. In this paper, the overall design of the algorithm is completed, and the preprocessing work such as data collection and annotation of kidney tumor image is completed. On the basis of v-net neural network, the feature fusion module and dual attention mechanism are introduced to improve the segmentation model. By setting experimental parameters to carry out experiments, the segmentation effect of the model is analyzed from the qualitative and quantitative perspectives. This study can provide technical support for the clinical diagnosis of renal tumor, and provide reference for algorithm optimization in related fields.
A Systematic Benchmark of Attention Mechanisms on the NEU-DET Strip Surface Defect Dataset Using YOLOv11n
Strip surface defect detection is an important part of the steel industry. Attention mechanisms have been widely used to enhance the feature representation capability of detection networks; however, their effectiveness on small industrial defect datasets lacks systematic evaluation. In this paper, we take the lightweight detector YOLOv11n as the baseline and integrate 15 attention mechanisms into a unified framework, covering channel attentions (SE and ECA), spatial attention (SimAM), hybrid attentions (CBAM and GAM), coordinate attention (CA), efficient multi-scale attention (EMA), and others. Strictly controlled experiments are conducted on the NEU-DET strip surface defect dataset. All models are trained under identical conditions: 50 epochs, 200×200 input resolution, batch size 16, 70/30 train/validation split, and random seed 42. Experimental results show that the original YOLOv11n baseline achieves the highest mAP@0.5 and mAP@0.5:0.95, outperforming all attention variants. The three best-performing attentions, namely ELA, TripletAttention, and SimAM, all achieve a mAP@0.5 of 0.735, only 0.003 lower than the baseline. In contrast, CBAM and SE, which have more parameters, drop by 0.066 and 0.076, respectively, and training curve analysis confirms that the degradation stems from overfitting. Per-class analysis reveals that crazing and rolled-in scale are the most challenging defect categories, with no attention mechanism improving detection accuracy on these classes over the baseline. The parameter-free SimAM shows the smallest drop, supporting the view that lightweight designs are safer on small datasets. Supplementary experiments at 640×640 resolution further validate the superiority of the baseline (baseline 0.772 vs. SimAM 0.761, ELA 0.741, SE 0.684). All models meet the real-time detection requirements. This baseline provides the real-world results of model selection for small industrial defect samples: a good baseline is often enough, attention should be emphasized, and parameter free or low-parameter designs should be chosen.
Research on Chinese Cuisine Image Recognition Using Semi-Supervised Convolutional Neural Networks
With the rapid development of image recognition and deep learning, food image classification has achieved remarkable results. Chinese cuisine image recognition faces challenges due to diverse dishes and similar ingredients. This paper constructs a dataset containing both labeled and unlabeled Chinese food images. To address slow training and inference of traditional CNNs, we adopt lightweight networks. MobileNetV3-small reduces training time by 41.1% and improves inference speed by 21% compared to ResNet-34.To better capture correlated features, we propose MobileNetV3-small-sa with a self-attention mechanism, which improves accuracy by 2.2% over the base model. Given the high cost of labeling, we apply semi-supervised learning. MixMatch uses both labeled and unlabeled data. With only 1/7 labeled data plus 10,952 unlabeled images, it achieves 75.9% accuracy, while supervised learning on the full labeled set yields 80.1%. MixMatch improves accuracy by over 10% when labeled data is scarce. Experiments show that the self-attention model outperforms traditional networks, achieving 3.8% higher accuracy than VGGNet16 and 3.2% higher than ResNet34.
Noise-Aware Causal Orthogonal Gating Network for Robust and Interpretable Multimodal Sentiment Analysis
Across online social platforms, the sheer expansion of multimodal content has made Multimodal Sentiment Analysis (MSA) both indispensable and technically difficult within computational social systems. Cross-modal heterogeneity is the core of the whole problem: textual, visual, and acoustic data streams almost never align completely in a clean, simple way. On top of this basic mismatch, things like noise, missing types of modality, and wrong inter-modal connections end up hurting both how reliable existing models are, and also how easy they are to understand. Given all these issues, this study puts forward the Noise-Aware Causal Orthogonal Gating Network (NCOG), which is a new framework made to add causal inference theory into adaptive multimodal fusion work. It does not use the traditional unimodal nonlinear gating that most methods rely on; instead, NCOG splits the gating work into two separate submodules that are orthogonal but can work together to complement each other. One of these submodules is called the Reliability Gate, and it filters signals using clear noise proxies — things like inter-frame variation, pitch energy fluctuation, and ASR confidence. The other one, named the Causal Contribution Gate, does not work the same way as this front-end screening step. It uses timestep-level counterfactual masking to work out the real causal effects between different modalities, and at the same time it gets rid of spurious correlations that do not mean anything. Along with this two-gate design, there is also a cross-modal attention module that dynamically adjusts how much weight each modality gets. When the input data has noise or some parts are missing, this module also makes the model more stable and harder to break. When we tested it on real data, NCOG-MSA got 88.12 percent accuracy, 87.11 percent F1 score, 0.681 for MAE, and 0.809 for PCC on the CMU-MOSI dataset. For the CMU-MOSEI dataset, the corresponding numbers are 87.44 percent accuracy, 89.04 percent F1, 0.483 MAE, and 0.816 PCC. If we compare it with the best existing fusion baselines, which include ACMG, TMFN, and HCAN, our framework shows better results on every single one of these evaluation metrics. Putting all these test results together, we can see that combining explicit noise modeling and causal reasoning makes multimodal sentiment analysis more robust, easier to understand, and works better on new data. This also gives a solid basic framework for building affective intelligence that can work in complex real-world social systems.
Design of College English Teaching Activities in Application-Oriented Undergraduate Universities Oriented by the Needs of the Guangdong-Hong Kong-Macao Greater Bay Area
The high-quality development of the foreign trade industry in the Guangdong-Hong Kong-Macao Greater Bay Area has put forward new requirements for scenario-based, post-oriented and practical college English teaching in application-oriented undergraduate universities. Centered on demand orientation, this study adopts Needs Analysis Theory, the Production-Oriented Approach and Situated Learning Theory to construct a college English teaching design framework compatible with the industrial needs of the Greater Bay Area. Based on 52 corporate recruitment data, this study extracts core post competency requirements, designs two teaching cases: English communication for the Canton Fair and written communication for cross-border e-commerce, and forms a progressive practical teaching path of "basic simulation – advanced practical combat". The research shows that this teaching design can effectively connect classroom teaching with the competency requirements of foreign trade posts, improve students’ English application ability and workplace adaptability. Under the practical condition of limited school-enterprise cooperation resources, it can provide practical references for college English teaching reform and serving regional economic development in application-oriented undergraduate universities.
Research on Configuration-Driven Dynamic Form Generation
Traditional static forms have two clear problems. They are not flexible enough. And they cost a lot to keep. They do not work well for complex business situations. And they cannot fit different devices well. These problems make traditional static forms hard to satisfy the needs of complex applications. This paper gives a configuration-driven way to make and improve dynamic forms. This method has two core technical parts. First, we build a standard form metadata model with JSON Schema. This model fully separates business logic from the front-end interface. It cuts the strong link between the two parts in traditional development. Second, we design a weight-driven adaptive layout algorithm. This algorithm lets forms change their layouts on their own. Forms can fit screens of different sizes well. We did comparative tests between this new method and traditional hard-coded forms. The test results show two clear advantages of the new method. It makes form rendering much faster. And it cuts down the time users need to fill out the form.
Sky Ranger: Industrial-Grade AI Drone Inspection Platform
Equipment inspection for power, petrochemicals and new energy is not just an operation; it is the lifeblood of industrial safety. But let's be honest, manual inspection is a bother. They are slow, risky, time-consuming and money-consuming. That is why we built Sky Ranger. With the help of artificial intelligence and the Internet of Things, a new kind of high-precision inspection technology that can cover all areas above, below and around structures has now been developed. The three main uses of SLAM 3D navigation are AI models that integrate all kinds of data and the NVIDIA Jetson edge device. It will find a fault in the field 98 per cent of the time. It is 8 times faster than a crew of people and saves 60% in costs. To be honest, the ROI is good. It will help the company go digital and will also be in line with China's "dual-carbon" goals.
Topology Optimization Design of Impellers Based on Selective Laser Melting Process
Aiming at the core contradiction between structural strength and material weight reduction in the lightweight design of impellers, this paper proposes a topology optimization method considering support-free forming and blade geometric shape retention based on selective laser melting (SLM) additive manufacturing technology. The variable density method is adopted for topology optimization design, and hollow structures and lattice filling strategies are integrated to optimize the internal material distribution of impellers. SLM process constraints are introduced in the optimization process to ensure that components can be formed without additional support structures. Static mechanical performance verification of the optimized model is conducted via finite element analysis. The results show that the overall weight of the optimized model is reduced by 36% while satisfying the requirements of impeller overall dimensions and mechanical properties, which effectively balances lightweight demand, structural strength and forming process adaptability.