Journal of Computing and Electronic Information Management
Journal of Computing and Electronic Information Management (JCEIM) 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, electronics, information management across the world. All articles published are rigorously and fast reviewed meeting the Journal Quality standards.
A Hybrid Pyramid and Strip Pooling Network for Accurate Building Extraction from Remote Sensing Images
Accurate extraction of building footprints from remote sensing imagery is important for urban planning, disaster management, and geographic information systems. However, complex building shapes, occlusions, and scale variation continue to challenge conventional segmentation models. This paper presents SRB-Net, a U-Net-based framework that combines three complementary components: (1) strip pooling (SP) for long-range horizontal and vertical context; (2) residual multi-scale atrous spatial pyramid pooling (RMASPP) with squeeze-and-excitation (SE) blocks for multi-scale and channel-aware feature learning; and (3) a bottleneck attention module (BAM) for refining skip-connection features. The model was trained with the Adam optimizer and evaluated on the aerial and Satellite Dataset II subsets of the WHU Building Dataset. Among the evaluated baselines, SRB-Net achieved the best overall performance, reaching 98.83% accuracy and 90.12% Intersection over Union (IoU) on the aerial dataset and 98.28% accuracy and 70.89% IoU on Satellite Dataset II. These results show consistent performance improvements across the two evaluated WHU subsets while avoiding claims beyond the within-dataset experimental setting.
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A Lightweight Feature Transformation Network with Progressive Training for Rainy Object Detection
Rainy weather causes occlusions and blur in images, severely degrading the performance of object detectors deployed in scenarios such as autonomous driving. Existing methods mostly adopt pixel-level deraining preprocessing or adversarial domain adaptation. The former introduces additional computational overhead and error accumulation, while the latter suffers from training instability and tends to forget clear-weather knowledge. This paper proposes a lightweight feature transformation module embedded in the detector backbone, which maps "rainy features" to "rain-free features" in the feature space, allowing the subsequent detection network to adapt to rainy conditions without any modification. To achieve both lightweight design and high accuracy, a knowledge distillation framework based on intermediate feature maps is introduced, where a large-capacity teacher network guides a small student network to learn the feature mapping. Meanwhile, a three-step progressive training strategy is designed to sequentially train the detector, distill the transformation module, and fine-tune the overall network, ensuring training stability and avoiding catastrophic forgetting. Extensive experiments show that our method has essentially reached the rain-free baseline level, while significantly reducing computational cost compared to preprocessing approaches. This work provides a new perspective for the efficient and robust deployment of vision systems under adverse weather conditions.
Adaptive Fusion SOH Assessment of Lithium-Ion Batteries Based on EIS Multi-Band Features
Electrochemical impedance spectroscopy (EIS) can quantitatively reflect internal aging mechanisms of lithium-ion batteries, serving as an effective non-destructive tool for State of Health (SOH) evaluation. Single prediction models either fail to balance precision for fresh and aged cells or lack adaptability under variable temperature and SOC conditions. This paper proposes an adaptive dual-model fusion SOH estimation method combining weighted Mahalanobis-distance KNN and power-law capacity fading model. Four-dimensional features extracted from medium-high frequency EIS, real-time SOC and ambient temperature are standardized via Z-score transformation. Dynamic weight allocation is implemented according to battery health intervals: higher weight is assigned to KNN for cells with SOH ≥ 95%, while power-law model dominates for severely degraded cells with SOH < 95%. Multi-temperature full-cycle aging tests on 314 Ah LiFePO4 cells verify the proposed method. The full-life SOH prediction error is controlled below 5%, which outperforms single KNN and pure power-law fitting. This fusion algorithm can be embedded into embedded EIS measurement hardware, supporting online health diagnosis for energy storage and vehicle power batteries.
Application and Analysis of XGBoost Models Optimized with TPE in Complex System Prediction
This paper proposes an XGBoost model based on TPE hyperparameter optimization for constructing a system effectiveness prediction framework, focusing on integrating precise prediction with interpretable analysis. First, TPE algorithm-based hyperparameter optimization enhances the XGBoost model's predictive performance, elevating the model's decision coefficient from the baseline 0.827 to 0.875. Second, a Monte Carlo simulation combined with confidence interval estimation constructs an uncertainty-quantified prediction system, effectively overcoming limitations of traditional methods to deliver more reliable result intervals. Finally, the SHAP model was applied to analyze feature importance in the XGBoost model, revealing nonlinear relationships between multidimensional features and target responses, thereby enhancing interpretability. This model quantifies the marginal effects of coaching factor inputs through hypothesis testing and develops optimal resource allocation strategies based on reward-punishment balance. The framework significantly improves prediction accuracy, interpretability depth, and strategy feasibility, providing methodological support for complex decision-making scenarios. The innovation of this research lies in integrating XGBoost with the TPE optimization algorithm, which not only enhances the predictive model's performance but also overcomes the limitations of traditional point estimation. It provides a comprehensive perspective encompassing uncertainty quantification and feature analysis, offering broad application value.
A Data-Driven Closed-Loop Parameter Adaptive Framework for Automated Visual Standardization Systems
Automated visual standardization — the conversion of arbitrary user-provided portraits into images that satisfy strict format specifications — is a key component of high-reliability identity-management systems. Conventional pipelines operate as feedforward open-loop systems: they map facial landmarks to a fixed scaling ratio and synthesize the output in a single pass, implicitly assuming a linear relationship between facial scale and body anatomy. Under high-dimensional stochastic geometric variation (narrow or wide shoulders, long hair, atypical clothing), this assumption breaks down and produces shoulder truncation or bottom-edge gaps that violate format compliance. This paper reformulates visual standardization as a feedback-controlled parameter-optimization process. Our framework cascades perception-aware color normalization, BiRefNet-based foreground segmentation with distance-transform edge purification, and a closed-loop geometric adaptation stage in which an edge-integrity sensor measures posterior canvas coverage and a controller iteratively refines the affine scale parameter until convergence. On 120 real-world samples spanning 12 challenging categories, the closed-loop framework raises the bottom-edge touch rate from 99.17% to 100% and bottom-row foreground coverage from 98.73% to 99.99%, while reducing the boundary contamination ratio from 67.5% to 53.8%. Ablation studies confirm that the geometric feedback loop is the sole factor eliminating bottom-edge gaps and that edge purification independently accounts for a 15.9-percentage-point reduction in boundary contamination. The results demonstrate that posterior feedback converts residual geometric uncertainty into a convergent control problem, enabling deterministic compliance under strict business constraints.
DriftGuard-TriAudit: Concept-Drift-Aware Continual Multimodal Learning for Evolving Financial Statement Fraud Detection
Financial statement fraud detection is usually trained as a static supervised classification task, yet real reporting behavior evolves as firms change disclosure style, enforcement priorities shift, and fraudsters adapt to known audit screens. This paper proposes DriftGuard-TriAudit, a concept-drift-aware continual multimodal learning framework for evolving financial statement fraud detection. Building positively on the recent TriAudit design, which demonstrates the value of jointly reasoning over financial ratios, management discussion text, and document-layout evidence, the proposed framework adds three mechanisms for non-stationary audit settings: modality-wise drift diagnostics, reliability-aware gated fusion, and replay-regularized continual updating. The model monitors population stability and calibration performance in numerical, textual, and visual-layout streams, then adapts the contribution of each modality while preserving prior fraud evidence through a compact replay memory. Because a full EDGAR-plus-AAER real-data construction was not available in the execution environment, the empirical section reports only a reproducible synthetic benchmark that explicitly simulates four temporal fraud regimes and does not claim to be real company data. Across five seeds, DriftGuard-TriAudit obtains the strongest average precision (0.628±0.087) and F1 score (0.579±0.061) among evaluated practical models, with substantially higher precision than naive continual fine-tuning (0.547 vs. 0.428) while maintaining comparable AUC-ROC (0.915 vs. 0.924). The results indicate that drift-aware multimodal fusion can reduce false positives under evolving fraud mechanisms, and the released code provides a direct path for replacing the synthetic generator with EDGAR/AAER features.
DriftGuard-AEDL: Concept-Drift-Aware Continual Neuro-Symbolic Causal Inference for Evolving Time Series
Structural causal models estimated on non-stationary time series are threatened by concept drift: the data-generating structure itself changes over time, silently invalidating a once-correct model. Recent neuro-symbolic pipelines such as Adaptive Event-Driven Labeling (AEDL) combine language-model reasoning with econometric identification, but they are trained one-shot and assume a fixed causal graph and a static anchor set. We propose DriftGuard-AEDL, a concept-drift-aware, continual extension that (i) estimates a contemporaneous causal graph online under a symbolic prior that encodes a known causal ordering and sign constraints; (ii) detects drift with a two-signal detector that couples a residual Page-Hinkley test with a lagged-reference test on the reduced-form coefficient stream; (iii) uses a symbolic verifier as an acceptance gate that admits an adaptation only when the re-estimated structure is economically coherent and materially changed; and (iv) adapts continually with anchor re-calibration and replay. On a controlled regime-switching structural VAR with known ground truth, DriftGuard-AEDL recovers the causal structure far better than drift-agnostic baselines (structural Hamming distance 1.9 vs. 7.3–8.2 and edge-F1 0.75 vs. ≤0.15) and matches a continuously retrained sliding-window model in predictive error while using 6 updates instead of 114; the symbolic gate cuts the false-alarm rate from 0.71 to 0.50 and the update count from 9.4 to 6.0. On the real Elec2 electricity-market benchmark (45,312 records), it recovers most of the adaptation gain of blind retraining at roughly one-fifth of the update cost and four times the speed. The results show that pairing lightweight symbolic constraints with drift-triggered continual learning yields causal inference that stays valid as the world changes.
Multi-Fidelity Physics-Informed Neural Networks for Fast Electromagnetic Simulation of RF Integrated-Circuit Passives
Full-wave electromagnetic (EM) analysis of radio-frequency (RF) integrated-circuit passives is accurate but too slow for the many-query loops of modern design automation, whereas quasi-static approximations are fast but miss frequency-dependent loss and dispersion. We present a composite multi-fidelity physics-informed neural network (MF-PINN) that fuses an abundant low-fidelity (LF) quasi-static model with a small set of high-fidelity (HF) full-model samples to build a fast, accurate frequency-domain S-parameter surrogate over a microstrip design space. The surrogate is regularized by two physical constraints derived from network theory—passivity (energy conservation) and non-negative group delay (causality)—evaluated by automatic and finite-difference differentiation on unlabeled collocation points. Ground-truth data are generated with a validated open-source microstrip model (Hammerstad-Jensen, Kirschning-Jansen, skin-effect loss). On 30 held-out geometries the MF-PINN attains 3.37% relative L2 error (0.11 dB magnitude, 0.55° phase) using only eight HF components, versus 13.17% for a single-fidelity network with the same HF budget. A data-efficiency study shows multi-fidelity training reaches the accuracy of single-fidelity models with 4–8× fewer HF samples, and the physics constraints reduce the mean passivity violation by two-to-three orders of magnitude. Once trained, the surrogate evaluates S-parameters 41× faster than the reference solver. We also report a negative result: aggressive Fourier-feature encoding, useful for oscillatory PDEs, degrades accuracy here because the RF response is smooth in the band, so a compact multilayer perceptron is preferable.
GraphRoute-Transfer: Topology-Generalizable Routing Convergence Optimization via Graph Reinforcement Learning
Fast and stable routing convergence is critical in large IP networks, and the interior-gateway-protocol (IGP) timers that govern failure detection (Hello/Dead intervals) expose a fundamental tension: aggressive timers detect failures quickly but inflate control overhead and trigger route flaps, whereas conservative timers are stable but slow. Recent work such as DRL-Adapt has shown that deep reinforcement learning can tune these timers better than static defaults, but it operates on a flat, globally-aggregated network state and emits a single network-wide timer, so it can neither exploit the spatial heterogeneity of real topologies nor transfer architecturally across networks of different size. We propose GraphRoute-Transfer, a graph-neural-network policy that assigns per-node timers from local structural features and is by construction permutation- and size-invariant. Because control-plane fragility and failure criticality are spatially heterogeneous, the cost-minimizing timer assignment varies across the graph; our policy learns this mapping and applies it zero-shot to unseen topologies of arbitrary size. Training is guided by a coordinate-descent search oracle on a convergence-cost objective, so the expensive per-topology optimization is amortized into a sub-millisecond inference. On 231 real topologies from the Internet Topology Zoo, GraphRoute-Transfer reduces mean convergence time by 37.3% relative to the OSPF default and to a flat DRL baseline, lowers the composite convergence-cost objective by 16.4% over the flat baseline, and attains 1.317 cost—within 0.3% of the search oracle—while running about 8,160× faster than the search. Crucially, a policy trained only on networks with ≤70 nodes maintains its gains on unseen networks up to 140 nodes, whereas the flat baseline degenerates to a global constant that cannot adapt.
Intelligent Decision Support Driven by AI Agents in Financial Operations
Artificial intelligence is now used in all parts of a typical company and has changed the form of business. Systematically study the new model of intelligent decision-support systems driven by autonomous, multi-agent AI frameworks in this paper. In the past, because of old, error-prone rule-based computing methods, complex financial risk management and long-term capital allocation were relatively constrained. However, with the emergence of powerful large language models and high-end reinforcement learning algorithms, dynamic and constantly improving AI agents are now being deployed. These intelligent agents cooperate and discuss actively to process large volumes of unstructured market data in sequence, thereby producing highly optimised and perfectly rational financial strategies. The above research indicates that, at present, most models of individual prediction are disconnected from larger-scale, multi-agent automated trading and credit risk systems. In addition, this paper will also investigate how to build an explainable and trustworthy AI system that meets the high standards for international financial regulations. Based on the present study of multi-agent reinforcement learning for algorithmic trading and dynamic risk control, this paper puts forward an all-encompassing system to redesign corporate financial decisions in light of the increasing complexity of the global economy.
Research and Practice on Curriculum Ideology and Politics in Big Data Platform Deployment and Operation Based on the Three-Color Lines
As a core course for the Big Data Technology major in higher vocational colleges aligning with the smart shipping industry chain, Big Data Platform Deployment and Operation possesses the dual characteristics of high technical abstraction and outstanding engineering practicality. However, traditional instruction commonly suffers from a "two-skin" phenomenon between ideological-political education and professional technical training. To address this practical dilemma, this paper—based on the overall institutional ideological and political educational framework of "Three Colors of Light Illuminating All-Round Education" at Jiangsu Maritime Vocational and Technical College—proposes a curriculum-based ideological and political design framework structured around the "Blue Maritime Power Line, Red Technology Power Line, and Cyan Digital Power Line." This framework deeply integrates real-world shipping industry scenarios, Information Technology Application Innovation (ITAI) technologies, and green development philosophies into six core instructional modules of low-level big data architecture operation and maintenance, such as Hadoop and Spark. At the level of instructional implementation, the course establishes a multi-dimensional framework consisting of "One Center, Two Lines, Three Vectors, Four Subjects, and Six Steps," paired with the innovative application of the "IDEAL" (Identify, Define, Explore, Execute, Reflect) project-driven model. Taking the deployment of a real-time navigation monitoring system in complex bridge areas along the Jiangsu section of the Yangtze River as a typical case study, the course achieves the simultaneous resonance of technical skill and moral virtue through extreme stress testing and crisis injection. Practical results demonstrate that this model effectively resolves long-standing bottlenecks in engineering curriculum ideology and politics, such as "mechanical integration" and "indistinct industry characteristics," thereby providing a reproducible paradigm reference for curriculum-based ideological and political construction in big data majors within industry-specific higher vocational institutions.
A few-shot image classification method based on Ball k-means algorithm
Ball K-means algorithm is an accelerated and precise clustering method that uses spheres to represent clusters, thereby reducing the distance calculations between data points and centroids. Initially designed for graph data, it was not efficient or accurate for clustering centroids in image data. This paper adjusts the ball K-means algorithm and integrates it with image analysis, expanding its application domain and introducing a novel theoretical framework. The method represents each class of query samples using spherical clusters, identifies neighboring clusters, and distinguishes between active and stable regions. It adaptively updates centroids based on the correlation between active region sample points and neighboring cluster centroids. The resulting centroids are combined with class representatives from training samples to form more distinctive spherical prototypes. The Euclidean distance between query samples and these prototypes is computed for classification based on the nearest-neighbor assignment principle, enhancing model generalization through adaptive features. Experimental results demonstrate that our method achieves improved classification accuracy and robustness on miniImageNet and tieredImageNet datasets.
A Job-Oriented Knowledge Graph for Computer Networking Courses in Higher Vocational Education
Students in higher vocational computer networking programs often learn technical topics such as TCP/IP, virtual local area networks, access control lists, firewall configuration, wireless access points, Linux services, and network monitoring without clearly understanding how these topics support real occupational roles. Teachers also need evidence-based methods to explain why a course matters and how its modules are connected with job abilities. To address this problem, this paper proposes a job-oriented knowledge graph for computer networking courses in higher vocational education. The proposed system separates raw job evidence from the student-facing teaching graph. Public and low-risk job evidence is organized in a controlled evidence layer, while the published graph presents normalized jobs, abilities, course modules, knowledge points, tools, certificates, tasks, and vendors. The student-facing explanation process connects target jobs with required abilities, course modules, and knowledge points, allowing students to understand how course learning supports career preparation. A Zhejiang pilot system was implemented using Vue 3, ECharts, Express APIs, and Neo4j. The current pilot graph contains 123 nodes and 367 relationships, including 6 job nodes, 10 ability nodes, 7 course module nodes, 34 knowledge nodes, and 98 course-to-knowledge teaching relationships. The system supports target-job course path recommendation, quiz-based ability gap diagnosis, personalized learning route generation, remedial resource recommendation, and teacher-facing curriculum improvement analysis. The pilot results show that the proposed graph can provide explainable learning paths and curriculum evidence, although larger job samples, formal teacher coding agreement, and real classroom evaluation are still required.
Latency-Bounded Embedding Table Partitioning Across Heterogeneous Accelerators for Large-Scale Recommendation Serving
Large-scale recommendation systems (RS) are among the most computationally demanding workloads deployed in modern data centers, characterized by the co-existence of memory-intensive embedding table lookups and compute-intensive dense neural network operations. As model sizes grow to encompass hundreds of embedding tables with billions of parameters, deploying these models under strict service-level objective (SLO) constraints becomes increasingly challenging. This paper proposes a latency-bounded embedding table partitioning framework, termed LatEmbed, that intelligently distributes embedding tables across heterogeneous accelerator pools comprising graphics processing units (GPU), central processing units (CPU), and field-programmable gate arrays (FPGA). Our approach constructs an offline profiling-guided cost model that captures per-table access latency, memory bandwidth consumption, and interconnect transfer overhead, then formulates the partitioning problem as a constrained optimization objective minimizing tail latency subject to memory capacity and bandwidth limits. A dynamic rebalancing mechanism further adapts placements to runtime access distribution shifts without violating end-to-end SLO bounds. Experiments on industrial-scale workloads demonstrate that LatEmbed reduces P99 inference latency by up to 43% compared to GPU-only baselines and achieves 2.1× improvement in memory efficiency over CPU-only configurations, while maintaining SLO compliance above 99.5% under peak traffic conditions.
Optimizing RAG-Assisted Code Generation for Cloud-Native ML Pipelines Through Dynamic Context Window Management
The proliferation of cloud-native machine learning (ML) infrastructure has created significant demand for automated code generation systems capable of producing syntactically and semantically correct pipeline configurations. Retrieval-Augmented Generation (RAG) offers a principled mechanism for grounding large language model (LMM) inference in dynamically retrieved contextual knowledge; however, naive implementations suffer from context window saturation and relevance dilution when applied to complex multi-component ML pipeline repositories. This paper proposes a Dynamic Context Window Management (DCWM) framework integrated within a RAG-assisted code generation system targeting cloud-native ML workflows. The framework introduces a multi-stage retrieval mechanism coupled with an adaptive context compression module that selectively ranks and prioritizes retrieved code artifacts through attention-weighted scoring. An abstract syntax tree guided generation pipeline further enforces syntactic validity of produced pipeline code. Empirical evaluation across three benchmark task categories demonstrates a 23.7% improvement in pass@1 accuracy and a 19.4% reduction in hallucinated API calls relative to static context window baselines. These findings establish dynamic context management as a first-class design consideration in RAG-enhanced code generation for cloud-native environments.
Dynamic Reliability and Resilience Assessment of Jack-up Production Platform Legs Based on Dynamic Bayesian Networks
This paper proposes a dynamic reliability and system resilience assessment method for the leg structures of a mobile offshore production unit (MOPU) that is subjected to long-term service in complex marine environments. A fault tree of the leg system is first constructed and mapped to a Bayesian network (BN). Time slices are then introduced to expand the network into a dynamic Bayesian network (DBN). To capture history-dependent cumulative effects, a non-homogeneous state transition mechanism driven by physical damage laws is embedded within the DBN framework, within which the Melchers power-law corrosion model and the Palmgren-Miner fatigue model are dynamically modelled. A restoration sub-network comprising diagnosis capability, resource accessibility and maintenance capability is built, and a semi-Markov process is adopted to describe the transition probabilities of degradation and recovery. A quantitative resilience indicator is defined based on the “resilience triangle” theory. Numerical simulations are carried out for four typical scenarios: normal operation, frequent jacking operations, fishing-vessel collision, and blowout. The results show that fatigue degradation is the dominant factor affecting reliability; welded joints are the most sensitive nodes, and under collision/blowout conditions the sensitivity of stress-concentration nodes rises to the second highest; bottom-side collision combined with blowout.
Optimization path of network security governance system under the background of digital transformation
Digital transformation not only releases the efficiency dividend of enterprises, but also significantly expands the network attack surface, which makes the traditional network security governance model face severe challenges. This paper systematically analyzes the four core pain points in the current governance system and the dilemma of governance logic reconstruction in zero trust transformation. Through the comparative study of mainstream governance paradigms such as NIST CSF 2.0, ISO 27001:2022 and zero trust architecture, this paper proposes a three-tier collaborative governance model covering strategic layer, operational layer and technical layer, emphasizing the horizontal integration of business-security-compliance. An empirical study of Group A shows that the model can shorten the decision-making period of cross-departmental security policy by 71.9%, reduce the time-consuming of new business security assessment by 84.9%, reduce the cost of multi-jurisdiction compliance integration by 50%, and reduce the response time of unauthorized access incident detection by 91.3%. The research provides a theoretical framework and practical path for the optimization of network security governance of digital transformation enterprises.
Adaptive Crow Search Algorithm Based on Population Diversity
As an emerging metaheuristic optimization algorithm, the Crow Search Algorithm (CSA) has received widespread academic attention for its intuitive model and concise parameter settings. However, its inherent limitations, including the tendency to fall into local optima and insufficient convergence accuracy in solving complex optimization problems, have severely restricted its popularization and application in practical engineering scenarios. Aiming at the above shortcomings of the basic CSA, this thesis conducts in-depth and systematic research, proposes two targeted improved algorithms, and systematically verifies their theoretical performance and engineering application value.To address the performance bottleneck of the basic CSA, this thesis proposes an Adaptive Crow Search Algorithm (ACSA) based on population diversity. By constructing an adaptive framework with population diversity as the feedback signal, the algorithm achieves three core improvements. First, a dynamic dual-mode guidance mechanism is designed to intelligently switch between global exploration and local exploitation strategies according to the population diversity index. Second, the Sigmoid function is introduced to realize adaptive adjustment of flight length, enabling the parameter settings to match the real-time search state of the algorithm. Third, the golden sine strategy is adopted to optimize the individual position update rule, which is combined with a reflection boundary handling mechanism to enhance the iterative stability of the algorithm.
Graph Convolutional Network-Based Multi-Agent Collaborative Task Allocation Optimization Algorithm
Due to the complex interdependencies between tasks, the dynamic state changes of the tasks, and the lack of flexibility of the traditional static matching method in the collaborative task allocation with multi-agent, this study aims to explore an optimization algorithm for collaborative task allocation driven by a graph convolutional network. The paper details the construction of multi-agent task graphs, the design of node features and edge weights as well as the graph convolutional feature aggregation mechanism. It also introduces updating of dynamic collaborative relationship, construction of allocation utility function and collaborative optimization process. The results of comparative and ablation experiments in a two-dimensional simulation platform show that the algorithm has a 97.4% task completion ratio and an average utility of 86.7 with an excellent allocation efficiency and convergence stability.
Research and Implementation of an Agricultural Pest and Disease Detection Algorithm Based on Deep Learning
Food security is increasingly threatened by crop pests and diseases, while conventional field inspection remains labor intensive, experience dependent, and prone to missed detection during early disease stages. To support accurate and real-time pest and disease monitoring in precision agriculture, this study proposes RSC-YOLOv11-EMA, an improved lightweight detection framework for soybean leaf pest and disease images. First, YOLOv11 is selected as the baseline after comparison with YOLOv5 and YOLOv8 on the same dataset. Second, the backbone network is reconstructed with RepVGG so that multi-branch training can be converted into a single-path inference structure, reducing computational cost for edge deployment. Third, SENet channel attention is embedded into multi-scale feature outputs to strengthen discriminative lesion channels and suppress background interference. Fourth, K-means++ is used to analyze the true scale distribution of lesions and guide scale-aware augmentation for small targets. On this basis, an Efficient Multi-scale Attention module, warm-up cosine annealing, and exponential moving average weight updating are introduced to improve spatial sensitivity, convergence stability, and generalization on a small-sample dataset. Experiments show that the final RSC-YOLOv11-EMA model achieves 93.6% precision, 90.4% recall, 91.2% mAP@0.5, and 31.2 FPS, improving mAP@0.5 by 6.5 percentage points over the YOLOv11 baseline while preserving real-time inference. A PyQt5-based detection system is further implemented, with an end-to-end response time of 145 ms on edge equipment. The results demonstrate that the proposed method provides an effective and deployable solution for early agricultural pest and disease detection.
Design and Simulation of a 24 GHz Microstrip Array Antenna
To meet the demand for high gain and low sidelobe levels of array antennas in radar systems, hydrological monitoring, and next-generation wireless communication systems, this paper proposes a 24 GHz microstrip array antenna based on a series–parallel hybrid feeding network. The proposed design combines the advantages of both series and parallel feeding structures to optimize array performance while maintaining a compact configuration. Simulation results show that the antenna achieves a reflection coefficient better than −10 dB over the frequency range of 23.9–24.3 GHz, with a maximum gain of 19.58 dBi and a sidelobe level close to −20 dB. The results indicate that the proposed antenna can basically meet the design requirements for high gain and low sidelobe performance in radar systems.
Secrecy Analysis for a Fixed UAV-Assisted DF Relay Network with Hardware Impairments over Nakagami-m Channels
A fixed UAV-assisted decode-and-forward relay network is examined from the perspective of physical-layer security under residual transceiver distortion. The source communicates with the destination only through the UAV relay, and the eavesdropper overhears the relay transmission. Independent Nakagami-m fading is used for the three wireless links so that different UAV propagation conditions can be represented within one model. Based on the SNDRs of the two legitimate hops and the wiretap hop, expressions for SOP and SPSC are derived, and simplified finite-sum integral forms are given for integer fading parameters. The numerical study confirms the analytical results and shows how fading severity, secrecy-rate demand, eavesdropper SNR, and impairment level affect secure transmission.
Generalized dissipativity state estimation of delayed neural networks under a novel event-triggered strategy
This paper investigates the generalized dissipativity state estimation problem for delayed neural networks (DNNs) under a novel event-triggered strategy. Unlike traditional Lyapunov-Krasovskii Functionals (LKFs) which impose positive definiteness constraints on matrices, this paper constructs a novel delay-dependent LKF. The positive definiteness constraint on the LKF matrix is relaxed via a quadratic inequality, which offers more flexibility in the functional construction. Meanwhile, to guarantee satisfactory estimator performance under limited communication resources, a novel adaptive event-triggered strategy (AETS) with memory-based triggering logic is proposed. Unlike existing event-triggered (ET) strategies, the proposed AETS can dynamically adjust triggering parameters and further reduce communication frequency. Accordingly, two sufficient criteria are derived via linear matrix inequalities (LMIs) to guarantee the dissipativity of the estimation error system. Finally, numerical examples are provided to demonstrate the superiority of the proposed methods.
Research on Personalized NIPT Detection Timing and Anomaly Judgment Based on Bayesian Optimization
With rising obesity-related NIPT failures due to insufficient fetal DNA, we introduce a pioneering clinical application of Bayesian optimization—traditionally confined to engineering and machine learning—to personalized prenatal screening. Linear mixed-effects models quantified nonlinear relationships between fetal DNA concentration and gestational age, BMI, and maternal age. Gaussian mixture clustering stratified pregnancies into five BMI-specific subgroups. Uniquely applying Bayesian optimization to determine optimal 10–25-week sampling windows for each subgroup, we minimized weighted risks of early/late testing and low DNA concentration. Results reveal distinct optimal timing across BMI categories that diverge from current uniform guidelines, with higher BMI requiring earlier collection. This innovative integration of machine learning optimization into clinical obstetrics establishes a data-driven 'one person, one time' framework, representing the first utilization of Bayesian methods for individualized NIPT scheduling decisions.
Enhancing Engagement in Speech Rehabilitation through Gamified Interaction and Emotional Design
Speech rehabilitation systems often suffer from low user engagement due to repetitive training processes and limited emotional support. To address this issue, this study proposes an integrated interaction design framework combining gamification, emotional design, and multisensory compensation mechanisms for speech rehabilitation. Based on User-Centered Design (UCD) principles and Norman’s emotional design theory, a mobile application named Yinshu was developed, incorporating gamified tasks, adaptive difficulty adjustment, AI emotional companion interaction, and a “visualizing sound” feedback mechanism. To evaluate the system, 40 participants completed a customized short-form User Engagement Scale (UES) questionnaire after using the application. The results showed positive evaluations across all dimensions, with mean scores above 4.0 on a 5-point Likert scale. Gamified rewards improved motivation and self-efficacy, while emotional companion interaction reduced feelings of isolation and enhanced psychological engagement. In addition, the visual feedback system effectively improved users’ understanding of pronunciation accuracy and reduced cognitive barriers during training. The findings suggest that integrating gamification and emotional design can effectively enhance engagement in speech rehabilitation, providing practical insights for future digital rehabilitation system design.
A Hybrid Data-Driven Machine Learning Approach for Anomaly Detection
Anomaly detection is a vital application field of machine learning. Given the widespread problems of extreme class imbalance and complex nonlinearity in high-dimensional data, traditional anomaly detection methods can hardly achieve both high-precision identification and model interpretability. Taking non-invasive prenatal testing data as the research background, this paper proposes a two-stage expert model and constructs an anomaly detection strategy integrating statistical analysis and machine learning. After appropriate data oversampling, the random forest algorithm is used to preliminarily verify the reliability of the results, and the recall rate of each expert model on the test set exceeds 0.9. SHAP is further introduced to analyze the main effects and interaction effects, indicating that the Z-score of each chromosome plays a core role in its corresponding model. Combined with index quality control in NIPT theory, this paper innovatively applies SHAP single-feature dependence analysis to explore the stability of quality control indicators, constructs a standard quality control score, and divides samples into high, medium, and low risk regions, with an abnormality rate of only 7.6% in the low-risk region. On this basis, the results of multi-index collaborative quality control are analyzed and predicted.
AI Empowered Breakthroughs and Innovations in Drug Development
The development of novel drugs has long faced the severe challenge of the "Double Ten Law," with pharmaceutical R&D persistently encountering the "triple dilemma" of prolonged cycles, high costs, and low success rates, falling into the trap of "anti-Moore's Law." The rise of artificial intelligence (AI) technology offers a comprehensive solution to this predicament, achieving efficiency revolutions across all stages—from target discovery and molecular design to clinical trials. To outline the technological evolution of AI in drug discovery and design and analyze its role in driving innovative models for new drug discovery, this study conducts a systematic literature review and case analysis. It focuses on the core pain points of traditional drug development, elaborates on the application pathways and technological innovations of AI throughout the drug development process, presents practical achievements and potential enabled by AI based on real-world data, examines current challenges in data, technology, and talent, and outlines future prospects. The findings aim to provide insights for technological innovation and industrial upgrading in the field of drug development.
WorldSense: Enabling Safe Autonomous Navigation Under Rare Event Scenarios
Autonomous vehicle (AV) safety in rare or long-tail driving scenarios remains one of the most intractable challenges in modern intelligent transportation research. This paper presents WorldSense, a unified framework that integrates predictive world modeling, temporal anomaly detection (TAD), and safety-aware planning to enable reliable navigation under infrequent but high-risk conditions. WorldSense encodes the driving environment into a compact latent representation using a convolutional neural network (CNN)-based multi-camera perception backbone, predicts future scene evolution through a recurrent gated memory module, and monitors reconstruction divergence in real time to generate a rarity score that triggers conservative trajectory planning when anomalous events are detected. Evaluations conducted on the CARLA simulator using a custom rare event test suite (RETS) and on the nuScenes benchmark demonstrate that WorldSense reduces collision rates by 34.2% and improves route completion by 14.6 percentage points relative to transformer-based planning baselines under rare event conditions. These results establish WorldSense as a principled and scalable framework for safety-critical autonomous navigation in scenarios underrepresented in standard training distributions.
Dynamic Knowledge Graph Augmentation Enhances Factual Accuracy in Retrieval Based Generation
Large language models (LLMs) demonstrate exceptional fluency in natural language generation but remain susceptible to producing factually incorrect outputs due to static parametric knowledge frozen at training time. Retrieval-augmented generation (RAG) partially mitigates this limitation by conditioning generation on externally retrieved evidence, yet conventional RAG systems depend on unstructured, flat document corpora that fail to represent the relational and temporal dynamics characterizing real-world knowledge. This paper proposes Dynamic Knowledge Graph Augmentation (DKGA), a framework that integrates continuously updated knowledge graphs (KGs) with retrieval-based generation pipelines to substantially improve factual accuracy. DKGA employs a graph neural network (GNN) encoder for subgraph-conditioned entity representation learning, a temporal update module for incremental knowledge refresh, and a cross-modal relevance-aware fusion mechanism that jointly conditions the generator on structured KG evidence and unstructured text passages. Experiments on the WebQA and TriviaQA benchmarks demonstrate that DKGA achieves an 11.4% improvement in exact match factual accuracy and an 18.2% reduction in hallucination rate over strong RAG baselines. These results provide compelling evidence that dynamic, structured knowledge representations are a critical and underutilized resource for knowledge-intensive language generation.
Privacy Preserving Language Models Detect Early Warning Signs in Digital Mental Health
The proliferation of digital communication platforms has generated large-scale behavioral and linguistic data streams that carry meaningful signals about users' mental health trajectories. Deploying language models to analyze such data, however, introduces serious privacy challenges, as mental health-related text constitutes among the most sensitive categories of personal information. This paper proposes an integrated framework combining federated learning and differential privacy mechanisms to enable privacy-preserving language model training for early warning sign detection across depression, anxiety, and suicidal ideation. The architecture allows gradient updates to be computed locally on distributed client nodes and aggregated without centralizing raw user content, while formal DP guarantees bound the statistical influence of any individual's data on the released model. Experimental evaluations demonstrate competitive detection performance, with an area under the receiver operating characteristic curve of 0.847 at epsilon equal to five, approaching the non-private centralized baseline of 0.891. The results confirm that privacy-preserving approaches are technically feasible and clinically viable for real-world digital mental health monitoring applications.